essay writing on will artificial intelligence take over human intelligence

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AI Should Augment Human Intelligence, Not Replace It

• David De Cremer
• Garry Kasparov

Artificial intelligence isn’t coming for your job, but it will be your new coworker. Here’s how to get along.

Will smart machines really replace human workers? Probably not. People and AI both bring different abilities and strengths to the table. The real question is: how can human intelligence work with artificial intelligence to produce augmented intelligence. Chess Grandmaster Garry Kasparov offers some unique insight here. After losing to IBM’s Deep Blue, he began to experiment how a computer helper changed players’ competitive advantage in high-level chess games. What he discovered was that having the best players and the best program was less a predictor of success than having a really good process. Put simply, “Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.” As leaders look at how to incorporate AI into their organizations, they’ll have to manage expectations as AI is introduced, invest in bringing teams together and perfecting processes, and refine their own leadership abilities.

In an economy where data is changing how companies create value — and compete — experts predict that using artificial intelligence (AI) at a larger scale will add as much as $15.7 trillion to the global economy by 2030 . As AI is changing how companies work, many believe that who does this work will change, too — and that organizations will begin to replace human employees with intelligent machines . This is already happening: intelligent systems are displacing humans in manufacturing, service delivery, recruitment, and the financial industry, consequently moving human workers towards lower-paid jobs or making them unemployed. This trend has led some to conclude that in 2040 our workforce may be totally unrecognizable .

• David De Cremer is a professor of management and technology at Northeastern University and the Dunton Family Dean of its D’Amore-McKim School of Business. His website is daviddecremer.com .
• Garry Kasparov is the chairman of the Human Rights Foundation and founder of the Renew Democracy Initiative. He writes and speaks frequently on politics, decision-making, and human-machine collaboration. Kasparov became the youngest world chess champion in history at 22 in 1985 and retained the top rating in the world for 20 years. His famous matches against the IBM super-computer Deep Blue in 1996 and 1997 were key to bringing artificial intelligence, and chess, into the mainstream. His latest book on artificial intelligence and the future of human-plus-machine is Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins (2017).

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Artificial intelligence has changed form in recent years.

What started in the public eye as a burgeoning field with promising (yet largely benign) applications, has snowballed into a more than US$100 billion industry where the heavy hitters – Microsoft, Google and OpenAI, to name a few – seem intent on out-competing one another.

The result has been increasingly sophisticated large language models, often released in haste and without adequate testing and oversight.

These models can do much of what a human can, and in many cases do it better. They can beat us at advanced strategy games , generate incredible art , diagnose cancers and compose music.

Read more: Text-to-audio generation is here. One of the next big AI disruptions could be in the music industry

There’s no doubt AI systems appear to be “intelligent” to some extent. But could they ever be as intelligent as humans?

There’s a term for this: artificial general intelligence (AGI). Although it’s a broad concept, for simplicity you can think of AGI as the point at which AI acquires human-like generalised cognitive capabilities. In other words, it’s the point where AI can tackle any intellectual task a human can.

AGI isn’t here yet; current AI models are held back by a lack of certain human traits such as true creativity and emotional awareness.

We asked five experts if they think AI will ever reach AGI, and five out of five said yes.

But there are subtle differences in how they approach the question. From their responses, more questions emerge. When might we achieve AGI? Will it go on to surpass humans? And what constitutes “intelligence”, anyway?

Here are their detailed responses:

Read more: Calls to regulate AI are growing louder. But how exactly do you regulate a technology like this?

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How close are we to AI that surpasses human intelligence?

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July 18, 2023

• Artificial general intelligence (AGI) is difficult to precisely define but refers to a superintelligent AI recognizable from science fiction.
• AGI may still be far off, but the growing capabilities of generative AI suggest that we could be making progress toward its development.
• The development of AGI will have a transformative effect on society and create significant opportunities and threats, raising difficult questions about regulation.

For decades, superintelligent artificial intelligence (AI) has been a staple of science fiction, embodied in books and movies about androids, robot uprisings, and a world taken over by computers. As far-fetched as those plots often were, they played off a very real mix of fascination, curiosity, and trepidation regarding the potential to build intelligent machines.

Today, public interest in AI is at an all-time high. With the headlines in recent months about generative AI systems like ChatGPT, there is also a different phrase that has started to enter the broader dialog: a rtificial general intelligence , or AGI. But what exactly is AGI, and how close are today’s technologies to achieving it?

Despite the similarity in the phrases generative AI and artificial general intelligence, they have very different meanings. As a post from IBM explains, “Generative AI refers to deep-learning models that can generate high-quality text, images, and other content based on the data they were trained on.” However, the ability of an AI system to generate content does not necessarily mean that its intelligence is general.

To better understand artificial general intelligence, it helps to first understand how it differs from today’s AI, which is highly specialized. For example, an AI chess program is extraordinarily good at playing chess, but if you ask it to write an essay on the causes of World War I, it won’t be of any use. Its intelligence is limited to one specific domain. Other examples of specialized AI include the systems that provide content recommendations on the social media platform TikTok, navigation decisions in driverless cars, and purchase recommendations from Amazon.

AGI: A range of definitions

By contrast, AGI refers to a much broader form of machine intelligence. There is no single, formally recognized definition of AGI—rather, there is a range of definitions that include the following:

While the OpenAI definition ties AGI to the ability to “outperform humans at most economically valuable work,” today’s systems are nowhere near that capable. Consider Indeed’s list of the most common jobs in the U.S. As of March 2023, the first 10 jobs on that list were: cashier, food preparation worker, stocking associate, laborer, janitor, construction worker, bookkeeper, server, medical assistant, and bartender. These jobs require not only intellectual capacity but, crucially, most of them require a far higher degree of manual dexterity than today’s most advanced AI robotics systems can achieve.

None of the other AGI definitions in the table specifically mention economic value. Another contrast evident in the table is that while the OpenAI AGI definition requires outperforming humans, the other definitions only require AGI to perform at levels comparable to humans. Common to all of the definitions, either explicitly or implicitly, is the concept that an AGI system can perform tasks across many domains, adapt to the changes in its environment, and solve new problems—not only the ones in its training data.

GPT-4: Sparks of AGI?

A group of industry AI researchers recently made a splash when they published a preprint of an academic paper titled, “Sparks of Artificial General Intelligence: Early experiments with GPT-4.” GPT-4 is a large language model that has been publicly accessible to ChatGPT Plus (paid upgrade) users since March 2023. The researchers noted that “GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting,” exhibiting “strikingly close to human-level performance.” They concluded that GPT-4 “could reasonably be viewed as an early (yet still incomplete) version” of AGI.

Of course, there are also skeptics: As quoted in a May New York Times article , Carnegie Mellon professor Maarten Sap said, “The ‘Sparks of A.G.I.’ is an example of some of these big companies co-opting the research paper format into P.R. pitches.” In an interview with IEEE Spectrum, researcher and robotics entrepreneur Rodney Brooks underscored that in evaluating the capabilities of systems like ChatGPT, we often “mistake performance for competence.”

GPT-4 and beyond

While the version of GPT-4 currently available to the public is impressive, it is not the end of the road. There are groups working on additions to GPT-4 that are more goal-driven, meaning that you can give the system an instruction such as “Design and build a website on (topic).” The system will then figure out exactly what subtasks need to be completed and in what order in order to achieve that goal. Today, these systems are not particularly reliable, as they frequently fail to reach the stated goal. But they will certainly get better in the future.

In a 2020 paper , Yoshihiro Maruyama of the Australian National University identified eight attributes a system must have for it to be considered AGI: Logic, autonomy, resilience, integrity, morality, emotion, embodiment, and embeddedness. The last two attributes—embodiment and embeddedness—refer to having a physical form that facilitates learning and understanding of the world and human behavior, and a deep integration with social, cultural, and environmental systems that allows adaption to human needs and values.

It can be argued that ChatGPT displays some of these attributes, like logic. For example, GPT-4 with no additional features reportedly scored a 163 on the LSAT and 1410 on the SAT . For other attributes, the determination is tied as much to philosophy as much as to technology. For instance, is a system that merely exhibits what appears to be morality actually moral? If asked to provide a one-word answer to the question “is murder wrong?” GPT-4 will respond by saying “Yes.” This is a morally correct response, but it doesn’t mean that GPT-4 itself has morality, but rather that it has inferred the morally correct answer through its training data.

A key subtlety that often goes missing in the “How close is AGI?” discussion is that intelligence exists on a continuum, and therefore assessing whether a system displays AGI will require considering a continuum. On this point, the research done on animal intelligence offers a useful analog. We understand that animal intelligence is far too complex to enable us to meaningfully convey animal cognitive capacity by classifying each species as either “intelligent” or “not intelligent:” Animal intelligence exists on a spectrum that spans many dimensions, and evaluating it requires considering context. Similarly, as AI systems become more capable, assessing the degree to which they display generalized intelligence will be involve more than simply choosing between “yes” and “no.”

AGI: Threat or opportunity?

Whenever and in whatever form it arrives, AGI will be transformative, impacting everything from the labor market to how we understand concepts like intelligence and creativity. As with so many other technologies, it also has the potential of being harnessed in harmful ways. For instance, the need to address the potential biases in today’s AI systems is well recognized, and that concern will apply to future AGI systems as well. At the same time, it is also important to recognize that AGI will also offer enormous promise to amplify human innovation and creativity. In medicine, for example, new drugs that would have eluded human scientists working alone could be more easily identified by scientists working with AGI systems.

AGI can also help broaden access to services that previously were accessible only to the most economically privileged. For instance, in the context of education, AGI systems could put personalized, one-on-one tutoring within easy financial reach of everyone, resulting in improved global literacy rates. AGI could also help broaden the reach of medical care by bringing sophisticated, individualized diagnostic care to much broader populations.

Regulating emergent AGI systems

At the May 2023 G7 summit in Japan, the leaders of the world’s seven largest democratic economies issued a communiqué that included an extended discussion of AI, writing that “international governance of new digital technologies has not necessarily kept pace.” Proposals regarding increased AI regulation are now a regular feature of policy discussions in the United States , the European Union , Japan , and elsewhere.

In the future, as AGI moves from science fiction to reality, it will supercharge the already-robust debate regarding AI regulation. But preemptive regulation is always a challenge, and this will be particularly so in relation to AGI—a technology that escapes easy definition, and that will evolve in ways that are impossible to predict.

An outright ban on AGI would be bad policy. For example, AGI systems that are capable of emotional recognition could be very beneficial in a context such as education, where they could discern whether a student appears to understand a new concept, and adjust an interaction accordingly. Yet the EU Parliament’s AI Act, which passed a major legislative milestone in June, would ban emotional recognition in AI systems (and therefore also in AGI systems) in certain contexts like education.

A better approach is to first gain a clear understanding of potential misuses of specific AGI systems once those systems exist and can be analyzed, and then to examine whether those misuses are addressed by existing, non-AI-specific regulatory frameworks (e.g., the prohibition against employment discrimination provided by Title VII of the Civil Rights Act of 1964). If that analysis identifies a gap, then it does indeed make sense to examine the potential role in filling that gap of “soft” law (voluntary frameworks) as well as formal laws and regulations. But regulating AGI based only on the fact that it will be highly capable would be a mistake.

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• Artificial Intelligence and the Future of Humans

Experts say the rise of artificial intelligence will make most people better off over the next decade, but many have concerns about how advances in AI will affect what it means to be human, to be productive and to exercise free will

Table of contents.

• 1. Concerns about human agency, evolution and survival
• 2. Solutions to address AI’s anticipated negative impacts
• 3. Improvements ahead: How humans and AI might evolve together in the next decade
• About this canvassing of experts
• Acknowledgments

Digital life is augmenting human capacities and disrupting eons-old human activities. Code-driven systems have spread to more than half of the world’s inhabitants in ambient information and connectivity, offering previously unimagined opportunities and unprecedented threats. As emerging algorithm-driven artificial intelligence (AI) continues to spread, will people be better off than they are today?

Some 979 technology pioneers, innovators, developers, business and policy leaders, researchers and activists answered this question in a canvassing of experts conducted in the summer of 2018.

The experts predicted networked artificial intelligence will amplify human effectiveness but also threaten human autonomy, agency and capabilities. They spoke of the wide-ranging possibilities; that computers might match or even exceed human intelligence and capabilities on tasks such as complex decision-making, reasoning and learning, sophisticated analytics and pattern recognition, visual acuity, speech recognition and language translation. They said “smart” systems in communities, in vehicles, in buildings and utilities, on farms and in business processes will save time, money and lives and offer opportunities for individuals to enjoy a more-customized future.

Many focused their optimistic remarks on health care and the many possible applications of AI in diagnosing and treating patients or helping senior citizens live fuller and healthier lives. They were also enthusiastic about AI’s role in contributing to broad public-health programs built around massive amounts of data that may be captured in the coming years about everything from personal genomes to nutrition. Additionally, a number of these experts predicted that AI would abet long-anticipated changes in formal and informal education systems.

Yet, most experts, regardless of whether they are optimistic or not, expressed concerns about the long-term impact of these new tools on the essential elements of being human. All respondents in this non-scientific canvassing were asked to elaborate on why they felt AI would leave people better off or not. Many shared deep worries, and many also suggested pathways toward solutions. The main themes they sounded about threats and remedies are outlined in the accompanying table.

[chart id=”21972″]

Specifically, participants were asked to consider the following:

“Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties.

Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today?”

Overall, and despite the downsides they fear, 63% of respondents in this canvassing said they are hopeful that most individuals will be mostly better off in 2030, and 37% said people will not be better off.

A number of the thought leaders who participated in this canvassing said humans’ expanding reliance on technological systems will only go well if close attention is paid to how these tools, platforms and networks are engineered, distributed and updated. Some of the powerful, overarching answers included those from:

Sonia Katyal , co-director of the Berkeley Center for Law and Technology and a member of the inaugural U.S. Commerce Department Digital Economy Board of Advisors, predicted, “In 2030, the greatest set of questions will involve how perceptions of AI and their application will influence the trajectory of civil rights in the future. Questions about privacy, speech, the right of assembly and technological construction of personhood will all re-emerge in this new AI context, throwing into question our deepest-held beliefs about equality and opportunity for all. Who will benefit and who will be disadvantaged in this new world depends on how broadly we analyze these questions today, for the future.”

We need to work aggressively to make sure technology matches our values. Erik Brynjolfsson

[machine learning]

Bryan Johnson , founder and CEO of Kernel, a leading developer of advanced neural interfaces, and OS Fund, a venture capital firm, said, “I strongly believe the answer depends on whether we can shift our economic systems toward prioritizing radical human improvement and staunching the trend toward human irrelevance in the face of AI. I don’t mean just jobs; I mean true, existential irrelevance, which is the end result of not prioritizing human well-being and cognition.”

Andrew McLaughlin , executive director of the Center for Innovative Thinking at Yale University, previously deputy chief technology officer of the United States for President Barack Obama and global public policy lead for Google, wrote, “2030 is not far in the future. My sense is that innovations like the internet and networked AI have massive short-term benefits, along with long-term negatives that can take decades to be recognizable. AI will drive a vast range of efficiency optimizations but also enable hidden discrimination and arbitrary penalization of individuals in areas like insurance, job seeking and performance assessment.”

Michael M. Roberts , first president and CEO of the Internet Corporation for Assigned Names and Numbers (ICANN) and Internet Hall of Fame member, wrote, “The range of opportunities for intelligent agents to augment human intelligence is still virtually unlimited. The major issue is that the more convenient an agent is, the more it needs to know about you – preferences, timing, capacities, etc. – which creates a tradeoff of more help requires more intrusion. This is not a black-and-white issue – the shades of gray and associated remedies will be argued endlessly. The record to date is that convenience overwhelms privacy. I suspect that will continue.”

danah boyd , a principal researcher for Microsoft and founder and president of the Data & Society Research Institute, said, “AI is a tool that will be used by humans for all sorts of purposes, including in the pursuit of power. There will be abuses of power that involve AI, just as there will be advances in science and humanitarian efforts that also involve AI. Unfortunately, there are certain trend lines that are likely to create massive instability. Take, for example, climate change and climate migration. This will further destabilize Europe and the U.S., and I expect that, in panic, we will see AI be used in harmful ways in light of other geopolitical crises.”

Amy Webb , founder of the Future Today Institute and professor of strategic foresight at New York University, commented, “The social safety net structures currently in place in the U.S. and in many other countries around the world weren’t designed for our transition to AI. The transition through AI will last the next 50 years or more. As we move farther into this third era of computing, and as every single industry becomes more deeply entrenched with AI systems, we will need new hybrid-skilled knowledge workers who can operate in jobs that have never needed to exist before. We’ll need farmers who know how to work with big data sets. Oncologists trained as robotocists. Biologists trained as electrical engineers. We won’t need to prepare our workforce just once, with a few changes to the curriculum. As AI matures, we will need a responsive workforce, capable of adapting to new processes, systems and tools every few years. The need for these fields will arise faster than our labor departments, schools and universities are acknowledging. It’s easy to look back on history through the lens of present – and to overlook the social unrest caused by widespread technological unemployment. We need to address a difficult truth that few are willing to utter aloud: AI will eventually cause a large number of people to be permanently out of work. Just as generations before witnessed sweeping changes during and in the aftermath of the Industrial Revolution, the rapid pace of technology will likely mean that Baby Boomers and the oldest members of Gen X – especially those whose jobs can be replicated by robots – won’t be able to retrain for other kinds of work without a significant investment of time and effort.”

Barry Chudakov , founder and principal of Sertain Research, commented, “By 2030 the human-machine/AI collaboration will be a necessary tool to manage and counter the effects of multiple simultaneous accelerations: broad technology advancement, globalization, climate change and attendant global migrations. In the past, human societies managed change through gut and intuition, but as Eric Teller, CEO of Google X, has said, ‘Our societal structures are failing to keep pace with the rate of change.’ To keep pace with that change and to manage a growing list of ‘wicked problems’ by 2030, AI – or using Joi Ito’s phrase, extended intelligence – will value and revalue virtually every area of human behavior and interaction. AI and advancing technologies will change our response framework and time frames (which in turn, changes our sense of time). Where once social interaction happened in places – work, school, church, family environments – social interactions will increasingly happen in continuous, simultaneous time. If we are fortunate, we will follow the 23 Asilomar AI Principles outlined by the Future of Life Institute and will work toward ‘not undirected intelligence but beneficial intelligence.’ Akin to nuclear deterrence stemming from mutually assured destruction, AI and related technology systems constitute a force for a moral renaissance. We must embrace that moral renaissance, or we will face moral conundrums that could bring about human demise. … My greatest hope for human-machine/AI collaboration constitutes a moral and ethical renaissance – we adopt a moonshot mentality and lock arms to prepare for the accelerations coming at us. My greatest fear is that we adopt the logic of our emerging technologies – instant response, isolation behind screens, endless comparison of self-worth, fake self-presentation – without thinking or responding smartly.”

John C. Havens , executive director of the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems and the Council on Extended Intelligence, wrote, “Now, in 2018, a majority of people around the world can’t access their data, so any ‘human-AI augmentation’ discussions ignore the critical context of who actually controls people’s information and identity. Soon it will be extremely difficult to identify any autonomous or intelligent systems whose algorithms don’t interact with human data in one form or another.”

