Artificial intelligence (AI) Who Invented the Turing Test? Turing test questions Turing edu index php t

TURING

TURING(Turing) Alan (1912-54), English mathematician and logician, who formulated the theories that later became the basis of computer technology. In 1937 he came up with Turing machine - a hypothetical machine capable of converting a set of input commands. She was the harbinger of modern computers. Turing also used the idea of ​​a computer to give an alternative and simpler proof of Gödel's incompleteness theorem. Turing played a major role in unraveling the Enigma, a complex encryption method used by Germany during World War II. In 1948, he participated in the creation of one of the world's first computers. In 1950 he came up with Turing test - it was supposed to be a test of a computer's ability to "think". In essence, it stated that a person would not be able to distinguish between a dialogue with a machine and a dialogue with another person. This work paved the way for the creation of ARTIFICIAL INTELLIGENCE. Turing was also involved in theoretical biology. In work "The chemical basis of morphogenesis"(1952) he proposed a model describing the origin of various organismal patterns in biology. Since then, such models have often been used to describe and explain many systems observed in nature. Turing committed suicide after being formally accused of homosexuality.


Scientific and technical encyclopedic dictionary.

See what "TURING" is in other dictionaries:

    Turing, Alan Mathison Alan Turing Alan Mathison Turing Monument in Sackville Park Date of birth ... Wikipedia

    - (Turing) Alan Mathison (1912 54), English mathematician. In 1936 1937 he introduced the mathematical concept of an abstract equivalent of an algorithm, or a computable function, which later became known as a Turing machine ... Modern Encyclopedia

    - (Turing), Alan Mathison (June 23, 1912 - June 7, 1954) - English. logician and mathematician. In 1936–37, he proposed an idealized machine model to calculate. process - a computational scheme close to the actions of a person performing calculations, and put forward ... ... Philosophical Encyclopedia

    Turing A.- Turing A. English mathematician. Topics information security EN Turing … Technical Translator's Handbook

    Alan Turing Alan Turing Monument in Sackville Park Date of birth: June 23, 1912 Place of birth: London, England Date of death: June 7, 1954 ... Wikipedia

    Turing- English mathematician Alan M. Turing, one of the founders logical foundations computer technology, in particular, gave one of the formal definitions of the algorithm; proved that there is a class of computers that can imitate ... ... Lem's world - dictionary and guide

    - (Turing) Alan Mathison (6/23/1912, London, 6/7/1954, Wilmslow, near Manchester), English mathematician. Fellow of the Royal Society (1951). After graduating from Cambridge University (1935), he worked on his doctoral dissertation at Princeton ... ... Great Soviet Encyclopedia

    Turing A.M.- TURING (Turing) Alan Mathison (1912–54), Eng. mathematician. Main tr. by math. logic, calculation. mathematics. In 1936–37 he introduced mathematics. the concept of an abstract equivalent of an algorithm, or a computable function, which was then called. machine T... Biographical Dictionary

    - (full. Alan Mathison Turing, Alan Mathison Turing) (June 23, 1912, London June 7, 1954, Wilmslow, UK), British mathematician, author of works on mathematical logic, computational mathematics. In 1936-1937 he introduced the mathematical concept ... encyclopedic Dictionary

Books

  • Can a machine think? General and logical theory of automata. Issue 14, Turing A., This book, containing the work of Alan Turing and John von Neumann, who were at the origins of the creation of the first thinking machines of computers, belongs to the classics of philosophical and cybernetic ... Category: Databases Series: Artificial Sciences Publisher: URSS, Manufacturer: URSS,
  • Can a machine think? General and logical theory of automata. Issue No. 14, Turing A., This book, containing the works of Alan Turing and John von Neumann, who were at the origins of the creation of the first "thinking machines" of computers, belongs to the classics of the philosophical and cybernetic direction ... Category:

