Artificial Intelligence

What is Artificial Intelligence ?

Source – Wikipedia

Artificial intelligence (AI) is intelligence demonstrated by computers, as opposed to human or animal intelligence. “Intelligence” encompasses the ability to learn and to reason, to generalize, and to infer meaning.[1] AI applications include advanced web search engines (e.g., Google Search), recommendation systems (used by YouTubeAmazon, and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Waymo), generative or creative tools (ChatGPT and AI art), and competing at the highest level in strategic game systems (such as chess and Go).[2]

Artificial intelligence was founded as an academic discipline in 1956, and in the years since it has experienced several waves of optimism,[3][4] followed by disappointment and the loss of funding (known as an “AI winter“),[5][6] followed by new approaches, success, and renewed funding.[4][7] AI research has tried and discarded many different approaches, including simulating the brain, modeling human problem solvingformal logiclarge databases of knowledge, and imitating animal behavior. In the first decades of the 21st century, highly mathematical and statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.[7][8]

The various sub-fields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoningknowledge representationplanninglearningnatural language processingperception, and the ability to move and manipulate objects.[a] General intelligence (the ability to solve an arbitrary problem) is among the field’s long-term goals.[9] To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statisticsprobability, and economics. AI also draws upon computer sciencepsychologylinguisticsphilosophy, and many other fields.

The field was founded on the assumption that human intelligence “can be so precisely described that a machine can be made to simulate it”.[b] This raised philosophical arguments about the mind and the ethical consequences of creating artificial beings endowed with human-like intelligence; these issues have previously been explored by mythfiction (science fiction), and philosophy since antiquity.

[11] Computer scientists and philosophers have since suggested that AI may become an existential risk to humanity if its rational capacities are not steered towards goals beneficial to humankind.[c] Economists have frequently highlighted the risks of redundancies from AI, and speculated about unemployment if there is no adequate social policy for full employment.[12] The term artificial intelligence has also been criticized for overhyping AI’s true technological capabilities.

History
Know about the history of Artificial Intelligence

Artificial Intelligence
Artificial Intelligence

Artificial intelligence (AI) is intelligence demonstrated by computers, as opposed to human or animal intelligence. “Intelligence” encompasses the ability to learn and to reason, to generalize, and to infer meaning.[1] AI applications include advanced web search engines (e.g., Google Search), recommendation systems (used by YouTubeAmazon, and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Waymo), generative or creative tools (ChatGPT and AI art), and competing at the highest level in strategic game systems (such as chess and Go).[2]

Artificial intelligence was founded as an academic discipline in 1956, and in the years since it has experienced several waves of optimism,[3][4] followed by disappointment and the loss of funding (known as an “AI winter“),[5][6] followed by new approaches, success, and renewed funding.[4][7] AI research has tried and discarded many different approaches, including simulating the brain, modeling human problem solvingformal logiclarge databases of knowledge, and imitating animal behavior. In the first decades of the 21st century, highly mathematical and statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.[7][8]

The various sub-fields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoningknowledge representationplanninglearningnatural language processingperception, and the ability to move and manipulate objects.[a] General intelligence (the ability to solve an arbitrary problem) is among the field’s long-term goals.[9] 

To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statisticsprobability, and economics. AI also draws upon computer sciencepsychologylinguisticsphilosophy, and many other fields.

The field was founded on the assumption that human intelligence “can be so precisely described that a machine can be made to simulate it”.[b] This raised philosophical arguments about the mind and the ethical consequences of creating artificial beings endowed with human-like intelligence; these issues have previously been explored by mythfiction (science fiction), and philosophy since antiquity.[11]

 Computer scientists and philosophers have since suggested that AI may become an existential risk to humanity if its rational capacities are not steered towards goals beneficial to humankind.[c] Economists have frequently highlighted the risks of redundancies from AI, and speculated about unemployment if there is no adequate social policy for full employment.[12] The term artificial intelligence has also been criticized for overhyping AI’s true technological capabilities.[13][14][15]

Silver didrachma from Crete depicting Talos, a mythical intelligent automaton (c. 300 BC)

Artificial beings with intelligence appeared as storytelling devices in antiquity,[16] and have been common in fiction, as in Mary Shelley‘s Frankenstein or Karel Čapek‘s R.U.R.[17] These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.[18]

The study of mechanical or “formal” reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing‘s theory of computation, which suggested that a machine, by shuffling symbols as simple as “0” and “1”, could simulate any conceivable act of mathematical deduction. This insight that digital computers can simulate any process of formal reasoning is known as the Church–Turing thesis.[19]