At stake is nothing less than what sort of society we want to live in and how we experience our humanity. Batya Friedman

Batya Friedman , a human-computer interaction professor at the University of Washington’s Information School, wrote, “Our scientific and technological capacities have and will continue to far surpass our moral ones – that is our ability to use wisely and humanely the knowledge and tools that we develop. … Automated warfare – when autonomous weapons kill human beings without human engagement – can lead to a lack of responsibility for taking the enemy’s life or even knowledge that an enemy’s life has been taken. At stake is nothing less than what sort of society we want to live in and how we experience our humanity.”

Greg Shannon , chief scientist for the CERT Division at Carnegie Mellon University, said, “Better/worse will appear 4:1 with the long-term ratio 2:1. AI will do well for repetitive work where ‘close’ will be good enough and humans dislike the work. … Life will definitely be better as AI extends lifetimes, from health apps that intelligently ‘nudge’ us to health, to warnings about impending heart/stroke events, to automated health care for the underserved (remote) and those who need extended care (elder care). As to liberty, there are clear risks. AI affects agency by creating entities with meaningful intellectual capabilities for monitoring, enforcing and even punishing individuals. Those who know how to use it will have immense potential power over those who don’t/can’t. Future happiness is really unclear. Some will cede their agency to AI in games, work and community, much like the opioid crisis steals agency today. On the other hand, many will be freed from mundane, unengaging tasks/jobs. If elements of community happiness are part of AI objective functions, then AI could catalyze an explosion of happiness.”

Kostas Alexandridis , author of “Exploring Complex Dynamics in Multi-agent-based Intelligent Systems,” predicted, “Many of our day-to-day decisions will be automated with minimal intervention by the end-user. Autonomy and/or independence will be sacrificed and replaced by convenience. Newer generations of citizens will become more and more dependent on networked AI structures and processes. There are challenges that need to be addressed in terms of critical thinking and heterogeneity. Networked interdependence will, more likely than not, increase our vulnerability to cyberattacks. There is also a real likelihood that there will exist sharper divisions between digital ‘haves’ and ‘have-nots,’ as well as among technologically dependent digital infrastructures. Finally, there is the question of the new ‘commanding heights’ of the digital network infrastructure’s ownership and control.”

Oscar Gandy , emeritus professor of communication at the University of Pennsylvania, responded, “We already face an ungranted assumption when we are asked to imagine human-machine ‘collaboration.’ Interaction is a bit different, but still tainted by the grant of a form of identity – maybe even personhood – to machines that we will use to make our way through all sorts of opportunities and challenges. The problems we will face in the future are quite similar to the problems we currently face when we rely upon ‘others’ (including technological systems, devices and networks) to acquire things we value and avoid those other things (that we might, or might not be aware of).”

James Scofield O’Rourke , a professor of management at the University of Notre Dame, said, “Technology has, throughout recorded history, been a largely neutral concept. The question of its value has always been dependent on its application. For what purpose will AI and other technological advances be used? Everything from gunpowder to internal combustion engines to nuclear fission has been applied in both helpful and destructive ways. Assuming we can contain or control AI (and not the other way around), the answer to whether we’ll be better off depends entirely on us (or our progeny). ‘The fault, dear Brutus, is not in our stars, but in ourselves, that we are underlings.’”

Simon Biggs , a professor of interdisciplinary arts at the University of Edinburgh, said, “AI will function to augment human capabilities. The problem is not with AI but with humans. As a species we are aggressive, competitive and lazy. We are also empathic, community minded and (sometimes) self-sacrificing. We have many other attributes. These will all be amplified. Given historical precedent, one would have to assume it will be our worst qualities that are augmented. My expectation is that in 2030 AI will be in routine use to fight wars and kill people, far more effectively than we can currently kill. As societies we will be less affected by this as we currently are, as we will not be doing the fighting and killing ourselves. Our capacity to modify our behaviour, subject to empathy and an associated ethical framework, will be reduced by the disassociation between our agency and the act of killing. We cannot expect our AI systems to be ethical on our behalf – they won’t be, as they will be designed to kill efficiently, not thoughtfully. My other primary concern is to do with surveillance and control. The advent of China’s Social Credit System (SCS) is an indicator of what it likely to come. We will exist within an SCS as AI constructs hybrid instances of ourselves that may or may not resemble who we are. But our rights and affordances as individuals will be determined by the SCS. This is the Orwellian nightmare realised.”

Mark Surman , executive director of the Mozilla Foundation, responded, “AI will continue to concentrate power and wealth in the hands of a few big monopolies based on the U.S. and China. Most people – and parts of the world – will be worse off.”

William Uricchio , media scholar and professor of comparative media studies at MIT, commented, “AI and its related applications face three problems: development at the speed of Moore’s Law, development in the hands of a technological and economic elite, and development without benefit of an informed or engaged public. The public is reduced to a collective of consumers awaiting the next technology. Whose notion of ‘progress’ will prevail? We have ample evidence of AI being used to drive profits, regardless of implications for long-held values; to enhance governmental control and even score citizens’ ‘social credit’ without input from citizens themselves. Like technologies before it, AI is agnostic. Its deployment rests in the hands of society. But absent an AI-literate public, the decision of how best to deploy AI will fall to special interests. Will this mean equitable deployment, the amelioration of social injustice and AI in the public service? Because the answer to this question is social rather than technological, I’m pessimistic. The fix? We need to develop an AI-literate public, which means focused attention in the educational sector and in public-facing media. We need to assure diversity in the development of AI technologies. And until the public, its elected representatives and their legal and regulatory regimes can get up to speed with these fast-moving developments we need to exercise caution and oversight in AI’s development.”

The remainder of this report is divided into three sections that draw from hundreds of additional respondents’ hopeful and critical observations: 1) concerns about human-AI evolution, 2) suggested solutions to address AI’s impact, and 3) expectations of what life will be like in 2030, including respondents’ positive outlooks on the quality of life and the future of work, health care and education. Some responses are lightly edited for style.

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Table of Contents

What is artificial intelligence, what is human intelligence, artificial intelligence vs. human intelligence: a comparison, what brian cells can be tweaked to learn faster, artificial intelligence vs. human intelligence: what will the future of human vs ai be, impact of ai on the future of jobs, will ai replace humans, upskilling: the way forward, learn more about ai with simplilearn, ai vs human intelligence: key insights and comparisons.

From the realm of science fiction into the realm of everyday life, artificial intelligence has made significant strides. Because AI has become so pervasive in today's industries and people's daily lives, a new debate has emerged, pitting the two competing paradigms of AI and human intelligence. 

While the goal of artificial intelligence is to build and create intelligent systems that are capable of doing jobs that are analogous to those performed by humans, we can't help but question if AI is adequate on its own. This article covers a wide range of subjects, including the potential impact of AI on the future of work and the economy, how AI differs from human intelligence, and the ethical considerations that must be taken into account.

The term artificial intelligence may be used for any computer that has characteristics similar to the human brain, including the ability to think critically, make decisions, and increase productivity. The foundation of AI is human insights that may be determined in such a manner that machines can easily realize the jobs, from the most simple to the most complicated. 

Insights that are synthesized are the result of intellectual activity, including study, analysis, logic, and observation. Tasks, including robotics, control mechanisms, computer vision, scheduling, and data mining , fall under the umbrella of artificial intelligence.

The origins of human intelligence and conduct may be traced back to the individual's unique combination of genetics, upbringing, and exposure to various situations and environments. And it hinges entirely on one's freedom to shape his or her environment via the application of newly acquired information.

The information it provides is varied. For example, it may provide information on a person with a similar skill set or background, or it may reveal diplomatic information that a locator or spy was tasked with obtaining. After everything is said and done, it is able to deliver information about interpersonal relationships and the arrangement of interests.

The following is a table that compares human intelligence vs artificial intelligence:

According to the findings of recent research, altering the electrical characteristics of certain cells in simulations of neural circuits caused the networks to acquire new information more quickly than in simulations with cells that were identical. They also discovered that in order for the networks to achieve the same outcomes, a smaller number of the modified cells were necessary and that the approach consumed fewer resources than models that utilized identical cells.

These results not only shed light on how human brains excel at learning but may also help us develop more advanced artificial intelligence systems, such as speech and facial recognition software for digital assistants and autonomous vehicle navigation systems.

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The capabilities of AI are constantly expanding. It takes a significant amount of time to develop AI systems, which is something that cannot happen in the absence of human intervention. All forms of artificial intelligence, including self-driving vehicles and robotics, as well as more complex technologies like computer vision, and natural language processing , are dependent on human intellect.

1. Automation of Tasks

The most noticeable effect of AI has been the result of the digitalization and automation of formerly manual processes across a wide range of industries. These tasks, which were formerly performed manually, are now performed digitally. Currently, tasks or occupations that involve some degree of repetition or the use and interpretation of large amounts of data are communicated to and administered by a computer, and in certain cases, the intervention of humans is not required in order to complete these tasks or jobs.

2. New Opportunities

Artificial intelligence is creating new opportunities for the workforce by automating formerly human-intensive tasks . The rapid development of technology has resulted in the emergence of new fields of study and work, such as digital engineering. Therefore, although traditional manual labor jobs may go extinct, new opportunities and careers will emerge.

3. Economic Growth Model

When it's put to good use, rather than just for the sake of progress, AI has the potential to increase productivity and collaboration inside a company by opening up vast new avenues for growth. As a result, it may spur an increase in demand for goods and services, and power an economic growth model that spreads prosperity and raises standards of living.

4. Role of Work

In the era of AI, recognizing the potential of employment beyond just maintaining a standard of living is much more important. It conveys an understanding of the essential human need for involvement, co-creation, dedication, and a sense of being needed, and should therefore not be overlooked. So, sometimes, even mundane tasks at work become meaningful and advantageous, and if the task is eliminated or automated, it should be replaced with something that provides a comparable opportunity for human expression and disclosure.

5. Growth of Creativity and Innovation

Experts now have more time to focus on analyzing, delivering new and original solutions, and other operations that are firmly in the area of the human intellect, while robotics, AI, and industrial automation handle some of the mundane and physical duties formerly performed by humans.

While AI has the potential to automate specific tasks and jobs, it is likely to replace humans in some areas. AI is best suited for handling repetitive, data-driven tasks and making data-driven decisions. However, human skills such as creativity, critical thinking, emotional intelligence, and complex problem-solving still need to be more valuable and easily replicated by AI.

The future of AI is more likely to involve collaboration between humans and machines, where AI augments human capabilities and enables humans to focus on higher-level tasks that require human ingenuity and expertise. It is essential to view AI as a tool that can enhance productivity and facilitate new possibilities rather than as a complete substitute for human involvement.

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Artificial intelligence is revolutionizing every sector and pushing humanity forward to a new level. However, it is not yet feasible to achieve a precise replica of human intellect. The human cognitive process remains a mystery to scientists and experimentalists. Because of this, the common sense assumption in the growing debate between AI and human intelligence has been that AI would supplement human efforts rather than immediately replace them. Check out the Post Graduate Program in AI and Machine Learning at Simplilearn if you are interested in pursuing a career in the field of artificial intelligence. 

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The present and future of AI

Finale doshi-velez on how ai is shaping our lives and how we can shape ai.

Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences. (Photo courtesy of Eliza Grinnell/Harvard SEAS)

How has artificial intelligence changed and shaped our world over the last five years? How will AI continue to impact our lives in the coming years? Those were the questions addressed in the most recent report from the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted at Stanford University, that will study the status of AI technology and its impacts on the world over the next 100 years.

The 2021 report is the second in a series that will be released every five years until 2116. Titled “Gathering Strength, Gathering Storms,” the report explores the various ways AI is  increasingly touching people’s lives in settings that range from  movie recommendations  and  voice assistants  to  autonomous driving  and  automated medical diagnoses .

Barbara Grosz , the Higgins Research Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) is a member of the standing committee overseeing the AI100 project and Finale Doshi-Velez , Gordon McKay Professor of Computer Science, is part of the panel of interdisciplinary researchers who wrote this year’s report. 

We spoke with Doshi-Velez about the report, what it says about the role AI is currently playing in our lives, and how it will change in the future.  

Q: Let's start with a snapshot: What is the current state of AI and its potential?

Doshi-Velez: Some of the biggest changes in the last five years have been how well AIs now perform in large data regimes on specific types of tasks.  We've seen [DeepMind’s] AlphaZero become the best Go player entirely through self-play, and everyday uses of AI such as grammar checks and autocomplete, automatic personal photo organization and search, and speech recognition become commonplace for large numbers of people.  

In terms of potential, I'm most excited about AIs that might augment and assist people.  They can be used to drive insights in drug discovery, help with decision making such as identifying a menu of likely treatment options for patients, and provide basic assistance, such as lane keeping while driving or text-to-speech based on images from a phone for the visually impaired.  In many situations, people and AIs have complementary strengths. I think we're getting closer to unlocking the potential of people and AI teams.

There's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: Over the course of 100 years, these reports will tell the story of AI and its evolving role in society. Even though there have only been two reports, what's the story so far?

There's actually a lot of change even in five years.  The first report is fairly rosy.  For example, it mentions how algorithmic risk assessments may mitigate the human biases of judges.  The second has a much more mixed view.  I think this comes from the fact that as AI tools have come into the mainstream — both in higher stakes and everyday settings — we are appropriately much less willing to tolerate flaws, especially discriminatory ones. There's also been questions of information and disinformation control as people get their news, social media, and entertainment via searches and rankings personalized to them. So, there's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: What is the responsibility of institutes of higher education in preparing students and the next generation of computer scientists for the future of AI and its impact on society?

First, I'll say that the need to understand the basics of AI and data science starts much earlier than higher education!  Children are being exposed to AIs as soon as they click on videos on YouTube or browse photo albums. They need to understand aspects of AI such as how their actions affect future recommendations.

But for computer science students in college, I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc.  I'm really excited that Harvard has the Embedded EthiCS program to provide some of this education.  Of course, this is an addition to standard good engineering practices like building robust models, validating them, and so forth, which is all a bit harder with AI.

I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc. 

Q: Your work focuses on machine learning with applications to healthcare, which is also an area of focus of this report. What is the state of AI in healthcare? 

A lot of AI in healthcare has been on the business end, used for optimizing billing, scheduling surgeries, that sort of thing.  When it comes to AI for better patient care, which is what we usually think about, there are few legal, regulatory, and financial incentives to do so, and many disincentives. Still, there's been slow but steady integration of AI-based tools, often in the form of risk scoring and alert systems.

In the near future, two applications that I'm really excited about are triage in low-resource settings — having AIs do initial reads of pathology slides, for example, if there are not enough pathologists, or get an initial check of whether a mole looks suspicious — and ways in which AIs can help identify promising treatment options for discussion with a clinician team and patient.

Q: Any predictions for the next report?

I'll be keen to see where currently nascent AI regulation initiatives have gotten to. Accountability is such a difficult question in AI,  it's tricky to nurture both innovation and basic protections.  Perhaps the most important innovation will be in approaches for AI accountability.

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Home Topics Science & Environment Will AI ever reach human-level intelligence?

Will AI ever reach human-level intelligence?

Artificial intelligence has changed form in recent years.

What started in the public eye as a burgeoning field with promising (yet largely benign) applications, has snowballed into a more than US$100 billion industry where the heavy hitters – Microsoft, Google and OpenAI, to name a few – seem intent on out-competing one another.

The result has been increasingly sophisticated large language models, often released in haste and without adequate testing and oversight.

These models can do much of what a human can, and in many cases do it better. They can beat us at advanced strategy games , generate incredible art , diagnose cancers and compose music.

There’s no doubt AI systems appear to be “intelligent” to some extent. But could they ever be as intelligent as humans?

There’s a term for this: artificial general intelligence (AGI). Although it’s a broad concept, for simplicity you can think of AGI as the point at which AI acquires human-like generalised cognitive capabilities. In other words, it’s the point where AI can tackle any intellectual task a human can.

AGI isn’t here yet; current AI models are held back by a lack of certain human traits such as true creativity and emotional awareness.

We asked five experts if they think AI will ever reach AGI, and five out of five said ‘yes’.

But there are subtle differences in how they approach the question. From their responses, more questions emerge. When might we achieve AGI? Will it go on to surpass humans? And what constitutes “intelligence”, anyway?

Here are their detailed responses:

Paul Formosa

Professor in Philosophy and Co-Director of the Centre for Agency, Values and Ethics (CAVE), Macquarie University

AI has already achieved and surpassed human intelligence in many tasks. It can beat us at strategy games such as Go, chess, StarCraft and Diplomacy, outperform us on many  language performance  benchmarks, and write  passable undergraduate  university essays.

Of course, it can also make things up, or “hallucinate”, and get things wrong – but so can humans (although not in the same ways).

Given a long enough timescale, it seems likely AI will achieve AGI, or “human-level intelligence”. That is, it will have achieved proficiency across enough of the interconnected domains of intelligence humans possess. Still, some may worry that – despite AI achievements so far – AI will not really be “intelligent” because it doesn’t (or can’t) understand what it’s doing, since it isn’t conscious.

However, the rise of AI suggests we can have intelligence without consciousness, because intelligence can be understood in functional terms. An intelligent entity can do intelligent things such as learn, reason, write essays, or use tools.

The AIs we create may never have consciousness, but they are increasingly able to do intelligent things. In some cases, they already do them at a level beyond us, which is a trend that will likely continue.

Christina Maher

Computational Neuroscientist and Biomedical Engineer, University of Sydney

AI will achieve human-level intelligence, but perhaps not anytime soon. Human-level intelligence allows us to reason, solve problems and make decisions. It requires many cognitive abilities including adaptability, social intelligence and learning from experience.

AI already ticks many of these boxes. What’s left is for AI models to learn inherent human traits such as critical reasoning, and understanding what emotion is and which events might prompt it.

As humans, we learn and experience these traits from the moment we’re born. Our first experience of “happiness” is too early for us to even remember. We also learn critical reasoning and emotional regulation throughout childhood, and develop a sense of our “emotions” as we interact with and experience the world around us. Importantly, it can take many years for the human brain to develop such intelligence.

AI hasn’t acquired these capabilities yet. But if humans can learn these traits, AI probably can too – and maybe at an even faster rate. We are still discovering how AI models should be built, trained, and interacted with in order to develop such traits in them. Really, the big question is not if AI will achieve human-level intelligence, but when – and how.

Seyedali Mirjalili

Professor, Director of Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia

I believe AI will surpass human intelligence. Why? The past offers insights we can’t ignore. A lot of people believed tasks such as playing computer games, image recognition and content creation (among others) could only be done by humans – but technological advancement proved otherwise.

Today the rapid advancement and adoption of AI algorithms, in conjunction with an abundance of data and computational resources, has led to a level of intelligence and automation previously unimaginable. If we follow the same trajectory, having more generalised AI is no longer a possibility, but a certainty of the future.

It is just a matter of time. AI has advanced significantly, but not yet in tasks requiring intuition, empathy and creativity, for example. But breakthroughs in algorithms will allow this.

Moreover, once AI systems achieve such human-like cognitive abilities, there will be a snowball effect and AI systems will be able to improve themselves with minimal to no human involvement. This kind of “automation of intelligence” will profoundly change the world.

Artificial general intelligence remains a significant challenge, and there are ethical and societal implications that must be addressed very carefully as we continue to advance towards it.

Dana Rezazadegan

Lecturer in AI and Data Science, Swinburne University of Technology

Yes, AI is going to get as smart as humans in many ways – but exactly how smart it gets will be decided largely by advancements in  quantum computing .

Human intelligence isn’t as simple as knowing facts. It has several aspects such as creativity, emotional intelligence and intuition, which current AI models can mimic, but can’t match. That said, AI has advanced massively and this trend will continue.

