Extraverts and Intraverts

Количество смертей на миллион километров пути

Магічні 150. Чому ми не можемо підтримувати стосунки з більшим числом людей?

Крістін РоBBC Future

Теорія числа Данбара стверджує, що наше коло спілкування водночас не може перебільшувати 150 людей. Але чи працює це правило у добу соцмереж?

Якщо колись вам відмовляли в романтичних стосунках, пропонуючи натомість бути друзями, ми могли відповісти поширеною фразою: "Дякую, друзів у мене достатньо".

Але чи дійсно люди мають обмежений емоційний ресурс, якого вистачає лише на певну кількість френдів?

Виявляється, що так. Ба більше, число людей, з якими ми здатні водночас підтримувати контакти, точно визначено і дорівнює 150.

Назвав його британський антрополог Робін Данбар.

Дослідження, які він проводив на приматах, показали, що існує пряма залежність між розміром мозку та кількістю особин, з якою здатні взаємодіяти ці тварини.

Данбар визначив цей коефіцієнт за допомогою нейровізуалізації та спостереження за поведінкою приматів у період залицянь. Вчений дійшов висновку, що неокортекс - ділянка мозку, що відповідає за пізнання та мову, - пов'язаний із розміром згуртованої соціальної групи.

Цей коефіцієнт також визначає складність соціальної системи певного виду.

Данбар та його колеги застосовували цей принцип і до людей.

Правило 150 залишається справедливим як для ранніх суспільств мисливців-збирачів, так і для сучасних офісів

Вони вивчили історичні, антропологічні та сучасні психологічні дані, що стосуються розміру груп, зокрема, до якої кількості зростає певна соціальна група, перш ніж вона розділиться або розпадеться.

Вчені виявили дивовижну стійкість навколо числа 150.

На думку Данбара та багатьох дослідників, на яких він вплинув, правило 150 залишається справедливим як для ранніх суспільств мисливців-збирачів, так і для сучасних угрупувань: офісів, комун, фабрик, житлових кемпінгів, військових організацій або англійських селищ XI століття чи навіть розсилок різдвяних вітань.

Збільште число понад 150, і мережа навряд чи проіснує довго або злагоджено.

Перше коло - це п'ять найближчих нам людей, наших коханих, останнє - півтори тисячі осіб, яких ми здатні лише впізнавати

В умовах урбанізації мешканці великих міст можуть уникнути відчуження та напруження, лише створюючи невеликі громади, квазіселища, всередині них.

Фото За теорією Данбара, люди здатні підтримувати стосунки приблизно зі 150 особами - чи то в ранніх суспільствах мисливців-збирачів, чи в сучасних офісах

Утім, число 150 - лише частина історії.

Гіпотеза соціального мозку складається й з інших чисел. Так, приміром, вузьке коло найближчих людей - наших коханих - може вміщати лише п'ять осіб.

Хороших друзів у нас може бути не більше 15, просто друзів - 50, важливих знайомих - 150, просто знайомих - 500. А півтори тисячі людей - це рівно стільки, скільки ми здатні впізнати.

Люди можуть мігрувати з однієї групи в іншу, але ідея полягає в тому, що для нових учасників має бути виділений простір.

Данбар не впевнений, чому всі ці шари чисел кратні п'яти, але зазначає, що "число п'ять є взагалі важливим для мавп, зокрема й людиноподібних.

Деякі американські соціальні мережі групуються по 290 осіб, а не 150

Ці числа, звичайно, доволі умовні. У екстравертів мережа буде ширшою, а стосунки більш поверхневі. Тоді як інтроверти зосереджені на меншому числі більш тісних стосунків.

У жінок загалом буде трохи більше контактів у найближчих колах.

"У реальному житті ці кола визначає частота, з якою ви бачите певних людей, - пояснює Данбар. - Адже нам усім доводиться постійно вирішувати, скільки часу ми готові пожертвувати для тих чи інших стосунків".

Деякі організації серйозно прислухалися до цієї теорії. Наприклад, Шведська податкова адміністрація реструктуризувала свої офіси так, щоби кількість працівник у них не перебільшувала 150 осіб.

