Artificial intelligence (AI, eng. Artificial intelligence, AI) - the science and technology of creating intelligent machines, especially intelligent computer programs. AI is related to the similar task of using computers to understand human intelligence, but is not necessarily limited to biologically plausible methods.

What is artificial intelligence

• (J. McCarthy) AI develops machines that have intelligent behavior
• (Britannica) AI is the ability of digital computers to solve problems commonly associated with highly intelligent human beings.
• (Feigenbaum) AI - develops intelligent computer systems with capabilities that we traditionally associate with the human mind: understanding language, learning, the ability to reason, solve problems, etc.
• (Elaine Rich) AI is the science of how to teach computers to do things in which this moment a person is more successful

Intelligence(from Latin intellectus - sensation, perception, understanding, understanding, concept, reason), or mind - the quality of the psyche, consisting of the ability to adapt to new situations, the ability to learn and remember based on experience, understand and apply abstract concepts and use one's own knowledge for environmental management. Intelligence is a general ability for cognition and solving difficulties, which combines all the cognitive abilities of a person: sensation, perception, memory, representation, thinking, imagination.

In the early 1980s Computing scientists Barr and Feigenbaum proposed the following definition of artificial intelligence (AI):

Later, a number of algorithms and software systems began to be referred to as AI, the distinguishing feature of which is that they can solve some problems in the same way as a person thinking about their solution would do.

The main properties of AI are language understanding, learning and the ability to think and, importantly, act.

AI is a complex of related technologies and processes that are developing qualitatively and rapidly, for example:

• natural language text processing
• expert systems
• virtual agents (chatbots and virtual assistants)
• recommendation systems.

AI Methods: NLP, CV, Data Science

Natural Language (NLP) Speech Technologies

• texts: recognize, automatically translate
• speech: recognize, generate

• find, track, classify, identify objects
• extract data from images
• analyze the received information

It is applied for

• object recognition
• descriptions of the content of images and videos
• gesture and handwriting recognition
• intelligent image processing

• extract knowledge
• find patterns in data
• predict

Use methods

• Statistics
• econometrics
• Machine learning , Deep learning

National Strategy for the Development of Artificial Intelligence

• Main article: National Strategy for the Development of Artificial Intelligence

AI Research

• Main article: Research in the field of artificial intelligence

AI standardization

Standards in the field of artificial intelligence in healthcare

2019

Top 3 Artificial Intelligence Trends in 4 Minutes

Rosstandart approved the first standards in the field of AI

The Federal Agency for Technical Regulation and Metrology (Rosstandart) approved in December 2019 the first national standards in the field of artificial intelligence - GOST R 58776-2019 “Means for monitoring people's behavior and predicting people's intentions. Terms and definitions” and GOST R 58777-2019 “Air transport. Airports. Technical means inspection. Methodology for determining the quality indicators of recognition of illegal investments by shadow x-ray images.

The standard is designed to ensure effective communication of intelligent robotic systems (including unmanned vehicles) with a person. The interaction of intelligent systems consists in predicting each other's intentions and determining further actions based on this forecast. Behavior prediction can also be used to identify individuals with criminal intent.

The second adopted standard, GOST R 58777-2019, establishes uniform requirements for systems and algorithms for recognizing illegal contents of baggage and hand luggage from x-ray images. The standard will also increase the reliability of system and algorithm test results.

Terminological standard “Artificial intelligence. Concepts and terminology” is fundamental for the entire family of international regulatory and technical documents in the field of artificial intelligence. In addition to terms and definitions, this document contains conceptual approaches and principles for building systems with elements, a description of the relationship between AI and other end-to-end technologies, as well as basic principles and framework approaches to the regulatory and technical regulation of artificial intelligence.

Following the meeting of the relevant ISO/IEC subcommittee in Dublin, ISO/IEC experts supported the proposal of the delegation from Russia on the simultaneous development of a terminological standard in the field of AI not only in English, but also in Russian. The document is expected to be approved in early 2021.

The development of products and services based on artificial intelligence requires an unambiguous interpretation of the concepts used by all market participants. The terminology standard will unify the "language" used by developers, customers and the professional community, classify such properties of AI-based products as "security", "reproducibility", "authenticity" and "confidentiality". A unified terminology will also become an important factor for the development of artificial intelligence technologies within the framework of the National Technology Initiative - more than 80% of companies within the NTI perimeter use AI algorithms. In addition, the ISO/IEC decision will strengthen the authority and expand the influence of Russian experts in the further development of international standards.

During the meeting, ISO/IEC experts also supported the development of the draft international document Information Technology - Artificial Intelligence (AI) - Overview of Computational Approaches for AI Systems, in which Russia acts as a co-editor. The document provides an overview state of the art artificial intelligence systems, describing the main characteristics of systems, algorithms and approaches, as well as examples of specialized applications in the field of AI. The working group 5 “Computational approaches and computational characteristics of AI systems” specially created within the framework of the subcommittee will develop this draft document.

As part of the work at the international level, the delegation from Russia managed to achieve a number of landmark decisions that will have a long-term effect on the development of artificial intelligence technologies in the country. The development of the Russian-language version of the standard, even from such an early stage, is a guarantee of synchronization with the international field, and the development of the ISO / IEC subcommittee and the initiation of international documents with Russian co-editorship is the foundation for further promoting the interests of Russian developers abroad,” commented.

Artificial intelligence technologies are widely demanded in various sectors of the digital economy. Among the main factors hindering their full-scale practical use is the underdevelopment of the regulatory framework. At the same time, it is the well-developed regulatory and technical base that ensures the specified quality of technology application and the corresponding economic effect.

Towards artificial intelligence TC "Cyber-Physical Systems" on the basis of RVC is developing a number of national standards, the approval of which is scheduled for the end of 2019 - the beginning of 2020. In addition, together with market players, work is underway to form a National Standardization Plan (PNS) for 2020 and beyond. TC "Cyber-Physical Systems" is open to proposals for the development of documents from interested organizations.

2018: Development of standards in the field of quantum communications, AI and the smart city

On December 6, 2018, the Technical Committee "Cyber-Physical Systems" on the basis of RVC together with the Regional Engineering Center "SafeNet" began developing a set of standards for the markets of the National Technology Initiative (NTI) and the digital economy. By March 2019, it is planned to develop technical standardization documents in the field of quantum communications, and , RVC reported. Read more.

