Who coined the term strong artificial intelligence. AI in brewing

Artificial intelligence

Artificial intelligence is a branch of computer science that studies the possibility of providing intelligent reasoning and action using computing systems and other artificial devices. In most cases, the algorithm for solving the problem is unknown in advance.

There is no exact definition of this science, since the question of the nature and status of human intelligence has not been resolved in philosophy. There is also no exact criterion for computers to achieve “intelligence,” although at the dawn of artificial intelligence a number of hypotheses were proposed, for example, the Turing test or the Newell-Simon hypothesis. At the moment, there are many approaches to both understanding the AI problem and creating intelligent systems.

Thus, one of the classifications identifies two approaches to AI development:

top-down, semiotic - the creation of symbolic systems that model high-level mental processes: thinking, reasoning, speech, emotions, creativity, etc.;

bottom-up, biological - the study of neural networks and evolutionary computations that model intelligent behavior based on smaller "non-intelligent" elements.

This science is related to psychology, neurophysiology, transhumanism and others. Like all computer sciences, it uses mathematics. Philosophy and robotics are of particular importance to her.

Artificial intelligence is a very young field of research, which was launched in 1956. Her historical path resembles a sine wave, each “takeoff” of which was initiated by some new idea. At the moment, its development is in decline, giving way to the application of already achieved results in other areas of science, industry, business and even everyday life.

Study approaches

Exist different approaches to building AI systems. At the moment, there are 4 quite different approaches:

1. Logical approach. The basis for the logical approach is Boolean algebra. Every programmer is familiar with it and with logical operators from the time he mastered the IF operator. Boolean algebra received its further development in the form of predicate calculus - in which it was expanded by introducing subject symbols, relations between them, quantifiers of existence and universality. Almost every AI system built on a logical principle is a theorem proving machine. In this case, the source data is stored in the database in the form of axioms, logical inference rules as relationships between them. In addition, each such machine has a goal generation unit, and the inference system tries to prove this goal as a theorem. If the goal is proven, then tracing the applied rules allows us to obtain a chain of actions necessary to achieve the goal (such a system is known as expert systems). The power of such a system is determined by the capabilities of the goal generator and the theorem proving machine. A relatively new direction, such as fuzzy logic, allows the logical approach to achieve greater expressiveness. Its main difference is that the truthfulness of a statement can take, in addition to yes/no (1/0), also intermediate values - I don’t know (0.5), the patient is more likely alive than dead (0.75), the patient rather dead than alive (0.25). This approach is more similar to human thinking, since it rarely answers questions with only yes or no.

2. By structural approach we mean here attempts to build AI by modeling the structure of the human brain. One of the first such attempts was Frank Rosenblatt's perceptron. The main modeled structural unit in perceptrons (as in most other brain modeling options) is the neuron. Later, other models arose, which are known to most under the term neural networks(NS). These models differ in the structure of individual neurons, in the topology of connections between them, and in learning algorithms. Among the most well-known NN options now are NNs with backpropagation of errors, Hopfield networks, and stochastic neural networks. In a broader sense, this approach is known as Connectivism.

3. Evolutionary approach. When building AI systems using this approach, the main attention is paid to building the initial model and the rules by which it can change (evolve). Moreover, the model can be compiled using a variety of methods, it can be a neural network, a set of logical rules, or any other model. After that, we turn on the computer and, based on checking the models, it selects the best of them, on the basis of which new models are generated according to a variety of rules. Among evolutionary algorithms, the genetic algorithm is considered classic.

4. Simulation approach. This approach is classic for cybernetics with one of its basic concepts black box. The object whose behavior is simulated is precisely a “black box”. It doesn’t matter to us what it and the model have inside and how it functions, the main thing is that our model behaves exactly the same in similar situations. Thus, another human property is modeled here - the ability to copy what others do, without going into detail about why this is needed. Often this ability saves him a lot of time, especially early in his life.

Within the framework of hybrid intelligent systems, they are trying to combine these areas. Expert inference rules can be generated by neural networks, and generative rules are obtained using statistical learning.

A promising new approach called intelligence amplification views the achievement of AI through evolutionary development as by-effect enhancing human intelligence with technology.

Research directions

Analyzing the history of AI, we can highlight such a broad area as reasoning modeling. Long years the development of this science moved precisely along this path, and now it is one of the most developed areas in modern AI. Modeling reasoning involves the creation of symbolic systems, the input of which is a certain problem, and the output requires its solution. As a rule, the proposed task has already been formalized, i.e., translated into mathematical form, but either does not have a solution algorithm, or it is too complex, time-consuming, etc. This area includes: proof of theorems, decision making and game theory, planning and dispatch, forecasting.

An important area is natural language processing, which involves analyzing the capabilities of understanding, processing and generating texts in “human” language. In particular, the problem of machine translation of texts from one language to another has not yet been solved. In the modern world, the development of information retrieval methods plays an important role. By its nature, the original Turing test is related to this direction.

According to many scientists, an important property of intelligence is the ability to learn. Thus, knowledge engineering comes to the fore, combining the tasks of obtaining knowledge from simple information, its systematization and use. Advances in this area affect almost every other area of AI research. Here, too, two important subareas cannot be overlooked. The first of them - machine learning - concerns the process of independent acquisition of knowledge by an intelligent system in the process of its operation. The second is associated with the creation of expert systems - programs that use specialized knowledge bases to obtain reliable conclusions on any problem.