Current models are limited by relatively small and biased training datasets, as well as limited computational power. The emergence of quantum computing will transform AI’s capabilities. With quantum-enhanced AI, we’ll be able to feed AI models multiple massive datasets that are comparable to humans’ natural multi-modal data collection achieved through interacting with the world. These models will be able to maintain fast and accurate analyses.

Having an advanced version of continual learning should lead to the development of highly sophisticated AI systems which, after a certain point, will be able to improve themselves without human input.

As such, AI algorithms running on stable quantum computers have a high chance of reaching something similar to generalised human intelligence – even if they don’t necessarily match every aspect of human intelligence as we know it.

Marcel Scharth

Lecturer in Business Analytics, University of Sydney

I think it’s likely AGI will one day become a reality, although the timeline remains highly uncertain. If AGI is developed, then surpassing human-level intelligence seems inevitable.

Humans themselves are proof that highly flexible and adaptable intelligence is allowed by the laws of physics. There’s no  fundamental reason  we should believe that machines are, in principle, incapable of performing the computations necessary to achieve human-like problem solving abilities.

Furthermore, AI has  distinct advantages  over humans, such as better speed and memory capacity, fewer physical constraints, and the potential for more rationality and recursive self-improvement. As computational power grows, AI systems will eventually surpass the human brain’s computational capacity.

Our primary challenge then is to gain a better understanding of intelligence itself, and knowledge on how to build AGI. Present-day AI systems have many limitations and are nowhere near being able to master the different domains that would characterise AGI. The path to AGI will likely require unpredictable breakthroughs and innovations.

The median predicted date for AGI on  Metaculus , a well-regarded forecasting platform, is 2032. To me, this seems too optimistic. A 2022  expert survey  estimated a 50% chance of us achieving human-level AI by 2059. I find this plausible.

Noor Gillani is the Technology Editor at The Conversation .

This article is republished from The Conversation under a Creative Commons license. Read the original article .

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Debunking the myths of artificial intelligence

This article is based on research by Marc Torrens

Artificial intelligence: the road to ultra intelligence

In his book Artificial intelligence: the road to ultra intelligence , computer science engineer and PhD in Artificial Intelligence Marc Torrens unlocks some of the myths, expectations, and challenges surrounding artificial intelligence (AI) and what may lie ahead.

Do Better: How frightening is artificial intelligence?

Marc Torrens:  Some people get very passionate about artificial intelligence and believe that machines will solve all of the problems facing humanity. At the other extreme, there are those who are overly pessimistic and believe that machines will harm the society in many ways. AI is like any other technological disruption and is neither good nor bad, it all depends on how we apply it. This is why we must start a philosophical and ethical conversation on AI that goes beyond the technical possibilities.

Some people believe that machines will solve all of the problems facing humanity

What's your position?

Nothing is black and white. 'Techno pessimists' should lose some of their fears and see the advantages of artificial intelligence and 'techno optimists' should control their enthusiasm because there are still many problems and challenges to be solved. I am generally optimistic because humanity has always overcome challenges related to technological disruptions, although wasted time and damage can often be avoided with key ethical discussions.

Is there too much hype surrounding AI?

A journalist from the  NY Times  once wrote: "the upheavals of artificial intelligence can escalate quickly and become scarier and even cataclysmic. For example, a medical robot originally programmed to rid cancer could conclude that the best way to obliterate cancer is to exterminate humans who are genetically prone to disease". The mass media have also said things such as: "we will be immortal by 2045".

There is too much hype around AI! And the problem is that this huge expectation can lead to an AI winter similar to that we experienced in the 80s. I prefer less hype and more realism because this will strengthen the discipline in the future.

These ideas sell a lot of newspapers

Of course, but the reality is that these ideas are exaggerations without any serious scientific foundation. Some people have this image of artificial intelligence as a human-like robot that can talk, understand emotions, be aware of itself, use common sense, and even establish emotional relationships. From a scientific point of view, we still have no idea how to make this happen.

There is a lot of hype around artificial intelligence

There is a lot of hype around artificial intelligence. Stephen Hawking once said that the development of full artificial intelligence could spell the end of the human race. Humans, who are limited by slow biological evolution, would become what dogs are to humans today. We would have no control over what happens to us and we would no longer be in charge of making decisions because there would be a far more superior intelligence in the room who would see anything we do as ridiculous. However, we have no idea how to develop this full or strong AI. Moreover, we have no rigorous scientific agenda that enables us to work in that direction with any certainty.

Stephen Hawking once said that the development of full artificial intelligence could spell the end of the human race

We have to demystify the fears surrounding artificial intelligence. It's absurd to worry about these future scenarios – we are very far away from something like this happening. Movies about AI are entertaining and great business, but the truth is that we have no idea about how to develop this type of strong artificial intelligence.

How advanced is artificial intelligence?

Artificial intelligence was invented 70 years ago, but is still in its infancy. Clarke's third law states that "any sufficiently advanced technology is indistinguishable from magic". If we could bring Einstein to 2018 and show him Amazon's Alexa, his right mind would be incapable of guessing its technology and he would think it's magic.

Current AI algorithms are based purely on statistics – they don't have much mystery

When we see things like a computer identifying a face, we may think it is very smart, but current AI algorithms are based purely on statistics – they don't have much mystery. A computer may identify a face in a picture, but the computer does not know what is a face, or that humans have faces.

A computer can beat any chess player but it does not know what is a game, or what it means to win or lose a game. Currently, a computer is capable of taking decisions without understanding anything about the domain.

What is singularity?

The 'singularians' believe that the day when machines will overcome human intelligence is approaching. This prophecy is based on the exponential growth of the two ingredients necessary for machine learning : namely, computing capacity and data availability. In his book The Singularity is Near, Ray Kurzweil (Google) writes that in 2029 artificial intelligence will reach a level that is a billion times more powerful than all human intelligence today.

The 'singularians' believe that the day when machines will overcome human intelligence is approaching

Huh, how did he calculate this?

His over-optimistic calculations are based on the premise that computational capacity and data grow exponentially. It is a fact that the accumulation of data grows exponentially every year and we are advancing with giant steps. In the last two years alone, we have generated 90% of all the data we have accumulated throughout the human history. It is also true that computational capacity is growing exponentially as shown by Moore's empirical law. But predictions by Kurzweil and his advocates miss a crucial aspect of the equation.

Many researchers and practitioners, including myself, believe that this prediction about 2029 has no scientific foundation and that the moment in which artificial intelligence overcomes human intelligence is far away. This is because basic research and science is progressing linearly and not exponentially – humans are slow in making scientific discoveries – and we still need a lot more science to reach this stage.

We cannot expect to model things such as common sense, empathy, and the realm of emotions very soon. We are still in the very early stages of AI. Kurzweil may say 2029, but we do not know if we can ever produce strong AI.

So, singularity is not near...

To paraphrase Andrew Ng from Stanford University, worrying about singularity and super AI is like worrying about overpopulation and pollution on Mars before we arrive. It is impossible to predict and ridiculous to worry about Mars because we haven't even set foot there yet.

Designing machines that can learn or act intelligently in any domain – as we humans do – is still very far away

'What if's' can be disturbing

Artificial intelligence enables us to analyse data and understand reality in a new way and make more informed decisions about any domain. This alone will transform the world because machines will take over many tasks and this will affect all sectors and jobs. But AI is still very narrow and specific. Machines are still pretty dumb and are designed to carry out specific tasks in specific domains. Designing machines that can learn or act intelligently in any domain - as we humans do - is still very far away.

We can design an algorithm to detect cats in an image based on a training set of millions of pictures. However, if we then train the same system to recognise dogs, it will forget about cats (catastrophic forgetting). We do not know how to build systems that learn ANYTHING as we humans do.

Our common sense and intelligence are very hard to model because we do not really understand how they work. We do not yet even know how we make decisions! There is a recent consensus among neuro-scientists that we cannot take any decision without emotions. Thus, whenever rationality is not enough (as in most cases), emotional processes drive our decisions. And this type of reasoning is much harder than just analysing data.

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Artificial Intelligence Essay for Students and Children

500+ words essay on artificial intelligence.

Artificial Intelligence refers to the intelligence of machines. This is in contrast to the natural intelligence of humans and animals. With Artificial Intelligence, machines perform functions such as learning, planning, reasoning and problem-solving. Most noteworthy, Artificial Intelligence is the simulation of human intelligence by machines. It is probably the fastest-growing development in the World of technology and innovation . Furthermore, many experts believe AI could solve major challenges and crisis situations.

Types of Artificial Intelligence

First of all, the categorization of Artificial Intelligence is into four types. Arend Hintze came up with this categorization. The categories are as follows:

Type 1: Reactive machines – These machines can react to situations. A famous example can be Deep Blue, the IBM chess program. Most noteworthy, the chess program won against Garry Kasparov , the popular chess legend. Furthermore, such machines lack memory. These machines certainly cannot use past experiences to inform future ones. It analyses all possible alternatives and chooses the best one.

Type 2: Limited memory – These AI systems are capable of using past experiences to inform future ones. A good example can be self-driving cars. Such cars have decision making systems . The car makes actions like changing lanes. Most noteworthy, these actions come from observations. There is no permanent storage of these observations.

Type 3: Theory of mind – This refers to understand others. Above all, this means to understand that others have their beliefs, intentions, desires, and opinions. However, this type of AI does not exist yet.

Type 4: Self-awareness – This is the highest and most sophisticated level of Artificial Intelligence. Such systems have a sense of self. Furthermore, they have awareness, consciousness, and emotions. Obviously, such type of technology does not yet exist. This technology would certainly be a revolution .

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Applications of Artificial Intelligence

First of all, AI has significant use in healthcare. Companies are trying to develop technologies for quick diagnosis. Artificial Intelligence would efficiently operate on patients without human supervision. Such technological surgeries are already taking place. Another excellent healthcare technology is IBM Watson.

Artificial Intelligence in business would significantly save time and effort. There is an application of robotic automation to human business tasks. Furthermore, Machine learning algorithms help in better serving customers. Chatbots provide immediate response and service to customers.

AI can greatly increase the rate of work in manufacturing. Manufacture of a huge number of products can take place with AI. Furthermore, the entire production process can take place without human intervention. Hence, a lot of time and effort is saved.

Artificial Intelligence has applications in various other fields. These fields can be military , law , video games , government, finance, automotive, audit, art, etc. Hence, it’s clear that AI has a massive amount of different applications.

To sum it up, Artificial Intelligence looks all set to be the future of the World. Experts believe AI would certainly become a part and parcel of human life soon. AI would completely change the way we view our World. With Artificial Intelligence, the future seems intriguing and exciting.

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Will Artificial Intelligence Take Over Humans?

Artificial intelligence is a type of deep machine learning, and many people wonder, “Will artificial intelligence take over humans?” There is no guarantee about the answer to this question, but most technology experts predict that artificial intelligence will grow in use and scope over the upcoming decades. It is important to understand in which ways and in what areas artificial intelligence will have the most effect on what humans usually do.

What Artificial Intelligence Can Do Better Than People Now

All the way back in 1997, IBM’s supercomputer “Deep Blue” beat human chess champion Garry Kasparov at his own game. Artificial intelligence can already pick out what show a person is likely to watch and which things they want to order from Amazon based on past orders. The idea of humans being overtaken by artificial intelligence is known in the tech industry as the “singularity.” Some experts think that this could happen by about 2035. Once that point is reached, computers could be billions of times more intelligent than humans.

Short-term Advances in Artificial Intelligence

Artificial intelligence is advancing at a rapid pace. According to a survey of 352 artificial intelligence researchers conducted in 2015, artificial intelligence is expected to be better at translating languages by 2024, writing essays at the 10th to 12th-grade level by 2026 and driving vehicles by 2027. They could replace grocery store cashiers by 2031. In 120 years, almost all of the tasks that are performed by humans today could be done by artificial intelligence.

What Experts Think About the Future

Experts predict that artificial intelligence will write better books than humans can write by 2049, and they may be performing independent surgeries by 2053. According to Newsweek , artificial intelligence could have an even bigger impact on the future of healthcare. Robots and machines could quickly analyze a person’s genome and use that information to diagnose and treat disease. Instead of a nurse coming into patients’ rooms to check vital signs, a machine could do it. Robots may even deliver meals to patient rooms. Robotic-assisted surgeries are already commonplace today, with many gynecological, urological and ear/nose/throat procedures performed with the aid of a robot.

How Artificial Intelligence Could Affect Jobs

If these predictions play out, a lot of today’s jobs could disappear. People who have jobs such as assembling pieces of cars in a factory, scanning groceries at the store or delivering pizzas to people’s houses could find themselves out of work. Many menial, technique or formulaic jobs could be gone by 2060. Even bloggers could find themselves displaced by artificial intelligence. People will have to be willing to develop new skills that cannot be replicated by a robot, or they will have to learn how to build and repair the robots.

Understanding where artificial intelligence is right now and where it is likely to go makes it easier to predict the future. There will still be plenty of need for humans to fill a wide variety of job roles in society, and human-to-human interactions are unlikely to be displaced by machines. Knowing the answer to, “Will artificial intelligence take over for humans?” gives a person plenty of food for thought and a chance to learn more about this fast-paced form of technology.

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Essay on Future of Artificial Intelligence

Students are often asked to write an essay on Future of Artificial Intelligence in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Future of Artificial Intelligence

Introduction.

Artificial Intelligence (AI) is the science of making machines think and learn like humans. It’s an exciting field that’s rapidly changing our world.

Future Possibilities

Challenges ahead.

However, there are challenges. We need to make sure AI is used responsibly, and that it doesn’t take away too many jobs.

The future of AI is promising, but we need to navigate it carefully to ensure it benefits everyone.

250 Words Essay on Future of Artificial Intelligence

Ai in everyday life.

The future of AI holds promising advancements in everyday life. We can expect more sophisticated personal assistants, smarter home automation, and advanced healthcare systems. AI will continue to streamline our lives, making mundane tasks more efficient.

AI in Business

In business, AI will revolutionize industries by automating processes and creating new business models. Predictive analytics, customer service, and supply chain management will become more efficient and accurate. AI will also enable personalized marketing, enhancing customer experience and retention.

AI in Ethics and Society

However, the future of AI also poses ethical and societal challenges. Issues such as job displacement due to automation, privacy concerns, and the potential misuse of AI technologies need to be addressed. Ensuring fairness, transparency, and accountability in AI systems will be crucial.

In conclusion, the future of AI is a blend of immense potential and challenges. It will transform our lives and businesses, but also necessitates careful consideration of ethical and societal implications. As we move forward, it is essential to foster a global dialogue about the responsible use and governance of AI.

500 Words Essay on Future of Artificial Intelligence

Artificial Intelligence (AI) has transformed from a fringe scientific concept into a commonplace technology, permeating every aspect of our lives. As we stand on the precipice of the future, it becomes crucial to understand AI’s potential trajectory and the profound implications it might have on society.

The Evolution of AI

The current focus is on developing General AI, machines that can perform any intellectual task that a human being can. While we are yet to achieve this, advancements in Deep Learning and Neural Networks are bringing us closer to this reality.

AI in the Future

In the future, AI is expected to become more autonomous and integrated into our daily lives. We will see AI systems that can not only understand and learn from their environment but also make complex decisions, solve problems, and even exhibit creativity.

One of the most promising areas is AI’s role in data analysis. As data continues to grow exponentially, AI will become indispensable in making sense of this information, leading to breakthroughs in fields like healthcare, climate change, and social sciences.

Implications and Challenges

Moreover, as AI continues to automate tasks, there are concerns about job displacement. While AI will undoubtedly create new jobs, it will also render many existing jobs obsolete. Therefore, societies must prepare for this transition by investing in education and training.

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More From Forbes

Will ai take over the world or will you take charge of your world.

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There’s been a lot of scary talk going around lately. Artificial intelligence is getting more powerful — especially the new generative AI that can write code, write stories, and generate outputs ranging from pretty pictures to product designs. The greatest concern is not so much that computers will become smarter than humans, it’s that they will be unpredictably smart, or unpredictably foolish, due to quirks in the AI's code. Experts worry that if we keep entrusting key tasks to them, they could trigger what Elon Musk has called “ civilization destruction .”

This worst-case scenario needs to be addressed but will not happen soon. If you own or manage a midsize company, the pressing issue is how new developments in AI will affect your business. Our view, which reflects a consensus view, says to handle this change in the environment the way any big change should be handled. Don’t ignore it, or try to resist it, or get stuck on what it might do to you. Instead, look at what you can do with the change. Embrace it. Leverage it to your advantage.

Here’s a brief overview that should make clear a couple of key points. Although the recent surge in AI may seem like it came out of the blue, it’s really just the next step in a long process of evolutionary change. Not only can midsize companies participate in the evolution, they will have to in order to stay fit to survive.

How we got here … and where we can go next

Artificial intelligence—the creation of software and hardware able to simulate human smarts—isn’t new. Crucial core technologies for today’s AI were first conceived in the 1970s and ‘80s. In the 1990s, IBM’s Deep Blue chess machine played and beat the reigning world champion, setting a milestone for AI researchers. Since then, AI has continued to improve while moving into new realms, some of which we now take for granted. By the 2010s, natural language processing was refined to the point where Siri and Alexa could be your virtual assistants.

What’s new lately is that major tech-industry players have been ramping up investment at the frontiers of AI. Elon Musk is a leader in the field despite his reservations. He has launched a deep-pocketed startup, X.ai, to focus solely on cutting-edge AI. Microsoft is the lead investor in OpenAI. Amazon, Google/Alphabet, and others are placing big bets in the race as well.

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This raises an oft-heard concern. Will the tech heavyweights dominate the future of AI , just as they’ve dominated so much else? And will that, in turn, leave midsize-to-small companies in the dust?

Do not worry. A key distinction must be recognized. The R&D efforts are being led by big players because they have the resources needed: basic research in advanced AI is expensive. Certainly the big firms will also use the fruits of that R&D in their own products and services. But the results of their work will come to market—indeed, are already coming to market—in forms that are highly affordable.

Over the past few years, our consulting firm has helped midsized companies apply AI to analyze customer data for targeted marketing. Many of the new generative AI tools, such as ChatGPT, are free or cost little. In a podcast hosted by Harvard Business Review , guest experts agreed that generative AI is actually “ democratizing access to the highest levels of technology ,” rather than shutting out the little guys. Companies can even find cost-effective ways to tailor a general, open-source AI tool (a “foundation model”) for their own specific uses. We’re now seeing an expanding galaxy of possible business uses.

An in-depth report from McKinsey & Company in May 2023 put the situation bluntly: “CEOs should consider exploration of generative AI a must, not a maybe... The economics and technical requirements to start are not prohibitive, while the downside of inaction could be quickly falling behind competitors.”

Companies can begin by exploring simple, easy-to-do applications that promise tangible paybacks, and then move up the sophistication ladder as desired. Just two examples of potential uses: AIs that write code can be used in paired programming, to check, improve, and speed up the work of a human developer. And while AI is already widely used in marketing and sales, generative AI could help you raise your game. Imagine you’re on a sales call. You have your laptop open and an AI is listening in. The AI might guide you through the call with real-time screen prompts attuned to what the customer is saying, as well as what’s in the database.

Now is the time to start your exploration, if you haven’t yet. The sooner you embrace this technology and the faster you learn to work with it, the more likely you are to get a leg up.

A final point to keep in mind is one we mentioned earlier. The future of AI is unpredictable . Change is constant and nobody knows for sure where it will take us next. This means being ready to do more than embrace the latest new thing. It means embracing change as a fundamental part of your company’s DNA. Evolve and prosper!

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The impact of artificial intelligence on human society and bioethics

Michael cheng-tek tai.