The impact of artificial intelligence

Risk to the development of human civilization

Impact on the economy and business

• The impact of artificial intelligence technologies on the economy and business

Impact on the labor market

Artificial intelligence bias

At the heart of everything that is the practice of AI (machine translation, speech recognition, natural language processing, computer vision, automating driving, and more) is deep learning. This is a subset of machine learning, characterized by the use of neural network models, which can be said to mimic the way the brain works, so they can hardly be classified as AI. Any neural network model is trained on large datasets, so it acquires some “skills”, but how it uses them is not clear to the creators, which ultimately becomes one of the most important problems for many deep learning applications. The reason is that such a model works with images formally, without any understanding of what it does. Is such an AI system and can systems built on the basis of machine learning be trusted? The significance of the answer to the last question goes beyond scientific laboratories. Therefore, the attention of the media to the phenomenon, called AI bias, has noticeably escalated. It can be translated as "AI bias" or "AI bias". Read more.

Artificial intelligence technology market

AI market in Russia

The global AI market

Applications of AI

The areas of application of AI are quite wide and cover both technologies that are familiar to hearing, and emerging new areas that are far from mass application, in other words, this is the whole range of solutions, from vacuum cleaners to space stations. It is possible to divide all their diversity according to the criterion of key points of development.

AI is not a monolithic subject area. Moreover, some technological areas of AI appear as new sub-sectors of the economy and separate entities while simultaneously serving most sectors of the economy.

The development of the use of AI leads to the adaptation of technologies in classical sectors of the economy along the entire value chain and transforms them, leading to the algorithmization of almost all functionality, from logistics to company management.

The use of AI for defense and military purposes

Use in education

Use of AI in business

AI in the fight against fraud

On July 11, 2019, it became known that in just two years, artificial intelligence and machine learning will be used to counter fraud three times more than in July 2019. These data were obtained during a joint study by SAS and the Association of Certified Fraud Examiners (ACFE). As of July 2019, such anti-fraud tools are already used in 13% of the organizations that took part in the survey, and another 25% said they plan to implement them within the next year or two. Read more.

AI in the power industry

• At the design level: improved forecasting of generation and demand for energy resources, assessment of the reliability of power generating equipment, automation of generation increase in the event of a demand surge.
• At the production level: optimizing preventive maintenance of equipment, increasing generation efficiency, reducing losses, preventing theft of energy resources.
• At the promotion level: optimization of pricing depending on the time of day and dynamic billing.
• At the level of service delivery: automatic selection of the most profitable supplier, detailed consumption statistics, automated customer service, energy optimization based on customer habits and behavior.

AI in manufacturing

• At the design level: improve the efficiency of new product development, automated evaluation of suppliers and analysis of requirements for spare parts and parts.
• At the production level: improving the process of executing tasks, automating assembly lines, reducing the number of errors, reducing the delivery time of raw materials.
• At the promotion level: forecasting the volume of support and maintenance services, pricing management.
• At the level of service delivery: improving fleet route planning, demand for fleet resources, improving the quality of training of service engineers.

AI in banks

AI in transport

• The auto industry is on the verge of a revolution: 5 challenges of the era of self-driving driving

AI in logistics

AI in the judiciary

Developments in the field of artificial intelligence will help to radically change the judicial system, make it more fair and free from corruption schemes. This opinion was expressed in the summer of 2017 by Dr. technical sciences, Artezio technical consultant Vladimir Krylov.

The scientist believes that the AI ​​solutions that already exist can be successfully applied in various sectors of the economy and public life. The expert points out that AI is successfully used in medicine, but in the future it can completely change the judicial system.

“Viewing daily news reports about developments in the field of AI, one is only amazed at the inexhaustibility of the imagination and the fruitfulness of researchers and developers in this field. Messages about scientific research constantly alternating with publications about new products breaking into the market and reports of amazing results obtained using AI in various fields. If we talk about the expected events, accompanied by a noticeable hype in the media, in which AI will again become the hero of the news, then I probably will not risk making technological forecasts. I can assume that the next event will be the appearance somewhere of an extremely competent court in the form of artificial intelligence, fair and incorruptible. This will probably happen in 2020-2025. And the processes that will take place in this court will lead to unexpected reflections and the desire of many people to transfer most of the processes of managing human society to AI.

The scientist recognizes the use of artificial intelligence in the judicial system as a "logical step" in the development of legislative equality and justice. The machine mind is not subject to corruption and emotions, can strictly adhere to the legislative framework and make decisions taking into account many factors, including the data that characterize the participants in the dispute. By analogy with the medical field, robot judges can operate with big data from public service repositories. It can be assumed that machine intelligence will be able to quickly process data and take into account much more factors than a human judge.

Psychological experts, however, believe that the absence of an emotional component in the consideration of court cases will negatively affect the quality of the decision. The verdict of the machine court may turn out to be too straightforward, not taking into account the importance of people's feelings and moods.

Music

Painting

In 2015, the Google team tested neural networks to see if they could create images on their own. Then artificial intelligence was trained on the example of a large number of different pictures. However, when the machine was “asked” to depict something on its own, it turned out that it interprets the world around us in a somewhat strange way. For example, for the task of drawing dumbbells, the developers received an image in which the metal was connected by human hands. This probably happened due to the fact that at the training stage, the analyzed pictures with dumbbells contained hands, and the neural network misinterpreted this.

On February 26, 2016, at a special auction in San Francisco, Google representatives raised about $98,000 from psychedelic paintings painted by artificial intelligence. These funds were donated to charity. One of the most successful pictures of the car is presented below.

A picture painted by Google artificial intelligence.

Since the invention of computers, their ability to perform various tasks has continued to grow exponentially. Humans are developing the power of computer systems by increasing the performance of tasks and decreasing the size of computers. The main goal of researchers in the field of artificial intelligence is to create computers or machines as intelligent as a person.

The author of the term "artificial intelligence" is John McCarthy, the inventor of the Lisp language, the founder of functional programming and the winner of the Turing Award for his great contribution to the field of artificial intelligence research.

Artificial intelligence is a way to make a computer, computer-controlled robot or program capable of thinking intelligently like a human as well.

Research in the field of AI is carried out by studying the mental abilities of a person, and then the results of this research are used as the basis for the development of intelligent programs and systems.

Philosophy of AI

During the operation of powerful computer systems, everyone asked the question: “Can a machine think and behave in the same way as a person? ".

Thus, the development of AI began with the intention of creating a similar intelligence in machines, similar to the human.

Main goals of AI

• Creation of expert systems - systems that demonstrate intelligent behavior: learn, show, explain and give advice;
• Realization of human intelligence in machines - the creation of a machine capable of understanding, thinking, teaching and behaving like a human.

What contributes to the development of AI?

Artificial intelligence is a science and technology based on such disciplines as computer science, biology, psychology, linguistics, mathematics, mechanical engineering. One of the main areas of artificial intelligence is the development of computer functions related to human intelligence, such as: reasoning, learning and problem solving.

Program with AI and without AI

Programs with and without AI differ in the following properties:

Applications with AI

AI has become dominant in various fields such as:

Games - AI plays a crucial role in strategy games such as chess, poker, tic-tac-toe, etc., where the computer is able to calculate a large number of possible solutions based on heuristic knowledge.