There are great and interesting achievements in the field of modeling biological systems. Strictly speaking, this can include several independent directions. Neural networks are used to solve fuzzy and complex problems, such as geometric shape recognition or object clustering. The genetic approach is based on the idea that an algorithm can become more efficient if it borrows best characteristics from other algorithms (“parents”). A relatively new approach, where the task is to create an autonomous program - an agent that interacts with the external environment, is called the agent approach. And if you properly force a lot of “not very intelligent” agents to interact together, you can get “ant” intelligence.

Pattern recognition problems are already partially solved in other areas. This includes character recognition, handwritten text, speech, and text analysis. Particularly worth mentioning is computer vision, which is related to machine learning and robotics.

In general, robotics and artificial intelligence are often associated with each other. The integration of these two sciences, the creation of intelligent robots, can be considered another area of AI.

Machine creativity stands apart, due to the fact that the nature of human creativity is even less studied than the nature of intelligence. Nevertheless, this area exists, and the problems of computer writing music, literary works (often poetry or fairy tales), and artistic creation are posed here.

Finally, there are many applications of artificial intelligence, each of which forms an almost independent direction. Examples include programming intelligence in computer games, nonlinear control, and intelligent security systems.

It can be seen that many areas of research overlap. This is typical for any science. But in artificial intelligence, the relationship between seemingly different areas is especially strong, and this is associated with the philosophical debate about strong and weak AI.

At the beginning of the 17th century, Rene Descartes suggested that an animal is a kind of complex mechanism, thereby formulating a mechanistic theory. In 1623, Wilhelm Schickard built the first mechanical digital computer, followed by machines by Blaise Pascal (1643) and Leibniz (1671). Leibniz was also the first to describe the modern binary number system, although before him many great scientists were periodically interested in this system. In the 19th century, Charles Babbage and Ada Lovelace worked on a programmable mechanical computer.

In 1910-1913 Bertrand Russell and A. N. Whitehead published Principia Mathematica, which revolutionized formal logic. In 1941, Konrad Zuse built the first working software-controlled computer. Warren McCulloch and Walter Pitts published A Logical Calculus of the Ideas Immanent in Nervous Activity in 1943, which laid the foundation for neural networks.

Current state of affairs

At the moment (2008) in the creation of artificial intelligence (in the original sense of the word, expert systems and chess programs do not belong here) there is a shortage of ideas. Almost all approaches have been tried, but none have led to the emergence of artificial intelligence. research group it never came up.

Some of the most impressive civilian AI systems are:

Deep Blue - defeated the world chess champion. (The match between Kasparov and supercomputers did not bring satisfaction to either computer scientists or chess players, and the system was not recognized by Kasparov, although the original compact chess programs are an integral element of chess creativity. Then the IBM line of supercomputers appeared in the brute force projects BluGene (molecular modeling) and modeling of the pyramidal cell system in Swiss Blue Brain Center. This story- an example of the intricate and secretive relationship between AI, business, and national strategic objectives.)

Mycin was one of the early expert systems that could diagnose a small set of diseases, often as accurately as doctors.

20q is a project based on AI ideas, based on the classic game “20 Questions”. It became very popular after appearing on the Internet on the website 20q.net.

Speech recognition. Systems such as ViaVoice are capable of serving consumers.

Robots compete in a simplified form of football in the annual RoboCup tournament.

Application of AI

Banks use artificial intelligence systems (AI) in insurance activities (actuarial mathematics) when playing on the stock exchange and property management. In August 2001, robots beat humans in an impromptu trading competition (BBC News, 2001). Pattern recognition methods (including both more complex and specialized and neural networks) are widely used in optical and acoustic recognition (including text and speech), medical diagnostics, spam filters, in air defense systems (target identification), and also to ensure a number of other national security tasks.

Computer game developers are forced to use AI of varying degrees of sophistication. Standard tasks of AI in games are finding a path in two-dimensional or three-dimensional space, simulating the behavior of a combat unit, calculating the correct economic strategy, and so on.

Prospects for AI

Two directions of AI development are visible:

the first is to solve problems associated with bringing specialized AI systems closer to human capabilities and their integration, which is realized by human nature.

the second is the creation of Artificial Intelligence, which represents the integration of already created AI systems into unified system capable of solving humanity's problems.

Connections with other sciences

Artificial intelligence is closely related to transhumanism. And together with neurophysiology and cognitive psychology, it forms a more general science called cognitive science. Philosophy plays a special role in artificial intelligence.

Philosophical questions

The science of “creating artificial intelligence” could not help but attract the attention of philosophers. With the advent of the first intelligent systems, fundamental questions about man and knowledge, and partly about the world order, were raised. On the one hand, they are inextricably linked with this science, and on the other, they introduce some chaos into it. Among AI researchers, there is still no dominant point of view on the criteria of intelligence, the systematization of goals and tasks to be solved, there is not even a strict definition of science.

Can a machine think?

The most heated debate in the philosophy of artificial intelligence is the question of the possibility of thinking created by human hands. The question “Can a machine think?”, which prompted researchers to create the science of simulating the human mind, was posed by Alan Turing in 1950. The two main points of view on this issue are called the hypotheses of strong and weak artificial intelligence.

The term “strong artificial intelligence” was introduced by John Searle, and the approach is characterized in his words:

“Moreover, such a program would not just be a model of the mind; she, in the literal sense of the word, herself will be the mind, in the same sense in which human mind- this is the mind."

In contrast, proponents of weak AI prefer to view programs only as tools that allow them to solve certain problems that do not require the full range of human cognitive abilities.

In his "Chinese Room" thought experiment, John Searle shows that passing the Turing test is not a criterion for a machine to have a genuine reasoning process.