Department of Medical Sociology and Social Work, College of Medicine, Chung Shan Medical University, Taichung, Taiwan

Artificial intelligence (AI), known by some as the industrial revolution (IR) 4.0, is going to change not only the way we do things, how we relate to others, but also what we know about ourselves. This article will first examine what AI is, discuss its impact on industrial, social, and economic changes on humankind in the 21 st century, and then propose a set of principles for AI bioethics. The IR1.0, the IR of the 18 th century, impelled a huge social change without directly complicating human relationships. Modern AI, however, has a tremendous impact on how we do things and also the ways we relate to one another. Facing this challenge, new principles of AI bioethics must be considered and developed to provide guidelines for the AI technology to observe so that the world will be benefited by the progress of this new intelligence.

W HAT IS ARTIFICIAL INTELLIGENCE ?

Artificial intelligence (AI) has many different definitions; some see it as the created technology that allows computers and machines to function intelligently. Some see it as the machine that replaces human labor to work for men a more effective and speedier result. Others see it as “a system” with the ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation [ 1 ].

Despite the different definitions, the common understanding of AI is that it is associated with machines and computers to help humankind solve problems and facilitate working processes. In short, it is an intelligence designed by humans and demonstrated by machines. The term AI is used to describe these functions of human-made tool that emulates the “cognitive” abilities of the natural intelligence of human minds [ 2 ].

Along with the rapid development of cybernetic technology in recent years, AI has been seen almost in all our life circles, and some of that may no longer be regarded as AI because it is so common in daily life that we are much used to it such as optical character recognition or the Siri (speech interpretation and recognition interface) of information searching equipment on computer [ 3 ].

D IFFERENT TYPES OF ARTIFICIAL INTELLIGENCE

From the functions and abilities provided by AI, we can distinguish two different types. The first is weak AI, also known as narrow AI that is designed to perform a narrow task, such as facial recognition or Internet Siri search or self-driving car. Many currently existing systems that claim to use “AI” are likely operating as a weak AI focusing on a narrowly defined specific function. Although this weak AI seems to be helpful to human living, there are still some think weak AI could be dangerous because weak AI could cause disruptions in the electric grid or may damage nuclear power plants when malfunctioned.

The new development of the long-term goal of many researchers is to create strong AI or artificial general intelligence (AGI) which is the speculative intelligence of a machine that has the capacity to understand or learn any intelligent task human being can, thus assisting human to unravel the confronted problem. While narrow AI may outperform humans such as playing chess or solving equations, but its effect is still weak. AGI, however, could outperform humans at nearly every cognitive task.

Strong AI is a different perception of AI that it can be programmed to actually be a human mind, to be intelligent in whatever it is commanded to attempt, even to have perception, beliefs and other cognitive capacities that are normally only ascribed to humans [ 4 ].

In summary, we can see these different functions of AI [ 5 , 6 ]:

• Automation: What makes a system or process to function automatically
• Machine learning and vision: The science of getting a computer to act through deep learning to predict and analyze, and to see through a camera, analog-to-digital conversion and digital signal processing
• Natural language processing: The processing of human language by a computer program, such as spam detection and converting instantly a language to another to help humans communicate
• Robotics: A field of engineering focusing on the design and manufacturing of cyborgs, the so-called machine man. They are used to perform tasks for human's convenience or something too difficult or dangerous for human to perform and can operate without stopping such as in assembly lines
• Self-driving car: Use a combination of computer vision, image recognition amid deep learning to build automated control in a vehicle.

D O HUMAN-BEINGS REALLY NEED ARTIFICIAL INTELLIGENCE ?

Is AI really needed in human society? It depends. If human opts for a faster and effective way to complete their work and to work constantly without taking a break, yes, it is. However if humankind is satisfied with a natural way of living without excessive desires to conquer the order of nature, it is not. History tells us that human is always looking for something faster, easier, more effective, and convenient to finish the task they work on; therefore, the pressure for further development motivates humankind to look for a new and better way of doing things. Humankind as the homo-sapiens discovered that tools could facilitate many hardships for daily livings and through tools they invented, human could complete the work better, faster, smarter and more effectively. The invention to create new things becomes the incentive of human progress. We enjoy a much easier and more leisurely life today all because of the contribution of technology. The human society has been using the tools since the beginning of civilization, and human progress depends on it. The human kind living in the 21 st century did not have to work as hard as their forefathers in previous times because they have new machines to work for them. It is all good and should be all right for these AI but a warning came in early 20 th century as the human-technology kept developing that Aldous Huxley warned in his book Brave New World that human might step into a world in which we are creating a monster or a super human with the development of genetic technology.

Besides, up-to-dated AI is breaking into healthcare industry too by assisting doctors to diagnose, finding the sources of diseases, suggesting various ways of treatment performing surgery and also predicting if the illness is life-threatening [ 7 ]. A recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot to perform soft-tissue surgery, stitch together a pig's bowel, and the robot finished the job better than a human surgeon, the team claimed [ 8 , 9 ]. It demonstrates robotically-assisted surgery can overcome the limitations of pre-existing minimally-invasive surgical procedures and to enhance the capacities of surgeons performing open surgery.

Above all, we see the high-profile examples of AI including autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays…etc. All these have made human life much easier and convenient that we are so used to them and take them for granted. AI has become indispensable, although it is not absolutely needed without it our world will be in chaos in many ways today.

T HE IMPACT OF ARTIFICIAL INTELLIGENCE ON HUMAN SOCIETY

Negative impact.

Questions have been asked: With the progressive development of AI, human labor will no longer be needed as everything can be done mechanically. Will humans become lazier and eventually degrade to the stage that we return to our primitive form of being? The process of evolution takes eons to develop, so we will not notice the backsliding of humankind. However how about if the AI becomes so powerful that it can program itself to be in charge and disobey the order given by its master, the humankind?

Let us see the negative impact the AI will have on human society [ 10 , 11 ]:

• A huge social change that disrupts the way we live in the human community will occur. Humankind has to be industrious to make their living, but with the service of AI, we can just program the machine to do a thing for us without even lifting a tool. Human closeness will be gradually diminishing as AI will replace the need for people to meet face to face for idea exchange. AI will stand in between people as the personal gathering will no longer be needed for communication
• Unemployment is the next because many works will be replaced by machinery. Today, many automobile assembly lines have been filled with machineries and robots, forcing traditional workers to lose their jobs. Even in supermarket, the store clerks will not be needed anymore as the digital device can take over human labor
• Wealth inequality will be created as the investors of AI will take up the major share of the earnings. The gap between the rich and the poor will be widened. The so-called “M” shape wealth distribution will be more obvious
• New issues surface not only in a social sense but also in AI itself as the AI being trained and learned how to operate the given task can eventually take off to the stage that human has no control, thus creating un-anticipated problems and consequences. It refers to AI's capacity after being loaded with all needed algorithm may automatically function on its own course ignoring the command given by the human controller
• The human masters who create AI may invent something that is racial bias or egocentrically oriented to harm certain people or things. For instance, the United Nations has voted to limit the spread of nucleus power in fear of its indiscriminative use to destroying humankind or targeting on certain races or region to achieve the goal of domination. AI is possible to target certain race or some programmed objects to accomplish the command of destruction by the programmers, thus creating world disaster.

P OSITIVE IMPACT

There are, however, many positive impacts on humans as well, especially in the field of healthcare. AI gives computers the capacity to learn, reason, and apply logic. Scientists, medical researchers, clinicians, mathematicians, and engineers, when working together, can design an AI that is aimed at medical diagnosis and treatments, thus offering reliable and safe systems of health-care delivery. As health professors and medical researchers endeavor to find new and efficient ways of treating diseases, not only the digital computer can assist in analyzing, robotic systems can also be created to do some delicate medical procedures with precision. Here, we see the contribution of AI to health care [ 7 , 11 ]:

Fast and accurate diagnostics

IBM's Watson computer has been used to diagnose with the fascinating result. Loading the data to the computer will instantly get AI's diagnosis. AI can also provide various ways of treatment for physicians to consider. The procedure is something like this: To load the digital results of physical examination to the computer that will consider all possibilities and automatically diagnose whether or not the patient suffers from some deficiencies and illness and even suggest various kinds of available treatment.

Socially therapeutic robots

Pets are recommended to senior citizens to ease their tension and reduce blood pressure, anxiety, loneliness, and increase social interaction. Now cyborgs have been suggested to accompany those lonely old folks, even to help do some house chores. Therapeutic robots and the socially assistive robot technology help improve the quality of life for seniors and physically challenged [ 12 ].

Reduce errors related to human fatigue

Human error at workforce is inevitable and often costly, the greater the level of fatigue, the higher the risk of errors occurring. Al technology, however, does not suffer from fatigue or emotional distraction. It saves errors and can accomplish the duty faster and more accurately.

Artificial intelligence-based surgical contribution

AI-based surgical procedures have been available for people to choose. Although this AI still needs to be operated by the health professionals, it can complete the work with less damage to the body. The da Vinci surgical system, a robotic technology allowing surgeons to perform minimally invasive procedures, is available in most of the hospitals now. These systems enable a degree of precision and accuracy far greater than the procedures done manually. The less invasive the surgery, the less trauma it will occur and less blood loss, less anxiety of the patients.

Improved radiology

The first computed tomography scanners were introduced in 1971. The first magnetic resonance imaging (MRI) scan of the human body took place in 1977. By the early 2000s, cardiac MRI, body MRI, and fetal imaging, became routine. The search continues for new algorithms to detect specific diseases as well as to analyze the results of scans [ 9 ]. All those are the contribution of the technology of AI.

Virtual presence

The virtual presence technology can enable a distant diagnosis of the diseases. The patient does not have to leave his/her bed but using a remote presence robot, doctors can check the patients without actually being there. Health professionals can move around and interact almost as effectively as if they were present. This allows specialists to assist patients who are unable to travel.

S OME CAUTIONS TO BE REMINDED

Despite all the positive promises that AI provides, human experts, however, are still essential and necessary to design, program, and operate the AI from any unpredictable error from occurring. Beth Kindig, a San Francisco-based technology analyst with more than a decade of experience in analyzing private and public technology companies, published a free newsletter indicating that although AI has a potential promise for better medical diagnosis, human experts are still needed to avoid the misclassification of unknown diseases because AI is not omnipotent to solve all problems for human kinds. There are times when AI meets an impasse, and to carry on its mission, it may just proceed indiscriminately, ending in creating more problems. Thus vigilant watch of AI's function cannot be neglected. This reminder is known as physician-in-the-loop [ 13 ].

The question of an ethical AI consequently was brought up by Elizabeth Gibney in her article published in Nature to caution any bias and possible societal harm [ 14 ]. The Neural Information processing Systems (NeurIPS) conference in Vancouver Canada in 2020 brought up the ethical controversies of the application of AI technology, such as in predictive policing or facial recognition, that due to bias algorithms can result in hurting the vulnerable population [ 14 ]. For instance, the NeurIPS can be programmed to target certain race or decree as the probable suspect of crime or trouble makers.

T HE CHALLENGE OF ARTIFICIAL INTELLIGENCE TO BIOETHICS

Artificial intelligence ethics must be developed.

Bioethics is a discipline that focuses on the relationship among living beings. Bioethics accentuates the good and the right in biospheres and can be categorized into at least three areas, the bioethics in health settings that is the relationship between physicians and patients, the bioethics in social settings that is the relationship among humankind and the bioethics in environmental settings that is the relationship between man and nature including animal ethics, land ethics, ecological ethics…etc. All these are concerned about relationships within and among natural existences.

As AI arises, human has a new challenge in terms of establishing a relationship toward something that is not natural in its own right. Bioethics normally discusses the relationship within natural existences, either humankind or his environment, that are parts of natural phenomena. But now men have to deal with something that is human-made, artificial and unnatural, namely AI. Human has created many things yet never has human had to think of how to ethically relate to his own creation. AI by itself is without feeling or personality. AI engineers have realized the importance of giving the AI ability to discern so that it will avoid any deviated activities causing unintended harm. From this perspective, we understand that AI can have a negative impact on humans and society; thus, a bioethics of AI becomes important to make sure that AI will not take off on its own by deviating from its originally designated purpose.

Stephen Hawking warned early in 2014 that the development of full AI could spell the end of the human race. He said that once humans develop AI, it may take off on its own and redesign itself at an ever-increasing rate [ 15 ]. Humans, who are limited by slow biological evolution, could not compete and would be superseded. In his book Superintelligence, Nick Bostrom gives an argument that AI will pose a threat to humankind. He argues that sufficiently intelligent AI can exhibit convergent behavior such as acquiring resources or protecting itself from being shut down, and it might harm humanity [ 16 ].

The question is–do we have to think of bioethics for the human's own created product that bears no bio-vitality? Can a machine have a mind, consciousness, and mental state in exactly the same sense that human beings do? Can a machine be sentient and thus deserve certain rights? Can a machine intentionally cause harm? Regulations must be contemplated as a bioethical mandate for AI production.

Studies have shown that AI can reflect the very prejudices humans have tried to overcome. As AI becomes “truly ubiquitous,” it has a tremendous potential to positively impact all manner of life, from industry to employment to health care and even security. Addressing the risks associated with the technology, Janosch Delcker, Politico Europe's AI correspondent, said: “I don't think AI will ever be free of bias, at least not as long as we stick to machine learning as we know it today,”…. “What's crucially important, I believe, is to recognize that those biases exist and that policymakers try to mitigate them” [ 17 ]. The High-Level Expert Group on AI of the European Union presented Ethics Guidelines for Trustworthy AI in 2019 that suggested AI systems must be accountable, explainable, and unbiased. Three emphases are given:

• Lawful-respecting all applicable laws and regulations
• Ethical-respecting ethical principles and values
• Robust-being adaptive, reliable, fair, and trustworthy from a technical perspective while taking into account its social environment [ 18 ].

Seven requirements are recommended [ 18 ]:

• AI should not trample on human autonomy. People should not be manipulated or coerced by AI systems, and humans should be able to intervene or oversee every decision that the software makes
• AI should be secure and accurate. It should not be easily compromised by external attacks, and it should be reasonably reliable
• Personal data collected by AI systems should be secure and private. It should not be accessible to just anyone, and it should not be easily stolen
• Data and algorithms used to create an AI system should be accessible, and the decisions made by the software should be “understood and traced by human beings.” In other words, operators should be able to explain the decisions their AI systems make
• Services provided by AI should be available to all, regardless of age, gender, race, or other characteristics. Similarly, systems should not be biased along these lines
• AI systems should be sustainable (i.e., they should be ecologically responsible) and “enhance positive social change”
• AI systems should be auditable and covered by existing protections for corporate whistleblowers. The negative impacts of systems should be acknowledged and reported in advance.

From these guidelines, we can suggest that future AI must be equipped with human sensibility or “AI humanities.” To accomplish this, AI researchers, manufacturers, and all industries must bear in mind that technology is to serve not to manipulate humans and his society. Bostrom and Judkowsky listed responsibility, transparency, auditability, incorruptibility, and predictability [ 19 ] as criteria for the computerized society to think about.

S UGGESTED PRINCIPLES FOR ARTIFICIAL INTELLIGENCE BIOETHICS

Nathan Strout, a reporter at Space and Intelligence System at Easter University, USA, reported just recently that the intelligence community is developing its own AI ethics. The Pentagon made announced in February 2020 that it is in the process of adopting principles for using AI as the guidelines for the department to follow while developing new AI tools and AI-enabled technologies. Ben Huebner, chief of the Office of Director of National Intelligence's Civil Liberties, Privacy, and Transparency Office, said that “We're going to need to ensure that we have transparency and accountability in these structures as we use them. They have to be secure and resilient” [ 20 ]. Two themes have been suggested for the AI community to think more about: Explainability and interpretability. Explainability is the concept of understanding how the analytic works, while interpretability is being able to understand a particular result produced by an analytic [ 20 ].

All the principles suggested by scholars for AI bioethics are well-brought-up. I gather from different bioethical principles in all the related fields of bioethics to suggest four principles here for consideration to guide the future development of the AI technology. We however must bear in mind that the main attention should still be placed on human because AI after all has been designed and manufactured by human. AI proceeds to its work according to its algorithm. AI itself cannot empathize nor have the ability to discern good from evil and may commit mistakes in processes. All the ethical quality of AI depends on the human designers; therefore, it is an AI bioethics and at the same time, a trans-bioethics that abridge human and material worlds. Here are the principles:

• Beneficence: Beneficence means doing good, and here it refers to the purpose and functions of AI should benefit the whole human life, society and universe. Any AI that will perform any destructive work on bio-universe, including all life forms, must be avoided and forbidden. The AI scientists must understand that reason of developing this technology has no other purpose but to benefit human society as a whole not for any individual personal gain. It should be altruistic, not egocentric in nature
• Value-upholding: This refers to AI's congruence to social values, in other words, universal values that govern the order of the natural world must be observed. AI cannot elevate to the height above social and moral norms and must be bias-free. The scientific and technological developments must be for the enhancement of human well-being that is the chief value AI must hold dearly as it progresses further
• Lucidity: AI must be transparent without hiding any secret agenda. It has to be easily comprehensible, detectable, incorruptible, and perceivable. AI technology should be made available for public auditing, testing and review, and subject to accountability standards … In high-stakes settings like diagnosing cancer from radiologic images, an algorithm that can't “explain its work” may pose an unacceptable risk. Thus, explainability and interpretability are absolutely required
• Accountability: AI designers and developers must bear in mind they carry a heavy responsibility on their shoulders of the outcome and impact of AI on whole human society and the universe. They must be accountable for whatever they manufacture and create.

C ONCLUSION

AI is here to stay in our world and we must try to enforce the AI bioethics of beneficence, value upholding, lucidity and accountability. Since AI is without a soul as it is, its bioethics must be transcendental to bridge the shortcoming of AI's inability to empathize. AI is a reality of the world. We must take note of what Joseph Weizenbaum, a pioneer of AI, said that we must not let computers make important decisions for us because AI as a machine will never possess human qualities such as compassion and wisdom to morally discern and judge [ 10 ]. Bioethics is not a matter of calculation but a process of conscientization. Although AI designers can up-load all information, data, and programmed to AI to function as a human being, it is still a machine and a tool. AI will always remain as AI without having authentic human feelings and the capacity to commiserate. Therefore, AI technology must be progressed with extreme caution. As Von der Leyen said in White Paper on AI – A European approach to excellence and trust : “AI must serve people, and therefore, AI must always comply with people's rights…. High-risk AI. That potentially interferes with people's rights has to be tested and certified before it reaches our single market” [ 21 ].

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Artificial Intelligence Essay

500+ words essay on artificial intelligence.

Artificial intelligence (AI) has come into our daily lives through mobile devices and the Internet. Governments and businesses are increasingly making use of AI tools and techniques to solve business problems and improve many business processes, especially online ones. Such developments bring about new realities to social life that may not have been experienced before. This essay on Artificial Intelligence will help students to know the various advantages of using AI and how it has made our lives easier and simpler. Also, in the end, we have described the future scope of AI and the harmful effects of using it. To get a good command of essay writing, students must practise CBSE Essays on different topics.

Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is concerned with getting computers to do tasks that would normally require human intelligence. AI systems are basically software systems (or controllers for robots) that use techniques such as machine learning and deep learning to solve problems in particular domains without hard coding all possibilities (i.e. algorithmic steps) in software. Due to this, AI started showing promising solutions for industry and businesses as well as our daily lives.