Natural language processing is the ability to communicate with a computer that understands the natural language spoken by humans.

Speech recognition - some intelligent systems able to hear and understand the language in which a person communicates with them. They can handle various accents, slang, etc.

Handwriting Recognition - The software reads text written on paper with a pen or on a screen with a stylus. It can recognize letter shapes and convert it into editable text.

Smart robots are robots capable of performing tasks assigned by humans. They have sensors to detect physical data from the real world, such as light, heat, motion, sound, shock, and pressure. They have high performance processors, multiple sensors and huge memory. In addition, they are able to learn from their own mistakes and adapt to the new environment.

History of AI development

Here is the history of AI development during the 20th century

	Karel Capek is directing a play in London called "Universal Robots", the first use of the word "robot" in English.
	Isaac Asimov, a graduate of Columbia University, coined the term robotics.
	Alan Turing develops the Turing test to measure intelligence. Claude Shannon publishes a detailed analysis of the intellectual chess game.
	John McCarthy coined the term artificial intelligence. Demonstration of the first launch of an AI program at Carnegie Mellon University.
	John McCarthy invents the lisp programming language for AI.
	Danny Bobrov's dissertation at MIT shows that computers can understand natural language quite well.
	Joseph Weizenbaum at MIT is developing Eliza, an interactive assistant that communicates in English.
	Scientists at the Stanford Research Institute have developed Sheki, a motorized robot capable of perceiving and solving some problems.
	A team of researchers at the University of Edinburgh built Freddie, the famous Scottish robot that can use its eyesight to find and assemble models.
	The first computer-controlled autonomous vehicle, the Stanford Cart, was built.
	Harold Cohen developed and demonstrated programming, Aaron.
	A chess program that beats world chess champion Garry Kasparov.
	Interactive robotic pets will become commercially available. MIT displays Kismet, a robot with a face that expresses emotions. Robot Nomad explores remote areas of Antarctica and finds meteorites.

The essence of artificial intelligence in the format of questions and answers. The history of creation, research technologies, whether artificial intelligence is associated with IQ and whether it can be compared with a human one. Answered questions Stanford University professor John McCarthy.

What is artificial intelligence (AI)?

Artificial intelligence is a field of science and engineering that deals with the creation of machines and computer programs that have intelligence. It is related to the task of using computers to understand human intelligence. At the same time, artificial intelligence should not be limited to biologically observable methods.

Yes, but what is intelligence?

Intelligence is the ability to come to a decision with the help of calculations. Intelligence different kind and levels have people, many animals and some machines.

Is there not a definition of intelligence that does not depend on relating it to human intelligence?

Until now, there is no understanding of what types of computational procedures we want to call intelligent. We know far from all the mechanisms of intelligence.

Is intelligence an unambiguous concept so that the question "Does this machine have intelligence?" could you answer yes or no?

No. AI research has shown how to use only some of the mechanisms. When only well-studied models are required to complete a task, the results are very impressive. Such programs have "little" intelligence.

Is artificial intelligence an attempt to mimic human intelligence?

Sometimes, but not always. On the one hand, we will learn how to make machines solve problems by watching people or our own algorithms at work. On the other hand, AI researchers use algorithms that are not observed in humans or require much more computational resources.

Do computer programs have an IQ?

No. IQ is based on the rate of development of intelligence in children. This is the ratio of the age at which a child usually scores a certain result to the age of the child. This assessment is appropriately extended to adults. IQ correlates well with various measures of success or failure in life. But building computers that can score high on IQ tests will have little to do with their usefulness. For example, a child's ability to repeat a long sequence of numbers correlates well with other intellectual abilities. It shows how much information a child can remember at one time. At the same time, keeping numbers in memory is a trivial task even for the most primitive computers.

How to compare human and computer intelligence?

Arthur R. Jensen, a leading researcher in the field of human intelligence, claims as a "heuristic hypothesis" that ordinary people share the same mechanisms of intelligence and that intellectual differences are due to "quantitative biochemical and physiological conditions." These include speed of thought, short-term memory, and the ability to form accurate and retrievable long-term memories.

Whether or not Jensen's view of human intelligence is correct, the situation in AI today is the opposite.

Computer programs have a lot of speed and memory, but their abilities correspond to the intellectual mechanisms that program developers understand and can put into them. Some abilities that children don't usually develop until adolescence are introduced. Others, owned by two-year-olds, are still missing. The matter is further exacerbated by the fact that the cognitive sciences still cannot determine exactly what human abilities are. Most likely, the organization of intellectual mechanisms of AI compares favorably with that of humans.

When a human is able to solve a problem faster than a computer, it indicates that developers lack understanding of the mechanisms of intelligence necessary to perform this task efficiently.

When did AI research start?

After World War II, several people began working independently on intelligent machines. The English mathematician Alan Turing may have been the first of these. He gave his lecture in 1947. Turing was one of the first to decide that AI was best explored by programming computers rather than constructing machines. By the late 1950s, there were many AI researchers, and most of them based their work on computer programming.

Is the purpose of AI to put the human mind into a computer?

The human mind has many features, it is hardly realistic to imitate each of them.

What is the Turing test?

A. Alan Turing's 1950 paper "Computing and Intelligence" discussed the conditions for a machine to have intelligence. He argued that if a machine can successfully pretend to be human to an intelligent observer, then you must, of course, consider it intelligent. This criterion will satisfy most people, but not all philosophers. The observer must interact with the machine or human through the I/O facility to eliminate the need for the machine to imitate appearance or human voice. The task of both the machine and the man is to make the observer consider himself a man.

The Turing test is one-sided. A machine that passes the test should definitely be considered sentient, even if it doesn't know enough about humans to imitate them.

Daniel Dennett's book "Brainchildren" has an excellent discussion of the Turing test and its various parts that have been implemented successfully, i.e. with limitations on the observer's knowledge of AI and the subject matter. It turns out that some people are pretty easy to convince that a fairly primitive program is reasonable.

Is the goal of AI to reach human levels of intelligence?

Yes. The ultimate goal is to create computer programs that can solve problems and achieve goals in the same way that humans can. However, scientists conducting research in narrow areas set much less ambitious goals.

How far is artificial intelligence from reaching the human level? When will it happen?

Human-level intelligence can be achieved by writing a lot of programs, and collecting vast knowledge bases of facts in the languages ​​that are used to express knowledge today.However, most AI researchers believe that new fundamental ideas are needed. Therefore, it is impossible to predict when human-level intelligence will be created.

Is the computer a machine that can become intelligent?

Computers can be programmed to simulate any type of machine.

Does the speed of computers allow them to be intelligent?

Some people think that both faster computers and new ideas are required. Computers were fast enough even 30 years ago. If only we knew how to program them.