FEDERAL AGENCY FOR EDUCATION STATE EDUCATIONAL INSTITUTION OF HIGHER PROFESSIONAL EDUCATION "VORONEZH STATE UNIVERSITY" T.K. Katsaran, L.N. Stroeva TURING MACHINE AND RECURSIVE FUNCTIONS Tutorial for universities Publishing and printing center of the Voronezh state university 2008 Approved by the scientific and methodological council of the PMM faculty on May 25, 2008, protocol No. 9 Reviewer Doctor of Technical Sciences, prof. Department of Mathematical Methods for Operations Research T.M. Ledeneva The textbook was prepared at the Department of Nonlinear Oscillations of the PMM Faculty of Voronezh State University. Recommended for 1st year students of the PMM Faculty of VSU, Starooskolsky and Liskinsky branches of VSU. For the specialty 010500 - Applied Mathematics and Informatics INTRODUCTION The word "algorithm" comes from algorithmi - the Latin spelling of the name of the Uzbek mathematician and astronomer, who lived in the VIII-IX centuries (783-850), Muhammad bin Musa al-Khwarizmi. Under this name in Medieval Europe they knew the greatest mathematician from Khorezm (a city in modern Uzbekistan). In his book On Indian Counting, he formulated the rules for writing natural numbers using Arabic numerals and the rules for working with them. Then the concept of an algorithm began to be used in a broader sense and not only in mathematics. For both mathematicians and practitioners, the concept of an algorithm is important. Thus, we can say that an algorithm is an exact prescription for the execution in a certain order of a certain system of operations for solving all problems of the same type, which determines the sequence of actions that provides the required result from the initial data. Note that this is not a definition of the concept of "algorithm", but only its description, its intuitive meaning. The algorithm can be designed to be executed by both a human and an automatic device. This representation of the algorithm is not rigorous from a mathematical point of view, since it uses such concepts as “exact prescription” and “initial data”, which, generally speaking, are not strictly defined. A feature of any algorithm is its ability to solve a certain class of problems. For example, it can be an algorithm for solving systems of linear equations, finding the shortest path in a graph, etc. Creating an algorithm, even the simplest one, is a creative process. It is available exclusively to living beings, and for a long time it was believed that only to humans. Another thing is the implementation of an already existing algorithm. It can be entrusted to a subject or object, which is not obliged to delve into the essence of the matter, and perhaps not able to understand it. Such a subject or object is usually called a formal executor. An example of a formal performer is an automatic washing machine, which strictly performs its prescribed actions, even if you forgot to put powder in it. A person can also act as a formal executor, but first of all, formal executors are various automatic devices, including a computer. Each algorithm is created based on a very specific performer. Those actions that the performer can perform are called his admissible actions. The set of admissible actions forms a system of commands for the executor. The algorithm should contain only those actions that are valid for a given executor. Therefore, several general properties of algorithms are usually formulated, which make it possible to distinguish algorithms from other instructions. The algorithm must have the following properties. Discreteness (discontinuity, separation) - the algorithm should represent the process of solving the problem as a sequential execution of simple (or previously defined) steps. Each action provided by the algorithm is executed only after the execution of the previous one has ended. Certainty - each rule of the algorithm should be clear, unambiguous and leave no room for arbitrariness. Due to this property, the execution of the algorithm is mechanical in nature and does not require any additional instructions or information about the problem being solved. Efficiency (finiteness) – the algorithm should lead to the solution of the problem in a finite number of steps. 4 Mass character - the algorithm for solving the problem is developed in a general form, that is, it must be applicable to a certain class of problems that differ only in the initial data. In this case, the initial data can be selected from a certain area, which is called the area of ​​applicability of the algorithm. The theory of algorithms is a branch of mathematics that studies the general properties of algorithms. There are qualitative and metric theory of algorithms. The main problem of the qualitative theory of algorithms is the problem of constructing an algorithm with given properties. Such a problem is called algorithmic. The metric theory of algorithms examines an algorithm in terms of their complexity. This branch of the theory of algorithms is also known as algorithmic complexity theory. When looking for solutions to some problems, it took a long time to find an appropriate algorithm. Examples of such tasks are: a) to indicate a method according to which for any predicate formula in a finite number of actions it is possible to find out whether it is identically true or not; b) is the Diophantine equation (an algebraic equation with integer coefficients) solvable in integers? Since it was not possible to find algorithms for solving these problems, the assumption arose that such algorithms do not exist at all, which is proved: the first problem was solved by A. Church, and the second by Yu.V. Matiyasevich and G.V. Chudnovsky. In principle, it is impossible to prove this using the intuitive concept of an algorithm. Therefore, attempts were made to give an exact mathematical definition of the concept of an algorithm. In the mid-1930s, S.K. Kleene, A.A. Markov, E. Post, A. Turing, A. Church and others have proposed various mathematical definitions of the 5 concept of an algorithm. Subsequently, it was proved that these different formal mathematical definitions are in some sense equivalent: they calculate the same set of functions. This suggests that, apparently, the main features of the intuitive concept of the algorithm are correctly reflected in these definitions. Next, consider the mathematical refinement of the algorithm proposed by A. Turing, which is called the Turing machine. 