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Presentation on the topic: Artificial intelligence
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Artificial intelligence Intellectus (from Latin knowledge, understanding, reason) - the ability to think, rational knowledge. The subject of science is " artificial intelligence' is human thinking. Scientists are looking for an answer to the question: how does a person think? The purpose of these studies is to create a model of human intelligence and implement it on a computer. (In other words: teach a machine to think).
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Artificial intelligence - the main function The fifties witnessed the appearance on the horizon of post-war science of a supernova - Cybernetics, its rapid rise and equally rapid disintegration into parts, one of which is associated with the birth of artificial intelligence (AI). And although a variety of hopes were associated (and continue to be associated) with the catchy name of a newborn, it soon became clear that no matter how broadly one interprets this area, the apparatus for representing and processing knowledge should become its core.
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At the same time, the most ambitious apologists believe that the goal of artificial intelligence is the formation of a metaknowledge apparatus capable of uniting philosophy, psychology, mathematics and spreading “ new order” symbiosis of man and computer for all sciences, activities and even art. Thus, it turned out that the main task of AI - the development of formal means of representing and processing knowledge - is very close to the function of mathematics itself.
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However, there is a rather significant difference in their methodological positions: in dealing with the theory and development of formal apparatuses, mathematics only on the periphery pays attention to the application of these apparatuses to the problems of other disciplines; the methodology of artificial intelligence is characterized by the opposite direction - from the study various forms knowledge to the development of a set of formal tools, ideally covering the entire spectrum of areas of activity.
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There are many human activities that cannot be planned in advance. Composing music and poetry, proving a theorem, literary translation from a foreign language, diagnosing and treating an illness, and much more... For example, when playing chess, a chess player knows the rules of the game and has the goal of winning the game. His actions are not pre-programmed. They depend on the actions of the opponent, on the emerging position on the board, on the wit and personal experience of the chess player.
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Any artificial intelligence system operates within a certain subject area(medical diagnostics, legislation, mathematics, economics, etc.) Like a specialist, a computer must have knowledge in a given area. Knowledge in a particular subject area, formalized in a certain way and stored in the computer's memory, is called a computer knowledge base.
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For example, you want to use a computer to solve problems in geometry. The problem book contains 500 tasks of different content. An artificial intelligence specialist will put the knowledge of geometry into the computer (it is assumed that this is how the teacher's knowledge is laid in you). Based on this knowledge and with the help of a special algorithm of logical reasoning, the computer will solve any of the 500 problems. To do this, it is enough to tell him only the condition of the problem. Artificial intelligence systems work on the basis of the knowledge bases embedded in them.
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How to create an intelligent system on a computer? Human thinking is based on two components: a stock of knowledge and the ability to reason logically. From this follow two main tasks in creating intelligent systems on a computer: knowledge modeling (development of knowledge formalization methods for entering them into computer memory as a knowledge base); reasoning modeling (creating computer programs imitating the logic of human thinking when solving various problems).
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One of the types of artificial intelligence systems is expert systems. The purpose of expert systems is to consult the user, help in making decisions. Such assistance becomes especially important in extreme situations, for example, in conditions of a technical accident, an emergency operation, when driving vehicles. The computer is not subject to stress. He will quickly find the optimal, safe solution and offer it to the person.
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For those who are interested: Artificial intelligence is the main function Knowledge modeling Fuzzy mathematics Information technology- change of epochs "Non-algorithmic" control... Tasks for specialists of the highest classComputer NOT von Neumann architecture
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The central task of AI - the creation of a knowledge apparatus (AZ) - almost immediately required clarification - but what, in fact, knowledge are we talking about? Speaking of exact, formal ones, then these territories already have a mistress - Mathematics, with a professional army, with which the conquistadors of the new lands had no desire to get involved. If informal knowledge is meant, then it can be classified as: sufficiently studied and specific, but (so far) poorly formalized - for example, natural language syntax or medical diagnostics, and poorly formalized in principle, that is, the main part of the concepts of all areas activities - from humanities to art and domestic spheres of life.
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This almost hopeless situation was saved by L. Zadeh, who proposed in the mid-60s the concept of a linguistic variable and the apparatus of fuzzy mathematics. Artificial intelligence received a real magic wand as a gift - it quickly became clear that the desert of solid white spots on the knowledge map can be easily turned into fuzzy (and, alas, only virtually) flowering fields.