What about creating a "child machine" that could be improved by reading and learning from experience?

This idea has been proposed repeatedly since the 1940s. Eventually, it will be implemented. However, AI programs have not yet reached the level of learning much of what a child learns in the course of life. Existing programs do not understand the language well enough to learn much through reading.

Are computability theory and computational complexity the keys to AI?

No. These theories are relevant but do not address the fundamental problems of AI.

In the 1930s, mathematical logicians Kurt Gödel and Alan Turing established that there were no algorithms that would guarantee the solution of all problems in some important mathematical areas. For example, answers to questions in the spirit of: “is the sentence of first-order logic a theorem” or “does a polynomial equation in some variables have integer solutions in others.” Since people are capable of solving problems of this kind, given fact has been offered as an argument that computers are inherently incapable of doing what humans do. Roger Penrose also speaks of this. However, humans cannot guarantee solutionsarbitrarytasks in these areas.

In the 1960s, computer scientists such as Steve Cook and Richard Karp developed the domain theory for NP-complete problems. Problems in these areas are solvable, but, apparently, their solution requires time that grows exponentially with the dimension of the problem. The simplest example of the domain of an NP-complete problem is the question: what statements of propositional logic are satisfiable? People often solve problems in the area of ​​NP-complete problems many times faster than is guaranteed by the main algorithms, but cannot solve them quickly in the general case.

For AI, it is important that when solving problems algorithms were just as effective as human mind. Determining the sub-fields where good algorithms exist is important, but many AI problem solvers do not fall into easily identifiable sub-fields.

The theory of complexity of general classes of problems is called computational complexity. So far, this theory has not interacted with AI as much as one might hope. Success in problem solving by human and AI programs appears to depend on problem properties and problem solving techniques that neither complexity researchers nor the AI ​​community can accurately define.

Also relevant is the theory of algorithmic complexity, developed independently of each other. Solomonov, Kolmogorov and Chaitin. It defines the complexity of a symbolic object as the length of the shortest program that can generate it. Proving that a candidate program is the shortest, or close to it, is an impossible task, but representing objects by the short programs that generate them can sometimes clear things up, even if you can't prove that your program is the shortest.

This year, Yandex launched the Alice voice assistant. The new service allows the user to listen to news and weather, get answers to questions and simply communicate with the bot. "Alice" sometimes cheeky, sometimes it seems almost reasonable and humanly sarcastic, but often she cannot figure out what she is being asked about, and sits in a puddle.

All this gave rise not only to a wave of jokes, but also to a new round of discussions about the development of artificial intelligence. News about what smart algorithms have achieved is coming almost every day today, and machine learning is called one of the most promising directions to which you can devote yourself.

To clarify the main questions about artificial intelligence, we talked with Sergey Markov, a specialist in artificial intelligence and machine learning methods, the author of one of the most powerful Russian chess programs SmarThink and the creator of the 22nd Century project.

Sergei Markov,

artificial intelligence specialist

Debunking myths about AI

So what is "artificial intelligence"?

The concept of "artificial intelligence" is somewhat unlucky. Initially originating in the scientific community, it eventually penetrated into science fiction literature, and through it into pop culture, where it underwent a number of changes, acquired many interpretations, and in the end was completely mystified.

That is why we often hear such statements from non-specialists as: “AI does not exist”, “AI cannot be created”. Misunderstanding of the essence of research conducted in the field of AI easily leads people to other extremes - for example, modern AI systems are credited with the presence of consciousness, free will and secret motives.

Let's try to separate the flies from the cutlets.

In science, artificial intelligence refers to systems designed to solve intellectual problems.

In turn, an intellectual task is a task that people solve with the help of their own intellect. Note that in this case, experts deliberately avoid defining the concept of "intelligence", because before the advent of AI systems, the only example of intelligence was the human intellect, and defining the concept of intelligence based on a single example is the same as trying to draw a straight line through a single point. There can be as many such lines as you like, which means that the debate about the concept of intelligence could be waged for centuries.

"strong" and "weak" artificial intelligence

AI systems are divided into two large groups.

Applied artificial intelligence(they also use the term "weak AI" or "narrow AI", in the English tradition - weak / applied / narrow AI) is an AI designed to solve any one intellectual task or a small number of them. This class includes systems for playing chess, go, image recognition, speech, decision-making on issuing or not issuing a bank loan, and so on.

As opposed to applied AI, the concept is introduced universal artificial intelligence(also "strong AI", in English - strong AI / Artificial General Intelligence) - that is, a hypothetical (so far) AI capable of solving any intellectual problems.

Often people, not knowing the terminology, identify AI with strong AI, because of this, judgments in the spirit of “AI does not exist” arise.

Strong AI does not really exist yet. Virtually all of the advances we've seen in the last decade in the field of AI have been advances in applied systems. These successes cannot be underestimated, since applied systems in some cases are able to solve intellectual problems better than the universal human intelligence does.

I think you noticed that the concept of AI is quite broad. Let's say mental counting is also an intellectual task, which means that any calculating machine will be considered an AI system. What about accounts? abacus? Antikythera mechanism? Indeed, all this is formal, although primitive, but AI systems. However, usually, calling some system an AI system, we thereby emphasize the complexity of the task solved by this system.

It is quite obvious that the division of intellectual tasks into simple and complex ones is very artificial, and our ideas about the complexity of certain tasks are gradually changing. The mechanical calculating machine was a marvel of technology in the 17th century, but today, people who have been confronted with much more complex mechanisms since childhood, it is no longer able to impress. When the game of cars in Go or car autopilots cease to surprise the public, there will certainly be people who will wince at the fact that someone will attribute such systems to AI.

"Robots-excellent students": about the ability of AI to learn

Another funny misconception is that AI systems must have the ability to self-learn. On the one hand, this is not required property AI systems: there are many amazing systems that are not capable of self-learning, but, nevertheless, solve many problems better than the human brain. On the other hand, some people simply do not know that self-learning is a feature that many AI systems have acquired even more than fifty years ago.

When I wrote my first chess program in 1999, self-study was already a commonplace in this area - the programs were able to memorize dangerous positions, adjust opening variations for themselves, adjust the style of play, adjusting to the opponent. Of course, those programs were still very far from Alpha Zero. However, even systems that learn behavior based on interactions with other systems in so-called “reinforcement learning” experiments already existed. However, for some inexplicable reason, some people still think that the ability to self-learn is the prerogative of the human intellect.

Machine learning, a whole scientific discipline, deals with the processes of teaching machines to solve certain problems.

There are two big poles of machine learning - supervised learning and unsupervised learning.

At learning with a teacher the machine already has a number of conditionally correct solutions for some set of cases. The task of learning in this case is to teach the machine, based on the available examples, to make the right decisions in other, unknown situations.