Звичайно, гіпотезу Данбара підтримують не усі. Дехто взагалі скептично ставиться до можливості отримати певне число соціальної взаємодії.

Наприклад, одна людина може бути достатньо багатою, щоб найняти помічників, які б частково керували стосунками, або делегувати частину емоційної праці іншим.

Люди з найбільшою кількістю зв'язків і є найбільш привілейованими

Як і в інших аспектах соціального життя, люди з найбільшою кількістю зв'язків і є найбільш привілейованими.

Число Данбара, схоже, є найбільш придатним для суспільств до сучасної доби або для груп із середнім рівнем доходу в сучасних західних суспільствах.

Але й їх змінює інтернет-культура.

А як щодо онлайн-френдів?

Однією із сучасних версій печерних посиденьок біля багаття є Slack, корпоративний месенджер , який виснажує завалених роботою працівників з 2013 року.

Одна з його користувачок, американська дизайнерка Карлі Айрес знайшла спосіб, як подолати перевантаження інформацією в месенджері.

Що менше ваша група спілкування у соцмережах, то краще стосунки в ній

Вона створила групу для дизайнерів 100s Under 100 ("сотня в сотні"), яка поділяється на окремі канали, щойно вони починають розростатися.

Спостерігаючи за онлайн-спільнотами, Айрес погоджується, що число Данбара має сенс.

"Думаю, ми справді можемо утримувати в голові саме стільки інформації, саме стільки аватарів. Що більше ви когось узнаєте, то кращі ваші стосунки, але й число таких відносин є дійсно обмеженим", - каже вона.

Дедалі більше людей схиляються до того, що чим менше ваша група спілкування у соцмережах, то краще стосунки в ній.

Данбар і його колеги також провели дослідження у соцмережах.

Дослідники виявили, що якщо у людей є понад 150 друзів на Facebook або 150 підписників на Twitter, вони неминуче переміщаються у зовнішні кола контактів, ті 500-1500 осіб, з якими ви знайомі лише віддалено.

Для більшості людей близькі стосунки не можливі за межами числа 150, впевнений вчений.

Є пряма залежність між кількістю ваших знайомих і близькістю стосунків з ними

На думку дослідника, нефізичний характер стосунків в інтернеті не може замінити розмову віч-на-віч з усією невербальною інформацією, яка так важлива для спілкування.

Утім, власні дослідження Данбара свідчать і про відмінності між поколіннями. Люди віком від 18 до 24 років мають набагато більші соціальних контактів в інтернеті, ніж старше покоління віком від 55 років.

Таким чином потреба фізичного контакту в гіпотезі Данбара може менше стосуватися молодих людей, які ніколи не знали життя без інтернету. Адже для них віртуальні стосунки так само важливі, як і реальні.

Стаття є частиною серії BBC Future "Невидимі числа", в якій ми розповідатимемо про відсотки, коефіцієнти та рівняння, які несподіваним чином керують нашим повсякденним життям.

https://bbc.in/2oT6wiJ

What Is AI?

Ten years ago, if you mentioned the term “artificial intelligence” in a boardroom there’s a good chance you would have been laughed at. For most people it would bring to mind sentient, sci-fi machines such as 2001: A Space Odyssey’s HAL or Star Trek’s Data.

Today it is one of the hottest buzzwords in business and industry. AI technology is a crucial lynchpin of much of the digital transformation taking place today as organisations position themselves to capitalize on the ever-growing amount of data being generated and collected.

So how has this change come about? Well partly it is due to the Big Data revolution itself. The glut of data has led to intensified research into ways it can be processed, analysed and acted upon. Machines being far better suited to humans than this work, the focus was on training machines to do this in as “smart” a way as is possible.

This increased interest in research in the field – in academia, industry and among the open source community which sits in the middle – has led to breakthroughs and advances that are showing their potential to generate tremendous change. From healthcare to self-driving cars to predicting the outcome of legal cases, no one is laughing now!

What is Artificial Intelligence?

The concept of what defines AI has changed over time, but at the core there has always been the idea of building machines which are capable of thinking like humans.