6 1. TURING MACHINE § 1. Mathematical model of a Turing machine The idea of ​​creating a Turing machine, proposed by the English mathematician A. Turing in the thirties of the XX century, is connected with his attempt to give an exact mathematical definition of the concept of an algorithm. A Turing machine (MT) is a mathematical model of an idealized digital computer. A Turing machine is the same mathematical object as a function, derivative, integral, group, etc. Just like other mathematical concepts, the concept of a Turing machine reflects objective reality, models some real processes. To describe the MT algorithm, it is convenient to represent a certain device consisting of four parts: a tape, a reading head, a control device, and an internal memory. 1. The tape is assumed to be potentially infinite, divided into cells (equal cells). If necessary, an empty cell is attached to the first or last cell in which the symbols are located. The machine operates in time, which is considered discrete, and its moments are numbered 1, 2, 3, … . At each moment, the tape contains a finite number of cells. Only one character (letter) from the external alphabet A = (L, a1 , a 2 ,..., a n -1 ), n ³ 2 , can be written into the cells at a discrete moment of time. An empty cell is denoted by the symbol L, and the symbol L itself is called empty, while the remaining symbols are called non-empty. In this alphabet A, in the form of a word (a finite ordered set of characters), the information that is fed to the MT is encoded. The machine "processes" the information given in the form of a word into a new word. 2. The reading head (some reading element) moves along the tape in such a way that at each moment of time it scans 7 exactly one cell of the tape. The head can read the contents of the cell and write a new character from the alphabet A into it. In one cycle of operation, it can only move one cell to the right (R), left (L), or remain in place (H). Let us denote the set of displacements (shifts) of the head D = (P, L, N). If in this moment time t the head is in the last cell and moves to the missing cell, then a new empty cell is added, over which the head will be at the moment t + 1 . 3. The internal memory of the machine is a certain finite set of internal states Q = ( q0 , q1 , q 2 , ..., q m ), m ³ 1 . We will assume that the power |Q | ³ 2. Two states of the machine have a special meaning: q1 is the initial internal state (there can be several initial internal states), q0 is the final state or stop state (the final state is always one). At each moment of time, the MT is characterized by the position of the head and the internal state. For example, under the cell above which the head is located, the internal state of the machine is indicated. ¯ a2 a1 L a2 a3 q1 4. Depending on the character read at that moment on the tape and the internal state of the machine, the control device performs the following actions at each moment t: (in particular, leaves it unchanged, i.e., ai = aj); 2) moves the head in one of the following directions: N, L, P; 3) changes the internal state of the machine 8 qi at the moment t to a new one q j , in which the car will be at the moment t +1 (it may be that qi = q j). Such actions of the control device are called a command, which can be written as: qi ai ® a j D q j , (1) where qi is the internal state of the machine at the moment; a i is the symbol being read at this moment; a j is the symbol to which the symbol a i is changed (it can be ai = a j); the symbol D is either H, or L, or P and indicates the direction of movement of the head; q j is the internal state of the machine at the next moment (maybe qi = q j). The expressions qi ai and a j D q j are called the left and right parts of this command, respectively. The number of teams in which the left parts are pairwise distinct is a finite number, since the sets Q \ (q 0 ) and A are finite. There are no instructions with identical left-hand sides, i.e. if the program of the machine T contains the expressions qi ai ® aj D qj and qt at ® ak D qk , then qi ¹ qt or ai ¹ at and D O (P, L, N ) . The set of all instructions is called the Turing machine program. The maximum number of instructions in a program is (n + 1) x m , where n + 1 = A and m + 1 = Q . It is believed that the final state of the command q0 can only be on the right side of the command, the initial state q1 can be both on the left and on the right side of the command. The execution of a single command is called a step. The calculation (or operation) of a Turing machine is a sequence of steps one after the other without gaps, starting from the first one. So, MT is given if four finite sets are known: the external alphabet A , the internal alphabet Q , the set D of head movements and the machine program, which is a finite set of instructions. 9 § 2. The operation of the Turing machine The operation of the machine is completely determined by the task at the first (initial) moment: 1) words on the tape, i.e. sequences of characters written in the cells of the tape (the word is obtained by reading these characters in the cells of the tape from left to right) ; 2) the position of the head; 3) the internal state of the machine. The combination of these three conditions (at the moment) is called the configuration (at the moment). Usually at the initial moment the internal state of the machine is q1 , and the head is either above the first from the left, or above the first from the right cell of the tape. The given word on the tape with the initial state q1 and the position of the head above the first word is called the initial configuration. Otherwise, we say that the Turing machine is not applicable to the word of the initial configuration. In other words, at the initial moment, the configuration can be represented as follows: on a tape consisting of a certain number of cells, each cell contains one of the symbols of the external alphabet A , the head is located above the first left or first right cell of the tape and the inner the state of the car is q1 . The word on the tape and the position of the head resulting from the implementation of this command is called the final configuration. For example, if at the initial moment the word a1La 2 a1a1 is written on the tape, then the initial configuration will look like: a1 a2 L a1 a1 q1 (the internal state of the machine is indicated under the cell over which the head is located). 10