 This, along with concurrent discoveries in neurobiologyinformation theory and cybernetics, led researchers to consider the possibility of building an electronic brain.[20] The first work that is now generally recognized as AI was McCullouch and Pitts‘ 1943 formal design for Turing-complete “artificial neurons”.[21]

The field of AI research was born at a workshop at Dartmouth College in 1956.[d][23] The attendees became the founders and leaders of AI research.[e] They and their students produced programs that the press described as “astonishing”:[f] computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English.[g][26] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense[27] and laboratories had been established around the world.[28]

Researchers in the 1960s and the 1970s were convinced that their methods would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.[29] Herbert Simon predicted, “machines will be capable, within twenty years, of doing any work a man can do”.[30] Marvin Minsky agreed, writing, “within a generation … the problem of creating ‘artificial intelligence’ will substantially be solved”.[31]

They had failed to recognize the difficulty of some of the remaining tasks.[h] Progress slowed and in 1974, in response to the criticism of Sir James Lighthill[33] and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. Early research into artificial neural networks was discredited by Minsky‘s and Papert‘s book Perceptrons, which was perceived as proving that this technology would never work.[34] The next few years would later be called an “AI winter“, a period when obtaining funding for AI projects was difficult.[5]

In the early 1980s, AI research was revived by the commercial success of expert systems,[35] a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan’s fifth generation computer project inspired the U.S. and British governments to restore funding for academic research.[4] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.[6]

Many researchers began to doubt that the current practices would be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition.[36] A number of researchers began to look into “sub-symbolic” approaches to specific AI problems.[37] Robotics researchers, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move, survive, and learn their environment.[i] Tools were invented that could make reasonable guesses (known as “soft computing“) and deal with uncertain information.[42]

 Interest in neural networks and “connectionism” was revived by Geoffrey HintonDavid Rumelhart and others in the middle of the 1980s.[43] Connectionism began to succeed in the 90s when Yann LeCun successfully showed convolutional neural networks could recognize handwritten digits.[44]

AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. This “narrow” and “formal” focus allowed researchers to produce verifiable results and collaborate with other fields (such as statisticseconomics and mathematics).[45] By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as “artificial intelligence”.[8]

Several leading academic researchers became concerned that AI was no longer pursuing the original goal of creating versatile, fully intelligent machines. This concern has led to the founding of the subfield of artificial general intelligence (or “AGI”),[9] which had several well-funded institutions by the 2010s.

Deep learning methods started to dominate accuracy benchmarks in 2012.[46] Deep learning’s success depended on hardware improvements (faster computers,[47] graphics processing unitscloud computing[7]) and access to large amounts of data[48] (including curated datasets,[7] such as ImageNet). It soon proved to be highly successful at a wide range of tasks and was adopted throughout the field. For many specific tasks, other methods were abandoned.[j] 

The success of deep learning set off an explosion of interest and investment throughout the economy.[k] The number of software projects that use AI at Google increased from a “sporadic usage” in 2012 to more than 2,700 projects in 2015.[7]

 In a 2017 survey, one in five companies reported they had “incorporated AI in some offerings or processes”.[50] The amount of research into AI (measured by total publications) increased by 50% in the years 2015–2019.[51] According to AI Impacts at Stanford, around 2022 about $50 billion annually is invested in artificial intelligence in the US, and about 20% of new US Computer Science PhD graduates have specialized in artificial intelligence;[52] about 800,000 AI-related US job openings existed in 2022.[53]

In 2016, issues of fairness and the misuse of technology were catapulted into center stage at AI conferences, publications vastly increased, funding became available, and many researchers re-focussed their careers on these issues. The alignment problem became a serious field of academic study.

Goals

What are the goals of Artificial Intelligence

Artificial Intelligence
Artificial Intelligence

The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.[a]

Artificial intelligence Reasoning, problem-solving

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[55] By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[56]

Many of these algorithms proved to be insufficient for solving large reasoning problems because they experienced a “combinatorial explosion”: they became exponentially slower as the problems grew larger.[57] Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments.[58]

Artificial intelligence Knowledge Representation

Knowledge Representation of Artificial Intelligence through picture presentation

Artificial Intelligence
Artificial Intelligence

An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.

Knowledge representation and knowledge engineering[59] allow AI programs to answer questions intelligently and make deductions about real-world facts.

A representation of “what exists” is an ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them.[60] The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge and act as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern).

AI research, building on the work of mathematics and philosophy, have developed tools to represent specific domains, such as objects, properties, categories and relations between objects;[61] situations, events, states and time;[62] causes and effects;[63] knowledge about knowledge (what we know about what other people know); and [64] default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing);[65].