Thinking is the process of processing information stored in memory: analysis, synthesis and self-programming.

A similar position is taken by Roger Penrose, who in his book “The King's New Mind” argues for the impossibility of obtaining the thinking process on the basis of formal systems.

There are different points of view on this issue. The analytical approach involves the analysis of a person’s higher nervous activity to the lowest, indivisible level (the function of higher nervous activity, an elementary reaction to external irritants (stimuli), irritation of the synapses of a set of neurons connected by function) and the subsequent reproduction of these functions.

Some experts mistake the ability of rational, motivated choice in conditions of lack of information for intelligence. That is, an intellectual program is simply considered to be that program of activity (not necessarily implemented on modern computers) that can choose from a certain set of alternatives, for example, where to go in the case of “you will go left ...”, “you will go right ...”, “you will go straight ...”

Science of knowledge

Also, epistemology - the science of knowledge within the framework of philosophy - is closely related to the problems of artificial intelligence. Philosophers working on this topic are grappling with questions similar to those faced by AI engineers about how best to represent and use knowledge and information.

Attitudes towards AI in society

AI and religion

Among followers of Abrahamic religions, there are several points of view on the possibility of creating AI based on a structural approach.

According to one of them, the brain, whose work the systems are trying to imitate, in their opinion, does not participate in the thinking process, is not the source of consciousness and any other mental activity. Creating AI based on a structured approach is impossible.

According to another point of view, the brain is involved in the thinking process, but in the form of a “transmitter” of information from the soul. The brain is responsible for such “simple” functions as unconditioned reflexes, response to pain, etc. Creating AI based on a structural approach is possible if the system being designed can perform “transfer” functions.

Both positions do not correspond to the data of modern science, because the concept of soul is not considered modern science as a scientific category.

According to many Buddhists, AI is possible. Thus, the spiritual leader Dalai Lama XIV does not exclude the possibility of the existence of consciousness on a computer basis.

Raelites actively support developments in the field of artificial intelligence.

AI and science fiction

In science fiction literature, AI is most often depicted as a force that attempts to overthrow human power (Omnius, HAL 9000, Skynet, Colossus, The Matrix, and the Replicant) or a serving humanoid (C-3PO, Data, KITT, and KARR, Bicentennial Man). The inevitability of domination of the world by AI that has gotten out of control is disputed by such science fiction writers as Isaac Asimov and Kevin Warwick.

A curious vision of the future is presented in the novel "The Turing Selection" by science fiction writer Harry Garrison and scientist Marvin Minsky. The authors discuss the topic of the loss of humanity in a person into whose brain a computer was implanted, and the acquisition of humanity by an AI machine into whose memory information from the human brain was copied.

Some science fiction writers, such as Vernor Vinge, have also speculated on the implications of the emergence of AI, which is likely to cause dramatic changes in society. This period is called the technological singularity.

Artificial intelligence (AI, English 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

Intelligence(from Lat. intellectus - sensation, perception, understanding, understanding, concept, reason), or mind - a 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 knowledge for environmental management. Intelligence is the general capacity for cognition and problem solving that unites everything cognitive abilities human: sensation, perception, memory, representation, thinking, imagination.

In the early 1980s. Computational scientists Barr and Fajgenbaum proposed the following definition of artificial intelligence (AI):

Later, a number of algorithms and software systems began to be classified as AI, distinctive feature 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 understanding language, 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.

Technological directions of AI. Deloitte data

AI Research

Main article: Artificial Intelligence Research

Standardization in AI

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

On December 6, 2018, the Technical Committee “Cyber-Physical Systems” based on 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.

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, automated driving and much more) is deep learning. It is a subset of machine learning, characterized by the use of neural network models, which can be said to mimic the workings of the brain, so it would be a stretch to classify them as AI. Any neural network model is trained on large data sets, so it acquires some “skills,” but how it uses them remains unclear to its 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 a system AI and can systems built on the basis of machine learning? The value of the answer to last question goes beyond scientific laboratories. Therefore, media attention to the phenomenon called AI bias has noticeably intensified. It can be translated as “AI bias” or “AI bias”. Read more.

Artificial Intelligence Technology Market

AI market in Russia

Global AI market

Areas of application of AI

The areas of application of AI are quite wide and cover both technologies familiar to the ear 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. You can 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 areas in the economy.

Main commercial applications of artificial intelligence technologies

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.

Using AI for Defense and Military Affairs

Use in education

Using AI in business

AI in the electric 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 increased generation when demand surges.
At the production level: optimization of 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 service provision level: automatic selection of the most profitable supplier, detailed statistics consumption, automated customer service, optimization of energy consumption taking into account customer habits and behavior.

AI in manufacturing

At the design level: increasing the efficiency of new product development, automated supplier assessment and analysis of spare parts requirements.
At the production level: improving the process of completing tasks, automating assembly lines, reducing the number of errors, reducing delivery times for raw materials.
At the promotion level: forecasting the volume of support and maintenance services, pricing management.
At the level of service provision: improving planning of vehicle fleet routes, demand for fleet resources, improving the quality of training of service engineers.

AI in banks

Pattern recognition - used incl. to recognize customers in branches and convey specialized offers to them.

Main commercial areas of application of artificial intelligence technologies in banks

AI in transport

The auto industry is on the verge of a revolution: 5 challenges of the era of unmanned driving

AI in logistics

AI in brewing

Use of AI in public administration

AI in forensics

Pattern recognition - used incl. to identify criminals in public spaces.

In May 2018, it became known that the Dutch police were using artificial intelligence to investigate complex crimes.

