Artificial intelligence its capabilities and potential presentation. Presentation on the topic: Artificial intelligence. How intelligent systems are created
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A presentation on the topic "Artificial Intelligence" (Grade 8) can be downloaded absolutely free of charge on our website. Project subject: Informatics. Colorful slides and illustrations will help you keep your classmates or audience interested. To view the content, use the player, or if you want to download the report, click on the appropriate text under the player. The presentation contains 11 slide(s).
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Artificial Intelligence
The problem of creating the human mind
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How does a person think?
Scientists from all over the world are thinking about this question. The goal of their research is to create a model of human intelligence and implement it on a computer. Slightly simplified, the above named goal sounds like this: - To teach the machine to think.
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The purpose of creating artificial intelligence
construction of a universal computer intelligent system designed to solve certain types of problems, which would find solutions to all (or at least most) non-formalized problems, with efficiency comparable to human or superior to it
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Main approaches to AI development:
top-down (English Top-Down AI), semiotic - the creation of expert systems, knowledge bases and inference systems that imitate high-level mental processes: thinking, reasoning, speech, emotions, creativity, etc.; bottom-up AI, biological - the study of neural networks and evolutionary calculations that model intelligent behavior based on biological elements, as well as the creation of appropriate computing systems, such as a neurocomputer or biocomputer.
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Human activities
There are many human activities that cannot be programmed in advance. For example: composing music and poetry, proving a theorem, literary translation from a foreign language, diagnosing and treating a disease, and much more.
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Can a machine think on its own?
The developers of AI systems are just trying to teach the machine, like a person, to independently build a program of its actions, based on the conditions of the problem. The goal is to transform the computer from a formal executor into an intellectual executor.
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How intelligent systems are created
Artificial intelligence systems operate on the basis of the knowledge bases embedded in them, and human thinking is based on two components: a stock of knowledge and the ability to reason logically. Therefore, to create intelligent systems on a computer, two tasks must be solved: knowledge modeling (development of knowledge formalization methods for entering them into computer memory as a knowledge base); reasoning modeling (creation of computer programs that imitate the logic of human thinking when solving various problems).
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The main areas in which AI methods are applied:
Image Recognition Optical Character Recognition Handwriting Recognition Speech Recognition Face Recognition Natural Language Processing Machine Translation Nonlinear Control and Robotics Machine Vision, Virtual Reality and Image Processing Game Theory and Strategic Planning AI Diagnostics in Games and Bots in Computer Games Machine Creativity Network Security
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Functioning models of formal and intellectual executor
Artificial Intelligence Artificial intelligence is 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. Other definitions of artificial intelligence: O A scientific direction within which the tasks of hardware or software modeling of those types of human activity that are traditionally considered intellectual are set and solved. O The property of intelligent systems to perform functions that are traditionally considered the prerogative of a person. At the same time, an intellectual system is a technical or software system capable of solving problems that are traditionally considered creative, belonging to a specific subject area, knowledge about which is stored in the memory of such a system. O The science called "Artificial Intelligence" is included in the complex of computer science, and the technologies created on its basis are part of information technology. The task of this science is to recreate rational reasoning and actions with the help of computer systems and other artificial devices.
Origin and understanding of the term "Artificial Intelligence" Various types and degrees of intelligence exist in many people, animals and some machines, intelligent information systems and various models of expert systems with different knowledge bases. At the same time, as we can see, such a definition of intelligence is not related to the understanding of intelligence in humans, these are different things. Moreover, this science models human intelligence, because on the one hand, you can learn something about how to make machines solve problems by observing other people, and on the other hand, most of the work in AI concerns the study of problems that humanity needs to solve. in the industrial and technological sense. Therefore, AI researchers are free to use methods that are not observed in humans, if necessary to solve specific problems. It was in this sense that the term was introduced by John McCarthy in 1956 at a conference at Dartmouth University. One of the private definitions of intelligence, common to a person and a “machine”, can be formulated as follows: “Intelligence is the ability of a system to create programs during self-learning to solve problems of a certain class of complexity and solve these problems.”
Artificial intelligence in Russia Collegiate adviser S. N. Korsakov can rightly be considered the pioneer of artificial intelligence, who set the task of strengthening the capabilities of the mind through the development of scientific methods and devices, echoing the modern concept of artificial intelligence as an amplifier of the natural. Work in the field of artificial intelligence in Russia began in the 1990s, headed by Veniamin Pushkin and D. A. Pospelov. Until the 1990s, all AI research in the USSR was carried out within the framework of cybernetics. Only at the end of the 1990s in the USSR they began to talk about the scientific direction "artificial intelligence" as a branch of computer science. At the end of x, an explanatory dictionary on artificial intelligence, a three-volume reference book on artificial intelligence and an encyclopedic dictionary on computer science are created, in which the sections "Cybernetics" and "Artificial Intelligence" are part of computer science along with other sections.
Prerequisites for the development of the science of artificial intelligence 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 disputes about the nature of man and the process of knowing the world, neurophysiologists and psychologists developed a number of theories regarding the work of the human brain and thinking, economists and mathematicians asked questions of optimal calculations and representation of knowledge about the world in formalized form; finally, the foundation of the mathematical theory of computation, 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 crept into the scientific community: what are the limits of the capabilities of computers and will machines reach the level of human development? In 1950, one of the pioneers in the field of computer technology, the English scientist Alan Turing, writes an article entitled “Can a machine think?” In which he describes a procedure by which it will be possible to determine the moment when a machine becomes equal in terms of intelligence with a person, called the Turing test.
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 in it, considering the achievements of this science in its light. Despite the presence of many approaches both to understanding the tasks of AI and to creating intelligent information systems, two main approaches to the development of AI can be distinguished: speech, emotions, creativity, etc.; O ascending, biological study of neural networks and evolutionary computations that model intelligent behavior based on biological elements, as well as the creation of appropriate computing systems, such as a neurocomputer or biocomputer. The latter approach, strictly speaking, does not apply to the science of AI in the sense given by John McCarthy, they are united only by a common ultimate goal.