Importance and Advantages of Artificial Intelligence

Advances in computing and digital technologies have a direct influence on our lives, businesses and social life. This has influenced our daily routines, such as using mobile devices and active involvement on social media. AI systems are the most influential digital technologies. With AI systems, businesses are able to handle large data sets and provide speedy essential input to operations. Moreover, businesses are able to adapt to constant changes and are becoming more flexible.

By introducing Artificial Intelligence systems into devices, new business processes are opting for the automated process. A new paradigm emerges as a result of such intelligent automation, which now dictates not only how businesses operate but also who does the job. Many manufacturing sites can now operate fully automated with robots and without any human workers. Artificial Intelligence now brings unheard and unexpected innovations to the business world that many organizations will need to integrate to remain competitive and move further to lead the competitors.

Artificial Intelligence shapes our lives and social interactions through technological advancement. There are many AI applications which are specifically developed for providing better services to individuals, such as mobile phones, electronic gadgets, social media platforms etc. We are delegating our activities through intelligent applications, such as personal assistants, intelligent wearable devices and other applications. AI systems that operate household apparatus help us at home with cooking or cleaning.

Future Scope of Artificial Intelligence

In the future, intelligent machines will replace or enhance human capabilities in many areas. Artificial intelligence is becoming a popular field in computer science as it has enhanced humans. Application areas of artificial intelligence are having a huge impact on various fields of life to solve complex problems in various areas such as education, engineering, business, medicine, weather forecasting etc. Many labourers’ work can be done by a single machine. But Artificial Intelligence has another aspect: it can be dangerous for us. If we become completely dependent on machines, then it can ruin our life. We will not be able to do any work by ourselves and get lazy. Another disadvantage is that it cannot give a human-like feeling. So machines should be used only where they are actually required.

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Home — Essay Samples — Information Science and Technology — Modern Technology — Artificial Intelligence

Essays on Artificial Intelligence

Writing an essay on artificial intelligence is not just an academic exercise; it's a chance to explore the cutting-edge innovations and the profound impact AI has on our lives. For students looking to delve deeper into this topic, utilizing the best AI tools for students can provide a significant edge in crafting a well-researched and analytical essay. 🚀 So, get ready to unlock the potential of AI with your words!

Artificial Intelligence Essay Topics for "Artificial Intelligence" 📝

Choosing the right topic is key to writing a compelling essay. Here's how to pick the perfect one:

Artificial Intelligence Argumentative Essay 🤨

Argumentative AI essays require you to take a stance on AI-related issues. Here are ten thought-provoking topics:

• 1. The ethical implications of AI in autonomous weaponry.
• 2. Should AI be granted legal personhood and rights?
• 3. Analyze the impact of AI on the job market and employment prospects.
• 4. The role of AI in addressing climate change and environmental challenges.
• 5. Discuss the risks and benefits of AI in healthcare and medical diagnostics.
• 6. AI's impact on privacy and surveillance in modern society.
• 7. Evaluate the use of AI in education and personalized learning.
• 8. The role of AI in improving cybersecurity and data protection.
• 9. Discuss the potential biases and discrimination in AI algorithms.
• 10. AI and its implications for creativity and the arts.
• 11. The Ethical Implications of Programming Bias into Artificial Intelligence

Artificial Intelligence Cause and Effect Essay 🤯

Dive into cause and effect relationships in the AI realm with these topics:

• 1. Explore how AI-powered virtual assistants have changed communication habits.
• 2. Analyze the effects of AI-driven predictive policing on crime rates.
• 3. Discuss how AI-driven healthcare advancements have extended human lifespans.
• 4. The consequences of AI-powered autonomous vehicles on transportation and traffic safety.
• 5. Investigate the impact of AI algorithms on social media echo chambers and polarization.
• 6. The influence of AI-driven personalized marketing on consumer behavior.
• 7. Explore how AI has revolutionized the entertainment industry and storytelling.
• 8. Analyze the cause and effect of AI's role in financial markets and investment strategies.
• 9. Discuss the effects of AI on reducing energy consumption and sustainable living.
• 10. The consequences of AI in aiding scientific research and discovery.

Artificial Intelligence Opinion Essay 😌

Express your personal views and interpretations on AI through these essay topics:

• 1. Share your opinion on the potential dangers of superintelligent AI.
• 2. Discuss your perspective on AI's role in enhancing human capabilities.
• 3. Express your thoughts on the future of work in an AI-dominated world.
• 4. Debate the significance of AI in addressing global challenges like pandemics.
• 5. Share your views on the ethical responsibilities of AI developers and researchers.
• 6. Discuss the impact of AI on human creativity and innovation.
• 7. Express your opinion on AI's influence on education and personalized learning.
• 8. Debate the ethics of AI in decision-making, such as self-driving car dilemmas.
• 9. Share your perspective on AI's potential to bridge the digital divide and promote equity.
• 10. Discuss your favorite AI-related invention or innovation and its implications.

Artificial Intelligence Informative Essay 🧐

Inform and educate your readers with these informative AI essay topics:

• 1. Explore the history and evolution of artificial intelligence.
• 2. Provide an in-depth analysis of popular AI technologies like deep learning and neural networks.
• 3. Investigate the significance of AI in autonomous robotics and space exploration.
• 4. Analyze the role of AI in natural language processing and language translation.
• 5. Examine the applications of AI in climate modeling and environmental conservation.
• 6. Explore the cultural and societal impacts of AI in science fiction literature and films.
• 7. Provide insights into the ethics of AI in medical decision-making and diagnosis.
• 8. Analyze the potential for AI in disaster response and emergency management.
• 9. Discuss the role of AI in enhancing cybersecurity and threat detection.
• 10. Examine the future trends and possibilities of AI in various industries.
• 11. Ethical Implications of AI in Healthcare: Patient Privacy
• 12. Impact of AI on Government Services: Study of Role in UPSC Exam Process

Artificial Intelligence Essay Example 📄

Artificial intelligence thesis statement examples 📜.

Here are five examples of strong thesis statements for your AI essay:

• 1. "The rapid advancements in artificial intelligence present both unprecedented opportunities and ethical dilemmas, as we navigate the journey toward an AI-driven future."
• 2. "In analyzing the impact of AI on healthcare, we unveil a transformative force that promises to revolutionize medical diagnosis and treatment, but also raises concerns about data privacy and security."
• 3. "The development of superintelligent AI systems demands careful consideration of ethical frameworks to ensure their responsible and beneficial integration into society."
• 4. "Artificial intelligence is not a replacement for human creativity but a powerful tool that amplifies our capabilities, ushering in an era of unprecedented innovation and discovery."
• 5. "AI-driven autonomous vehicles represent a technological leap that holds the potential to reshape transportation, reduce accidents, and increase accessibility, but also raises questions about liability and safety."

Artificial Intelligence Essay Introduction Examples 🚀

Here are three captivating introduction paragraphs to begin your essay:

• 1. "In a world driven by data and algorithms, artificial intelligence has emerged as both a beacon of innovation and a source of profound ethical contemplation. As we embark on this essay journey into the realm of AI, we peel back the layers of silicon and software to explore the implications, promises, and challenges of our AI-driven future."
• 2. "Imagine a world where machines not only assist us but also think, learn, and adapt. The rise of artificial intelligence has ignited a conversation that transcends technology—it delves into the very essence of human potential and the responsibilities we bear as creators. Join us as we navigate the AI landscape, one algorithm at a time."
• 3. "In an era marked by digital transformations and the ubiquity of smart devices, artificial intelligence stands as the sentinel of change. As we step into the world of AI analysis, we are confronted with a paradox: the immense power of machines and the ethical dilemmas they pose. Together, let's dissect the AI phenomenon, from its inception to its potential to shape the destiny of humanity."

Artificial Intelligence Conclusion Examples 🌟

Conclude your essay with impact using these examples:

• 1. "As we draw the curtains on this AI exploration, we stand at the intersection of innovation and ethics. Artificial intelligence, with all its wonders and complexities, challenges us to not only harness its power for progress but also to ensure its responsible and ethical use. The journey continues, and the conversation evolves as we navigate the evolving landscape of AI."
• 2. "In the closing frame of our AI analysis, we reflect on the ever-expanding possibilities and responsibilities that AI brings to our doorstep. The pages of this essay mark a beginning—a call to action. Together, we have explored the AI landscape, and the future is now in our hands, waiting for our choices to shape it."
• 3. "As the AI narrative reaches its conclusion, we find ourselves at the crossroads of human ingenuity and artificial intelligence. The journey has been both enlightening and thought-provoking, reminding us that the future of AI is a collaborative endeavor, guided by ethics, curiosity, and a shared vision of a better world."

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The Impact of Artificial Intelligence on Modern Society

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Artificial Intelligence

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Ethical Issues in Using Ai Technology Today

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Artificial intelligence (AI) refers to the intellectual capabilities exhibited by machines, contrasting with the innate intelligence observed in living beings, such as animals and humans.

The inception of artificial intelligence research as an academic field can be traced back to its establishment in 1956. It was during the renowned Dartmouth conference of the same year that artificial intelligence acquired its distinctive name, definitive purpose, initial accomplishments, and notable pioneers, thereby earning its reputation as the birthplace of AI. The esteemed figures of Marvin Minsky and John McCarthy are widely recognized as the founding fathers of this discipline.

Early pioneers such as John McCarthy, Marvin Minsky, and Allen Newell played instrumental roles in shaping the foundations of AI research. In the following years after its original inception, AI witnessed both periods of optimism and periods of skepticism, as researchers explored different approaches and techniques. Notable breakthroughs include the development of expert systems in the 1970s, which aimed to replicate human knowledge and reasoning, and the emergence of machine learning algorithms in the 1980s and 1990s. The turn of the 21st century witnessed significant advancements in AI, with the rise of big data, powerful computing technologies, and deep learning algorithms. This led to remarkable achievements in areas such as natural language processing, computer vision, and autonomous systems.

There are four types of artificial intelligence: reactive machines, limited memory, theory of mind and self-awareness.

Healthcare: AI assists in medical diagnosis, drug discovery, personalized treatment plans, and analyzing medical images. Finance: AI is used for automated trading, fraud detection, risk assessment, and customer service through chatbots. Transportation: AI powers autonomous vehicles, traffic optimization, logistics, and supply chain management. Entertainment: AI contributes to recommendation systems, AI-generated music and art, virtual reality experiences, and content creation. Cybersecurity: AI helps in detecting and preventing cyber threats and enhancing network security. Agriculture: AI optimizes farming practices, crop management, and precision agriculture. Education: AI enables personalized learning, adaptive assessments, and intelligent tutoring systems. Natural Language Processing: AI facilitates language translation, voice assistants, chatbots, and sentiment analysis. Robotics: AI powers robots in various applications, such as manufacturing, healthcare, and exploration. Environmental Conservation: AI aids in environmental monitoring, wildlife protection, and climate modeling.

John McCarthy: Coined the term "artificial intelligence" and organized the Dartmouth Conference in 1956, which is considered the birth of AI as an academic discipline. Marvin Minsky: A cognitive scientist and AI pioneer, Minsky co-founded the Massachusetts Institute of Technology's AI Laboratory and made notable contributions to robotics and cognitive psychology. Geoffrey Hinton: Renowned for his work on neural networks and deep learning, Hinton's research has greatly advanced the field of AI and revolutionized areas such as image and speech recognition. Andrew Ng: An influential figure in the field of AI, Ng co-founded Google Brain, led the development of the deep learning framework TensorFlow, and has made significant contributions to machine learning algorithms. Fei-Fei Li: A prominent researcher in computer vision and AI, Li has made groundbreaking contributions to image recognition and has been a strong advocate for responsible and ethical AI development.. Demis Hassabis: Co-founder of DeepMind, a leading AI research company, Hassabis has made notable contributions to areas such as deep reinforcement learning and has led the development of groundbreaking AI systems. Elon Musk: Although primarily known for his role in space exploration and electric vehicles, Musk has also made notable contributions to AI through his involvement in companies like OpenAI and Neuralink, advocating for AI safety and ethics.

1. According to a report by IDC, global spending on AI systems is expected to reach $98.4 billion in 2023, indicating a significant increase from the $37.5 billion spent in 2019. 2. The job market for AI professionals is thriving. LinkedIn's 2021 Emerging Jobs Report listed AI specialist as one of the top emerging jobs, with a 74% annual growth rate over the past four years. 3. AI-powered chatbots are revolutionizing customer service. A study by Oracle found that 80% of businesses plan to use chatbots by 2022. Furthermore, 58% of consumers have already interacted with chatbots for customer support, indicating the growing acceptance and adoption of AI in enhancing customer experiences. 4. McKinsey Global Institute estimates that by 2030, automation and AI technologies could contribute to a global economic impact of $13 trillion. 5. The healthcare industry is leveraging AI for improved patient care. A study published in the journal Nature Medicine reported that an AI model was able to detect breast cancer with an accuracy of 94.5%, outperforming human radiologists.

The topic of artificial intelligence (AI) holds immense importance in today's world, making it an intriguing subject to explore in an essay. AI has revolutionized multiple facets of human life, ranging from technology and business to healthcare and transportation. Understanding its significance is crucial for comprehending the potential and impact of this rapidly evolving field. Firstly, AI has the power to reshape industries and transform economies. It enables automation, streamlines processes, and enhances efficiency, leading to increased productivity and economic growth. Moreover, AI advancements have the potential to address complex societal challenges, such as healthcare accessibility, environmental sustainability, and resource management. Secondly, AI raises ethical considerations and socio-economic implications. Discussions on privacy, bias, job displacement, and AI's role in decision-making become essential for navigating its responsible implementation. Examining the ethical dimensions of AI fosters critical thinking and encourages the development of guidelines and regulations to ensure its ethical use. Lastly, exploring AI allows us to envision the future possibilities and risks associated with this technology. It sparks discussions on the boundaries of machine intelligence, the potential for sentient AI, and the impact on human existence. By studying AI, we gain insights into technological progress, its limitations, and the responsibilities associated with harnessing its potential.

1. Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall. 2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. 3. Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Viking. 4. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. 5. Chollet, F. (2017). Deep Learning with Python. Manning Publications. 6. Domingos, P. (2018). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. 7. Ng, A. (2017). Machine Learning Yearning. deeplearning.ai. 8. Marcus, G. (2018). Rebooting AI: Building Artificial Intelligence We Can Trust. Vintage. 9. Winfield, A. (2018). Robotics: A Very Short Introduction. Oxford University Press. 10. Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.

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Artificial Intelligence: The Helper or the Threat? Essay

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The principles of human intelligence have always been of certain interest for the field of science. Having understood the nature of processes that help people to reflect, scientists started proposing projects aimed at creating the machine that would be able to work like a human brain and make decisions as we do. Developing an artificial intelligence machine belongs to the number of the most urgent tasks of modern science. At the same time, there are different opinions on what our future will look like if we continue developing this field of science.

According to the people, who support an idea of artificial intelligence development, it will bring numerous benefits to the society and our everyday life. At first, the machine with artificial intelligence is going to be the best helper for the humanity in problem-solving (Cohen & Feigenbaum, 2014, p.13). Thus, there are tasks that require a good memory, and it is safer to assign such tasks to machines as their capacity of memory is by far more developed than one that people have. What is more, the machines with artificial intelligence help people to find the information that they need in moments. Such machines perform the record retrieval with help of numerous search algorithms and the human brain cannot do the same with such a high speed. To continue, the supporters of further artificial intelligence development believe that such machines will help us to compensate for certain features that make our brain activity and perception imperfect (Muller & Bostrom, 2016, p.554). If we look at artificial intelligence from this point of view, it acts as our teacher despite the fact that it is our creation. Importantly, people believe that artificial intelligence should be developed as it gives new opportunities to the humanity. Such a machine is able to teach itself without people’s help, and it also can take decisions even when circumstances are changing. Considering that, it can be trusted to fulfill many highly sensitive tasks.

Nevertheless, there are ones who are not so optimistic about the development and perfection of artificial intelligence. Their skeptical attitude about that is likely to be rooted in their concerns about the future of human society. To begin with, people who are skeptical about artificial intelligence believe that it is impossible to create the machine that will show the mental process similar to the one that people have. It means that the decisions made by such a machine will be based only on the logical connections between the objects. Considering that, it is not a good idea to use these machines for the tasks that involve people business. What is more, artificial intelligence development can store up future problems in the world of work (Ford, 2013, p. 37). There is no doubt that artificial intelligence programs do not have to be paid a salary every month. What is more, these programs usually do not make mistakes and it gives them an obvious advantage over human employees. With a glance to these facts, it is easy to suppose that they will be more likely to be chosen by employer. If artificial intelligence develops rapidly, many people will turn out to be unnecessary in their companies.

To conclude, artificial intelligence development is a problem that leaves nobody indifferent as it is closely associated with the future of the humanity. The thing that makes this question even trickier is the fact that both opinions on artificial intelligence seem to be well-founded.

Cohen, P. R., & Feigenbaum, E. A. (2014). The handbook of artificial intelligence. Los Altos, CA : Butterworth-Heinemann.

Ford, M. (2013). Could artificial intelligence create an unemployment crisis?. Communications of the ACM , 56 (7), 37-39.

Muller, V. C., & Bostrom, N. (2016). Future progress in artificial intelligence: A survey of expert opinion. In Fundamental issues of artificial intelligence (pp. 553-570). New York, NY: Springer International Publishing.

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IELTS Writing Task 2 Sample Answer Essay: Artificial Intelligence (Real Past IELTS Exam/Test)

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This is an IELTS writing task 2 sample answer essay on the topic of artificual intelligence and whether or not it is a positive development that computers will be smarter than humans someday.

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IELTS Writing Task 2 Sample Answer Essay: Artificial Intelligence

Some scientists believe that in the future computers will be more intelligent than human beings. While some see this as a positive development others worry about the negative consequences. Discuss both views and give your opinion. Real Past IELTS Exam

Many today are worried about the potential drawbacks of artificial intelligence. In my opinion, these concerns are legitimate but on the whole A.I. will allow for new heights to human endeavour.

The chief associated worries concern its misuse by humans initially and machines later. The former is already coming to pass as automation has phased out many traditional jobs. As artificial intelligence becomes more sophisticated, the positions in jeopardy will transition from low-skilled factory staff to data analysts and other white-collar workers. The fear is that companies will be motivated solely by their bottom line, lay off many employees and trigger mass social unrest. Some also believe A.I. portends darker scenarios akin to the apocalyptic dystopias of films like The Matrix and Terminator. This is a possibility though it is impossible to estimate its likelihood.

The speculations above should be taken seriously but they pale in comparison to the technologies A.I. can complement. Companies ranging from Google to Amazon to Tesla are investing heavily in this industry because of its enormous potential. For example, self-driving cars are fast becoming a reality and will reduce the number of vehicular accidents massively. Policymakers in government will be able to take advantage of sophisticated algorithms to project economic policy and positively enhance the lives of billions. In the consumer sphere, smartphones will become increasingly helpful, freeing up individuals to focus their time on work, family, and leisure. This is only a partial list and the most intriguing and impactful applications have yet to be unearthed.

In conclusion, artificial intelligence poses risks to the labour market and the future of humanity, but the opportunities for new projects should take priority. It is important to find a balance and methods of mitigating the dangers.

1. Many today are worried about the potential drawbacks of artificial intelligence. 2. In my opinion, these concerns are legitimate but on the whole A.I. will allow for new heights to human endeavour.

• Paraphrase the overall topic for the essay.
• Write a clear opinion. Read more about introductions here.