ARTIFICIAL INTELLIGENCE

The Turing test is known to every person interested in artificial intelligence. It was formulated in 1938 by Alan Turing in the article “Can a machine think?”. The test is as follows. The experimenter communicates with the interlocutor without seeing him (for example, via a computer network), typing phrases on the keyboard and receiving a text response on the monitor. He then tries to determine who he was talking to. If an experimenter takes a computer program for a human, then it has passed the Turing test and can be considered intelligent.

A person will get a gold medal

Most famous program, which showed the real possibility of passing this test back in the 60s, was the legendary ELIZA. It was created in 1966 by scientists Winograd, Weizenbaum and Colby. ELIZA found key words in the phrase (for example, “mother”) and issued a template request, mechanically reacting to these words (“Tell me more about your mother”). Subsequently, Toddy Winograd based on ELIZA created a more advanced version of "Psychotherapist". The advent of ELIZA entered the history of artificial intelligence along with such events as the release of the first industrial robot in 1962 or the beginning of Pentagon funding for development in the field of pattern recognition and speech in 1975-1976.

In 1991, for the first time, a private but very respectable Turing test tournament was held, to which the authors of suitable computer programs (called bots) were invited. This tournament was founded by Hugh Loebner (www.loebner.net/Prizef/loebner-prize.html). A prize of $100,000 and a gold medal were relied upon to win it.

So far, no one has won the top prize. However, in 1994, Loebner made a major change to the rules, requiring that the program communicate with the judge not only in text format, but also by generating an image of a virtual person. She must also be able to synthesize and recognize speech information. These conditions were considered by many to be extremely difficult, and so far no contenders for the main prize have appeared under the new rules. And for the victory in the old, “text” mode, $25,000 and a silver medal are now promised. It should be noted that the probability of a judge's subjective error when communicating with the program according to the old rules is quite high. In addition, bots are improving quite quickly, and we will probably wait for the winner in the Turing test in the coming years.