Turing test and intuitive approach An empirical test, the idea of which was proposed by Alan Turing in the article "Computing Machines and the Mind", published in 1950 in a philosophical journal. The purpose of this test is to determine the possibility of artificial thinking, close to human. The standard interpretation of this test is as follows: “A person interacts with one computer and one person. Based on the answers to the questions, he must determine with whom he is talking: with a person or a computer program. The task of a computer program is to mislead a person into making the wrong choice.” All test participants do not see each other. Unintelligent Human Behavior Intelligent Behavior But Humans Don't Do It The Turing Test Human Behavior Intelligent Behavior
The symbolic approach Historically, the symbolic approach was the first in the era of digital computers, since it was after the creation of Lisp, the first language of symbolic calculations, that its author became confident in the possibility of practically starting to implement these means of intelligence. The symbolic approach allows one to operate with weakly formalized representations and their meanings. The ability to highlight only essential information depends on the effectiveness and efficiency of solving the problem. The main application of symbolic logic is the solution of problems on the development of rules. Most of the research focuses precisely on the impossibility of at least designating the new difficulties that have arisen by means of the symbolic systems chosen at the previous stages. Especially to solve them and even more so to train the computer to solve them, or at least identify and get out of such situations.
Logical approach The logical approach to the creation of artificial intelligence systems is aimed at creating expert systems with logical models of knowledge bases using the predicate language. In the 1990s, the logical programming language and system Prolog was adopted as the educational model of artificial intelligence systems. Knowledge bases written in the Prolog language represent sets of facts and inference rules written in the language of logical predicates. The logical model of knowledge bases allows you to record not only specific information and data in the form of facts in the Prolog language, but also generalized information using the rules and procedures of inference, including logical rules for defining concepts that express certain knowledge as specific and generalized information. In general, research into the problems of artificial intelligence within the framework of a logical approach to the design of knowledge bases and expert systems is aimed at the creation, development and operation of intelligent information systems, including the issues of teaching students and schoolchildren, as well as training users and developers of such intelligent information systems.
Agent-Based Approach The latest approach, developed since the early 1990s, is called the agent-based approach, or the intelligent agent approach. According to this approach, intelligence is the computational part of the ability to achieve the goals set for an intelligent machine. Such a machine itself will be an intelligent agent, perceiving the world around it with the help of sensors, and capable of influencing objects in the environment with the help of actuators. This approach focuses on those methods and algorithms that will help an intelligent agent survive in the environment while performing its task. So, pathfinding and decision-making algorithms are much more studied here. An illustration of the principle of finding a path in two-dimensional space
Applications of artificial intelligence Some of the most famous AI systems: O 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. The IBM line of supercomputers then manifested itself in the molecular modeling and pyramidal cell system modeling projects at the Swiss Blue Brain Center. O MYCIN is one of the early expert systems that could diagnose a small subset of diseases, often as accurately as doctors. O 20Q is an AI-inspired project inspired by the classic 20 Questions game. Became very popular after appearing on the Internet at 20q.net O Speech recognition. Systems such as ViaVoice are capable of serving consumers. o Robots in the annual RoboCup tournament compete in a simplified form of football.
Prospects for artificial intelligence Two directions for the development of AI can be distinguished: O solving problems related to the approximation of specialized AI systems to human capabilities, and their integration, which is realized by human nature O creation of artificial intelligence, representing the integration of already created AI systems into a single system capable of solving problems humanity
Conclusion Many disputes around the problem of creating artificial intelligence are emotionally motivated. Recognition of the possibility of artificial intelligence seems to be something degrading to human dignity. However, questions of the possibilities of artificial intelligence should not be confused with the question of the development and improvement of the human mind. The widespread use of AI creates the preconditions for the transition to a qualitatively new stage of progress, gives impetus to a new round of production automation, and hence an increase in labor productivity. Of course, artificial intelligence can be used for unsuitable purposes, but this is not a scientific problem, but rather a moral and ethical one.
Artificial Intelligence It is the science and development of intelligent machines and systems, especially intelligent computer programs, aimed at understanding human intelligence. However, the methods used do not have to be biologically plausible. It is the science and development of intelligent machines and systems, especially intelligent computer programs, aimed at understanding human intelligence. However, the methods used do not have to be biologically plausible. But the problem is that we don't know what computational procedures we want to call intelligent. And since we understand only some of the mechanisms of intelligence, then by intelligence within this science we understand only the computational part of the ability to achieve goals in the world. But the problem is that we don't know what computational procedures we want to call intelligent. And since we understand only some of the mechanisms of intelligence, then by intelligence within this science we understand only the computational part of the ability to achieve goals in the world.
Logical approach Aimed at creating expert systems with logical models of knowledge bases using the predicate language. Aimed at creating expert systems with logical models of knowledge bases using the predicate language. The language and system of logical Prolog was adopted as the training model for artificial intelligence systems in the 1980s. Knowledge bases written in the Prolog language represent sets of facts and inference rules written in the language of logical predicates. The language and system of logical Prolog was adopted as the training model for artificial intelligence systems in the 1980s. Knowledge bases written in the Prolog language represent sets of facts and inference rules written in the language of logical predicates. The logical model of knowledge bases allows you to record not only specific information and data in the form of facts in the Prolog language, but also generalized information using the rules and procedures of inference, including logical rules for defining concepts that express certain knowledge as specific and generalized information. The logical model of knowledge bases allows you to record not only specific information and data in the form of facts in the Prolog language, but also generalized information using the rules and procedures of inference, including logical rules for defining concepts that express certain knowledge as specific and generalized information. In general, research into the problems of artificial intelligence within the framework of a logical approach to the design of knowledge bases and expert systems is aimed at the creation, development and operation of intelligent information systems, including the issues of teaching students and schoolchildren, as well as training users and developers of such intelligent information systems. In general, research into the problems of artificial intelligence within the framework of a logical approach to the design of knowledge bases and expert systems is aimed at the creation, development and operation of intelligent information systems, including the issues of teaching students and schoolchildren, as well as training users and developers of such intelligent information systems.