Among the most difficult problems in AI are: the breadth of commonsense knowledge (the set of atomic facts that the average person knows) is enormous;[66] and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as “facts” or “statements” that they could express verbally).[58]

Formal knowledge representations are used in content-based indexing and retrieval,[67] scene interpretation,[68] clinical decision support,[69] knowledge discovery (mining “interesting” and actionable inferences from large databases),[70] and other areas.[71]

Artificial intelligence Learning

Main article: Machine learning

Machine learning (ML), a fundamental concept of AI research since the field’s inception,[l] is the study of computer algorithms that improve automatically through experience.[m] [75]

Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance.[76]

Supervised learning requires a human to label the input data first, and comes in two main varieties: classification and regression.[77] Classification is used to determine what category something belongs in – the program sees a number of examples of things from several categories and will learn to classify new inputs.

Regression is the attempt to produce a numeric function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. Both classifiers and regression learners can be viewed as “function approximators” trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, “spam” or “not spam”.[78]

In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent classifies its responses to form a strategy for operating in its problem space.[79]

Transfer learning is when the knowledge gained from one problem is applied to a new problem.[80]

Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.

Artificial intelligence Future

Superintelligence

A superintelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind.[210]

If research into artificial general intelligence produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to recursive self-improvement.[222] Its intelligence would increase exponentially in an intelligence explosion and could dramatically surpass humans. Science fiction writer Vernor Vinge named this scenario the “singularity”.[223]

 Because it is difficult or impossible to know the limits of intelligence or the capabilities of superintelligent machines, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable.[224]

Robot designer Hans Moravec, cyberneticist Kevin Warwick, and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in Aldous Huxley and Robert Ettinger.[225]

Edward Fredkin argues that “artificial intelligence is the next stage in evolution”, an idea first proposed by Samuel Butler‘s “Darwin among the Machines” as far back as 1863, and expanded upon by George Dyson in his book of the same name in 1998.

Artificial intelligence Risks

Technological unemployment

In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that “we’re in uncharted territory” with Artificial intelligence.[227] A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit if productivity gains are redistributed.[228]

 Risk estimates vary; for example, in the 2010s Michael Osborne and Carl Benedikt Frey estimated 47% of U.S. jobs are at “high risk” of potential automation, while an OECD report classified only 9% of U.S. jobs as “high risk”.[v][230] The methodology of speculating about future employment levels has been criticised as lacking evidential foundation, and for implying that technology (rather than social policy) creates unemployment (as opposed to redundancies).[12]

Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; The Economist stated in 2015 that “the worry that Artificial intelligence could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution” is “worth taking seriously”.[231] Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.

Bad actors and weaponized Artificial intelligence

AI provides a number of tools that are particularly useful for authoritarian governments: smart spywareface recognition and voice recognition allow widespread surveillance; such surveillance allows machine learning to classify potential enemies of the state and can prevent them from hiding; recommendation systems can precisely target propaganda and misinformation for maximum effect; deepfakes aid in producing misinformation; advanced AI can make centralized decision making more competitive with liberal and decentralized systems such as markets.[233]

Terrorists, criminals and rogue states may use other forms of weaponized AI such as advanced digital warfare and lethal autonomous weapons. By 2015, over fifty countries were reported to be researching battlefield robots.[234]

Machine-learning Artificial intelligence is also able to design tens of thousands of toxic molecules in a matter of hours.

Algorithmic bias

AI programs can become biased after learning from real-world data.[236] It may not be introduced by the system designers but learned by the program, and thus the programmers may not be aware that the bias exists.[237] Bias can be inadvertently introduced by the way training data is selected and by the way a model is deployed.[238] [236] It can also emerge from correlations: Artificial intelligence is used to classify individuals into groups and then make predictions assuming that the individual will resemble other members of the group. In some cases, this assumption may be unfair.[239]

 An example of this is COMPAS, a commercial program widely used by U.S. courts to assess the likelihood of a defendant becoming a recidivistProPublica claims that the COMPAS-assigned recidivism risk level of black defendants is far more likely to be overestimated than that of white defendants, despite the fact that the program was not told the races of the defendants.[240]

Health equity issues may also be exacerbated when many-to-many mapping is done without taking steps to ensure equity for populations at risk for bias. At this time equity-focused tools and regulations are not in place to ensure equity application representation and usage.[241] Other examples where algorithmic bias can lead to unfair outcomes are when Artificial intelligence is used for credit rating, CV screening, hiring and applications for public housing.[236]