According to edition The Next Web, law enforcement agencies began digitizing more than 1,500 reports and 30 million pages related to unsolved cases. Materials from 1988 onwards, in which the crime was not solved for at least three years, and the offender was sentenced to more than 12 years in prison, are transferred into computer format.

Solve a complex crime in a day. Police are adopting AI

Once all the content is digitized, it will be connected to a machine learning system that will analyze the records and decide which cases use the most reliable evidence. This should reduce the time it takes to process cases and solve past and future crimes from several weeks to one day.

Artificial intelligence will categorize cases according to their “solvability” and indicate possible results of DNA testing. Then it is planned to automate the analysis in other areas forensics and perhaps even cover data in areas such as social Sciences and witness statements.

In addition, as one of the system developers, Jeroen Hammer, said, API functions for partners may be released in the future.

The Dutch police have special unit, specializing in the development of new technologies for solving crimes. It was he who created the AI system for quickly searching for criminals based on evidence.

AI in the judiciary

Developments in the field of artificial intelligence will help radically change the judicial system, making it fairer and free from corruption schemes. This opinion was expressed in the summer of 2017 by Vladimir Krylov, Doctor of Technical Sciences, technical consultant at Artezio.

The scientist believes that existing solutions in the field of AI can be successfully applied in various spheres 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.

“Looking at news reports every day about developments in the field of AI, you are only amazed at the inexhaustible imagination and fruitfulness of researchers and developers in this field. Messages about scientific research are constantly interspersed with publications about new products breaking into the market and reports of amazing results obtained through the use of AI in various areas. If we talk about expected events, accompanied by noticeable hype in the media, in which AI will again become the hero of the news, then I probably won’t risk making technological forecasts. I can imagine that the next event will be the emergence somewhere of an extremely competent court in the form of artificial intelligence, fair and incorruptible. This will happen, apparently, 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 to AI most of the processes of managing human society.”

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

Expert psychologists, however, believe that the absence of an emotional component when considering court cases will negatively affect the quality of the decision. The verdict of a machine court may be too straightforward, not taking into account the importance of people’s feelings and moods.

Painting

In 2015, the Google team tested neural networks to see if they could create images on their own. Then artificial intelligence was trained using a large number of different pictures. However, when the machine was “asked” to depict something on its own, it turned out that it interpreted 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 interpreted this incorrectly.

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

A painting painted by Google's artificial intelligence.

The definition of artificial intelligence cited in the preamble, given by John McCarthy in 1956 at a conference at Dartmouth University, is not directly related to the understanding of human intelligence. According to McCarthy, AI researchers are free to use techniques not seen in humans if needed to solve specific problems.

At the same time, there is a point of view according to which intelligence can only be a biological phenomenon.

As the chairman of the St. Petersburg branch of the Russian Association of Artificial Intelligence T. A. Gavrilova points out, in English language phrase artificial intelligence does not have that slightly fantastic anthropomorphic overtones that it acquired in the rather unsuccessful Russian translation. Word intelligence means “the ability to reason rationally”, and not at all “intelligence”, for which there is an English analogue intelligence .

Participants of the Russian Association of Artificial Intelligence give following definitions artificial intelligence:

One of the particular definitions of intelligence, common to man and “machine,” can be formulated as follows: “Intelligence is the ability of a system to create programs (primarily heuristic) during self-learning to solve problems of a certain class of complexity and solve these problems.”

Prerequisites for the development of artificial intelligence science

The history of artificial intelligence as a new scientific direction begins in the middle of the 20th century. By this time, many prerequisites for its origin had already been formed: among philosophers there had long been debates about the nature of man and the process of understanding the world, neurophysiologists and psychologists had developed a number of theories regarding the work of the human brain and thinking, economists and mathematicians asked questions about optimal calculations and the presentation of knowledge about the world in in a formalized form; finally, the foundation of the mathematical theory of calculations - the theory of algorithms - was born and the first computers were created.

The capabilities of new machines in terms of computing speed turned out to be greater than human ones, so the question arose in the scientific community: what are the limits of computer capabilities and will machines reach the level of human development? In 1950, one of the pioneers in the field computer technology, English scientist Alan Turing, writes an article entitled “Can a Machine Think?” , which describes a procedure by which it will be possible to determine the moment when a machine becomes equal to a person in terms of intelligence, called the Turing test.

History of the development of artificial intelligence in the USSR and Russia

In the USSR, work in the field of artificial intelligence began in the 1960s. A number of pioneering studies were carried out at Moscow University and the Academy of Sciences, headed by Veniamin Pushkin and D. A. Pospelov. Since the early 1960s, M. L. Tsetlin and his colleagues have been developing issues related to training finite state machines.

In 1964, the work of the Leningrad logician Sergei Maslov “ Reverse method establishing deducibility in classical predicate calculus”, in which the method was first proposed automatic search proofs of theorems in predicate calculus.

Until the 1970s in the USSR, all AI research was carried out within the framework of cybernetics. According to D. A. Pospelov, the sciences “computer science” and “cybernetics” were mixed at that time due to a number of academic disputes. Only at the end of the 1970s in the USSR they began to talk about scientific direction"artificial intelligence" as a branch of computer science. At the same time, computer science itself was born, subordinating its ancestor “cybernetics”. At the end of the 1970s it was created Dictionary on artificial intelligence, a three-volume reference book on artificial intelligence and an encyclopedic dictionary on computer science, in which the sections “Cybernetics” and “Artificial Intelligence” are included, along with other sections, in computer science. The term “computer science” became widespread in the 1980s, and the term “cybernetics” gradually disappeared from circulation, remaining only in the names of those institutions that arose during the era of the “cybernetic boom” of the late 1950s - early 1960s. This view of artificial intelligence, cybernetics and computer science is not shared by everyone. This is due to the fact that in the West the boundaries of these sciences are somewhat different.