Judging at the competition is very strict. Experts prepare in advance for the tournament and select very tricky questions in order to understand who they are talking to. Their conversation with the programs resembles the interrogation of the investigator. Judges like, for example, to repeat some questions after a certain time, since weak bots do not know how to follow the history of the dialogue and can be caught on monotonous answers.

But the developers do not waste time, setting up their virtual wards in such a way that provocative questions like “are you a machine or a person?” the program very convincingly answered: “Of course, man!”.

On November 8, 1991, at the Boston Computer Museum, eight programs went head-to-head with live experts. The result was stunning. Five out of ten judges decided that a person was hiding under the pseudonym Whimsical Conversation. It was actually Therapist, running an IBM PC 386 and created in 1986 based on the legendary ELIZA by Joseph Weintraub, a psychologist. However, this result is largely due to the skepticism and prejudice of experts who believed that programs communicate at a much more primitive level than it was in reality. In the future, the judges got used to the computer style of communication, and since then the bots have not been able to achieve such a successful result.

Therapist originally consisted of seven modules written in C, Turbo-Prolog, and QuickBasic. Later it was supplemented by a speech synthesis system and a knowledge base. The program uses a dictionary of 70,000 words and remembers everything that is told to it, using this information in subsequent dialogues and adjusting to the interests of a particular interlocutor. The Therapist has won the Loebner Prize back in 1992, 1993 and 1995. It was then reinforced with specialized modules capable of supporting conversations on the topics of "man against woman" and "liberals against conservatives".

Here is an example from one of her winning dialogues:

Judge: Do you think that American soldiers should go to Bosnia?

Therapist: I think that what clinton has done in sending 20,000 peacekeepers to bosnia will ensure a lasting peace.

Judge: Do you think most Americans feel the way you do?

Therapist: What did you mean when you said do you think that american soldiers should go to bosnia? Did anyone ever tell you that you think too much? Oh, I think most americans feel the way i do?

In 1994, Thomas Wylen, a doctor of experimental psychology who specializes in the study of natural languages, won the tournament. His TIPS program was trained to store and parse phrases (there were several thousand in the database) and search for lexical patterns.

In 1996, Jason Hutchence and his SEPO bot won a bronze medal, which communicated something like this:

Judge: I think. I do research. I write. I love. I live.

SEPO: I wonder whether you think sometimes! I am feeling like the judge here!

In 1997, David Levy became the champion with the Converse bot. In 1998 and 1999, the winner was Robbie Garnet, who believes that passing the Turing test does not require real intelligence. It is interesting that almost all the authors, mentioning scientific approaches to learning (neural networks, genetic algorithms), managed in their decisions with fairly simple heuristic methods.

Garnet entered the Computational Behaviorist program, based on a stimulus-response approach similar to that of TIPS and ELIZA. Only his bot was looking for not one, but up to three keywords in a phrase. At the same time, realizing that the program required something more than a monotonous answer to questions, he built into it a number of additional heuristic algorithms that created a more complete illusion of communication with a person.

During the development of Behaviorist, technical difficulties arose due to the complexity of implementing knowledge search in large databases at that time, which led to noticeable time delays in communication, which immediately gave out a computer interlocutor. Therefore, Garnet combined two public bots - Albert, written in C ++, and one of the Pascal versions of ELIZA and implemented them in the Visual DataFlex development environment, which allowed using standard database query algorithms.

In 2000 and 2001, the small prize went to Richard Wallace's ALICE program. Today, on the basis of ALICE, the ALICE AI Foundation (http://alice.sunlitsurf.com/) is organized, which is engaged in the standardization of activities for creating bots. In particular, ALICE is supplemented by means of supporting a database in the AIML format (Artificial Intelligence Markup Language) - a subset of XML aimed at formalizing the presentation of key phrases and answers. Now anyone who is unfamiliar with programming can take the basic version of ALICE and fill it with their own knowledge base in any language using a regular editor.