At its 2022 Conference on Fairness, Accountability, and Transparency (ACM FAccT 2022) the Association for Computing Machinery, in Seoul, South Korea, presented and published findings recommending that until AI and robotics systems are demonstrated to be free of bias mistakes, they are unsafe and the use of self-learning neural networks trained on vast, unregulated sources of flawed internet data should be curtailed.[

Existential risk

It has been argued Artificial intelligence will become so powerful that humanity may irreversibly lose control of it. This could, as the physicist Stephen Hawking puts it, “spell the end of the human race“.[243] According to the philosopher Nick Bostrom, for almost any goals that a sufficiently intelligent AI may have, it is instrumentally incentivized to protect itself from being shut down and to acquire more resources, as intermediary steps to better achieve these goals. Sentience or emotions are then not required for an advanced AI to be dangerous. In order to be safe for humanity, a superintelligence would have to be genuinely aligned with humanity’s morality and values so that it is “fundamentally on our side”.[244] 

The political scientist Charles T. Rubin argued that “any sufficiently advanced benevolence may be indistinguishable from malevolence” and warned that we should not be confident that intelligent machines will by default treat us favorably.[245]

The opinions amongst experts and industry insiders are mixed, with sizable fractions both concerned and unconcerned by risk from eventual superintelligent AI.[246] Personalities such as Stephen HawkingBill GatesElon Musk have expressed concern about existential risk from AI.[247] In 2023, AI pioneers including Geoffrey HintonYoshua BengioDemis Hassabis, and Sam Altman issued the joint statement that “Mitigating the risk of extinction from Artificial intelligence should be a global priority alongside other societal-scale risks such as pandemics and nuclear war”; some others such as Yann LeCun consider this to be unfounded.[248] 

Mark Zuckerberg stated that artificial intelligence is helpful in its current form and will continue to assist humans.[249] Some experts have argued that the risks are too distant in the future to warrant research, or that humans will be valuable from the perspective of a superintelligent machine.[250] Rodney Brooks, in particular, said in 2014 that “malevolent” AI is still centuries away.

In order to leverage as large a dataset as is feasible, generative Artificial intelligence is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under a rationale of “fair use“. Experts disagree about how well, and under what circumstances, this rationale will hold up in courts of law; relevant factors may include “the purpose and character of the use of the copyrighted work” and “the effect upon the potential market for the copyrighted work”

Artificial intelligence Ethical machines

Friendly Artificial intelligence are machines that have been designed from the beginning to minimize risks and to make choices that benefit humans. Eliezer Yudkowsky, who coined the term, argues that developing friendly AI should be a higher research priority: it may require a large investment and it must be completed before AI becomes an existential risk

Machines with intelligence have the potential to use their intelligence to make ethical decisions. The field of machine ethics provides machines with ethical principles and procedures for resolving ethical dilemmas. and was founded at an AAAI symposium in 2005.The field of machine ethics is also called computational morality, and was founded at an AAAI symposium in 2005.

Artificial intelligence Regulation

The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating artificial intelligence (AI); it is therefore related to the broader regulation of algorithms. The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally. According to AI Index at Stanford, the annual number of AI-related laws passed in the 127 survey countries jumped from one passed in 2016 to 37 passed in 2022 alone.[260][261] 

Between 2016 and 2020, more than 30 countries adopted dedicated strategies for AI Most EU member states had released national AI strategies, as had Canada, China, India, Japan, Mauritius, the Russian Federation, Saudi Arabia, United Arab Emirates, US and Vietnam. Others were in the process of elaborating their own AI strategy, including Bangladesh, Malaysia and Tunisia. The Global Partnership on Artificial Intelligence was launched in June 2020, stating a need for AI to be developed in accordance with human rights and democratic values, to ensure public confidence and trust in the technology.[51]

 Henry KissingerEric Schmidt, and Daniel Huttenlocher published a joint statement in November 2021 calling for a government commission to regulate AI. In 2023, OpenAI leaders published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years.

In a 2022 Ipsos survey, attitudes towards AI varied greatly by country; 78% of Chinese citizens, but only 35% of Americans, agreed that “products and services using AI have more benefits than drawbacks”. A 2023 Reuters/Ipsos poll found that 61% of Americans agree, and 22% disagree, that AI poses risks to humanity. In a 2023 Fox News poll, 35% of Americans thought it “very important”, and an additional 41% thought it “somewhat important”, for the federal government to regulate AI, versus 13% responding “not very important” and 8% responding “not at all important”.

Most Famous Artificial Intelligence in 2023

  • Google Assistant by Google
  • ChatGpt by OpenAI
  • Siri by Apple
  • Alexa by Amazon
  • Whatson by IBM

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