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Fuzzy-Morgana quickly seized the masses: by the beginning of the 80s, the fuzzy bibliography included about twenty thousand titles, the number of which has certainly increased since then by no less than two or three times. In the whirlpool of enthusiasm, a certain inherent defect of the new universal tool went unnoticed - the semantics and pragmatics of the fuzzy apparatus from the very beginning were themselves quite fuzzy: what remained blurred was WHAT, in fact, fuzziness represents, WHAT it operates on and WHY exactly THAT way, and not otherwise. The vagueness of the apparatus inevitably led to a complete ambiguity of the results of its application, which was not noticed simply because it remained unclear how, in fact, to check these results.
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Although imperative (algorithmic) control from the very beginning was the basis of programming for computers of the von Neumann architecture, in the late 60s and early 70s there were attempts to develop alternative ways of organizing the computing process. First of all, this was due to research on AI and parallel programming for multiprocessor systems. However, qualitative progress in solving this problem was provided by the apparatus of underdetermined models and recent work in the field of programming in constraints, since they are built on a decentralized, asynchronous, maximally parallel data-driven computing process. As the next step in this revolution, a transition to event-based management is possible, which significantly increases the level of associative apparatus that organizes the process of managing data.
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Parallelism Unsolvability - the problem of parallelization of imperative software technologies has formed an insurmountable barrier to the widespread use of multiprocessor systems. Over the past 15 years, software and hardware have changed places: the level of hardware design automation and the cost of the element base have for many years allowed mass production of computers with any number of processors, however, adapting modern computers for them and developing new software products remains a task that can be solved only by specialists of the highest class and then only in some special cases. In the new IT paradigm, concurrency is no longer a problem, but a natural feature of any software system.
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The computer is NOT von Neumann architecture. Data-driven (and event-based control in the future) radically changes the very organization of the computing process, making it asynchronous, decentralized and independent of the number of processors. A fundamental restructuring of the familiar von Neumann architecture of modern machines will be required. Thus, there is a prospect of not just a change of generations, but a change of eras, leading to a real revolution - a shock to the “unshakable foundations” of IT: Algorithm, von Neumann architecture, a deterministic and sequential process go down in history forever, giving way to the Model, multi-agency and associatively self-organizing non-deterministic parallel process.
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Text content of presentation slides: Presentation for the competition “Present and Future” Topic: “Development of Artificial Intelligence” GPOU TO “Krapivinsky Forestry Technical School” Teacher Blazhevich L.S. Information about AI at the present time Artificial intelligence is a discipline that studies the possibility of creating programs to solve problems that require certain intellectual efforts when performed by a person. Nowadays, artificial intelligence (AI) is necessary in all areas of human activity - management, production, education, etc. The intellectual systems constructed by means of these technologies are designed to enhance the mental abilities of a person, to help him find effective solutions to the so-called poorly formalized and semi-structured tasks, characterized by the presence of various types of uncertainties and huge search spaces. The main preference in research is given to neural networks. Neural networks are a mathematical structure that imitates some aspects of the human brain and demonstrates its capabilities such as the ability to informally learn, the ability to generalize and cluster unclassified information, and the ability to independently build forecasts based on already presented time series. The most important difference from other methods, such as expert systems, is that neural networks, in principle, do not need to famous model, but they build it themselves only on the basis of the presented information. That is why neural networks and genetic algorithms have entered into practice wherever it is necessary to solve problems of forecasting, classification, and control. In practice, neural networks are used in two forms - as software products running on conventional computers, and as specialized hardware and software systems. The main task of neurocomputers is image processing based on learning. Similar to biological networks, artificial neural networks are aimed at parallel processing of wideband images. The next most important technology is evolutionary computing (EC). EVs touch upon the practical problems of self-assembly, self-configuration and self-healing of systems consisting of many simultaneously functioning nodes. At the same time, it is possible to apply scientific achievements from the field of digital machines. Another aspect of EV is the use of autonomous agents for solving everyday tasks as personal secretaries, managing personal accounts, assistants who select the necessary information in networks using third-generation search algorithms, work planners, personal teachers, virtual sellers, etc. This also applies robotics and all related fields. The main directions of development are the development of standards, open architectures, intelligent shells, scripting / query languages, methodologies