Approaches and directions

Approaches to understanding the problem

There is no single answer to the question of what artificial intelligence does. Almost every author who writes a book about AI starts from some definition, considering the achievements of this science in its light.

top-down AI), semiotic - creation of expert systems, knowledge bases and systems logical inference simulating high-level mental processes: thinking, reasoning, speech, emotions, creativity, etc.;
Bottom-Up AI), biological - the study of neural networks and evolutionary computations that model intelligent behavior based on biological elements, as well as the creation of corresponding computing systems, such as a neurocomputer or biocomputer.

The latter approach, strictly speaking, does not belong to the science of AI in the sense given by John McCarthy - they are united only by a common final goal.

The Turing Test and the Intuitive Approach

This approach focuses on those methods and algorithms that will help an intelligent agent survive in its environment while performing its task. So, here the algorithms for finding a path and making decisions are studied much more carefully.

Hybrid approach

Hybrid approach assumes that only the synergistic combination of neural and symbolic models achieves the full range of cognitive and computational capabilities. For example, expert inference rules can be generated by neural networks, and generative rules are obtained using statistical learning. Proponents of this approach believe that hybrid Information Systems will be significantly more powerful than the sum of the various concepts taken separately.

Research models and methods

Symbolic modeling of thought processes

Analyzing the history of AI, we can identify such a broad area as modeling reasoning. For many years, the development of this science has moved precisely along this path, and now it is one of the most developed areas in modern AI. Modeling reasoning involves the creation of symbolic systems, the input of which is set to a certain task, and the output requires its solution. As a rule, the proposed problem has already been formalized, that is, translated into mathematical form, but either does not have a solution algorithm, or it is too complex, time-consuming, etc. This area includes: proving theorems, making decisions and game theory, planning and dispatching, forecasting.

Working with Natural Languages

An important direction is natural language processing, within which the analysis of the capabilities of understanding, processing and generating texts in “human” language is carried out. This direction aims to process natural language in such a way that one would be able to acquire knowledge independently by reading existing text available on the Internet. Some direct applications of natural language processing include information retrieval (including deep text mining) and machine translation.

Representation and use of knowledge

Direction knowledge engineering combines the tasks of obtaining knowledge from simple information, their systematization and use. This direction is historically associated with the creation expert systems- programs that use specialized knowledge bases to obtain reliable conclusions on any problem.

Producing knowledge from data is one of the basic problems of data mining. There are various approaches to solving this problem, including those based on neural network technology, using neural network verbalization procedures.

Machine learning

Issues machine learning concerns the process independent acquisition of knowledge by an intelligent system in the process of its operation. This direction has been central since the very beginning of the development of AI. In 1956, at the Dartmund Summer Conference, Ray Solomonoff wrote a report on a probabilistic unsupervised learning machine, calling it "The Inductive Inference Machine."

Robotics

Machine creativity

Nature human creativity even less studied than the nature of intelligence. Nevertheless, this area exists, and the problems of computer writing music, literary works (often poetry or fairy tales), and artistic creation are posed here. Creating realistic images is widely used in the film and gaming industries.

Separately, the study of problems is highlighted technical creativity artificial intelligence systems. The theory of solving inventive problems, proposed in 1946 by G. S. Altshuller, marked the beginning of such research.

Adding this capability to any intelligent system allows you to very clearly demonstrate what exactly the system perceives and how it understands it. By adding noise instead of missing information or filtering noise with knowledge available in the system, it produces concrete images from abstract knowledge that are easily perceived by a person, this is especially useful for intuitive and low-value knowledge, the verification of which in a formal form requires significant mental effort.

Other areas of research

In the future, it is expected that the development of artificial intelligence will be closely connected with the development of a quantum computer, since some properties of artificial intelligence have similar operating principles to quantum computers.

It can be seen that many areas of research overlap. This is typical of any science. But in artificial intelligence, the relationship between seemingly different areas is especially strong, and this is associated with the philosophical debate about strong and weak AI.

Modern artificial intelligence

Two directions of AI development can be distinguished:

solving problems related to proximity specialized systems AI to human capabilities, and their integration, which is realized by human nature ( see Intelligence Enhancement);
the creation of artificial intelligence, representing the integration of already created AI systems into a single system capable of solving the problems of humanity ( see Strong and weak artificial intelligence).

But at the moment, the field of artificial intelligence is seeing the involvement of many subject areas, having a practical relationship to AI rather than a fundamental one. Many approaches have been tested, but no research group has yet approached the emergence of artificial intelligence. Below are just some of the most famous developments in the field of AI.

Application

Some of the most famous AI systems are:

Banks use artificial intelligence systems (AI) in insurance activities (actuarial mathematics), when playing on the stock exchange and in property management. Pattern recognition methods (including both more complex and specialized and neural networks) are widely used in optical and acoustic recognition (including text and speech), medical diagnostics, spam filters, in air defense systems (target identification), as well as to ensure a number of other national security tasks.

Psychology and cognitive science

Cognitive modeling methodology is designed for analysis and decision making in poor certain situations. It was proposed by Axelrod.

It is based on modeling the subjective ideas of experts about the situation and includes: a methodology for structuring the situation: a model for representing the expert’s knowledge in the form of a signed digraph (cognitive map) (F, W), where F is the set of factors of the situation, W is the set of cause-and-effect relationships between the factors of the situation ; methods of situation analysis. Currently, the methodology of cognitive modeling is developing in the direction of improving the apparatus for analyzing and modeling the situation. Models for forecasting the development of the situation are proposed here; methods for solving inverse problems.