The other extreme - learning without a teacher. That is, the machine is put in a situation where the correct solutions are unknown, there are only data in a raw, unlabeled form. It turns out that in such cases it is possible to achieve some success. For example, you can teach a machine to identify semantic relationships between words in a language based on the analysis of a very large set of texts.

One type of supervised learning is reinforcement learning. The idea is that the AI ​​system acts as an agent placed in some model environment, in which it can interact with other agents, for example, with copies of itself, and receive from the environment some feedback through the reward function. For example, a chess program that plays with itself, gradually adjusting its parameters and thereby gradually strengthening its own game.

Reinforcement learning is a fairly broad field and uses many interesting techniques ranging from evolutionary algorithms to Bayesian optimization. Recent advances in AI for games are precisely related to the amplification of AI during reinforcement learning.

Technology Risks: Should We Be Afraid of Doomsday?

I am not one of the AI ​​alarmists, and in this sense I am by no means alone. For example, Andrew Ng, creator of the Stanford Machine Learning course, compares the dangers of AI to the problem of overpopulation on Mars.

Indeed, in the future, it is likely that humans will colonize Mars. It is also likely that sooner or later the problem of overpopulation may arise on Mars, but it is not entirely clear why we should deal with this problem now? Agree with Yn and Yang LeKun - the creator of convolutional neural networks, and his boss Mark Zuckerberg, and Joshua Beno - a person, largely thanks to whose research modern neural networks are able to solve challenging tasks in the field of word processing.

It will probably take several hours to present my views on this problem, so I will focus only on the main theses.

1. DO NOT LIMIT AI DEVELOPMENT

Alarmists consider the risks associated with the potential disruption of AI while ignoring the risks associated with trying to limit or even stop progress in this area. The technological power of mankind is increasing at an extremely rapid pace, which leads to an effect that I call "cheapening the cost of the apocalypse."

150 years ago, with all the will, humanity could not cause irreparable damage to either the biosphere or itself as a species. To implement the catastrophic scenario 50 years ago, it would have been necessary to concentrate all the technological power of the nuclear powers. Tomorrow, a small handful of fanatics may be enough to bring a global man-made disaster to life.

Our technological power is growing much faster than the ability of human intelligence to control this power.

Unless human intelligence, with its prejudices, aggression, delusions and narrow-mindedness, is replaced by a system capable of making more informed decisions (whether it be AI or, what I consider more likely, a technologically improved human intelligence integrated with machines into a single system), we can waiting for a global catastrophe.

2. the creation of superintelligence is fundamentally impossible

There is an idea that the AI ​​of the future will certainly be super-intelligent, superior to humans even more than humans are superior to ants. In this case, I'm afraid to disappoint technological optimists - our Universe contains a number of fundamental physical limitations, which, apparently, will make the creation of superintelligence impossible.

For example, the speed of signal transmission is limited by the speed of light, and the Heisenberg uncertainty appears on the Planck scale. This implies the first fundamental limit - the Bremermann limit, which imposes restrictions on the maximum computational speed for an autonomous system of a given mass m.

Another limit is related to Landauer's principle, according to which there is a minimum amount of heat released when processing 1 bit of information. Too fast calculations will cause unacceptable heating and destruction of the system. In fact, modern processors are less than a thousand times behind the Landauer limit. It would seem that 1000 is quite a lot, but another problem is that many intellectual tasks belong to the EXPTIME complexity class. This means that the time required to solve them is an exponential function of the dimension of the problem. Accelerating the system several times gives only a constant increase in "intelligence".

In general, there are very serious reasons to believe that a super-intelligent strong AI will not work, although, of course, the level of human intelligence may well be surpassed. How dangerous is it? Most likely not very much.

Imagine that you suddenly started thinking 100 times faster than other people. Does this mean that you will easily be able to persuade any passer-by to give you their wallet?

3. we worry about something else

Unfortunately, as a result of the alarmists' speculation on the fears of the public, brought up on the Terminator and Clark and Kubrick's famous HAL 9000, there is a shift in the focus of AI security towards the analysis of unlikely but spectacular scenarios. At the same time, the real dangers slip out of sight.

Any sufficiently complex technology that claims to occupy an important place in our technological landscape certainly brings with it specific risks. Many lives were destroyed by steam engines - in manufacturing, transportation, and so on - before effective safety rules and measures were put in place.

If we talk about progress in the field of applied AI, we can pay attention to the related problem of the so-called "Digital Secret Court". More and more applied AI systems make decisions on issues affecting the life and health of people. This includes medical diagnostic systems, and, for example, systems that make decisions in banks on issuing or not issuing a loan to a client.

At the same time, the structure of the models used, the sets of factors used, and other details of the decision-making procedure are hidden from the person whose fate is at stake.

The models used can base their decisions on the opinions of expert teachers who made systematic mistakes or had certain prejudices - racial, gender.

An AI trained on the decisions of such experts will conscientiously reproduce these prejudices in its decisions. After all, these models may contain specific defects.

Few people are now dealing with these problems, since, of course, SkyNet, which unleashes nuclear war, it is certainly much more spectacular.

Neural networks as a "hot trend"

On the one hand, neural networks are one of the oldest models used to build AI systems. Initially appeared as a result of applying the bionic approach, they quickly ran away from their biological prototypes. The only exception here are impulse neural networks (however, they have not yet found wide application in the industry).

The progress of recent decades is associated with the development of deep learning technologies - an approach in which neural networks are assembled from a large number of layers, each of which is built on the basis of certain regular patterns.

In addition to the creation of new neural network models, important progress has also been made in the field of learning technologies. Today, neural networks are no longer taught with the help of central processors of computers, but with the use of specialized processors capable of quickly performing matrix and tensor calculations. The most common type of such devices today is video cards. However, even more specialized devices for training neural networks are being actively developed.

In general, of course, neural networks today are one of the main technologies in the field of machine learning, to which we owe the solution of many problems that were previously solved unsatisfactorily. On the other hand, of course, you need to understand that neural networks are not a panacea. For some tasks, they are far from the most effective tool.

So how smart are today's robots really?

Everything is relative. Against the background of the technologies of the year 2000, the current achievements look like a real miracle. There will always be people who like to grumble. 5 years ago, they were talking with might and main that machines will never beat people in Go (or at least they won't win very soon). It was said that a machine would never be able to draw a picture from scratch, while today people are practically unable to distinguish between pictures created by machines and paintings by artists unknown to them. At the end of last year, machines learned to synthesize speech, almost indistinguishable from human, and in last years ears do not wither from the music created by machines.

Let's see what happens tomorrow. I look at these applications of AI with great optimism.

Promising directions: where to start diving into the field of AI?