After all, human beings have proven uniquely capable of interpreting the world around us and using the information we pick up to effect change. If we want to build machines to help us to this more efficiently, then it makes sense to use ourselves as a blueprint!

AI, then, can be thought of as simulating the capacity for abstract, creative, deductive thought – and particularly the ability to learn – using the digital, binary logic of computers.

Research and development work in AI is split between two branches. One is labelled “applied AI” which uses these principles of simulating human thought to carry out one specific task. The other is known as “generalised AI” – which seeks to develop machine intelligences that can turn their hands to any task, much like a person.

Research into applied, specialised AI is already providing breakthroughs in fields of study from quantum physics where it is used to model and predict the behaviour of systems comprised of billions of subatomic particles, to medicine where it being used to diagnose patients based on genomic data.

In industry, it is employed in the financial world for uses ranging from fraud detection to improving customer service by predicting what services customers will need. In manufacturing it is used to manage workforces and production processes as well as for predicting faults before they occur, therefore enabling predictive maintenance.

In the consumer world more and more of the technology we are adopting into our everyday lives is becoming powered by AI – from smartphone assistants like Apple’s Siri and Google’s Google Assistant, to self-driving and autonomous cars which many are predicting will outnumber manually driven cars within our lifetimes.

Generalised AI is a bit further off – to carry out a complete simulation of the human brain would require both a more complete understanding of the organ than we currently have, and more computing power than is commonly available to researchers. But that may not be the case for long, given the speed with which computer technology is evolving. A new generation of computer chip technology known as neuromorphic processors are being designed to more efficiently run brain-simulator code. And systems such as IBM’s Watson cognitive computing platform use high-level simulations of human neurological processes to carry out an ever-growing range of tasks without being specifically taught how to do them.

What are the key developments in AI?

All of these advances have been made possible due to the focus on imitating human thought processes. The field of research which has been most fruitful in recent years is what has become known as “machine learning”. In fact, it’s become so integral to contemporary AI that the terms “artificial intelligence” and “machine learning” are sometimes used interchangeably.

However, this is an imprecise use of language, and the best way to think of it is that machine learning represents the current state-of-the-art in the wider field of AI. The foundation of machine learning is that rather than have to be taught to do everything step by step, machines, if they can be programmed to think like us, can learn to work by observing, classifying and learning from its mistakes, just like we do.

The application of neuroscience to IT system architecture has led to the development of artificial neural networks– and although work in this field has evolved over the last half century it is only recently that computers with adequate power have been available to make the task a day-to-day reality for anyone except those with access to the most expensive, specialised tools.

Perhaps the single biggest enabling factor has been the explosion of data which has been unleashed since mainstream society merged itself with the digital world. This availability of data – from things we share on social media to machine data generated by connected industrial machinery – means computers now have a universe of information available to them, to help them learn more efficiently and make better decisions.

What is the future of AI?

That depends on who you ask, and the answer will vary wildly!

Real fears that development of intelligence which equals or exceeds our own, but has the capacity to work at far higher speeds, could have negative implications for the future of humanity have been voiced, and not just by apocalyptic sci-fi such as The Matrix or The Terminator, but respected scientists like Stephen Hawking.

Even if robots don’t eradicate us or turn us into living batteries, a less dramatic but still nightmarish scenario is that automation of labour (mental as well as physical) will lead to profound societal change – perhaps for the better, or perhaps for the worse.

This understandable concern has led to the foundation last year, by a number of tech giants including Google, IBM, Microsoft, Facebook and Amazon, of the Partnership in AI. This group will research and advocate for ethical implementations of AI, and to set guidelines for future research and deployment of robots and AI.

Artificial intelligence

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.^[1] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".^[2]

As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect.^[3] A quip in Tesler's Theorem says "AI is whatever hasn't been done yet."^[4] For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology.^[5] Modern machine capabilities generally classified as AI include successfully understanding human speech,^[6] competing at the highest level in strategic game systems (such as chess and Go),^[7] autonomously operating cars, intelligent routing in content delivery networks, and military simulations.