Agent-Based Approach The latest approach, developed since the early 1990s, is called the agent-based approach, or the approach based on the use of intelligent (rational) agents. According to this approach, intelligence is the computational part (roughly speaking, planning) of the ability to achieve the goals set for an intelligent machine. Such a machine itself will be an intelligent agent, perceiving the world around it with the help of sensors, and capable of influencing objects in the environment with the help of actuators. The latest approach, developed since the early 1990s, is called the agent-based approach, or the approach based on the use of intelligent (rational) agents. According to this approach, intelligence is the computational part (roughly speaking, planning) of the ability to achieve the goals set for an intelligent machine. Such a machine itself will be an intelligent agent, perceiving the world around it with the help of sensors, and capable of influencing objects in the environment with the help of actuators. This approach focuses on those methods and algorithms that will help an intelligent agent survive in the environment while performing its task. So, pathfinding and decision-making algorithms are much more studied here. This approach focuses on those methods and algorithms that will help an intelligent agent survive in the environment while performing its task. So, pathfinding and decision-making algorithms are much more studied here.
Intuitive approach An empirical test, the idea of which was proposed by Alan Turing, in the article "Computing Machinery and Intelligence" (eng. Computing Machinery and Intelligence), published in 1950 in the philosophical journal "Mind". The purpose of this test is to determine the possibility of artificial thinking, close to human. An empirical test, the idea of which was proposed by Alan Turing, in the article "Computing Machinery and Intelligence" (eng. Computing Machinery and Intelligence), published in 1950 in the philosophical journal "Mind". The purpose of this test is to determine the possibility of artificial thinking close to human. The standard interpretation of this test is as follows: “A person interacts with one computer and one person. Based on the answers to the questions, he must determine with whom he is talking: with a person or a computer program. The task of a computer program is to mislead a person, forcing him to make the wrong choice. All test participants do not see each other. The standard interpretation of this test is as follows: “A person interacts with one computer and one person. Based on the answers to the questions, he must determine with whom he is talking: with a person or a computer program. The task of a computer program is to mislead a person, forcing him to make the wrong choice. All test participants do not see each other. The most general approach assumes that AI will be able to exhibit behavior that is no different from human, moreover, in normal situations. This idea is a generalization of the Turing test approach, which states that a machine will become intelligent when it is able to carry on a conversation with an ordinary person, and he will not be able to understand that he is talking to the machine (the conversation is carried out by correspondence). The most general approach assumes that AI will be able to exhibit behavior that is no different from human, moreover, in normal situations. This idea is a generalization of the Turing test approach, which states that a machine will become intelligent when it is able to carry on a conversation with an ordinary person, and he will not be able to understand that he is talking to the machine (the conversation is carried out by correspondence).
Turing test The standard interpretation of this test is as follows: “A person interacts with one computer and one person. Based on the answers to the questions, he must determine with whom he is talking: with a person or a computer program. The task of a computer program is to mislead a person, forcing him to make the wrong choice. The standard interpretation of this test is as follows: “A person interacts with one computer and one person. Based on the answers to the questions, he must determine with whom he is talking: with a person or a computer program. The task of a computer program is to mislead a person, forcing him to make the wrong choice. All test participants do not see each other. If the judge cannot say for sure which of the interlocutors is human, then the car is considered to have passed the test. In order to test the intelligence of the machine, and not its ability to recognize oral speech, the conversation is conducted in the "text only" mode, for example, using the keyboard and screen (intermediary computer). Correspondence must take place at controlled intervals so that the judge cannot draw conclusions based on the speed of responses. In Turing's time, computers reacted more slowly than humans. Now this rule is necessary, because they react much faster than a person. All test participants do not see each other. If the judge cannot say for sure which of the interlocutors is human, then the car is considered to have passed the test. In order to test the intelligence of the machine, and not its ability to recognize oral speech, the conversation is conducted in the "text only" mode, for example, using the keyboard and screen (intermediary computer). Correspondence must take place at controlled intervals so that the judge cannot draw conclusions based on the speed of responses. In Turing's time, computers reacted more slowly than humans. Now this rule is necessary, because they react much faster than a person. So far, no existing computer system has come close to passing the test. So far, no existing computer system has come close to passing the test.
Modern artificial intelligence At the moment, in the creation of artificial intelligence, there is an intensive grinding of all subject areas that have at least some relation to AI into knowledge bases. Almost all approaches have been tried, but not a single research group has approached the emergence of artificial intelligence. At the moment, in the creation of artificial intelligence, there is an intensive grinding of all subject areas that have at least some relation to AI into knowledge bases. Almost all approaches have been tried, but not a single research group has approached the emergence of artificial intelligence. AI research has joined the general flow of singularity technologies (species leap, exponential human development), such as computer science, expert systems, nanotechnology, molecular bioelectronics, theoretical biology, quantum theory. AI research has joined the general flow of singularity technologies (species leap, exponential human development), such as computer science, expert systems, nanotechnology, molecular bioelectronics, theoretical biology, quantum theory. The results of developments in the field of AI have entered the higher and secondary education of Russia in the form of computer science textbooks, which now study the issues of working and creating knowledge bases, expert systems based on personal computers based on domestic logic programming systems, as well as studying fundamental issues of mathematics and computer science using examples. work with models of knowledge bases and expert systems in schools and universities. The results of developments in the field of AI have entered the higher and secondary education of Russia in the form of computer science textbooks, which now study the issues of working and creating knowledge bases, expert systems based on personal computers based on domestic logic programming systems, as well as studying fundamental issues of mathematics and computer science using examples. work with models of knowledge bases and expert systems in schools and universities.