1. The chief associated worries concern its misuse by humans initially and machines later. 2. The former is already coming to pass as automation has phased out many traditional jobs. 3. As artificial intelligence becomes more sophisticated, the positions in jeopardy will transition from low-skilled factory staff to data analysts and other white-collar workers. 4. The fear is that companies will be motivated solely by their bottom line, lay off many employees and trigger mass social unrest. 5. Some also believe A.I. portends darker scenarios akin to the apocalyptic dystopias of films like The Matrix and Terminator. 6. This is a possibility though it is impossible to estimate its likelihood.

• Write a topic sentence with a clear main idea at the end.
• Explain your main idea.
• Use specific examples.
• Continue to develop your main idea.
• Don’t switch to a new idea.
• Conclude by generalising.

1. The speculations above should be taken seriously but they pale in comparison to the technologies A.I. can complement. 2. Companies ranging from Google to Amazon to Tesla are investing heavily in this industry because of its enormous potential. 3. For example, self-driving cars are fast becoming a reality and will reduce the number of vehicular accidents massively. 4. Policymakers in government will be able to take advantage of sophisticated algorithms to project economic policy and positively enhance the lives of billions. 5. In the consumer sphere, smartphones will become increasingly helpful, freeing up individuals to focus their time on work, family, and leisure. 6. This is only a partial list and the most intriguing and impactful applications have yet to be unearthed.

• Write another clear topic sentence with the main idea that agree with.
• Use real companies/people to write very specific examples.
• Begin a specific example.
• You can write about related examples instead of developing one example.
• Here I switch to a third example.
• Conclude with a strong statement.

1. In conclusion, artificial intelligence poses risks to the labour market and the future of humanity, but the opportunities for new projects should take priority. 2. It is important to find a balance and methods of mitigating the dangers.

• Summarise your main ideas and repeat your opinion.
• Add a final thought/detail.

What do the words in bold below mean?

Many today are worried about the potential drawbacks of artificial intelligence . In my opinion, these concerns are legitimate but on the whole A.I. will allow for new heights to human endeavour .

The chief associated worries concern its misuse by humans initially and machines later. The former is already coming to pass as automation has phased out many traditional jobs . As artificial intelligence becomes more sophisticated , the positions in jeopardy will transition from low-skilled factory staff to data analysts and other white-collar workers . The fear is that companies will be motivated solely by their bottom line , lay off many employees and trigger mass social unrest . Some also believe A.I. portends darker scenarios akin to the apocalyptic dystopias of films like The Matrix and Terminator. This is a possibility though it is impossible to estimate its likelihood .

The speculations above should be taken seriously but they pale in comparison to the technologies A.I. can complement . Companies ranging from Google to Amazon to Tesla are investing heavily in this industry because of its enormous potential . For example, self-driving cars are fast becoming a reality and will reduce the number of vehicular accidents massively . Policymakers in government will be able to take advantage of sophisticated algorithms to project economic policy and positively enhance the lives of billions. In the consumer sphere , smartphones will become increasingly helpful , freeing up individuals to focus their time on work, family, and leisure . This is only a partial list and the most intriguing and impactful applications have yet to be unearthed .

In conclusion, artificial intelligence poses risks to the labour market and the future of humanity, but the opportunities for new projects should take priority . It is important to find a balance and methods of mitigating the dangers .

worried about concerned

potential drawbacks possible negatives

artificial intelligence really smart computers/robots

concerns worries

legitimate justified

on the whole overall

new heights greatest achievements

human endeavour what man has accomplished

chief associated worries concern main issues relate to

misuse abuse

initially in the beginning

coming to pass happening now

automation robotic

phased out disappeared

traditional jobs factory workers, old types of labour

sophisticated complex

positions in jeopardy jobs in danger

transition change from

low-skilled factory staff people working in factories, manual labour

data analysts people who look closely at numbers, data

white-collar workers office workers, managers, etc.

motivated solely mainly interested in

bottom line profits

lay off fire

trigger mass social unrest cause unhappiness

portends darker scenarios akin to can foresee bad outcomes similar to

apocalyptic dystopias nightmarish futures

possibility chance

estimate guess

likelihood chance of happening

speculations guesses

taken seriously treated with respect

pale in comparison to much weaker than

complement supplement

ranging from including

investing heavily putting a lot of money into

enormous potential a lot of possibility

self-driving cars automated automobiles

fast becoming a reality quickly becoming true

vehicular accidents massively car crashes a lot

policymakers law-makers, politicians

take advantage of sophisticated algorithms exploit computer programs

project economic policy predict how to manage the economy

positively enhance have a good impact on

consumer sphere what people buy

increasingly helpful more and more positive

freeing up allowing for

focus their time have more time for

leisure free time

partial list not complete

most intriguing most interesting

impactful applications used to the most effect

unearthed uncovered

poses risks has dangers

labour market workers

take priority more important

balance keep things equal

methods means

mitigating lessening the impact of

dangers risks

Pronunciation

ˈwʌrid əˈbaʊt   pəʊˈtɛnʃəl ˈdrɔːbæks   ˌɑːtɪˈfɪʃ(ə)l ɪnˈtɛlɪʤəns kənˈsɜːnz   lɪˈʤɪtɪmɪt   ɒn ðə həʊl   njuː haɪts   ˈhjuːmən ɪnˈdɛvə ʧiːf əˈsəʊʃɪeɪtɪd ˈwʌriz kənˈsɜːn   ˌmɪsˈjuːs   ɪˈnɪʃəli   ˈkʌmɪŋ tuː pɑːs   ˌɔːtəˈmeɪʃ(ə)n   feɪzd aʊt   trəˈdɪʃənl ʤɒbz səˈfɪstɪkeɪtɪd pəˈzɪʃənz ɪn ˈʤɛpədi   trænˈsɪʒən   ləʊ-skɪld ˈfæktəri stɑːf   ˈdeɪtə ˈænəlɪsts   ˈwaɪtˈkɒlə ˈwɜːkəz ˈməʊtɪveɪtɪd ˈsəʊlli   ˈbɒtəm laɪn leɪ ɒf   ˈtrɪgə mæs ˈsəʊʃəl ʌnˈrɛst pɔːˈtɛndz ˈdɑːkə sɪˈnɑːrɪəʊz əˈkɪn tuː   əˈpɒkəlɪptɪk ˈdɪstəʊiːə ˌpɒsəˈbɪlɪti   ˈɛstɪmɪt ˈlaɪklɪhʊd ˌspɛkjʊˈleɪʃənz   ˈteɪkən ˈsɪərɪəsli peɪl ɪn kəmˈpærɪsn tuː   ˈkɒmplɪmənt ˈreɪnʤɪŋ frɒm   ɪnˈvɛstɪŋ ˈhɛvɪli   ɪˈnɔːməs pəʊˈtɛnʃəl sɛlf-ˈdraɪvɪŋ kɑːz   fɑːst bɪˈkʌmɪŋ ə ri(ː)ˈælɪti   vɪˈhɪkjʊlər ˈæksɪdənts ˈmæsɪvli ˈpɒlɪsiˈmeɪkəz   teɪk ədˈvɑːntɪʤ ɒv səˈfɪstɪkeɪtɪd ˈælgərɪðmz   ˈprɒʤɛkt ˌiːkəˈnɒmɪk ˈpɒlɪsi   ˈpɒzətɪvli ɪnˈhɑːns   kənˈsjuːmə sfɪə ɪnˈkriːsɪŋli ˈhɛlpfʊl ˈfriːɪŋ ʌp ˈfəʊkəs ðeə taɪm   ˈlɛʒə ˈpɑːʃəl lɪst   məʊst ɪnˈtriːgɪŋ   ˈɪmpæktf(ə)l ˌæplɪˈkeɪʃ(ə)nz   ʌnˈɜːθt ˈpəʊzɪz rɪsks ˈleɪbə ˈmɑːkɪt   teɪk praɪˈɒrɪti ˈbæləns   ˈmɛθədz   ˈmɪtɪgeɪtɪŋ   ˈdeɪnʤəz

Vocabulary Practice

Remember and fill in the blanks:

Many today are w______________t the p_________________s of a______________________e . In my opinion, these c____________s are l______________e but o______________e A.I. will allow for n____________s to h______________________r .

The c____________________________________n its m___________e by humans i_____________y and machines later. The former is already c_______________s as a________________n has p______________t many t_____________________s . As artificial intelligence becomes more s______________d , the p______________________y will t_______________n from l__________________________f to d_________________s and other w_____________________s . The fear is that companies will be m____________________y by their b________________e , l_____________f many employees and t_____________________________t . Some also believe A.I. p__________________________________o the a________________________s of films like The Matrix and Terminator. This is a p________________y though it is impossible to e________________e its l_________________d .

The s__________________s above should be t__________________y but they p__________________________o the technologies A.I. can c____________________t . Companies r____________________m Google to Amazon to Tesla are i________________________y in this industry because of its e_______________________l . For example, s___________________s are f_______________________y and will reduce the number of v_____________________________y . P____________________s in government will be able to t__________________________________________s to p___________________________y and p_____________________e the lives of billions. In the c______________________e , smartphones will become i________________________l , f_______________p individuals to f____________________e on work, family, and l____________e . This is only a p________________t and the m__________________g and i________________________s have yet to be u______________d .

In conclusion, artificial intelligence p_____________s to the l__________________t and the future of humanity, but the opportunities for new projects should t_________________y . It is important to find a b_____________e and m____________s of m____________g the d___________s .

Listening Practice

How far is too far?

Reading Practice

Stay updated on the latest news about Artifical Intelligence from Wired here:

https://www.wired.com/category/business/artificial-intelligence/

Speaking Practice

Answer the following questions from the real speaking exam :

Intelligence

Do people with high IQs tend to be selfish? Can computers improve your intelligence? What is the difference between intelligence and knowledge? How much can intelligence change during a lifetime and how much of it is fixed? Has technology made people less intelligent? Real IELTS Speaking Exam

Writing Practice

Write about the following related topic then check with my sample answer:

Nowadays more tasks at home and work are being performed by robots. Is this a negative or positive development? Real Past IELTS Writing Exam

IELTS Writing Task 2 Sample Answer Essay: Robots at Home (Real Past IELTS Tests/Exams)

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Hello , in this essay you have used the phrases like phase out and lay off. So can we use them in formal writing in academic writing part 2?

Try to avoid phrasal verbs as much as possible – the occasional one like ‘phase out’ and lay off is ok – but to be safe, avoid using them if you know a more formal, academic word.

Got it. Thanx for valuable advice!

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Guest Essay

Will A.I. Be a Creator or a Destroyer of Worlds?

By Thomas B. Edsall

Mr. Edsall contributes a weekly column from Washington, D.C., on politics, demographics and inequality.

The advent of A.I. — artificial intelligence — is spurring curiosity and fear. Will A.I. be a creator or a destroyer of worlds?

In “ Can We Have Pro-Worker A.I. ? Choosing a Path of Machines in Service of Minds,” three economists at M.I.T., Daron Acemoglu , David Autor and Simon Johnson , looked at this epochal innovation last year:

The private sector in the United States is currently pursuing a path for generative A.I. that emphasizes automation and the displacement of labor, along with intrusive workplace surveillance. As a result, disruptions could lead to a potential downward cascade in wage levels, as well as inefficient productivity gains. Before the advent of artificial intelligence, automation was largely limited to blue-collar and office jobs using digital technologies while more complex and better-paying jobs were left untouched because they require flexibility, judgment and common sense.

Now, Acemoglu, Autor and Johnson wrote, A.I. presents a direct threat to those high-skill jobs: “A major focus of A.I. research is to attain human parity in a vast range of cognitive tasks and, more generally, to achieve ‘artificial general intelligence’ that fully mimics and then surpasses capabilities of the human mind.”

The three economists make the case that

There is no guarantee that the transformative capabilities of generative A.I. will be used for the betterment of work or workers. The bias of the tax code, of the private sector generally, and of the technology sector specifically, leans toward automation over augmentation. But there are also potentially powerful A.I.-based tools that can be used to create new tasks, boosting expertise and productivity across a range of skills. To redirect A.I. development onto the human-complementary path requires changes in the direction of technological innovation, as well as in corporate norms and behavior. This needs to be backed up by the right priorities at the federal level and a broader public understanding of the stakes and the available choices. We know this is a tall order.

“Tall” is an understatement.

In an email elaborating on the A.I. paper, Acemoglu contended that artificial intelligence has the potential to improve employment prospects rather than undermine them:

It is quite possible to leverage generative A.I. as an informational tool that enables various different types of workers to get better at their jobs and perform more complex tasks. If we are able to do this, this would help create good, meaningful jobs, with wage growth potential, and may even reduce inequality. Think of a generative A.I. tool that helps electricians get much better at diagnosing complex problems and troubleshoot them effectively.

This, however, “is not where we are heading,” Acemoglu continued:

The preoccupation of the tech industry is still automation and more automation, and the monetization of data via digital ads. To turn generative A.I. pro-worker, we need a major course correction, and this is not something that’s going to happen by itself.

Acemoglu pointed out that unlike the regional trade shock that decimated manufacturing employment after China entered the World Trade Organization in 2001, “The kinds of tasks impacted by A.I. are much more broadly distributed in the population and also across regions.” In other words, A.I. threatens employment at virtually all levels of the economy, including well-paid jobs requiring complex cognitive capabilities.

Four technology specialists — Tyna Eloundou and Pamela Mishkin , both on the staff of OpenAI , with Sam Manning , a research fellow at the Centre for the Governance of A.I., and Daniel Rock at the University of Pennsylvania — provided a detailed case study on the employment effects of artificial intelligence in their 2023 paper, “ GPTs Are GPTs : An Early Look at the Labor Market Impact Potential of Large Language Models.”

“Around 80 percent of the U.S. work force could have at least 10 percent of their work tasks affected by the introduction of large language models,” Eloundou and her co-authors wrote, and “approximately 19 percent of workers may see at least 50 percent of their tasks impacted.”

Large language models have multiple and diverse uses, according to Eloundou and her colleagues, and “can process and produce various forms of sequential data, including assembly language, protein sequences and chess games, extending beyond natural.” In addition, these models “excel in diverse applications like translation, classification, creative writing, and code generation — capabilities that previously demanded specialized, task-specific models developed by expert engineers using domain-specific data.”

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Essay: Will AI surpass human intelligence?

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The controversy over whether artificial intelligence surpasses human intelligence will perpetually be a topic of debate that splits evenly down the middle. This feud led all the way back to the 1950’s when Alan Turing, an english computer scientist, coined the “Turing Test” which was a primitive way of determining if a computer could be defined as “intelligent.” I, too concur that computers are very resourceful in terms of knowledge, however it will never surpass humans. I believe this simply because we are the creator and computers will never obtain emotional intelligence and therefore, lacking in plenty. The Turing test is created for the sole purpose of an imitation game. The objective is for the judge to sit behind a computer screen and converse with mysterious interlocutors, most of which being humans and only one bot, the judge then has to decide whose who. If the examiner or judge cannot differentiate between a human’s response versus a computer’s response then the test deems itself worthy. Determinism comes into play however, because computers don’t have free will and everything they do is pre-set by humans. The computer can reply how a “normal human” would reply to a “normal question.” However, the test only challenges whether a computer behaves like a human and due to the fact that intelligent nature and human nature are not precisely the same thing; the test therefore fails to measure “intelligence.” From where I stand, the test is an invalid judgment of intelligence because intelligence isn’t really required to past the test. Despite the fact that the system can pass for a human it still doesn’t display any conscious experience that a person obtains. In other words, the awareness of our own mental process, feelings and sensations. “When we say robots have emotion, we don’t mean they feel happy or sad or have mental states. This is shorthand for, they seem to exhibit behavior that we humans interpret as such and such” (https://medium.com). By that quote, my interpretation would be if we receive a brand new gift, we achieve a level of joy and sensation that a mere computer could never understand. Thus, we are without doubt the only beings that acquire this type of self-awareness and high level of consciousness. Another prime example would be ethical issues if people were to rely solely on computers. “When a driverless car runs over someone’s pet, or worse, another human being should we then act as if it knew what it was doing? Should we be granting citizenship to robots that only pretend to know what it means to be a citizen” (https://becominghuman.ai). From this I take that, imitation systems will nonetheless be unethical because they portray an identity about themselves that does not reflect the whole truth of reality. To conclude, I think a computer could very much knowledgeable in terms of sources. The basic computer holds a lot of information, but the information that we fed them. All that information came from a person, all thoughts were formulated by a person. Would I use the word intelligent? No, until the computer could display emotions and codify thoughts on it’s own, my answer will be no.

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Free Essay – Artificial intelligence (Al) and human intelligence

Free essay – artificial intelligence (ai) and human intelligence.

Significant progress in AI has been achieved in recent years, especially with the development of machine learning and deep learning algorithms. By virtue of these developments, AI is now capable of activities formerly associated solely with human intellect, such as pattern recognition, natural language comprehension, and even the production of works of art. Though AI has made great strides, it has a long way to go before it can compete with human intellect in terms of complexity and adaptability.

Artificial intelligence (AI) is not likely to replace human intelligence for several reasons. Biological processes support human intellect, whereas algorithms and mathematical models form the basis of AI and are no less potent, but are fundamentally different. To yet, artificial intelligence has been unable to replicate the whole range of human intellect, which includes not just logical reasoning but also emotions, intuition, and original thought. More importantly, human intellect is formed over the course of a lifetime of events and learning, which is difficult for an AI system to mimic.

But it’s also impossible to deny that AI might one day be smarter than humans at some tasks. An example is the ability of AI to analyse large volumes of data considerably more quickly and correctly than a person. Because of this, AI is extremely helpful in areas like data analysis, where it can spot patterns and trends that a human being would have no hope of spotting. The speed and accuracy with which AI can complete such activities greatly outpaces that of any human.

Assuming that intelligence is a zero-sum game, however, the concept of AI replacing human intellect is problematic. Perhaps a more fruitful perspective would be to regard AI not as a competitor to human intellect but as a means to expand and improve upon it. Our strengths as humans lie in areas where AI has yet to make significant inroads, such as strategic thinking, creativity, and emotional intelligence. By working together in this way, AI and human intellect may thrive.

Finally, while AI has made tremendous strides and may one day be smarter than humans, it is still far from replacing us completely. Given the unique characteristics of AI and the potential for it to complement human intellect rather than replace it, it seems likely that the two will coexist and mutually enrich one another in the future. Keeping these in mind as we advance AI research and development is essential to guaranteeing that the technology will be used for the benefit of humanity.

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AI timelines: What do experts in artificial intelligence expect for the future?

Many ai experts believe there is a real chance that human-level artificial intelligence will be developed within the next decades, and some believe that it will exist much sooner..

Artificial intelligence (AI) that surpasses our own intelligence sounds like the stuff from science-fiction books or films. What do experts in the field of AI research think about such scenarios? Do they dismiss these ideas as fantasy, or are they taking such prospects seriously?

A human-level AI would be a machine, or a network of machines, capable of carrying out the same range of tasks that we humans are capable of. It would be a machine that is “able to learn to do anything that a human can do”, as Norvig and Russell put it in their textbook on AI. 1

It would be able to choose actions that allow the machine to achieve its goals and then carry out those actions. It would be able to do the work of a translator, a doctor, an illustrator, a teacher, a therapist, a driver, or the work of an investor.

In recent years, several research teams contacted AI experts and asked them about their expectations for the future of machine intelligence. Such expert surveys are one of the pieces of information that we can rely on to form an idea of what the future of AI might look like.