Artificial intelligence can be classified into three different types of systems:

• Analytical
• Human-inspired
• Humanized artificial intelligence.^[8]

Analytical AI has only characteristics consistent with cognitive intelligence; generating a cognitive representation of the world and using learning based on past experience to inform future decisions. Human-inspired AI has elements from cognitive and emotional intelligence; understanding human emotions, in addition to cognitive elements, and considering them in their decision making. Humanized AI shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), is able to be self-conscious and is self-aware in interactions.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,^[9][10] followed by disappointment and the loss of funding (known as an "AI winter"),^[11][12] followed by new approaches, success and renewed funding.^[10][13] For most of its history, AI research has been divided into subfields that often fail to communicate with each other.^[14] These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"),^[15] the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences.^[16][17][18] Subfields have also been based on social factors (particular institutions or the work of particular researchers).^[14]

The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects.^[15] General intelligence is among the field's long-term goals.^[19] Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, 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".^[20] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence. These issues have been explored by myth, fiction and philosophy since antiquity.^[21] Some people also consider AI to be a danger to humanity if it progresses unabated.^[22] Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.^[23]

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.^[24][13]

Silver didrachma from Crete depicting Talos, an ancient mythical automaton with artificial intelligence

History

Thought-capable artificial beings appeared as storytelling devices in antiquity,^[25] and have been common in fiction, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R. (Rossum's Universal Robots).^[26] These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.^[21]

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.^[27] Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to consider the possibility of building an electronic brain. Turing proposed changing the question from whether a machine was intelligent, to "whether or not it is possible for machinery to show intelligent behaviour".^[28] The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete "artificial neurons".^[29]

The field of AI research was born at a workshop at Dartmouth College in 1956.^[30] Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research.^[31] They and their students produced programs that the press described as "astonishing":^[32] computers were learning checkers strategies (c. 1954)^[33] (and by 1959 were reportedly playing better than the average human),^[34] solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English.^[35] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense^[36] and laboratories had been established around the world.^[37] AI's founders were optimistic about the future: Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". Marvin Minsky agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".^[9]

They failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill^[38] 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. The next few years would later be called an "AI winter",^[11] a period when obtaining funding for AI projects was difficult.

In the early 1980s, AI research was revived by the commercial success of expert systems,^[39] 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.^[10] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began.^[12]

In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas.^[24] The success was due to increasing computational power (see Moore's law), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards.^[40] Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.^[41]

In 2011, a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin.^[42] Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012.^[43] The Kinect, which provides a 3D body–motion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from lengthy AI research^[44] as do intelligent personal assistants in smartphones.^[45] In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps.^[7][46] In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie,^[47] who at the time continuously held the world No. 1 ranking for two years.^[48][49] This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is a relatively complex game, more so than Chess.

According to Bloomberg's Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI Google increased from a "sporadic usage" in 2012 to more than 2,700 projects. Clark also presents factual data indicating the improvements of AI since 2012 supported by lower error rates in image processing tasks.^[50] He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets.^[13] Other cited examples include Microsoft's development of a Skype system that can automatically translate from one language to another and Facebook's system that can describe images to blind people.^[50] In a 2017 survey, one in five companies reported they had "incorporated AI in some offerings or processes".^[51][52] Around 2016, China greatly accelerated its government funding; given its large supply of data and its rapidly increasing research output, some observers believe it may be on track to becoming an "AI superpower".^[53][54] However, it has been acknowledged that reports regarding artificial intelligence have tended to be exaggerated.^[55][56][57]

Definitions

Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.^[1] A more elaborate definition characterizes AI as “a system’s 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.”^[58]

Basics

A typical AI analyzes its environment and takes actions that maximize its chance of success.^[1] An AI's intended utility function (or goal) can be simple ("1 if the AI wins a game of Go, 0 otherwise") or complex ("Do mathematically similar actions to the ones succeeded in the past"). Goals can be explicitly defined, or induced. If the AI is programmed for "reinforcement learning", goals can be implicitly induced by rewarding some types of behavior or punishing others.^[a] Alternatively, an evolutionary system can induce goals by using a "fitness function" to mutate and preferentially replicate high-scoring AI systems, similarly to how animals evolved to innately desire certain goals such as finding food.^[59] Some AI systems, such as nearest-neighbor, instead of reason by analogy, these systems are not generally given goals, except to the degree that goals are implicit in their training data.^[60] Such systems can still be benchmarked if the non-goal system is framed as a system whose "goal" is to successfully accomplish its narrow classification task.^[61]

AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute.^[b] A complex algorithm is often built on top of other, simpler, algorithms. A simple example of an algorithm is the following (optimal for first player) recipe for play at tic-tac-toe:^[62]

1. If someone has a "threat" (that is, two in a row), take the remaining square. Otherwise,
2. if a move "forks" to create two threats at once, play that move. Otherwise,
3. take the center square if it is free. Otherwise,
4. if your opponent has played in a corner, take the opposite corner. Otherwise,
5. take an empty corner if one exists. Otherwise,
6. take any empty square.

Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or "rules of thumb", that have worked well in the past), or can themselves write other algorithms. Some of the "learners" described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, (given infinite data, time, and memory) learn to approximate any function, including which combination of mathematical functions would best describe the world^{[citation needed]}. These learners could therefore, derive all possible knowledge, by considering every possible hypothesis and matching them against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of "combinatorial explosion", where the amount of time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering broad range of possibilities that are unlikely to be beneficial.^[63][64] For example, when viewing a map and looking for the shortest driving route from Denver to New York in the East, one can in most cases skip looking at any path through San Francisco or other areas far to the West; thus, an AI wielding a pathfinding algorithm like A* can avoid the combinatorial explosion that would ensue if every possible route had to be ponderously considered in turn.^[65]

The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): "If an otherwise healthy adult has a fever, then they may have influenza". A second, more general, approach is Bayesian inference: "If the current patient has a fever, adjust the probability they have influenza in such-and-such way". The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: "After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza". A fourth approach is harder to intuitively understand, but is inspired by how the brain's machinery works: the artificial neural network approach uses artificial "neurons" that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to "reinforce" connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms;^[66] the best approach is often different depending on the problem.^[67][68]

Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". They can be nuanced, such as "X% of families have geographically separate species with color variants, so there is an Y% chance that undiscovered black swans exist". Learners also work on the basis of "Occam's razor": The simplest theory that explains the data is the likeliest. Therefore, according to Occam's razor principle, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better.

The blue line could be an example of overfitting a linear function due to random noise.

Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is.^[69] Besides classic overfitting, learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses.^[70] A real-world example is that, unlike humans, current image classifiers don't determine the spatial relationship between components of the picture; instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects. Faintly superimposing such a pattern on a legitimate image results in an "adversarial" image that the system misclassifies.^{[c][71][72][73]}

Compared with humans, existing AI lacks several features of human "commonsense reasoning"; most notably, humans have powerful mechanisms for reasoning about "naïve physics" such as space, time, and physical interactions. This enables even young children to easily make inferences like "If I roll this pen off a table, it will fall on the floor". Humans also have a powerful mechanism of "folk psychology" that helps them to interpret natural-language sentences such as "The city councilmen refused the demonstrators a permit because they advocated violence". (A generic AI has difficulty discerning whether the ones alleged to be advocating violence are the councilmen or the demonstrators.)^[76][77][78] This lack of "common knowledge" means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.^[79][80][81]

Challenges of AI

The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. 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.^[15]

Reasoning, problem solving

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.^[82] By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.^[83]

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.^[63] In fact, even humans rarely use the step-by-step deduction that early AI research was able to model. They solve most of their problems using fast, intuitive judgements.^[84]

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

Knowledge representation

Knowledge representation^[85] and knowledge engineering^[86] are central to classical AI research. Some "expert systems" attempt to gather together explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the "commonsense knowledge" known to the average person into a database containing extensive knowledge about the world. Among the things a comprehensive commonsense knowledge base would contain are: objects, properties, categories and relations between objects;^[87] situations, events, states and time;^[88] causes and effects;^[89] knowledge about knowledge (what we know about what other people know);^[90] and many other, less well researched domains. 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. The semantics of these are captured as description logic concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the Web Ontology Language.^[91] The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge^[92] by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval,^[93] scene interpretation,^[94] clinical decision support,^[95] knowledge discovery (mining "interesting" and actionable inferences from large databases),^[96] and other areas.^[97]