Applications of artificial intelligence Some of the most famous intelligent systems: Some of the most famous intelligent systems: Deep Blue beat the world chess champion. The Kasparov vs. supercomputer match did not bring satisfaction to either computer scientists or chess players, and the system was not recognized by Kasparov. The IBM line of supercomputers then manifested itself in the brute force BluGene (molecular modeling) and pyramidal cell system modeling projects at Blue Brain, Switzerland. Deep Blue defeated the world chess champion. The Kasparov vs. supercomputer match did not bring satisfaction to either computer scientists or chess players, and the system was not recognized by Kasparov. The IBM line of supercomputers then manifested itself in the brute force BluGene (molecular modeling) and pyramidal cell system modeling projects at Blue Brain, Switzerland. MYCIN is one of the early expert systems that could diagnose a small subset of diseases, often as accurately as doctors. MYCIN is one of the early expert systems that could diagnose a small subset of diseases, often as accurately as doctors. 20Q is an AI-inspired project inspired by the classic 20 Questions game. Became very popular after appearing on the Internet at 20q.net. 20Q is an AI-inspired project inspired by the classic 20 Questions game. Became very popular after appearing on the Internet at 20q.net. Speech recognition. Systems such as ViaVoice are capable of serving consumers. Speech recognition. Systems such as ViaVoice are capable of serving consumers. Robots in the annual RoboCup tournament compete in a simplified form of football. Robots in the annual RoboCup tournament compete in a simplified form of football. Banks apply artificial intelligence systems (AI) in insurance activities (actuarial mathematics) when playing on the stock exchange and managing property. Pattern recognition methods (including both more complex and specialized ones and neural networks) are widely used in optical and acoustic recognition (including text and speech), medical diagnostics, spam filters, air defense systems (target identification), as well as to ensure a number of other national security tasks. Banks apply artificial intelligence systems (AI) in insurance activities (actuarial mathematics) when playing on the stock exchange and managing property. Pattern recognition methods (including both more complex and specialized ones and neural networks) are widely used in optical and acoustic recognition (including text and speech), medical diagnostics, spam filters, air defense systems (target identification), as well as to ensure a number of other national security tasks. Computer game developers use AI to varying degrees of sophistication. This forms the concept of "Game artificial intelligence". Standard AI tasks in games are finding a path in 2D or 3D space, simulating the behavior of a combat unit, calculating the right economic strategy, and so on. Computer game developers use AI to varying degrees of sophistication. This forms the concept of "Game artificial intelligence". Standard AI tasks in games are finding a path in 2D or 3D space, simulating the behavior of a combat unit, calculating the right economic strategy, and so on.
ASIMO Asimo (short for Advanced Step in Innovative MObility) is an android robot. Created by Honda Corporation, at Wako Fundamental Technical Research Center (Japan). Height 130 cm, weight 54 kg. Able to move at the speed of a fast walking person up to 6 km / h. Asimo (short for Advanced Step in Innovative MObility) is an android robot. Created by Honda Corporation, at Wako Fundamental Technical Research Center (Japan). Height 130 cm, weight 54 kg. Able to move at the speed of a fast walking person up to 6 km / h. According to 2007 information, there are 46 copies of ASIMO in the world. The production cost of each of them does not exceed one million dollars, and some robots can even be rented for $ per year (about $ per month). According to 2007 information, there are 46 copies of ASIMO in the world. The production cost of each of them does not exceed one million dollars, and some robots can even be rented for $ per year (about $ per month). Honda representatives say that this rule is only renting, not selling sometimes gives them problems. For example, during the demonstration of ASIMO to a certain Arab sheikh, it was very difficult for engineers to explain that the robot is not sold, in principle, for any money. Honda representatives say that this rule is only rent, but not sale sometimes gives them problems. For example, during the demonstration of ASIMO to a certain Arab sheikh, it was very difficult for engineers to explain that the robot is not sold, in principle, for any money. ASIMO is able to distinguish people by special cards that are worn on the chest. Asimo can walk up stairs. ASIMO is able to distinguish people by special cards that are worn on the chest. Asimo can walk up stairs.
ASIMO Recognition Technology With the 2000 ASIMO model, Honda added a host of features to the robot that allowed it to better communicate with people. These features fall into five categories: With the 2000 ASIMO model, Honda added a host of features to the robot that allowed it to better communicate with people. These functions are divided into five categories: Recognition of moving objects Recognition of moving objects ASIMO has a video camera built into its head. With its help, ASIMO can monitor the movements of a large number of objects, determining the distance to them and the direction. The practical applications of this feature are: the ability to follow people's movements (by panning the camera), the ability to follow a person, and the ability to "greet" a person when they come within range. ASIMO has a video camera built into its head. With its help, ASIMO can monitor the movements of a large number of objects, determining the distance to them and the direction. The practical applications of this feature are: the ability to follow people's movements (by panning the camera), the ability to follow a person, and the ability to "greet" a person when they come within range. Gesture recognition Gesture recognition ASIMO can also correctly interpret hand movements, thereby recognizing gestures. As a result, it is possible to give commands to ASIMO not only with your voice, but also with your hands. For example, ASIMO understands when the interlocutor is going to shake his hand, and when he waves his hand, saying "Goodbye." ASIMO can also recognize pointing gestures such as "go over there". ASIMO can also correctly interpret hand movements, thereby recognizing gestures. As a result, it is possible to give commands to ASIMO not only with your voice, but also with your hands. For example, ASIMO understands when the interlocutor is going to shake his hand, and when he waves his hand, saying "Goodbye." ASIMO can also recognize pointing gestures such as "go over there". Environment recognition Environment recognition ASIMO is able to recognize objects and surfaces, thanks to which it can act safely for itself and for others. For example, ASIMO owns the concept of "step" and will not fall down the stairs if it is not pushed. In addition, ASIMO knows how to move, bypassing people who stand in its way. ASIMO is able to recognize objects and surfaces, thanks to which it can act safely for itself and for others. For example, ASIMO owns the concept of "step" and will not fall down the stairs if it is not pushed. In addition, ASIMO knows how to move, bypassing people who stand in its way. Discrimination of sounds Discrimination of sounds Discrimination of sounds occurs thanks to the HARK system, which uses an array of eight microphones located on the head and body of the android. It detects where the sound came from and separates each voice from outside noise. At the same time, it does not specify the number of sound sources and their location. At the moment, HARK is able to reliably (70-80% accuracy) recognize three speech streams, that is, ASIMO is able to capture and perceive the speech of three people at once, which is not available to an ordinary person. The robot can respond to its own name, turn its head towards the people it is talking to, and turn around to unexpected and disturbing sounds, such as the sound of falling furniture. Discrimination of sounds occurs thanks to the HARK system, which uses an array of eight microphones located on the head and body of the android. It detects where the sound came from and separates each voice from outside noise. At the same time, it does not specify the number of sound sources and their location. At the moment, HARK is able to reliably (70-80% accuracy) recognize three speech streams, that is, ASIMO is able to capture and perceive the speech of three people at once, which is not available to an ordinary person. The robot can respond to its own name, turn its head towards the people it is talking to, and turn around to unexpected and disturbing sounds, such as the sound of falling furniture. Face Recognition Face Recognition ASIMO is able to recognize familiar faces, even while moving. That is, when ASIMO itself moves, a person's face moves, or both objects move. The robot can distinguish about ten different faces. As soon as ASIMO recognizes someone, he immediately turns to the person he recognizes by name. ASIMO is able to recognize familiar faces, even while moving. That is, when ASIMO itself moves, a person's face moves, or both objects move. The robot can distinguish about ten different faces. As soon as ASIMO recognizes someone, he immediately turns to the person he recognizes by name. Networking Networking ASIMO knows how to use the Internet and local networks. ASIMO knows how to use the Internet and local networks. After connecting to the local network at home, ASIMO will be able to talk with visitors through the intercom, and then report to the owner who came. After the owner agrees to receive guests, ASIMO will be able to open the door and bring the visitor to the right place. After connecting to the local network at home, ASIMO will be able to talk with visitors through the intercom, and then report to the owner who came. After the owner agrees to receive guests, ASIMO will be able to open the door and bring the visitor to the right place.