Philosophy

Philosophical problems of creating artificial intelligence can be divided into two groups, relatively speaking, “before and after the development of AI.” The first group answers the question: “What is AI, is it possible to create it, and, if possible, how to do it?” The second group (ethics of artificial intelligence) asks the question: “What are the consequences of creating AI for humanity?”

The term “strong artificial intelligence” was introduced by John Searle, and the approach is characterized in his words:

Moreover, such a program would not simply be a model of the mind; she, in the literal sense of the word, herself will be the mind, in the same sense in which the human mind is the mind.

At the same time, it is necessary to understand whether a “pure artificial” mind (“metamind”) is possible, understanding and solving real problems and, at the same time, devoid of emotions characteristic of a person and necessary for his individual survival [ ] .

In contrast, proponents of weak AI prefer to view programs only as tools that allow them to solve certain problems that do not require the full range of human cognitive abilities.

Ethics

Other traditional faiths rarely describe the issues of AI. But some theologians nevertheless pay attention to this. For example, Archpriest Mikhail Zakharov, arguing from the point of view of the Christian worldview, poses the following question: “Man is a rationally free being, created by God in His image and likeness. We are accustomed to attributing all these definitions to biological species. Homo sapiens. But how justified is this? . He answers this question like this:

If we assume that research in the field of artificial intelligence will someday lead to the emergence of an artificial being that is superior in intelligence to humans and has free will, would this mean that this being is a person? ... man is God's creation. Can we call this creature a creation of God? At first glance, it is a human creation. But even during the creation of man, it is hardly worth understanding literally that God sculpted the first man from clay with His own hands. This is probably an allegory indicating materiality human body created by the will of God. But without the will of God nothing happens in this world. Man, as a co-creator of this world, can, fulfilling the will of God, create new creatures. Such creatures, created by human hands according to God's will, can probably be called creations of God. After all, man creates new species of animals and plants. And we consider plants and animals to be God’s creations. The same can be applied to an artificial being of a non-biological nature.

Science fiction

The topic of AI is covered under different angles in the works of Robert Heinlein: the hypothesis of the emergence of self-awareness of AI when the structure becomes more complex beyond a certain critical level and there is interaction with the outside world and other carriers of intelligence (“The Moon Is a Harsh Mistress”, “Time Enough For Love”, the characters Mycroft, Dora and Aya in cycle “History of the Future”), problems of AI development after hypothetical self-awareness and some social and ethical issues (“Friday”). The socio-psychological problems of human interaction with AI are also considered in Philip K. Dick’s novel “Do Androids Dream of Electric Sheep? ", also known for the film adaptation of Blade Runner.

The works of science fiction writer and philosopher Stanislaw Lem describe and largely anticipate the creation of virtual reality, artificial intelligence, nanorobots and many other problems of the philosophy of artificial intelligence. It is especially worth noting the futurology of Sum technology. In addition, in the adventures of Iyon the Quiet, the relationship between living beings and machines is repeatedly described: the rebellion of the on-board computer with subsequent unexpected events (11th journey), the adaptation of robots to human society(“Washing tragedy” from “Memoirs of Ijon the Quiet”), building absolute order on the planet by processing living inhabitants (24th journey), inventions of Corcoran and Diagoras (“Memories of Ijon the Quiet”), a psychiatric clinic for robots (“Memoirs of Ijon the Quiet” "). In addition, there is a whole series of novels and stories Cyberiad, where almost all the characters are robots, who are distant descendants of robots that escaped from people (they call people pallids and consider them mythical creatures).

Movies

Since almost the 1960s, along with the writing fantasy stories and stories, films are being made about artificial intelligence. Many stories by authors recognized throughout the world are filmed and become classics of the genre, others become a milestone in development

Artificial Intelligence – in Lately one of the most popular topics in the technology world. Minds like Elon Musk, Stephen Hawking and Steve Wozniak are seriously concerned about AI research and argue that its creation threatens us mortal danger. At the same time, science fiction and Hollywood films have given rise to many misconceptions around AI. Are we really in danger and what inaccuracies are we making when we imagine the destruction of Skynet Earth, general unemployment, or, on the contrary, prosperity and carefreeness? Gizmodo has looked into human myths about artificial intelligence. We present full translation his articles.

It was called the most important test machine intelligence since Deep Blue's victory over Garry Kasparov in a chess match 20 years ago. Google AlphaGo defeated grandmaster Lee Sedol at the Go tournament with a crushing score of 4:1, showing how seriously artificial intelligence (AI) has advanced. The fateful day when machines will finally surpass humans in intelligence has never seemed so close. But we seem to be no closer to understanding the consequences of this epoch-making event.

In fact, we cling to serious and even dangerous misconceptions about artificial intelligence. Last year, SpaceX founder Elon Musk warned that AI could take over the world. His words caused a storm of comments, both opponents and supporters of this opinion. For such a future monumental event, there is a surprising amount of disagreement as to whether it will happen and, if so, in what form. This is especially troubling considering the incredible benefits humanity could gain from AI, and possible risks. Unlike other human inventions, AI has the potential to change humanity or destroy us.

It's hard to know what to believe. But thanks to early work by computer scientists, neuroscientists, and AI theorists, a clearer picture is beginning to emerge. Here are some common misconceptions and myths about artificial intelligence.