Among the most difficult problems in knowledge representation are:

Default reasoning and the qualification problem

Many of the things people know take the form of "working assumptions". For example, if a bird comes up in conversation, people typically picture an animal that is fist-sized, sings, and flies. None of these things are true about all birds. John McCarthy identified this problem in 1969^[98] as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem.^[99]

The breadth of commonsense knowledge

The number of atomic facts that the average person knows is very large. Research projects that attempt to build a complete knowledge base of commonsense knowledge (e.g., Cyc) require enormous amounts of laborious ontological engineering—they must be built, by hand, one complicated concept at a time.^[100]

The subsymbolic form of some commonsense knowledge

Much of what people know is not represented as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed"^[101] or an art critic can take one look at a statue and realize that it is a fake.^[102] These are non-conscious and sub-symbolic intuitions or tendencies in the human brain.^[103] Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that situated AI, computational intelligence, or statistical AI will provide ways to represent this kind of knowledge.^[103]

A hierarchical control system is a form of control system in which a set of devices and governing software is arranged in a hierarchy.

Planning

Intelligent agents must be able to set goals and achieve them.^[104] They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the utility (or "value") of available choices.^[105]

In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.^[106] However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions, but also evaluate its predictions and adapt based on its assessment.^[107]

Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.^[108]

Learning

Main article: Machine learning

Machine learning (ML), a fundamental concept of AI research since the field's inception,^[109] is the study of computer algorithms that improve automatically through experience.^[110][111]

Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first. Supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, and occurs after a program sees a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change.^[111] 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". Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.^[112] In reinforcement learning^[113] the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.

A parse tree represents the syntactic structure of a sentence according to some formal grammar.

Natural language processing

Natural language processing^[114] (NLP) gives machines the ability to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include information retrieval, text mining, question answering^[115] and machine translation.^[116] Many current approaches use word co-occurrence frequencies to construct syntactic representations of text. "Keyword spotting" strategies for search are popular and scalable but dumb; a search query for "dog" might only match documents with the literal word "dog" and miss a document with the word "poodle". "Lexical affinity" strategies use the occurrence of words such as "accident" to assess the sentiment of a document. Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well. Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications. Beyond semantic NLP, the ultimate goal of "narrative" NLP is to embody a full understanding of commonsense reasoning.^[117]

Feature detection (pictured: edge detection) helps AI compose informative abstract structures out of raw data.

Perception

Machine perception^[118] is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition,^[119] facial recognition, and object recognition.^[120] Computer vision is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its "object model" to assess that fifty-meter pedestrians do not exist.^[121]

Motion and manipulation

Main article: Robotics

AI is heavily used in robotics.^[122] Advanced robotic arms and other industrial robots, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage.^[123] A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and map its environment; however, dynamic environments, such as (in endoscopy) the interior of a patient's breathing body, pose a greater challenge. Motion planning is the process of breaking down a movement task into "primitives" such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical contact with an object.^{[124][125][126]} Moravec's paradox generalizes that low-level sensorimotor skills that humans take for granted are, counterintuitively, difficult to program into a robot; the paradox is named after Hans Moravec, who stated in 1988 that "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility".^[127][128] This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years.^[129]

Social intelligence

Moravec's paradox can be extended to many forms of social intelligence.^[131][132] Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.^[133] Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects.^{[134][135][136]} Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.^[137]

In the long run, social skills and an understanding of human emotion and game theory would be valuable to a social agent. Being able to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions. Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction.^[138] Similarly, some virtual assistants are programmed to speak conversationally or even to banter humorously; this tends to give naïve users an unrealistic conception of how intelligent existing computer agents actually are.^[139]