Android An android is a humanoid robot. The word comes from the Greek andr-, meaning "person, male, masculine", and the suffix -eides, meaning "similar, similar" (from eidos). The word robot droid from the epic "Star Wars Wars" George Lucas received by reduction from "android". Android is a humanoid robot. The word comes from the Greek andr-, meaning "person, male, masculine", and the suffix -eides, meaning "similar, similar" (from eidos). The word robot droid from the epic "Star Wars Wars" George Lucas received by reduction from "android". The first mention of the term android is attributed to Albert of Cologne (1270). A significant role in the popularization of the term was played by the French writer Philip Auguste Mathias Villiers de Lisle-Adam Mathias (Mathias Villiers de lIsle-Adam) (), in his work "Future Eve" ("L "Ève future") to refer to a humanoid robot, describing an artificial woman Adali (Hadaly).Adali spoke with the help of a phonograph, issuing one after another classical quotes.According to another version, the word android comes from the creator of the first mechanical toys, Henri Droz.The first mention of the term android is attributed to Albert of Cologne (1270).A significant role in the popularization of the term played by the French writer Philip Auguste Mathias Villiers de Lisle-Adam Mathias (Mathias Villiers de lIsle-Adam) (), in his work “Future Eve” (“L "Ève future”) to refer to a humanoid robot, describing the artificial woman Adali (Hadaly) . Adali spoke with the help of a phonograph, giving out one after another classical quotations. According to another version, the word android comes from the creator of the first mechanical toys, Henri Droz.
Modern humanoid robots Aiko is a robot-girl with an imitation of human feelings: touch, hearing, speech, vision. Aiko is a robot girl with an imitation of human senses: touch, hearing, speech, vision. Einstein Robot The head of a robot with the appearance of Einstein. A model for testing and reproducing human emotions by a robot. Einstein Robot The head of a robot with the appearance of Einstein. A model for testing and reproducing human emotions by a robot. EveR-1 is a robot that looks like a 20-year-old Korean woman: she is 1.6 meters tall and weighs about 50 kilograms. Machines like the EveR are expected to serve as guides, providing information in department stores and museums, as well as entertaining kids. EveR-1 is a robot that looks like a 20-year-old Korean woman: she is 1.6 meters tall and weighs about 50 kilograms. Machines like the EveR are expected to serve as guides, providing information in department stores and museums, as well as entertaining kids. HRP-4C robot girl designed to display clothes. The robot is 158 cm tall and weighs 43 kg including batteries. As for the degrees of freedom, there are 42 of them, for example, there are three of them in the hips and neck, and eight in the face, they make it possible to express emotions. HRP-4C robot girl designed to display clothes. The robot is 158 cm tall and weighs 43 kg including batteries. As for the degrees of freedom, there are 42 of them, for example, there are three of them in the hips and neck, and eight in the face, they make it possible to express emotions. Repliee R-1 is a humanoid robot with the appearance of a Japanese five-year-old girl, designed to care for the elderly and incapacitated people. Repliee R-1 is a humanoid robot with the appearance of a Japanese five-year-old girl, designed to care for the elderly and incapacitated people. The Repliee Q2 robot girl, tentatively titled Repliee Q1expo, was shown at the World Expo held in Aichi, Japan. At the demonstrations, he played the role of a television interviewer, while constantly interacting with people. The robot was equipped with omnidirectional cameras, microphones and sensors that allowed Repliee Q2 to detect human speech and gestures without much difficulty. The Repliee Q2 robot girl, tentatively titled Repliee Q1expo, was shown at the World Expo held in Aichi, Japan. At the demonstrations, he played the role of a television interviewer, while constantly interacting with people. The robot was equipped with omnidirectional cameras, microphones and sensors that allowed Repliee Q2 to detect human speech and gestures without much difficulty. Ibn Sina android, named after the ancient Arab philosopher and physician. One of the most advanced modern (2010) androids. Speaks Arabic. He is able to independently find his place on the plane, communicate with people. Recognizes the speaker's facial expression and uses facial expressions appropriate to the situation. His lips move in a rather monotonous way, but it is noted that he is especially good at raising his eyebrows and squinting his eyes. Ibn Sina android, named after the ancient Arab philosopher and physician. One of the most advanced modern (2010) androids. Speaks Arabic. He is able to independently find his place on the plane, communicate with people. Recognizes the speaker's facial expression and uses facial expressions appropriate to the situation. His lips move in a rather monotonous way, but it is noted that he is especially good at raising his eyebrows and squinting his eyes.