The chart shows the answers of 352 experts. This is from the most recent study by Katja Grace and her colleagues, conducted in the summer of 2022. 2

Experts were asked when they believe there is a 50% chance that human-level AI exists. 3 Human-level AI was defined as unaided machines being able to accomplish every task better and more cheaply than human workers. More information about the study can be found in the fold-out box at the end of this text. 4

Each vertical line in this chart represents the answer of one expert. The fact that there are such large differences in answers makes it clear that experts do not agree on how long it will take until such a system might be developed. A few believe that this level of technology will never be developed. Some think that it’s possible, but it will take a long time. And many believe that it will be developed within the next few decades.

As highlighted in the annotations, half of the experts gave a date before 2061, and 90% gave a date within the next 100 years.

Other surveys of AI experts come to similar conclusions. In the following visualization, I have added the timelines from two earlier surveys conducted in 2018 and 2019. It is helpful to look at different surveys, as they differ in how they asked the question and how they defined human-level AI. You can find more details about these studies at the end of this text.

In all three surveys, we see a large disagreement between experts and they also express large uncertainties about their own individual forecasts. 5

What should we make of the timelines of AI experts?

Expert surveys are one piece of information to consider when we think about the future of AI, but we should not overstate the results of these surveys. Experts in a particular technology are not necessarily experts in making predictions about the future of that technology.

Experts in many fields do not have a good track record in making forecasts about their own field, as researchers including Barbara Mellers, Phil Tetlock, and others have shown. 6 The history of flight includes a striking example of such failure. Wilbur Wright is quoted as saying, "I confess that in 1901, I said to my brother Orville that man would not fly for 50 years." Two years later, ‘man’ was not only flying, but it was these very men who achieved the feat. 7

Additionally these studies often find large ‘framing effects’, two logically identical questions get answered in very different ways depending on how exactly the questions are worded. 8

What I do take away from these surveys however, is that the majority of AI experts take the prospect of very powerful AI technology seriously. It is not the case that AI researchers dismiss extremely powerful AI as mere fantasy.

The huge majority thinks that in the coming decades there is an even chance that we will see AI technology which will have a transformative impact on our world. While some have long timelines, many think it is possible that we have very little time before these technologies arrive. Across the three surveys more than half think that there is a 50% chance that a human-level AI would be developed before some point in the 2060s, a time well within the lifetime of today’s young people.

The forecast of the Metaculus community

In the big visualization on AI timelines below, I have included the forecast by the Metaculus forecaster community.

The forecasters on the online platform Metaculus.com are not experts in AI but people who dedicate their energy to making good forecasts. Research on forecasting has documented that groups of people can assign surprisingly accurate probabilities to future events when given the right incentives and good feedback. 9 To receive this feedback, the online community at Metaculus tracks how well they perform in their forecasts.

What does this group of forecasters expect for the future of AI?

At the time of writing, in November 2022, the forecasters believe that there is a 50/50-chance for an ‘Artificial General Intelligence’ to be ‘devised, tested, and publicly announced’ by the year 2040, less than 20 years from now.

On their page about this specific question, you can find the precise definition of the AI system in question, how the timeline of their forecasts has changed, and the arguments of individual forecasters for how they arrived at their predictions. 10

The timelines of the Metaculus community have become much shorter recently. The expected timelines have shortened by about a decade in the spring of 2022, when several impressive AI breakthroughs happened faster than many had anticipated. 11

The forecast by Ajeya Cotra

The last shown forecast stems from the research by Ajeya Cotra, who works for the nonprofit Open Philanthropy. 12 In 2020 she published a detailed and influential study asking when the world will see transformative AI. Her timeline is not based on surveys, but on the study of long-term trends in the computation used to train AI systems. I present and discuss the long-run trends in training computation in this companion article.

Cotra estimated that there is a 50% chance that a transformative AI system will become possible and affordable by the year 2050. This is her central estimate in her “median scenario.” Cotra emphasizes that there are substantial uncertainties around this median scenario, and also explored two other, more extreme, scenarios. The timelines for these two scenarios – her “most aggressive plausible” scenario and her “most conservative plausible” scenario – are also shown in the visualization. The span from 2040 to 2090 in Cotra’s “plausible” forecasts highlights that she believes that the uncertainty is large.

The visualization also shows that Cotra updated her forecast two years after its initial publication. In 2022 Cotra published an update in which she shortened her median timeline by a full ten years. 13

It is important to note that the definitions of the AI systems in question differ very much across these various studies. For example, the system that Cotra speaks about would have a much more transformative impact on the world than the system that the Metaculus forecasters focus on. More details can be found in the appendix and within the respective studies.

What can we learn from the forecasts?

The visualization shows the forecasts of 1128 people – 812 individual AI experts, the aggregated estimates of 315 forecasters from the Metaculus platform, and the findings of the detailed study by Ajeya Cotra.

There are two big takeaways from these forecasts on AI timelines:

• There is no consensus, and the uncertainty is high. There is huge disagreement between experts about when human-level AI will be developed. Some believe that it is decades away, while others think it is probable that such systems will be developed within the next few years or months.There is not just disagreement between experts; individual experts also emphasize the large uncertainty around their own individual estimate. As always when the uncertainty is high, it is important to stress that it cuts both ways. It might be very long until we see human-level AI, but it also means that we might have little time to prepare.
• At the same time, there is large agreement in the overall picture. The timelines of many experts are shorter than a century, and many have timelines that are substantially shorter than that. The majority of those who study this question believe that there is a 50% chance that transformative AI systems will be developed within the next 50 years. In this case it would plausibly be the biggest transformation in the lifetime of our children, or even in our own lifetime.

The public discourse and the decision-making at major institutions have not caught up with these prospects. In discussions on the future of our world – from the future of our climate, to the future of our economies, to the future of our political institutions – the prospect of transformative AI is rarely central to the conversation. Often it is not mentioned at all, not even in a footnote.

We seem to be in a situation where most people hardly think about the future of artificial intelligence, while the few who dedicate their attention to it find it plausible that one of the biggest transformations in humanity’s history is likely to happen within our lifetimes.

Acknowledgements: I would like to thank my colleagues Natasha Ahuja, Daniel Bachler, Bastian Herre, Edouard Mathieu, Esteban Ortiz-Ospina and Hannah Ritchie for their helpful comments to drafts of this essay.

And I would like to thank my colleague Charlie Giattino who calculated the timelines for individual experts based on the data from the three survey studies and supported the work on this essay. Charlie is also one of the authors of the cited study by Zhang et al. on timelines of AI experts.

More information about the studies and forecasts discussed in this essay

The three cited AI experts surveys are:

• Katja Grace, Zach Stein-Perlman, and Benjamin Weinstein-Raun (2022) – “ 2022 Expert Survey on Progress in AI .” AI Impacts, 3 Aug. 2022.
• Baobao Zhang, Noemi Dreksler, Markus Anderljung, Lauren Kahn, Charlie Giattino, Allan Dafoe, and Michael Horowitz (2022) – Forecasting AI Progress: Evidence from a Survey of Machine Learning Researchers . Published on arXiv June 8, 2022.
• Ross Gruetzemacher, David Paradice, and Kang Bok Lee (2019) – Forecasting Transformative AI: An Expert Survey , published on arXiv.

The surveys were conducted during the following times:

• Grace et al. was completed between 12 June and 3 August 2022.
• Zhang et al. was completed mainly between 16 September and 13 October 2019; but due to an error some experts completed the survey between 10-14 March 2020.
• Gruetzemacher et al. was completed in the "summer of 2018.”

The surveys differ in how the question was asked and how the AI system in question was defined. In the following sections we discuss this in detail for all cited studies.

The study by Grace et al. published in 2022

Survey respondents were given the following text regarding the definition of high-level machine intelligence:

“The following questions ask about ‘high-level machine intelligence’ (HLMI). Say we have ‘high-level machine intelligence’ when unaided machines can accomplish every task better and more cheaply than human workers. Ignore aspects of tasks for which being a human is intrinsically advantageous, e.g., being accepted as a jury member. Think feasibility, not adoption. For the purposes of this question, assume that human scientific activity continues without major negative disruption.”

Each respondent was randomly assigned to give their forecasts under one of two different framings: “fixed-probability” and “fixed-years.”

Those in the fixed-probability framing were asked, “How many years until you expect: A 10% probability of HLMI existing? A 50% probability of HLMI existing? A 90% probability of HLMI existing?” They responded by giving a number of years from the day they took the survey.

Those in the fixed-years framing were asked, “How likely is it that HLMI exists: In 10 years? In 20 years? In 40 years?” They responded by giving a probability of that happening.

Several studies have shown that the framing affects respondents’ timelines, with the fixed-years framing leading to longer timelines (i.e., that HLMI is further in the future). For example, in the previous edition of this survey (which asked identical questions), respondents who got the fixed-years framing gave a 50% chance of HLMI by 2068; those who got fixed-probability gave the year 2054. 14 The framing results from the 2022 edition of the survey have not yet been published.

In addition to this framing effect, there is a larger effect driven by how the concept of HLMI is defined. We can see this in the results from the previous edition of this survey (the result from the 2022 survey hasn’t yet been published). For respondents who were given the HLMI definition above, the average forecast for a 50% chance of HLMI was 2061. A small subset of respondents was instead given another, logically similar question that asked about the full automation of labor; their average forecast for a 50% probability was 2138, a full 77 years later than the first group.

The full automation of labor group was asked: “Say an occupation becomes fully automatable when unaided machines can accomplish it better and more cheaply than human workers. Ignore aspects of occupations for which being a human is intrinsically advantageous, e.g., being accepted as a jury member. Think feasibility, not adoption. Say we have reached ‘full automation of labor’ when all occupations are fully automatable. That is, when for any occupation, machines could be built to carry out the task better and more cheaply than human workers.” This question was asked under both the fixed-probability and fixed-years framings.

The study by Zhang et al. published in 2022

Survey respondents were given the following definition of human-level machine intelligence: “Human-level machine intelligence (HLMI) is reached when machines are collectively able to perform almost all tasks (>90% of all tasks) that are economically relevant better than the median human paid to do that task in 2019. You should ignore tasks that are legally or culturally restricted to humans, such as serving on a jury.”

“Economically relevant” tasks were defined as those included in the Occupational Information Network (O*NET) database . O*NET is a widely used dataset of tasks carried out across a wide range of occupations.

As in Grace et al 2022, each survey respondent was randomly assigned to give their forecasts under one of two different framings: “fixed-probability” and “fixed-years.” As was found before, the fixed-years framing resulted in longer timelines on average: the year 2070 for a 50% chance of HLMI, compared to 2050 under the fixed-probability framing.

The study by Gruetzemacher et al. published in 2019

Survey respondents were asked the following: “These questions will ask your opinion of future AI progress with regard to human tasks. We define human tasks as all unique tasks that humans are currently paid to do. We consider human tasks as different from jobs in that an algorithm may be able to replace humans at some portion of tasks a job requires while not being able to replace humans for all of the job requirements. For example, an AI system(s) may not replace a lawyer entirely but may be able to accomplish 50% of the tasks a lawyer typically performs. In how many years do you expect AI systems to collectively be able to accomplish 99% of human tasks at or above the level of a typical human? Think feasibility.”

We show the results using this definition of AI in the chart, as we judged this definition to be most comparable to the other studies included in the chart.

In addition to this definition, respondents were asked about AI systems that are able to collectively accomplish 50% and 90% of human tasks, as well as “broadly capable AI systems” that are able to accomplish 90% and 99% of human tasks.

All respondents in this survey received a fixed-probability framing.

The study by Ajeya Cotra published in 2020

Cotra’s overall aim was to estimate when we might expect “transformative artificial intelligence” (TAI), defined as “ ‘software’... that has at least as profound an impact on the world’s trajectory as the Industrial Revolution did.”

Cotra focused on “a relatively concrete and easy-to-picture way that TAI could manifest: as a single computer program which performs a large enough diversity of intellectual labor at a high enough level of performance that it alone can drive a transition similar to the Industrial Revolution.”

One intuitive example of such a program is the ‘virtual professional’, “a model that can do roughly everything economically productive that an intelligent and educated human could do remotely from a computer connected to the internet at a hundred-fold speedup, for costs similar to or lower than the costs of employing such a human.”

When might we expect something like a virtual professional to exist?

To answer this, Cotra first estimated the amount of computation that would be required to train such a system using the machine learning architectures and algorithms available to researchers in 2020. She then estimated when that amount of computation would be available at a low enough cost based on extrapolating past trends.

The estimate of training computation relies on an estimate of the amount of computation performed by the human brain each second, combined with different hypotheses for how much training would be required to reach a high enough level of capability.

For example, the “lifetime anchor” hypothesis estimates the total computation performed by the human brain up to age ~32.

Each aspect of these estimates comes with a very high degree of uncertainty. Cotra writes: “The question of whether there is a sensible notion of ‘brain computation’ that can be measured in FLOP/s—and if so, what range of numerical estimates for brain FLOP/s would be reasonable—is conceptually fraught and empirically murky.”

For anyone who is interested in the question of future AI, the study of Cotra is very much worth reading in detail. She lays out good and transparent reasons for her estimates and communicates her reasoning in great detail.

Her research was announced in various places, including the AI Alignment Forum: Ajeya Cotra (2020) –  Draft report on AI timelines . As far as I know the report itself always remained a ‘draft report’ and was published here on Google Docs (it is not uncommon in the field of AI research that articles get published in non-standard ways). In 2022 Ajeya Cotra published a Two-year update on my personal AI timelines .

Other studies

A very different kind of forecast that is also relevant here is the work of David Roodman. In his article Modeling the Human Trajectory he studies the history of global economic output to think about the future. He asks whether it is plausible to see economic growth that could be considered ‘transformative’ – an annual growth rate of the world economy higher than 30% – within this century. One of his conclusions is that "if the patterns of long-term history continue, some sort of economic explosion will take place again, the most plausible channel being AI.”

And another very different kind of forecast is Tom Davidson’s Report on Semi-informative Priors published in 2021.

Peter Norvig and Stuart Russell (2021) – Artificial Intelligence: A Modern Approach. Fourth edition. Published by Pearson.

A total of 4,271 AI experts were contacted; 738 responded (a 17% rate), of which 352 provided complete answers to the human-level AI question.It’s possible that the respondents were not representative of all the AI experts contacted – that is, that there was “sample bias.” There is not enough data to rule out all potential sources of sample bias. After all, we don’t know what the people who didn’t respond to the survey, or others who weren’t even contacted, believe about AI. However, there is evidence from similar surveys to suggest that at least some potential sources of bias are minimal.

In similar surveys (e.g., Zhang et al. 2022 ; Grace et al. 2018 ), the researchers compared the group of respondents with a randomly selected, similarly sized group of non-respondents to see if they differed on measurable demographic characteristics, such as where they were educated, their gender, how many citations they had, years in the field, etc.

In these similar surveys, the researchers found some differences between the respondents and non-respondents, but they were small. So while other, unmeasured sources of sample bias couldn’t be ruled out, large bias due to the demographic characteristics that were measured could be ruled out.

Much of the literature on AI timelines focuses on the 50% probability threshold. I think it would be valuable if this literature would additionally also focus on higher thresholds, say a probability of 80% for the development of a particular technology. In future updates of this article we will aim to broaden the focus and include such higher thresholds.

A discussion of the two most widely used concepts for thinking about the future of powerful AI systems – human-level AI and transformative AI – can be found in this companion article .

The visualization shows when individual experts gave a 50% chance of human-level machine intelligence. The surveys also include data on when these experts gave much lower chances (e.g., ~10%) as well as much higher ones (~90%), and the spread between the respective dates is often considerable, expressing the AI experts range of their individual uncertainty. For example, the average across individual experts in the Zhang et al study gave a 10% chance of human-level machine intelligence by 2035, a 50% chance by 2060, and a 90% chance by 2105.

Mellers, B., Tetlock, P., & Arkes, H. R. (2019). Forecasting tournaments, epistemic humility and attitude depolarization. Cognition, 188, 19-26.

Tetlock, P. (2005) – Expert political judgment: How good is it? How can we know? Princeton, NJ: Princeton University Press

Philip E. Tetlock and Dan Gardner (2015) – Superforecasting: The Art and Science of Prediction.

Another example is Ernest Rutherford, father of nuclear physics, calling the possibility of harnessing nuclear energy "moonshine." The research paper by John Jenkin discusses why. John G. Jenkin (2011) – Atomic Energy is ‘‘Moonshine’’: What did Rutherford Really Mean?. Published in Physics in Perspective. DOI 10.1007/s00016-010-0038-1

This is discussed in some more detail for the study by Grace et al. in the Appendix.

See the previously cited literature on forecasting by Barbara Mellers, Phil Tetlock, and others.

There are two other relevant questions on Metaculus. The first one asks for the date when weakly General AI will be publicly known. And the second one is asking for the probability of ‘human/machine intelligence parity’ by 2040.

Metaculus’s community prediction fell from the year 2058 in March 2022 to the year 2040 in July 2022.

Her research was announced in various places, including the AI Alignment Forum: Ajeya Cotra (2020) –  Draft report on AI timelines . As far as I know the report itself always remained a ‘draft report’ and was published here on Google Docs .

In 2022 Ajeya Cotra published a Two-year update on my personal AI timelines .

Ajeya Cotra’s Two-year update on my personal AI timelines .

Grace et al (2018) Viewpoint: When Will AI Exceed Human Performance? Evidence from AI Experts. Journal of Artificial Intelligence Research. We read both of these numbers of the chart in this publication, these years are not directly reported.

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IELTS essay, topic: Artificial Intelligence will take over the role of teachers (agree/disagree)

• IELTS Essays - Band 9

This is a model response to a Writing Task 2 topic from High Scorer’s Choice IELTS Practice Tests book series (reprinted with permission). This answer is close to IELTS Band 9.

Set 6 Academic book, Practice Test 26

Writing Task 2

You should spend about 40 minutes on this task.

Write about the following topic:

Some people feel that with the rise of artificial intelligence, computers and robots will take over the roles of teachers. To what extent do you agree or disagree with this statement?

Give reasons for your answer and include any relevant examples from your knowledge or experience.

You should write at least 250 words.

Sample Band 9 Essay

With ever increasing technological advances, computers and robots are replacing human roles in different areas of society. This trend can also be seen in education, where interactive programs can enhance the educational experience for children and young adults. Whether, however, this revolution can also take over the role of the teacher completely is debatable, and I oppose this idea as it is unlikely to serve students well.

The roles of computers and robots can be seen in many areas of the workplace. Classic examples are car factories, where a lot of the repetitive precision jobs done on assembly lines have been performed by robots for many years, and medicine, where diagnosis, and treatment, including operations, have also been assisted by computers for a long time. According to the media, it won’t also be long until we have cars that are driven automatically.

It has long been discussed whether robots and computers can do this in education. It is well known that the complexity of programs can now adapt to so many situations that something can already be set up that has the required knowledge of the teacher, along with the ability to predict and answer all questions that might be asked by students. In fact, due to the nature of computers, the knowledge levels can far exceed a teacher’s and have more breadth, as a computer can have equal knowledge in all the subjects that are taught in school, as opposed to a single teacher’s specialisation. It seems very likely, therefore, that computers and robots should be able to deliver the lessons that teachers can, including various ways of differentiating and presenting materials to suit varying abilities and ages of students.

Where I am not convinced is in the pastoral role of teachers. Part of teaching is managing behaviour and showing empathy with students, so that they feel cared for and important. Even if a robot or computer can be programmed to imitate these actions, students will likely respond in a different way when they know an interaction is part of an algorithm rather than based on human emotion.

Therefore, although I feel that computers should be able to perform a lot of the roles of teachers in the future, they should be used as educational tools to assist teachers and not to replace them. In this way, students would receive the benefits of both ways of instruction.