Myth #1: “We will never create an AI with intelligence comparable to a human”

Reality: We already have computers that have equaled or exceeded human capabilities at chess, Go, stock trading, and conversation. Computers and the algorithms that run them can only get better. It's only a matter of time before they surpass humans at any task.

New York University research psychologist Gary Marcus said that “literally everyone” who works in AI believes that machines will eventually beat us: “The only real difference between the enthusiasts and the skeptics is the timing estimates.” Futurists like Ray Kurzweil believe this could happen within a few decades; others say it will take centuries.

AI skeptics are not convincing when they say that this is an unsolvable technological problem, but in nature biological brain there is something unique. Our brains - biological machines– they exist in the real world and adhere to the basic laws of physics. There is nothing unknowable about them.

Myth #2: “Artificial intelligence will have consciousness”

Reality: Most imagine that machine intelligence will be conscious and think the way humans think. Moreover, critics like Microsoft co-founder Paul Allen believe that we cannot yet achieve artificial general intelligence (capable of solving any mental problem a human can solve) because we lack a scientific theory of consciousness. But as Imperial College London cognitive robotics specialist Murray Shanahan says, we shouldn't equate the two concepts.

“Consciousness is certainly amazing and important thing, but I don't believe it's necessary for artificial intelligence human level. To be more precise, we use the word “consciousness” to refer to several psychological and cognitive attributes that a person “comes with,” explains the scientist.

It is possible to imagine a smart machine that lacks one or more of these features. Ultimately, we may create incredibly intelligent AI that is unable to perceive the world subjectively and consciously. Shanahan argues that mind and consciousness can be combined in a machine, but we must not forget that these are two different concepts.

Just because a machine passes the Turing Test, in which it is indistinguishable from a human, does not mean it is conscious. To us, advanced AI may appear conscious, but it will be no more self-aware than a rock or a calculator.

Myth #3: “We shouldn’t be afraid of AI”

Reality: In January founder of Facebook Mark Zuckerberg said that we should not be afraid of AI, because it will do an incredible amount of good things for the world. He's half right. We will benefit enormously from AI, from self-driving cars to the creation of new drugs, but there is no guarantee that every AI implementation will be benign.

A highly intelligent system can know everything about specific task, such as solving an unpleasant financial problem or hacking an enemy defense system. But outside the boundaries of these specializations, it will be deeply ignorant and unconscious. Google's DeepMind system is an expert in Go, but it has no ability or reason to explore areas outside its specialization.

Many of these systems may not be subject to security considerations. Good example– a complex and powerful Stuxnet virus, a militarized worm developed by the Israeli and US militaries to infiltrate and sabotage the work of Iranian nuclear power plants. This virus somehow (deliberately or accidentally) infected a Russian nuclear power plant.

Another example is the Flame program, used for cyber espionage in the Middle East. It's easy to imagine future versions of Stuxnet or Flame going beyond their intended purpose and causing massive harm to sensitive infrastructure. (To be clear, these viruses are not AI, but in the future they may have it, hence the concern).

The Flame virus was used for cyber espionage in the Middle East. Photo: Wired

Myth #4: “Artificial superintelligence will be too smart to make mistakes”

Reality: AI researcher and founder of Surfing Samurai Robots Richard Lucimore believes that most AI doomsday scenarios are inconsistent. They are always built on the assumption that the AI is saying: “I know that the destruction of humanity is caused by a failure in my design, but I am forced to do it anyway.” Lucimore says that if an AI behaves like this, reasoning about our destruction, then such logical contradictions will haunt it all its life. This in turn degrades his knowledge base and makes him too stupid to create a dangerous situation. The scientist also argues that people who say: “AI can only do what it is programmed to do” are just as mistaken as their colleagues at the dawn of the computer era. Back then, people used this phrase to argue that computers were not capable of demonstrating the slightest flexibility.

Peter Macintyre and Stuart Armstrong, who work at the Future of Humanity Institute at Oxford University, disagree with Lucimore. They argue that AI is largely bound by how it is programmed. McIntyre and Armstrong believe that AI will not be able to make mistakes or be too stupid to not know what we expect from it.

“By definition, artificial superintelligence (ASI) is a subject with intelligence significantly greater than that of the best human brain in any field of knowledge. He will know exactly what we wanted him to do,” says McIntyre. Both scientists believe that AI will only do what it is programmed to do. But if he becomes smart enough, he will understand how different this is from the spirit of the law or the intentions of people.

McIntyre compared the future situation of humans and AI to the current human-mouse interaction. The mouse's goal is to seek food and shelter. But it often conflicts with the desire of a person who wants his animal to run around freely. “We're smart enough to understand some of the mice's goals. So the ASI will also understand our desires, but be indifferent to them,” says the scientist.

As the plot of the movie Ex Machina shows, it will be extremely difficult for a person to hold onto a smarter AI

Myth #5: “A simple patch will solve the problem of AI control”

Reality: By creating artificial intelligence smarter than humans, we will face a problem known as the “control problem.” Futurists and AI theorists fall into a state of complete confusion if you ask them how we will contain and limit ASI if one appears. Or how to make sure that he will be friendly towards people. Recently, researchers at the Georgia Institute of Technology naively suggested that AI could learn human values and social rules by reading simple stories. In reality, it will be much more difficult.

“There have been a lot of simple tricks proposed that could ‘solve’ the whole AI control problem,” says Armstrong. Examples included programming an ASI so that its purpose was to please people, or so that it simply functioned as a tool in the hands of a person. Another option is to integrate the concepts of love or respect into the source code. To prevent AI from adopting a simplistic, one-sided view of the world, it has been proposed to program it to value intellectual, cultural and social diversity.