Unfortunately, this summer, as Wired reported, Mr. Wallace began to have mental problems (he threatened one of his fellow professors with physical violence, claiming that corruption is rampant in a number of American universities and that a large-scale conspiracy). While the scientist is under investigation.

One of the most likely contenders for victory this year (the tournament will be held in October) is Smith Joshua, the author of the Anna program (AIML extension of ALICE, freely available at http://annabot.sourceforge.net/). Mr. Joshua notes that, unlike his colleagues, he created a bot from the very beginning, impersonating a person in the process of communication. Anna really considers herself a living being, has a set of individual qualities and is quite brisk in conversation.

Are there similar Russian developments - bots that can communicate in Russian? The editors of PC Week / RE are ready to hold a Russian competition for passing the Turing test. Write to the author at: [email protected]

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

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 to manage environment. 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.

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

2019: ISO/IEC experts supported the proposal to develop a standard in Russian

On April 16, 2019, it became known that the ISO / IEC subcommittee on standardization in the field of artificial intelligence supported the proposal of the Cyber-Physical Systems Technical Committee, created on the basis of RVC, to develop the standard "Artificial intelligence. Concepts and terminology" in Russian in addition to the basic English version.

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 as part 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 credibility and 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 on 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, - 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.

In the field of artificial intelligence, TC Cyber-Physical Systems, based on 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 AI technologies appear as new sub-sectors of the economy and separate entities, while simultaneously serving most areas in 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 service delivery level: improving fleet route planning Vehicle, 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 send them specialized offers.

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 brewing

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 Vladimir Krylov, Doctor of Technical Sciences, technical consultant of Artezio.

The scientist believes that the AI ​​solutions that already exist can be successfully applied in various areas 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. Reports of scientific research are constantly interspersed with reports of 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

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 a large number various 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.

The Turing test, proposed by Alan Turing, has been developed as a satisfactory functional definition of intelligence. Turing decided that there was no point in developing an extensive list of requirements necessary for the creation of artificial intelligence, which, moreover, could be contradictory, and proposed a test based on the fact that the behavior of an object with artificial intelligence would eventually be indistinguishable from the behavior of such undeniably intelligent beings, like human beings. The computer will pass this test if the human experimenter, having asked it certain questions in writing, cannot determine whether the written answers are from another person or from some device.

Solving the problem of writing a program for a computer to pass this test requires a lot of work. A computer programmed in this way must have the following capabilities:

  • Facilities natural language text processing(Natural Language Processing - NLP), allowing you to successfully communicate with a computer, say in English.
  • Facilities knowledge representation, with which the computer can write to memory what it learns or reads.
  • Facilities automatic generation of logical conclusions, providing the ability to use the stored information to find answers to questions and draw new conclusions.
  • Facilities machine learning, which allow you to adapt to new circumstances, as well as to detect and extrapolate signs of standard situations.

In the Turing test, the direct physical interaction of the experimenter and the computer is deliberately excluded, since the creation of artificial intelligence does not require a physical imitation of a person. But in the so-called Turing complete test the use of a video signal is envisaged so that the experimenter can check the ability of the test object to perceive, and also be able to present physical objects "in an incomplete form" (pass them "through hatching"). To pass the Turing Complete Test, a computer must have the following abilities:

  • machine vision to perceive objects.
  • Facilities robotics for manipulating objects and moving in space.

The six lines of research listed in this section make up the bulk of artificial intelligence, and Turing deserves our thanks for proposing a test that is still relevant 50 years later. Nevertheless, artificial intelligence researchers practically do not deal with the problem of passing the Turing test, believing that it is much more important to learn the fundamental principles of intelligence than to duplicate one of the carriers of natural intelligence. In particular, the problem of "artificial flight" was successfully solved only after the Wright brothers and other researchers stopped imitating birds and began to study aerodynamics. In scientific and technical works on aeronautics, the goal of this area of ​​​​knowledge is not defined as "the creation of machines that, in their flight, are so reminiscent of doves that they can even deceive real birds."

Loading...Loading...