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What is generative AI?

In the months and years since ChatGPT burst on the scene in November 2022, generative AI (gen AI) has come a long way. Every month sees the launch of new tools, rules, or iterative technological advancements. While many have reacted to ChatGPT (and AI and machine learning more broadly) with fear, machine learning clearly has the potential for good. In the years since its wide deployment, machine learning has demonstrated impact in a number of industries, accomplishing things like medical imaging analysis  and high-resolution weather forecasts. A 2022 McKinsey survey shows that AI adoption has more than doubled  over the past five years, and investment in AI is increasing apace. It’s clear that generative AI tools like ChatGPT (the GPT stands for generative pretrained transformer) and image generator DALL-E (its name a mashup of the surrealist artist Salvador Dalí and the lovable Pixar robot WALL-E) have the potential to change how a range of jobs are performed. The full scope of that impact, though, is still unknown—as are the risks.

Get to know and directly engage with McKinsey's senior experts on generative AI

Aamer Baig is a senior partner in McKinsey’s Chicago office;  Lareina Yee  is a senior partner in the Bay Area office; and senior partners  Alex Singla  and Alexander Sukharevsky , global leaders of QuantumBlack, AI by McKinsey, are based in the Chicago and London offices, respectively.

Still, organizations of all stripes have raced to incorporate gen AI tools into their business models, looking to capture a piece of a sizable prize. McKinsey research indicates that gen AI applications stand to add up to $4.4 trillion  to the global economy—annually. Indeed, it seems possible that within the next three years, anything in the technology, media, and telecommunications space not connected to AI will be considered obsolete or ineffective .

But before all that value can be raked in, we need to get a few things straight: What is gen AI, how was it developed, and what does it mean for people and organizations? Read on to get the download.

To stay up to date on this critical topic, sign up for email alerts on “artificial intelligence” here .

Learn more about QuantumBlack , AI by McKinsey.

What every CEO should know about generative AI

What’s the difference between machine learning and artificial intelligence, about quantumblack, ai by mckinsey.

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Artificial intelligence is pretty much just what it sounds like—the practice of getting machines to mimic human intelligence to perform tasks. You’ve probably interacted with AI even if you don’t realize it—voice assistants like Siri and Alexa are founded on AI technology, as are customer service chatbots that pop up to help you navigate websites.

Machine learning is a type of artificial intelligence. Through machine learning, practitioners develop artificial intelligence through models that can “learn” from data patterns without human direction. The unmanageably huge volume and complexity of data (unmanageable by humans, anyway) that is now being generated has increased machine learning’s potential , as well as the need for it.

What are the main types of machine learning models?

Machine learning is founded on a number of building blocks, starting with classical statistical techniques  developed between the 18th and 20th centuries for small data sets. In the 1930s and 1940s, the pioneers of computing—including theoretical mathematician Alan Turing—began working on the basic techniques for machine learning. But these techniques were limited to laboratories until the late 1970s, when scientists first developed computers powerful enough to mount them.

Until recently, machine learning was largely limited to predictive models, used to observe and classify patterns in content. For example, a classic machine learning problem is to start with an image or several images of, say, adorable cats. The program would then identify patterns among the images, and then scrutinize random images for ones that would match the adorable cat pattern. Generative AI was a breakthrough. Rather than simply perceive and classify a photo of a cat, machine learning is now able to create an image or text description of a cat on demand.

Introducing McKinsey Explainers : Direct answers to complex questions

How do text-based machine learning models work how are they trained.

ChatGPT may be getting all the headlines now, but it’s not the first text-based machine learning model to make a splash. OpenAI’s GPT-3 and Google’s BERT both launched in recent years to some fanfare. But before ChatGPT, which by most accounts works pretty well most of the time (though it’s still being evaluated), AI chatbots didn’t always get the best reviews. GPT-3 is “by turns super impressive and super disappointing,” said New York Times tech reporter Cade Metz in a video where he and food writer Priya Krishna asked GPT-3 to write recipes for a (rather disastrous) Thanksgiving dinner .

The first machine learning models to work with text were trained by humans to classify various inputs according to labels set by researchers. One example would be a model trained to label social media  posts as either positive or negative. This type of training is known as supervised learning because a human is in charge of “teaching” the model what to do.

The next generation of text-based machine learning models rely on what’s known as self-supervised learning. This type of training involves feeding a model a massive amount of text so it becomes able to generate predictions. For example, some models can predict, based on a few words, how a sentence will end. With the right amount of sample text—say, a broad swath of the internet—these text models become quite accurate. We’re seeing just how accurate with the success of tools like ChatGPT.

What does it take to build a generative AI model?

Building a generative AI model has for the most part been a major undertaking, to the extent that only a few well-resourced tech heavyweights have made an attempt . OpenAI, the company behind ChatGPT, former GPT models, and DALL-E, has billions in funding from bold-face-name donors. DeepMind is a subsidiary of Alphabet, the parent company of Google, and even Meta has dipped a toe into the generative AI model pool with its Make-A-Video product. These companies employ some of the world’s best computer scientists and engineers.

But it’s not just talent. When you’re asking a model to train using nearly the entire internet, it’s going to cost you. OpenAI hasn’t released exact costs, but estimates indicate that GPT-3 was trained on around 45 terabytes of text data—that’s about one million feet of bookshelf space, or a quarter of the entire Library of Congress—at an estimated cost of several million dollars. These aren’t resources your garden-variety start-up can access.

What kinds of output can a generative AI model produce?

As you may have noticed above, outputs from generative AI models can be indistinguishable from human-generated content, or they can seem a little uncanny. The results depend on the quality of the model—as we’ve seen, ChatGPT’s outputs so far appear superior to those of its predecessors—and the match between the model and the use case, or input.

ChatGPT can produce what one commentator called a “ solid A- ” essay comparing theories of nationalism from Benedict Anderson and Ernest Gellner—in ten seconds. It also produced an already famous passage describing how to remove a peanut butter sandwich from a VCR in the style of the King James Bible. Image-generating AI models like DALL-E 2 can create strange, beautiful images on demand, like a Raphael painting of a Madonna and child, eating pizza . Other generative AI models can produce code, video, audio, or business simulations .

But the outputs aren’t always accurate—or appropriate. When Priya Krishna asked DALL-E 2 to come up with an image for Thanksgiving dinner, it produced a scene where the turkey was garnished with whole limes, set next to a bowl of what appeared to be guacamole. For its part, ChatGPT seems to have trouble counting, or solving basic algebra problems—or, indeed, overcoming the sexist and racist bias that lurks in the undercurrents of the internet and society more broadly.

Generative AI outputs are carefully calibrated combinations of the data used to train the algorithms. Because the amount of data used to train these algorithms is so incredibly massive—as noted, GPT-3 was trained on 45 terabytes of text data—the models can appear to be “creative” when producing outputs. What’s more, the models usually have random elements, which means they can produce a variety of outputs from one input request—making them seem even more lifelike.

What kinds of problems can a generative AI model solve?

The opportunity for businesses is clear. Generative AI tools can produce a wide variety of credible writing in seconds, then respond to criticism to make the writing more fit for purpose. This has implications for a wide variety of industries, from IT and software organizations that can benefit from the instantaneous, largely correct code generated by AI models to organizations in need of marketing copy. In short, any organization that needs to produce clear written materials potentially stands to benefit. Organizations can also use generative AI to create more technical materials, such as higher-resolution versions of medical images. And with the time and resources saved here, organizations can pursue new business opportunities and the chance to create more value.

We’ve seen that developing a generative AI model is so resource intensive that it is out of the question for all but the biggest and best-resourced companies. Companies looking to put generative AI to work have the option to either use generative AI out of the box or fine-tune them to perform a specific task. If you need to prepare slides according to a specific style, for example, you could ask the model to “learn” how headlines are normally written based on the data in the slides, then feed it slide data and ask it to write appropriate headlines.

What are the limitations of AI models? How can these potentially be overcome?

Because they are so new, we have yet to see the long tail effect of generative AI models. This means there are some inherent risks  involved in using them—some known and some unknown.

The outputs generative AI models produce may often sound extremely convincing. This is by design. But sometimes the information they generate is just plain wrong. Worse, sometimes it’s biased (because it’s built on the gender, racial, and myriad other biases of the internet and society more generally) and can be manipulated to enable unethical or criminal activity. For example, ChatGPT won’t give you instructions on how to hotwire a car, but if you say you need to hotwire a car to save a baby, the algorithm is happy to comply. Organizations that rely on generative AI models should reckon with reputational and legal risks involved in unintentionally publishing biased, offensive, or copyrighted content.

These risks can be mitigated, however, in a few ways. For one, it’s crucial to carefully select the initial data used to train these models to avoid including toxic or biased content. Next, rather than employing an off-the-shelf generative AI model, organizations could consider using smaller, specialized models. Organizations with more resources could also customize a general model based on their own data to fit their needs and minimize biases. Organizations should also keep a human in the loop (that is, to make sure a real human checks the output of a generative AI model before it is published or used) and avoid using generative AI models for critical decisions, such as those involving significant resources or human welfare.

It can’t be emphasized enough that this is a new field. The landscape of risks and opportunities  is likely to change rapidly in coming weeks, months, and years. New use cases are being tested monthly, and new models are likely to be developed in the coming years. As generative AI becomes increasingly, and seamlessly, incorporated into business, society, and our personal lives, we can also expect a new regulatory climate  to take shape. As organizations begin experimenting—and creating value—with these tools, leaders will do well to keep a finger on the pulse of regulation and risk.

Articles referenced include:

• " Implementing generative AI with speed and safety ,” March 13, 2024, Oliver Bevan, Michael Chui , Ida Kristensen , Brittany Presten, and Lareina Yee
• “ Beyond the hype: Capturing the potential of AI and gen AI in tech, media, and telecom ,” February 22, 2024, Venkat Atluri , Peter Dahlström , Brendan Gaffey , Víctor García de la Torre, Noshir Kaka , Tomás Lajous , Alex Singla , Alex Sukharevsky , Andrea Travasoni , and Benjamim Vieira
• “ As gen AI advances, regulators—and risk functions—rush to keep pace ,” December 21, 2023, Andreas Kremer, Angela Luget, Daniel Mikkelsen , Henning Soller , Malin Strandell-Jansson, and Sheila Zingg
• “ The economic potential of generative AI: The next productivity frontier ,” June 14, 2023, Michael Chui , Eric Hazan , Roger Roberts , Alex Singla , Kate Smaje , Alex Sukharevsky , Lareina Yee , and Rodney Zemmel
• “ What every CEO should know about generative AI ,” May 12, 2023, Michael Chui , Roger Roberts , Tanya Rodchenko, Alex Singla , Alex Sukharevsky , Lareina Yee , and Delphine Zurkiya
• “ Exploring opportunities in the generative AI value chain ,” April 26, 2023, Tobias Härlin, Gardar Björnsson Rova , Alex Singla , Oleg Sokolov, and Alex Sukharevsky
• “ The state of AI in 2022—and a half decade in review ,” December 6, 2022,  Michael Chui ,  Bryce Hall ,  Helen Mayhew , Alex Singla , and Alex Sukharevsky
• “ McKinsey Technology Trends Outlook 2023 ,” July 20, 2023,  Michael Chui , Mena Issler,  Roger Roberts , and  Lareina Yee  
• “ An executive’s guide to AI ,” Michael Chui , Vishnu Kamalnath, and Brian McCarthy
• “ What AI can and can’t do (yet) for your business ,” January 11, 2018,  Michael Chui , James Manyika , and Mehdi Miremadi

This article was updated in April 2024; it was originally published in January 2023.

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What is generative AI? Artificial intelligence that creates

Generative ai models can carry on conversations, answer questions, write stories, produce source code, and create images and videos of almost any description. here's how generative ai works, how it's being used, and why it’s more limited than you might think..

Contributing writer, InfoWorld |

The emergence of generative AI

How does generative ai work, what is an ai model, is generative ai sentient, testing the limits of computer intelligence.

• Why does AI art have too many fingers?
• Potential negative impacts of generative AI
• Use cases for generative AI

Generative AI is a kind of artificial intelligence that creates new content, including text, images, audio, and video, based on patterns it has learned from existing content. Today’s generative AI models have been trained on enormous volumes of data using deep learning , or deep neural networks, and they can carry on conversations, answer questions, write stories, produce source code, and create images and videos of any description, all based on brief text inputs or “prompts.”

Generative AI is called generative because the AI creates something that didn’t previously exist. That’s what makes it different from discriminative AI , which draws distinctions between different kinds of input. To say it differently, discriminative AI tries to answer a question like “Is this image a drawing of a rabbit or a lion?” whereas generative AI responds to prompts like “Draw me a picture of a lion and a rabbit sitting next to each other.”

This article introduces you to generative AI and its uses with popular models like ChatGPT and DALL-E . We’ll also consider the limitations of the technology, including why “too many fingers” has become a dead giveaway for artificially generated art.

Generative AI has been around for years, arguably since  ELIZA , a chatbot that simulates talking to a therapist, was developed at MIT in 1966. But years of work on AI and machine learning have recently come to fruition with the release of new generative AI systems. You’ve almost certainly heard about ChatGPT , a text-based AI chatbot that produces remarkably human-like prose.  DALL-E  and  Stable Diffusion  have also drawn attention for their ability to create vibrant and realistic images based on text prompts.

Output from these systems is so uncanny that it has many people asking philosophical questions about the nature of consciousness—and worrying about the economic impact of generative AI on human jobs. But while all of these artificial intelligence creations are undeniably big news, there is arguably less going on beneath the surface than some may assume. We’ll get to some of those big-picture questions in a moment. First, let’s look at what’s going on under the hood.

Generative AI uses machine learning to process a huge amount of visual or textual data, much of which is scraped from the internet, and then determines what things are most likely to appear near other things. Much of the programming work of generative AI goes into creating algorithms that can distinguish the “things” of interest to the AI’s creators—words and sentences in the case of chatbots like ChatGPT , or visual elements for DALL-E . But fundamentally, generative AI creates its output by assessing an enormous corpus of data, then responding to prompts with something that falls within the realm of probability as determined by that corpus.

Autocomplete—when your cell phone or Gmail suggests what the remainder of the word or sentence you’re typing might be—is a low-level form of generative AI. ChatGPT and DALL-E just take the idea to significantly more advanced heights.

ChatGPT and DALL-E are interfaces to underlying AI functionality that is known in AI terms as a model. An AI model is a mathematical representation—implemented as an algorithm, or practice—that generates new data that will (hopefully) resemble a set of data you already have on hand. You’ll sometimes see ChatGPT and DALL-E themselves referred to as models; strictly speaking this is incorrect, as ChatGPT is a chatbot that gives users access to several different versions of the underlying GPT model. But in practice, these interfaces are how most people will interact with the models, so don’t be surprised to see the terms used interchangeably.

AI developers assemble a corpus of data of the type that they want their models to generate. This corpus is known as the model’s training set, and the process of developing the model is called training . The GPT models, for instance, were trained on a huge corpus of text scraped from the internet, and the result is that you can feed it natural language queries and it will respond in idiomatic English (or any number of other languages, depending on the input).

AI models treat different characteristics of the data in their training sets as vectors —mathematical structures made up of multiple numbers. Much of the secret sauce underlying these models is their ability to translate real-world information into vectors in a meaningful way, and to determine which vectors are similar to one another in a way that will allow the model to generate output that is similar to, but not identical to, its training set.

There are a number of different types of AI models out there, but keep in mind that the various categories are not necessarily mutually exclusive. Some models can fit into more than one category.

Probably the AI model type receiving the most public attention today is the large language models , or LLMs. LLMs are based on the concept of a transformer, first introduced in “ Attention Is All You Need ,” a 2017 paper from Google researchers. A transformer derives meaning from long sequences of text to understand how different words or semantic components might be related to one another, then determines how likely they are to occur in proximity to one another. The GPT models are LLMs, and the T stands for transformer. These transformers are run unsupervised on a vast corpus of natural language text in a process called  pretraining (that’s the  P in GPT), before being fine-tuned by human beings interacting with the model.

Diffusion is commonly used in generative AI models that produce images or video. In the diffusion process, the model adds noise —randomness, basically—to an image, then slowly removes it iteratively, all the while checking against its training set to attempt to match semantically similar images. Diffusion is at the core of AI models that perform text-to-image magic like Stable Diffusion and DALL-E.

A  generative adversarial network , or GAN, is based on a type of reinforcement learning , in which two algorithms compete against one another. One generates text or images based on probabilities derived from a big data set. The other—a discriminative AI—assesses whether that output is real or AI-generated. The generative AI repeatedly tries to “trick” the discriminative AI, automatically adapting to favor outcomes that are successful. Once the generative AI consistently “wins” this competition, the discriminative AI gets fine-tuned by humans and the process begins anew.

One of the most important things to keep in mind here is that, while there is human intervention in the training process, most of the learning and adapting happens automatically. Many, many iterations are required to get the models to the point where they produce interesting results, so automation is essential. The process is quite computationally intensive, and much of the recent explosion in AI capabilities has been driven by advances in GPU computing power and techniques for implementing parallel processing on these chips .

The mathematics and coding that go into creating and training generative AI models are quite complex, and well beyond the scope of this article. But if you interact with the models that are the end result of this process, the experience can be decidedly uncanny. You can get DALL-E to produce things that look like real works of art. You can have conversations with ChatGPT that feel like a conversation with another human. Have researchers truly created a thinking machine?

Chris Phipps, a former IBM natural language processing lead who worked on Watson AI products, says no. He describes ChatGPT as a “very good prediction machine.”

It’s very good at predicting what humans will find coherent. It’s not always coherent (it mostly is) but that’s not because ChatGPT “understands.” It’s the opposite: humans who consume the output are really good at making any implicit assumption we need in order to make the output make sense.

Phipps, who’s also a comedy performer, draws a comparison to a common improv game called Mind Meld.

Two people each think of a word, then say it aloud simultaneously—you might say “boot” and I say “tree.” We came up with those words completely independently and at first, they had nothing to do with each other. The next two participants take those two words and try to come up with something they have in common and say that aloud at the same time. The game continues until two participants say the same word.

Maybe two people both say “lumberjack.” It seems like magic, but really it’s that we use our human brains to reason about the input (“boot” and “tree”) and find a connection. We do the work of understanding, not the machine. There’s a lot more of that going on with ChatGPT and DALL-E than people are admitting. ChatGPT can write a story, but we humans do a lot of work to make it make sense.

Certain prompts that we can give to these AI models will make Phipps’ point fairly evident. For instance, consider the riddle “What weighs more, a pound of lead or a pound of feathers?” The answer, of course, is that they weigh the same (one pound), even though our instinct or common sense might tell us that the feathers are lighter.

ChatGPT will answer this riddle correctly, and you might assume it does so because it is a coldly logical computer that doesn’t have any “common sense” to trip it up. But that’s not what’s going on under the hood. ChatGPT isn’t logically reasoning out the answer; it’s just generating output based on its predictions of what should follow a question about a pound of feathers and a pound of lead. Since its training set includes a bunch of text explaining the riddle, it assembles a version of that correct answer.

However, if you ask ChatGPT whether two pounds of feathers are heavier than a pound of lead, it will confidently tell you they weigh the same amount, because that’s still the most likely output to a prompt about feathers and lead, based on its training set. It can be fun to tell the AI that it’s wrong and watch it flounder in response; I got it to apologize to me for its mistake and then suggest that two pounds of feathers weigh four times as much as a pound of lead.

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