I would advise you to try to master at a good level one of the popular neural network frameworks and one of the programming languages ​​popular in the field of machine learning (the most popular today is the TensorFlow + Python combination).

Having mastered these tools and ideally having a strong base in the field of mathematical statistics and probability theory, you should direct your efforts to the area that will be most interesting to you personally.

Interest in the subject of work is one of your most important assistants.

The need for machine learning specialists exists in various fields - in medicine, in banking, in science, in manufacturing, so today a good specialist has more choice than ever. The potential benefits of any of these industries seem to me insignificant compared to the fact that the work will bring you pleasure.

Artificial intelligence is the ability of a digital computer or computer-controlled robot to perform tasks normally associated with sentient beings. The term is often applied to the project of developing systems endowed with human-specific intellectual processes, such as the ability to reason, generalize, or learn from past experience. In addition, the definition of the concept of AI (artificial intelligence) is reduced to a description of a set of related technologies and processes, such as, for example, machine learning, virtual agents and expert systems. talking in simple terms AI is a crude mapping of neurons in the brain. Signals are transmitted from neuron to neuron and finally output - a numerical, categorical or generative result is obtained. This can be illustrated with the following example. if the system takes a picture of a cat and is trained to recognize whether it is a cat or not, the first layer can identify the general gradients that determine general form cats. The next layer can identify larger objects such as ears and mouths. The third layer defines smaller objects (such as whiskers). Finally, based on this information, the program will print "yes" or "no" to tell if it is a cat or not. The programmer does not need to "tell" the neurons that these are the features they should be looking for. The AI ​​learned them on its own by training on many images (both with and without cats).

What is artificial intelligence?

Description of the artificial neuron

An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network. Artificial neurons are elementary units in artificial neural networks. An artificial neuron receives one or more inputs and sums them up to produce an output or activation representing the action potential of the neuron that is transmitted along its axon. Typically, each input is analyzed separately and the sum is passed through a non-linear function known as an activation function, or transfer function.

When did AI research start?

In 1935, the British researcher A.M. Turing described an abstract computing machine that consists of infinite memory and a scanner that moves back and forth through the memory, character by character. The scanner reads what it finds, writing further characters. The actions of the scanner are dictated by a program of instructions, which is also stored in memory as symbols. The earliest successful AI program was written in 1951 by Christopher Strachey. In 1952, this program could play checkers with a person, surprising everyone with its ability to predict moves. In 1953, Turing published a classic early paper on chess programming.

The difference between artificial intelligence and natural

Intelligence can be defined as the general mental capacity for reasoning, problem solving, and learning. By virtue of its general nature, intelligence integrates cognitive functions such as perception, attention, memory, language, or planning. natural intelligence is distinguished by a conscious attitude to the world. Human thinking is always emotionally colored, and it cannot be separated from physicality. In addition, a person is a social being, therefore society always influences thinking. AI is not related to the emotional sphere and is not socially oriented.

How to compare human and computer intelligence?

Human thinking can be compared with artificial intelligence based on several general parameters of the organization of the brain and machine. The activity of a computer, like the brain, includes four stages: encoding, storing, analyzing data, and issuing a result. In addition, the human brain and AI can self-learn depending on the data received from environment. Also, the human brain and machine intelligence solve problems (or tasks) using certain algorithms.

Do computer programs have an IQ?

No. IQ is related to the development of a person's intelligence depending on age. AI exceeds some human abilities in some ways, for example, it can store a huge number of numbers in memory, but this has nothing to do with IQ.

What is the Turing test?

Alan Turing developed an empirical test that shows whether the program is able to capture all the nuances of human behavior to such an extent that a person cannot determine with whom exactly he is communicating - with AI or with a live interlocutor. Turing suggested that an outside observer evaluate the conversation between a person and a machine that answers questions. The judge does not see who exactly answers, but knows that one of the interlocutors is an AI. The conversation is limited to only the text channel (computer keyboard and screen), so the result is not affected by the machine's ability to render words as human speech. If the program manages to deceive a person, it is considered that it has effectively coped with the test.

Symbolic approach

Symbolic approach to AI - a set of all methods for studying artificial intelligence, based on high-level symbolic (human-readable) ideas about tasks, logic and search. The symbolic approach was widely used in AI research in the 1950s and 80s. One popular form of the symbolic approach is expert systems, which use a combination of specific production rules. Production rules link symbols into logical relationships that are similar to the If-Then algorithm. The expert system processes the rules to draw inferences and determine which Additional Information she needs, that is, what questions to ask using human-readable characters.

logical approach

The term "logical approach" implies an appeal to logic, reasoning, solving problems with the help of logical steps. Logicians back in the 19th century developed precise notations for all kinds of objects in the world and the relationships between them. By 1965, there were programs that could solve any logical task(the peak of popularity of this approach came in the late 1950s–70s). Supporters of the logical approach within the framework of logical artificial intelligence hoped to build intelligent systems on such programs (in particular, written in the Prolog language). However, this approach has two limitations. First, it is not easy to take informal knowledge and put it into the formal terms required for AI processing. Secondly, there is a big difference between solving a problem in theory and solving it in practice. Even problems with a few hundred facts can exhaust the computational resources of any computer if it does not have any indication of which reasoning to use first.

Agent Based Approach

An agent is something that acts (from Latin agere, "to do"). Of course, all computer programs do something, but computer agents are expected to do more: work autonomously, perceive environmental signals (using special sensors), adapt to changes, create goals and execute them. A rational agent is one who acts in such a way as to achieve the best expected outcome.

Hybrid approach

It is assumed that this approach, which became popular in the late 80s, works most effectively, as it is a combination of symbolic and neural models. The hybrid approach increases the cognitive and computational capabilities of the machine.

Artificial intelligence technology market

The market is expected to grow to $190.61 billion by 2025, with an annual growth rate of 36.62%. The growth of the market is influenced by such factors as the introduction of cloud applications and services, the emergence of big data arrays and active demand for intelligent virtual assistants. However, there are still few experts developing and implementing AI technologies, and this is holding back the growth of the market. AI-powered systems require integration and maintenance support.

Processors for AI

Modern AI tasks require powerful processors that can process huge amounts of data. Processors must have access to large amounts of memory, and the device also needs high-speed data links.

In Russia

At the end of 2018, a series of Elbrus-804 servers was launched in Russia, showing high performance. Each of the computers is equipped with four eight-core processors. Using these devices, you can build computing clusters, they allow you to work with applications and databases.

World market

Drivers and market leaders are two corporations - Intel and AMD, manufacturers of the most powerful processors. Intel has traditionally focused on making machines with higher clock speeds, AMD is focused on constantly increasing the number of cores and providing multi-threaded performance.

National Development Concept

Three dozen countries have already approved national strategies for the development of AI. In October 2019, the draft National Strategy for the Development of AI should be adopted in Russia. It is assumed that Moscow will introduce a legal regime that facilitates the development and implementation of AI technologies.