Prospects Solving the problems associated with the approximation of specialized AI systems to human capabilities, and their integration, which is realized by human nature Solving the problems associated with the approximation of specialized AI systems to human capabilities, and their integration, which is implemented by human nature created AI systems into a single system capable of solving the problems of mankind Creation of artificial intelligence, representing the integration of already created AI systems into a single system capable of solving the problems of mankind
Blue Brain Project Blue Brain Project Can a brain that thinks, remembers, makes decisions, and exactly matches a biological brain be simulated by a supercomputer? In the basement of the University of Lausanne in Switzerland, there are four refrigerator-sized black boxes filled with 2,000 IBM microprocessors arranged in repeating rows. Together they form the processor core of a machine capable of performing 22.8 trillion operations per second. It contains no moving parts and is completely silent. When the computer is turned on, the only thing you can hear is the lingering hum of powerful air conditioners. This is the main computer of the Blue Brain project. Can a brain that thinks, remembers, makes decisions, and exactly matches a biological brain be simulated using a supercomputer? In the basement of the University of Lausanne in Switzerland, there are four refrigerator-sized black boxes filled with 2,000 IBM microprocessors arranged in repeating rows. Together they form the processor core of a machine capable of performing 22.8 trillion operations per second. It contains no moving parts and is completely silent. When the computer is turned on, the only thing you can hear is the lingering hum of powerful air conditioners. This is the main computer of the Blue Brain project. The name of this supercomputer should be taken literally: each of its microchips: each of its processors is programmed to act like a real neuron in a real brain. The behavior of this computer reproduces, with shocking accuracy, the cellular events unfolding inside the brain. “This is the first model of the brain built from the bottom up,” says Henry Markram, a neuroscientist at the Federal Polytechnic Institute in Lausanne and director of the Blue Brain Project. Many different models have been proposed, but this is the only one that is completely biologically accurate. We started our work with the most basic facts about the brain.” The name of this supercomputer should be taken literally: each of its microchips: each of its processors is programmed to act like a real neuron in a real brain. The behavior of this computer reproduces, with shocking accuracy, the cellular events unfolding inside the brain. “This is the first model of the brain built from the bottom up,” says Henry Markram, a neuroscientist at the Federal Polytechnic Institute in Lausanne and director of the Blue Brain Project. Many different models have been proposed, but this is the only one that is completely biologically accurate. We started our work with the most basic facts about the brain.”
Before the Blue Brain project was launched, Markram compared it to the human genome sequencing project, which many thought was ridiculous or a form of self-promotion. When he launched the project in the summer of 2005 as a joint venture with IBM, there were no shortage of skeptics either. Scholars have criticized the project as a costly self-deception, a flagrant waste of money and talent. They argued that neuroscience does not need computers; it needs more molecular biologists. Terry Sejnowski, a renowned computational neuroscientist at the Salk Institute, announced that the Blue Brain project is doomed to fail because the brain is too mysterious to model. But Markram's attitude to the problem was different. "I wanted to model the brain precisely because we don't understand it," he said. "The best way to understand how something works is to build it from scratch." Before the Blue Brain project was launched, Markram compared it to the human genome sequencing project, which many thought was ridiculous or a form of self-promotion. When he launched the project in the summer of 2005 as a joint venture with IBM, there were no shortage of skeptics either. Scholars have criticized the project as a costly self-deception, a flagrant waste of money and talent. They argued that neuroscience does not need computers; it needs more molecular biologists. Terry Sejnowski, a renowned computational neuroscientist at the Salk Institute, announced that the Blue Brain project is doomed to fail because the brain is too mysterious to model. But Markram's attitude to the problem was different. "I wanted to model the brain precisely because we don't understand it," he said. "The best way to understand how something works is to build it from scratch." At the moment, the Blue Brain project is at a critical crossroads. The first phase of the project, the “proof of possibility” phase, is coming to an end. Most of the skeptics' objections were refuted. It took less than two years for the Blue Brain supercomputer to simulate the neurocortical column, which is a microscopic piece of the brain containing about neurons, with 30 million synoptic connections between them. "The column is up and running," Markram said, "now we just need to scale it up." Scientists at the Blue Brain Project are confident that within the next few years they will be able to simulate the entire brain. If we make this brain right, it will do everything,” says Markram. I ask if this includes self-awareness: is it possible to infuse a spirit into a machine? “When I say everything, I mean everything,” Markram says, a mischievous smile on his face. At the moment, the Blue Brain project is at a critical crossroads. The first phase of the project, the “proof of possibility” phase, is coming to an end. Most of the skeptics' objections were refuted. It took less than two years for the Blue Brain supercomputer to simulate the neurocortical column, which is a microscopic piece of the brain containing about neurons with 30 million synoptic connections between them. "The column is up and running," Markram said, "now we just need to scale it up." Scientists at the Blue Brain Project are confident that within the next few years they will be able to simulate the entire brain. If we make this brain right, it will do everything,” says Markram. I ask if this includes self-awareness: is it possible to infuse a spirit into a machine? “When I say everything, I mean everything,” Markram says, a mischievous smile on his face.