General intelligence

Main articles: Artificial general intelligence and AI-complete

Historically, projects such as the Cyc knowledge base (1984–) and the massive Japanese Fifth Generation Computer Systems initiative (1982–1992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable "narrow AI" applications (such as medical diagnosis or automobile navigation).^[140] Many researchers predict that such "narrow AI" work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.^[19][141] Many advances have general, cross-domain significance. One high-profile example is that DeepMind in the 2010s developed a "generalized artificial intelligence" that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning.^{[142][143][144]} Besides transfer learning,^[145] hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to "slurp up" a comprehensive knowledge base from the entire unstructured Web.^[6] Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, "Master Algorithm" could lead to AGI.^[146] Finally, a few "emergent" approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges.^[147][148]

Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's original intent (social intelligence). A problem like machine translation is considered "AI-complete", because all of these problems need to be solved simultaneously in order to reach human-level machine performance.

Approaches

There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues.^[149] A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurobiology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering?^[16] Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?^[17]

Cybernetics and brain simulation

Main articles: Cybernetics and Computational neuroscience

In the 1940s and 1950s, a number of researchers explored the connection between neurobiology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England.^[150] By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.

Symbolic

Main article: Symbolic AI

When access to digital computers became possible in the mid 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and as described below, each one developed its own style of research. John Haugeland named these symbolic approaches to AI "good old fashioned AI" or "GOFAI".^[151] During the 1960s, symbolic approaches had achieved great success at simulating high-level "thinking" in small demonstration programs. Approaches based on cybernetics or artificial neural networks were abandoned or pushed into the background.^[152] Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.

Cognitive simulation

Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.^[153][154]

Logic-based

Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless whether people used the same algorithms.^[16] His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.^[155] Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.^[156]

Anti-logic or scruffy

Researchers at MIT (such as Marvin Minsky and Seymour Papert)^[157] found that solving difficult problems in vision and natural language processing required ad-hoc solutions—they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their "anti-logic" approaches as "scruffy" (as opposed to the "neat" paradigms at CMU and Stanford).^[17] Commonsense knowledge bases (such as Doug Lenat's Cyc) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time.^[158]

Knowledge-based

When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.^[159] This "knowledge revolution" led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software.^[39] A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules that illustrate AI.^[160] The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

Sub-symbolic

By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.^[18] Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.

Embodied intelligence

This includes embodied, situated, behavior-based, and nouvelle AI. Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.^[161] Their work revived the non-symbolic point of view of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.

Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).^{[162][163][164][165]}

Computational intelligence and soft computing

Interest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle of the 1980s.^[166] Artificial neural networks are an example of soft computing—they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other soft computing approaches to AI include fuzzy systems, Grey system theory, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence.^[167]

Statistical learning

Much of traditional GOFAI got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models (HMM), information theory, and normative Bayesian decision theory to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like mathematics, economics or operations research).^[d] Compared with GOFAI, new "statistical learning" techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as data mining, without necessarily acquiring a semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more scientific. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible.^[40][168] Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language.^[169] Critics note that the shift from GOFAI to statistical learning is often also a shift away from explainable AI. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.^[170][171]

Integrating the approaches

Intelligent agent paradigm

An intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as firms). The paradigm allows researchers to directly compare or even combine different approaches to isolated problems, by asking which agent is best at maximizing a given "goal function". An agent that solves a specific problem can use any approach that works—some agents are symbolic and logical, some are sub-symbolic artificial neural networks and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as decision theory and economics—that also use concepts of abstract agents. Building a complete agent requires researchers to address realistic problems of integration; for example, because sensory systems give uncertain information about the environment, planning systems must be able to function in the presence of uncertainty. The intelligent agent paradigm became widely accepted during the 1990s.^[172]

Agent architectures and cognitive architectures

Researchers have designed systems to build intelligent systems out of interacting intelligent agents in a multi-agent system.^[173] A hierarchical control system provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling.^[174] Some cognitive architectures are custom-built to solve a narrow problem; others, such as Soar, are designed to mimic human cognition and to provide insight into general intelligence. Modern extensions of Soar are hybrid intelligent systems that include both symbolic and sub-symbolic components.^{[175][176][177]}