AI Research

The questions of what is artificial intelligence and how it works excite scientists different countries for more than a decade now. The US government allocates $200 million annually for research. In Russia, for 10 years - from 2007 to 2017 - about 23 billion rubles were allocated. Sections on supporting AI research will become an important part of the concept of the national strategy. In the near future, new research centers will open in Russia, and the development of innovative software for AI will continue.

AI standardization

Norms and rules in the field of AI in Russia are in the process of constant improvement. It is assumed that in late 2019 - early 2020, national standards will be approved, which are now being developed by market leaders. In parallel, a National Standardization Plan for 2020 and beyond is being formed. The standard “Artificial Intelligence. Concept and terminology”, and in 2019 experts began to develop its Russified version. The document must be approved in 2021.

The impact of artificial intelligence

The introduction of AI is inextricably linked with scientific and technological progress, and the scope of application is expanding every year. We face this every day in life, when a large retail chain on the Internet recommends a product to us, or when we open the computer, we see an advertisement for a movie that we just wanted to watch. These recommendations are based on algorithms that analyze what the consumer bought or watched. Artificial intelligence is behind these algorithms.

Is there a risk to the development of human civilization?

Elon Musk believes that the development of AI may threaten humanity and the results may be worse than the use of nuclear weapons. Stephen Hawking, a British scientist, fears that people can create artificial intelligence with superintelligence that can harm a person.

On the economy and business

The penetration of AI technology into all areas of the economy will increase the volume of the global market for services and goods by $15.7 trillion by 2030. The US and China are still leaders in terms of all kinds of projects in the field of AI. Developed countries - Germany, Japan, Canada, Singapore - also strive to realize all the possibilities. Many moderately growing economies, such as Italy, India, Malaysia, are developing strengths in specific AI applications.

To the labor market

The global impact of AI on the labor market will follow two scenarios. First, the spread of some technologies will lead to the dismissal of a large number of people, since computers will take over many tasks. Secondly, due to the development of technological progress, AI specialists will be in great demand in many industries.

AI bias

AI system bias is likely to become an increasingly common problem as artificial intelligence moves out of the lab and into the real world. Researchers fear that without proper training in data assessment and identification of the potential for data bias, vulnerable groups in society may be harmed or their rights infringed. Until now, researchers have no data on whether systems built on the basis of machine learning will threaten humanity.

Applications

Artificial intelligence and its applications are undergoing a transformation. The definition of Weak AI ("weak AI") is used when we are talking about the implementation of narrow tasks in medical diagnostics, electronic trading platforms, robot control. Whereas Strong AI (“strong AI”) is defined by researchers as an intellect that is faced with global tasks, as if they were set for a person.

Defense and military use

By 2025, the rate of sales of relevant services, software and equipment on a global scale will rise to 18.82 billion dollars, and the annual market growth will be 14.75%. AI is used for data aggregation, bioinformatics, military training, and the defense sector.

In education

Many schools include educational course informatics introductory lessons on AI, and universities are widely using big data technologies. Some programs monitor student behavior, grade tests and essays, recognize pronunciation errors, and suggest corrections.

There are also online courses on artificial intelligence. For example, at the educational portal.

In business and trade

In the next five years, leading retailers will have mobile apps that work with digital assistants like Siri to make shopping easier. AI allows you to earn huge amounts of money on the Internet. One example is Amazon, which constantly analyzes consumer behavior and improves algorithms.

Where can I learn about #artificial intelligence

In the power industry

AI helps predict generation and demand for energy resources, reduce losses, and prevent resource theft. In the electric power industry, the use of AI in the analysis of statistical data helps to choose the most profitable supplier or automate customer service.

In the manufacturing sector

According to a McKinsey survey of 1,300 executives, 20% of businesses are already using AI. Recently, Mosselprom implemented AI in its production in the packaging shop. Uses AI's ability to recognize an image. The camera captures all the actions of the employee by scanning the barcode printed on the clothing and sends the data to the computer. The number of transactions performed directly affects the employee's remuneration.

In brewing

Carlsberg uses machine learning to select yeast and expand its range. The technology is implemented on the basis of a digital cloud platform.

In banking

The need for reliable data processing, the development of mobile technologies, the availability of information and the spread of open source software make AI a technology in demand in the banking sector. More and more banks are raising funds through mobile app development companies. New technologies are improving customer service, and analysts predict that within five years, AI in banks will make most decisions on its own.

On transport

The development of AI technologies is the driver of the transport industry. Road condition monitoring, detection of pedestrians or objects in the wrong places, autonomous driving, cloud services in the automotive industry are just a few examples of the use of AI in transport.

In logistics

The power of AI is enabling companies to better predict demand and build supply chains more cost-effectively. AI helps to reduce the number of vehicles needed for transportation, optimize delivery times, and reduce the operating costs of transport and storage facilities.

In the market of luxury goods and services

Luxury brands have also turned to digital to analyze customer needs. One of the challenges facing developers in this segment is managing and influencing customer emotions. Dior is already adapting AI to manage customer-brand interactions through chatbots. Luxury brands will compete in the future and the level of personalization they can achieve with AI will be decisive.

In public administration

The state apparatuses of many countries are not yet ready for the challenges that are hidden in AI technologies. Experts predict that many of the existing government structures and processes that have evolved over the past few centuries are likely to become irrelevant in the near future.

In forensics

Different AI approaches are used to identify criminals in in public places. In some countries, such as the Netherlands, the police are using AI to investigate complex crimes. Digital forensics is an emerging science that requires the mining of huge amounts of very complex datasets.

In the judiciary

Developments in the field of artificial intelligence will help to radically change the judicial system, make it more fair and free from corruption. One of the first AI in the judicial system began to use China. It can be assumed that robot judges will eventually be able to operate with big data from public service repositories. Machine intelligence analyzes a huge amount of data, and it does not experience emotions like a human judge. AI can have a huge impact on the processing of information and the collection of statistics, as well as predicting possible offenses based on data analysis.

In sports

The application of AI in sports has become commonplace in recent years. Sports teams (baseball, football, etc.) analyze individual player performance data, taking into account various factors in the selection. AI can predict the future potential of players by analyzing game technique, physical state and other data, as well as to evaluate their market value.

In healthcare medicine

This area of ​​application is growing rapidly. AI is being used in disease diagnosis, clinical research, drug development, and health insurance. In addition, there is now a boom in investment in numerous medical applications and devices.

Analysis of citizens' behavior

Observation of the behavior of citizens is widely used in the field of security, including behavior on websites (in in social networks) and in messengers. For example, in 2018, Chinese scientists managed to identify 20,000 potential suicides and provide them with psychological assistance. In March 2018, Vladimir Putin instructed to intensify the actions of state bodies to combat negative impact destructive movements in social networks.