But these solutions are too simple, like an attempt to cram all the complexity of human likes and dislikes into one superficial definition. Try, for example, to come up with a clear, logical, and workable definition of “respect.” This is extremely difficult.

The machines in The Matrix could easily destroy humanity

Myth #6: “Artificial intelligence will destroy us”

Reality: There is no guarantee that AI will destroy us, or that we will not be able to find a way to control it. As AI theorist Eliezer Yudkowsky said, “AI neither loves nor hates you, but you are made of atoms that it can use for other purposes.”

In his book “Artificial Intelligence. Stages. Threats. Strategies,” Oxford philosopher Nick Bostrom wrote that true artificial superintelligence, once it emerges, will pose greater risks than any other human invention. Prominent minds like Elon Musk, Bill Gates and Stephen Hawking (the latter of whom warned that AI could be our “worst mistake in history”) have also expressed concern.

McIntyre said that for most purposes that an ASI might have, there are good reasons to get rid of people.

“AI can predict, quite correctly, that we don't want it to maximize the profits of a particular company, no matter what the cost to customers. environment and animals. Therefore, he has a strong incentive to ensure that he is not interrupted, interfered with, turned off, or changed in his goals, since this would prevent his original goals from being achieved,” McIntyre argues.

Unless the ASI's goals closely mirror our own, it will have a good reason to prevent us from stopping it. Considering that his level of intelligence significantly exceeds ours, there is nothing we can do about it.

No one knows what form AI will take or how it might threaten humanity. As Musk noted, artificial intelligence can be used to control, regulate and monitor other AI. Or it may be imbued with human values or an overriding desire to be friendly to people.

Myth #7: “Artificial superintelligence will be friendly”

Reality: The philosopher Immanuel Kant believed that reason was strongly correlated with morality. Neuroscientist David Chalmers in his study “The Singularity: Philosophical analysis” took Kant’s famous idea and applied it to the emerging artificial superintelligence.

If this is true...we can expect an intellectual explosion to lead to a moral explosion. We can then expect that the emerging ASI systems will be super-moral as well as super-intelligent, which allows us to expect good quality from them.

But the idea that advanced AI will be enlightened and kind is not very plausible at its core. As Armstrong noted, there are many smart war criminals. The connection between intelligence and morality does not seem to exist among humans, so he questions the operation of this principle among other intelligent forms.

“Intelligent people who behave immorally can cause much pain large scale than their dumber counterparts. Reasonableness simply gives them the opportunity to be bad with great mind, it doesn’t turn them into good-natured people,” says Armstrong.

As MacIntyre explained, a subject's ability to achieve a goal is not relevant to whether the goal is reasonable to begin with. “We will be very lucky if our AIs are uniquely gifted and their level of morality increases along with their intelligence. Relying on luck is not the best approach for something that could shape our future,” he says.

Myth #8: “The risks of AI and robotics are equal”

Reality: It's special common mistake, propagated by uncritical media and Hollywood films like "Terminator".

If an artificial superintelligence like Skynet really wanted to destroy humanity, it wouldn't use androids with six-barreled machine guns. It would be much more effective to send a biological plague or nanotechnological gray goo. Or simply destroy the atmosphere.

Artificial intelligence is potentially dangerous not because it can affect the development of robotics, but because of how its appearance will affect the world in general.

Myth #9: “The portrayal of AI in science fiction is an accurate representation of the future.”

Many kinds of minds. Image: Eliezer Yudkowsky

Of course, authors and futurists have used science fiction to make fantastic predictions, but the event horizon that ASI establishes is a completely different story. Moreover, the non-human nature of AI makes it impossible for us to know, and therefore predict, its nature and form.

To entertain us stupid people in science fiction most AIs are depicted as similar to us. “There is a spectrum of all possible minds. Even among humans, you are quite different from your neighbor, but that variation is nothing compared to all the minds that can exist,” says McIntyre.

Most science fiction does not have to be scientifically accurate to tell a compelling story. The conflict usually unfolds between heroes of similar strength. “Imagine how boring a story would be where an AI with no consciousness, joy or hatred, ended humanity without any resistance to achieve an uninteresting goal,” Armstrong narrates, yawning.

Hundreds of robots work at the Tesla factory

Myth #10: “It’s terrible that AI will take all our jobs.”

Reality: The ability of AI to automate much of what we do and its potential to destroy humanity are two very different things. But according to Martin Ford, author of The Dawn of the Robots: Technology and the Threat of a Jobless Future, they are often viewed as a whole. It's good to think about the distant future of AI, as long as it doesn't distract us from the challenges we'll face in the coming decades. Chief among them is mass automation.

No one doubts that artificial intelligence will replace many existing jobs, from factory worker to the upper echelons of white-collar workers. Some experts predict that half of all US jobs are at risk of automation in the near future.

But this does not mean that we cannot cope with the shock. In general, getting rid of most of our work, both physical and mental, is a quasi-utopian goal for our species.

“AI will destroy a lot of jobs within a couple of decades, but that's not a bad thing,” Miller says. Self-driving cars will replace truck drivers, which will reduce delivery costs and, as a result, make many products cheaper. “If you are a truck driver and make a living, you will lose, but everyone else, on the contrary, will be able to buy more goods for the same salary. And the money they save will be spent on other goods and services that will create new jobs for people,” says Miller.

In all likelihood, artificial intelligence will create new opportunities for producing goods, freeing people to do other things. Advances in AI will be accompanied by advances in other areas, especially manufacturing. In the future, it will become easier, not harder, for us to meet our basic needs.