Artificial Nervous System Russian scientists have taken the first step in creating artificial intelligence by creating an artificial nervous system based on the example of a worm. Russian scientists have succeeded in creating an artificial nervous system, which is the first step towards creating artificial intelligence. Russian scientists have taken the first step in creating artificial intelligence by creating an artificial nervous system using the example of a worm. Russian scientists have succeeded in creating an artificial nervous system, which is the first step towards creating artificial intelligence. To do this, they thoroughly studied the body of a worm with simple nerves. Then, with the help of a computer, they built a virtual model of him and recreated the entire structure of his nervous system. The video shows how, under a microscope, a transparent worm twitches, then freezes, then curls up into a ball. For brain scientists, this video is like a Hollywood blockbuster. "A worm is not a hero of a computer game whose behavior is programmed in advance. Its actions are unpredictable, like those of a living thing... This is not yet artificial intelligence, but already an artificial nervous system," the scientists explain. To do this, they thoroughly studied the body of a worm with simple nerves. Then, with the help of a computer, they built a virtual model of him and recreated the entire structure of his nervous system. The video shows how, under a microscope, a transparent worm twitches, then freezes, then curls up into a ball. For brain scientists, this video is like a Hollywood blockbuster. "A worm is not a hero of a computer game whose behavior is programmed in advance. Its actions are unpredictable, like those of a living thing... This is not yet artificial intelligence, but already an artificial nervous system," the scientists explain. Andrey Palyanov, a researcher at the Institute of Informatics Systems of the Siberian Branch of the Russian Academy of Sciences named after A.P. Ershov, says: "These gray cone-shaped things symbolize the muscles of neurons, they have an object and 95 muscle cells - they are all represented here, and the small spheres and connections between them are the same neurons ". Andrey Palyanov, a researcher at the Institute of Informatics Systems of the Siberian Branch of the Russian Academy of Sciences named after A.P. Ershov, says: "These gray cone-shaped things symbolize the muscles of neurons, they have an object and 95 muscle cells - they are all represented here, and the small spheres and connections between them are the same neurons ". First, scientists built the body of a worm in virtual space. All proportions are observed, even the shape and principle of muscle contraction are the same as in a real nematode. But in order to revive this body, it was necessary to transfer the entire structure of the nervous system to the computer. "A living nematode includes such systems that we cannot yet reproduce - this is a system of digestion, reproduction, cell division," the scientist says. According to him, the volume of the human brain is ten to the eleventh degree of neurons. This is so much that today it is impossible to imagine a computer that can accommodate the entire human brain if it could be digitized.
General Motors proposes to replace cars with AI scooters. The American company General Motors already knows what will be the car of the future. They have already presented to everyone's attention the latest concept apparatus EN-V. This model is characterized by peculiar features: very small dimensions, only two wheels that are located in parallel, and the biggest plus is the greatest autonomy from human actions. At the moment, many are trying to imagine what the car will be like in the future, General Motors has come close to this, following the ecological path. According to Auto car General Motors, they created the EN-V together with the Chinese firm SAIC. In the opinion of many, this model has replaced the hybrid Chevrolet Volt in terms of radicalness. There are three versions, and each one is based on the chabolda platform. The height of each change is 1.82 m, width - 1.21 m, length - 1.21 m. Weight less than 400kg. Production material smoothness and carbon. The American company General Motors already knows what will be the car of the future. They have already presented to everyone's attention the latest concept apparatus EN-V. This model is characterized by peculiar features: very small dimensions, only two wheels that are located in parallel, and the biggest plus is the greatest autonomy from human actions. At the moment, many are trying to imagine what the car will be like in the future, General Motors has come close to this, following the ecological path. According to Auto car General Motors, they created the EN-V together with the Chinese firm SAIC. In the opinion of many, this model has replaced the hybrid Chevrolet Volt in terms of radicalness. There are three versions, and each one is based on the chabolda platform. The height of each change is 1.82 m, width - 1.21 m, length - 1.21 m. Weight less than 400kg. Production material smoothness and carbon. The specific compilation is the main oddity. Due to the presence of 2 cars, the EN-V is very similar to a Segway bike, which, thanks to hydroscopic, fluid sensors, can predetermine imbalance. Also their similarity is the complete absence of a cabin. But the main plus in maneuverability. In this model, two seats are located inside. The power of the electric motor that drives the rollers is 3 kW. And it is powered by an ion-lithium unit. The model is regulated not only by means of an autonomous electric connection, but also by gas and brakes together with a manual drive. General Motors promises a model speed of only 40 km/h. Most believe that this is very little for modern megacities. Of course, miniature size and high maneuverability is a big plus. But is this enough for the car of the future? EN-V is eco-friendly, futuristic and practical. Due to the uniqueness of internal reserves, this model can move independently on autopilot. In this case, the device itself will be able to take alternative routes in traffic jams of huge metropolitan areas, without the intervention of the driver. Small dimensions and maneuverability did not interfere with sufficient safety for both the driver and the passenger. So the chance of an accident is greatly reduced. Of course, the model still needs to be improved. And the question arises will mass production be great? After all, drivers do not really want to change their cars to EN-V. The specific compilation is the main oddity. Due to the presence of 2 cars, the EN-V is very similar to a Segway bike, which, thanks to hydroscopic, fluid sensors, can predetermine imbalance. Also their similarity is the complete absence of a cabin. But the main plus in maneuverability. In this model, two seats are located inside. The power of the electric motor that drives the rollers is 3 kW. And it is powered by an ion-lithium unit. The model is regulated not only by means of an autonomous electric connection, but also by gas and brakes together with a manual drive. General Motors promises a model speed of only 40 km/h. Most believe that this is very little for modern megacities. Of course, miniature size and high maneuverability is a big plus. But is this enough for the car of the future? EN-V is eco-friendly, futuristic and practical. Due to the uniqueness of internal reserves, this model can move independently on autopilot. In this case, the device itself will be able to take alternative routes in traffic jams of huge metropolitan areas, without the intervention of the driver. Small dimensions and maneuverability did not interfere with sufficient safety for both the driver and the passenger. So the chance of an accident is greatly reduced. Of course, the model still needs to be improved. And the question arises will mass production be great? After all, drivers do not really want to change their cars to EN-V.
Mobile communications and AI The winner of the Project Bluesky competition, whose goal was to "create the best phone of all." And Christina Ferraz created it. The winner of the Project Bluesky competition, whose goal was to "create the best phone among all." And Christina Ferraz created it. This phone supports user fingerprint recognition, which in turn activates his account, while in idle mode, the device is a blank, faded surface. This phone supports user fingerprint recognition, which in turn activates his account, while in idle mode, the device is a blank, faded surface. In the working mode, the interface of the device is a real three-dimensional system that uses artificial intelligence to change the appearance of settings and applications, as well as in accordance with user preferences and templates used. And, finally, the main advantage of the device is its touchscreen display with "growing" keys (this is really a three-dimensional interface, tangible, not drawn).