In the development of culture

AI algorithms start to generate works of art, which are difficult to distinguish from those created by man. AI offers creative people many tools to bring ideas to life. Right now, the understanding of the role of the artist in a broad sense is changing, since AI provides a lot of new methods, but also poses many new questions for humanity.

Painting

Art has long been considered the exclusive sphere of human creativity. But it turns out that machines can do a lot more in the creative field than people realize. In October 2018, Christie's sold the first AI painting for $432,500. A generative adversarial network algorithm was used, which analyzed 15,000 portraits created between the 15th and 20th centuries.

Music

Several music programs have been developed that use AI to create music. As in other areas, AI in this case also simulates a mental task. A notable feature is the ability of an AI algorithm to learn from information received, such as computer tracking technology that is capable of listening to and following a human performer. The AI ​​also drives what is known as interactive compositing technology, in which a computer composes music in response to a live musician performing. In early 2019, Warner Music signed the first-ever contract with a performer - the Endel algorithm. Under the terms of the contract, the Endel neural network will release 20 unique albums during the year.

Photo

AI is rapidly changing the way we think about photography. In just a couple of years, most of the advances in this field will be focused on AI, and not on optics or sensors, as before. For the first time, advances in photography technology will be unrelated to physics and will create a completely new way photothinking. Even now, the neural network recognizes the slightest changes when modeling faces in photo editors.

Video: face swap

In 2015, Facebook began testing DeepFace technology on the site. In 2017, Reddit user DeepFakes came up with an algorithm to create realistic face swap videos using neural networks and machine learning.

Media and literature

In 2016, Google AI, after analyzing 11,000 unpublished books, began writing its first literary works. Facebook AI Research researchers in 2017 came up with a neural network system that can write poetry on any topic. In November 2015, the direction of preparing automatic texts was opened by the Russian company Yandex.

Go games, poker, chess

In 2016, an AI beat a human in Go (a game with over 10,100 options). In chess, a supercomputer defeated a human player because of the ability to store in memory moves ever played by people and program new ones 10 steps ahead. Poker is now played by bots, although in the past it was considered almost impossible to train a computer to play this card game. Every year developers improve algorithms more and more.

Face recognition

Face recognition technology is used for both photo and video streams. Neural networks build a vector, or “digital”, face template, then these templates are compared within the system. She finds reference points on the face that define individual characteristics. The algorithm for calculating the characteristics is different for each of the systems and is the main secret of the developers.

For the further development and application of AI, it is necessary to train, first of all, a person

Sergey Shirkin

Dean of the Faculty of Artificial Intelligence

Artificial intelligence technologies in the form in which they are currently used have existed for about 5-10 years, but in order to apply them, oddly enough, a large number of people are required. Accordingly, the main costs in the field of artificial intelligence are the costs of specialists. Moreover, almost all the basic technologies of artificial intelligence (libraries, frameworks, algorithms) are free and are in the public domain. At one time, finding machine learning experts was almost impossible. But now, largely due to the development of MOOC (eng. Massive Open Online Course, massive open online course), there are more of them. higher educational institutions also supply specialists, but they often have to finish their studies in online courses.

Now artificial intelligence may well recognize that a person is planning to change jobs, and can offer him appropriate online courses, many of which can be taken with only a smartphone. And this means that you can practice even while on the road - for example, on the way to work. One of the first such projects was the online resource Coursera, but later many similar educational projects appeared, each of which occupies a certain niche in online education.

You need to understand that AI, like any program, is primarily a code, that is, a text designed in a certain way. This code needs development, maintenance and improvement. Unfortunately, this does not happen by itself; without a programmer, the code cannot “come to life”. Therefore, all fears about the omnipotence of AI are unfounded. Programs are created for strictly defined tasks, they do not have feelings and aspirations like a person, they do not perform actions that the programmer did not put into them.

It can be said that in our time, AI has only individual human skills, although it can outpace the average person in the speed of their application. True, many hours of effort of thousands of programmers are spent on developing each such skill. The most that AI is capable of so far is to automate some physical and mental operations, thereby freeing people from routine.

Does the use of AI carry any risks? Rather, now there is a risk of not seeing the possibility of using artificial intelligence technologies. Many companies are aware of this and are trying to develop in several directions at once, in the hope that one of them can "shoot". An illustrative example is online stores: now only those who realized the need to use AI, when it was not yet in trend, remained afloat, although it was quite possible to “save money” and not invite the necessary mathematicians-programmers for no reason.

The prospect of development of artificial intelligence

Computers can now do many things that only humans used to be able to do: play chess, recognize letters of the alphabet, check spelling, grammar, recognize faces, dictate, speak, win game shows, and more. But skeptics persist. Once a human ability is automated, skeptics say it's just another computer program and not an example of self-learning AI. AI technologies are only finding wide application and have huge growth potential in all areas. Over time, humanity will create more and more powerful computers, which will be more and more improved in the development of AI.

Is the purpose of AI to put the human mind into a computer?

There is only a rough understanding of how the human brain works. So far, not all properties of the mind can be imitated using AI.

Can AI reach human levels of intelligence?

Scientists strive to ensure that AI can solve even more diverse problems. But it is premature to talk about reaching the level of human intelligence, since thinking is not limited to only one algorithm.

When can artificial intelligence reach the level of human thinking?

At this stage of accumulation and analysis of information, which is now reached by mankind, AI is far from human thinking. However, in the future, breakthrough ideas may arise that will affect a sharp jump in the development of AI.

Can a computer become an intelligent machine?

A part of any complex machine is a computer system, and here it is possible to speak only of intelligent computer systems. The computer itself is not intelligent.

Is there a connection between speed and the development of intelligence in computers?

No, speed is responsible only for some properties of intelligence. By itself, the speed of processing and analyzing information is not enough for intelligence to appear.

Is it possible to create a children's machine that could develop through reading and self-learning?

This has been discussed by researchers for almost a hundred years. Probably, the idea will someday be implemented. Today, AI programs do not process and use as much information as children can.

How are computability theory and computational complexity related to AI?

Computational complexity theory focuses on classifying computational problems according to their inherent complexity and relating these classes to each other. A computational problem is a problem solved by a computer. The calculation problem is solvable by the mechanical application of mathematical steps, such as an algorithm.

Conclusion

Artificial intelligence has already had a huge impact on the development of our world, which was impossible to predict even a century ago. Smart phone networks route calls more efficiently than any human operator. Cars are built in unmanned factories by automated robots. Artificial intelligence is being integrated into the most common household items, such as a vacuum cleaner. The mechanisms of AI are not fully understood, but experts predict that the development of AI will come even closer to the development of the human brain in the coming years.