Conclusion The key factor determining the development of AI technologies today is the rate of growth in the computing power of computers, since the principles of the human psyche still remain unclear (at a level of detail accessible for modeling). Therefore, the topics of AI conferences look quite standard and have hardly changed in composition for quite a long time. But the increase in the performance of modern computers, combined with the improvement in the quality of algorithms, periodically makes it possible to apply various scientific methods in practice. It happened with intellectual toys, it happens with domestic robots. The key factor determining the development of AI technologies today is the rate of growth in the computing power of computers, since the principles of the human psyche still remain unclear (at the level of detail available for modeling). Therefore, the topics of AI conferences look quite standard and have hardly changed in composition for quite a long time. But the increase in the performance of modern computers, combined with the improvement in the quality of algorithms, periodically makes it possible to apply various scientific methods in practice. It happened with intellectual toys, it happens with domestic robots. Temporarily forgotten methods of simple enumeration of options (as in chess programs) will be intensively developed again, using an extremely simplified description of objects. But with the help of this approach (the main resource for its successful application is performance), it will be possible to solve, as expected, a lot of very different problems (for example, from the field of cryptography). Quite simple, but resource-intensive algorithms of adaptive behavior will help autonomous devices to operate confidently in a complex world. At the same time, the goal is to develop systems that do not look like a person, but act like a person. Temporarily forgotten methods of simple enumeration of options (as in chess programs) will be intensively developed again, using an extremely simplified description of objects. But with the help of this approach (the main resource for its successful application is performance), it will be possible to solve, as expected, a lot of very different problems (for example, from the field of cryptography). Quite simple, but resource-intensive algorithms of adaptive behavior will help autonomous devices to operate confidently in a complex world. At the same time, the goal is to develop systems that do not look like a person, but act like a person. Scientists are trying to look into the more distant future. Is it possible to create stand-alone devices that, if necessary, independently collect similar copies of themselves (multiply)? Is science able to create appropriate algorithms? Will we be able to control such machines? There are no answers to these questions yet. The active introduction of formal logic into applied systems for the representation and processing of knowledge will continue. At the same time, such logic is not able to fully reflect real life, and there will be an integration of various inference systems in single shells. In this case, it may be possible to move from the concept of a detailed representation of information about objects and techniques for manipulating this information to more abstract formal descriptions and the use of universal inference mechanisms, and the objects themselves will be characterized by a small array of data based on probability distributions of characteristics. Scientists are trying to look into the more distant future. Is it possible to create stand-alone devices that, if necessary, independently collect similar copies of themselves (multiply)? Is science able to create appropriate algorithms? Will we be able to control such machines? There are no answers to these questions yet. The active introduction of formal logic into applied systems for the representation and processing of knowledge will continue. At the same time, such logic is not able to fully reflect real life, and there will be an integration of various inference systems in single shells. In this case, it may be possible to move from the concept of a detailed representation of information about objects and techniques for manipulating this information to more abstract formal descriptions and the use of universal inference mechanisms, and the objects themselves will be characterized by a small array of data based on probability distributions of characteristics. The field of AI, which has become a mature science, is developing gradually - slowly but steadily moving forward. Therefore, the results are fairly well predictable, although sudden breakthroughs associated with strategic initiatives are not ruled out along the way. For example, in the 1980s, the US National Computing Initiative brought many areas of AI out of the lab and had a significant impact on the development of high-performance computing theory and its application in many applied projects. Such initiatives will most likely appear at the intersection of different mathematical disciplines - probability theory, neural networks, fuzzy logic. The field of AI, which has become a mature science, is developing gradually - slowly but steadily moving forward. Therefore, the results are fairly well predictable, although sudden breakthroughs associated with strategic initiatives are not ruled out along the way. For example, in the 1980s, the US National Computing Initiative brought many areas of AI out of the lab and had a significant impact on the development of high-performance computing theory and its application in many applied projects. Such initiatives will most likely appear at the intersection of different mathematical disciplines - probability theory, neural networks, fuzzy logic.
For the first time, the idea of creating an artificial likeness of the human mind was expressed by Raymond Lull
(1235-1315), who, back in the 14th century, tried to create a machine for solving various problems based on a general classification of concepts.
In the 17th century Gottfried Leibniz (1646-1716) and René Descartes (1596-1650) developed this idea independently from each other, proposing universal languages for the classification of all sciences.
These ideas formed the basis of theoretical developments in the field of artificial intelligence.
The development of artificial intelligence after the creation of computers
The development of AI as a scientific direction became possible only after the creation of computers
This happened in the 40s of the XX century.
At the same time, Norbert Wiener (1894-1964) created his fundamental works on the new science - cybernetics.
Cybernetics (from the Greek - “the art of management”) is the science of the general laws governing the processes of management and transmission of information in various systems, be it machines, living organisms or society.
The term "artificial intelligence"
The term "artificial intelligence" (artificial intelligence) was proposed in 1956 on
seminar of the same name in
Stanford University USA.
Soon after the recognition of artificial intelligence as an independent branch of science, there was a division into two main areas: neurocybernetics and black box cybernetics.
The main idea of neurocybernetics
The only thing capable of thinking is the human brain.
Therefore, any "thinking device" must somehow reproduce its structure.
Neurocybernetics is focused on hardware modeling of structures similar to the structure of the brain.
Elements similar to neurons and their combinations into functioning systems were created (neurons are brain cells interacting with each other). These systems are called neural networks.
Neural networks
The first neural networks were created in the late 50s. American scientists G. Rosenblatt and P. McCulloch. These were attempts to create systems that simulate the human eye and its interaction with the brain. The device is a perceptron.
In the 70-80s. the number of works in this direction began to decrease.
Neurocybernetics in Japan
In the mid 80s. in Japan, as part of the development of the 5th generation knowledge-based computer, the 6th generation computer, or neurocomputer, was created.
At this time, restrictions on memory and speed were practically removed.
Transputers appeared - parallel computers that interact with an unlimited number of microprocessors.
From transputers to neurocomputers - one step.
Three Modern Approaches to Building Neural Networks
Hardware - the creation of special computers, expansion cards, chipsets that implement all algorithms.
Software - the creation of programs and tools designed for high-performance computers. Neural networks are created in the computer's memory, all the work is done by its own processors.
Hybrid is a combination of the first two.
Black box cybernetics
The main idea is that it does not matter how the “thinking device” is arranged. The main thing is that it reacts to given input signals in the same way as the human brain.
This trend was focused on search for algorithms solving intellectual problems on existing computer models.
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