for effective interaction between programs and people. The next group of technologies, including fuzzy logic, image processing, etc., is used in control systems, image recognition systems, real-scale systems time, systems for obtaining and processing knowledge, and many others. This group of technologies is necessary when working with large amounts of information, its search, analysis, storage and structuring. The last group of technologies helps to solve a number of specific problems. For example, solving the problem of automation in production by introducing robotics based on AI, the so-called automated cyberfactories. Or the introduction of robotic technology in medicine will make it possible to carry out accurate diagnostics or perform very complex operations without direct human intervention. The key factor determining today the development of AI technologies and the possibility of their application in practice is the growth rate of computing power of computers, since the principles of the human psyche are still unclear. 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. Artificial intelligence in the future Artificial intelligence is usually called a branch of computer science that studies the possibilities of providing intelligent actions and reasoning with the help of computer systems and other artificial devices. In most cases, at the same time, the algorithm for solving problems is known in advance. It should be noted that in scientific circles there is no exact definition of this science, because there is also no solution to the question of the status and nature of the human brain. Similarly, there is no exact criterion for achieving computers"intelligence", despite the fact that in the early stages of the development of artificial intelligence, certain hypotheses were used, in particular, the Turing test (the goal is to determine whether a machine can think). This science has close relationships with psychology, transhumanism, and neurophysiology. Like all computer sciences, it uses a mathematical apparatus. Artificial intelligence is a fairly young field of research, which began in 1956. AT this moment time, the development of this science is in a state of so-called recession, when the results achieved earlier are applied in various fields of science, industry, business and everyday life. Currently, there are four main approaches to studying the construction of artificial intelligence systems: logistic, structural, evolutionary and simulation . The logistic approach basically contains the so-called Boolean algebra, which is well known to programmers. Most artificial intelligence systems built according to the logistic principle are a certain theorem proving machine: background information is contained in the form of axioms, and logical conclusions are formulated according to the rules of relations between these axioms. Each such machine has a goal generation unit, and the inference system proves this goal as a theorem. This system is better known as an expert system. The structural approach uses modeling of the structure of the human brain as the basis of an artificial intelligence system. Among the first such attempts, Rosenblatt's perceptron should be noted. The main structural modeled unit is a neuron. Over time, new models have emerged that are currently known as neural networks. In the case of using the evolutionary approach to build artificial intelligence systems, the main part of the attention is usually paid to building the initial model, as well as the rules by which this model can evolve . A classic example of an evolutionary algorithm is the genetic algorithm. Another project that started in 2010 is the DARPA project in cooperation with SRI International. Its essence lies in the development of breakthrough artificial intelligence, which will be able to process and transmit data, copying the mechanisms of the human brain. The SyNAPSE electronic adaptive neuromorphic scalable system, according to the developers, should surpass traditional data processing algorithms and be able to autonomously study a complex environment. At the moment, the military uses artificial intelligence to process a large amount of information, in particular, intelligence data and video. All this information must be quickly deciphered and analyzed. For a new system, this will not be difficult. It will use mathematical logic, solve simple theorems based on sensor data, make decisions and perform the necessary actions. Moreover, the Pentagon intends to use this artificial intelligence model as a virtual personal assistant that can respond to voice commands and act as a secretary. Recall that earlier DARPA, together with SRI International, has already been developing a personal assistant called CALO. The project was completed in 2009. The program is able to reason, understand instructions, learn, explain its actions, adequately respond to an unknown situation and discuss the conduct of the operation after its completion. This program takes the necessary data from the user's contacts, his Email, projects and tasks. Then a relational model of the user's environment is created, training takes place. As a result, Artificial Intelligence can negotiate and resolve conflicts on behalf of the user. Unfortunately, this program only works on personal computer without being integrated into the robot. In 2011, the first artificial brain prototype was developed in Japan. Artificial intelligence can process a huge amount of information, but robots are not yet endowed with the ability to think. The developers are not in a hurry with this yet ... According to the researchers, the robots of the near future will be in many ways similar to people: they will be able to walk on two legs, they will be able to distinguish faces, keep up a conversation, fulfill requests, but in essence they are just machines similar to a person . All their actions are subject to a pre-prepared algorithm, and therefore are primitive. And only if it is possible to implement the technology of bimolecular computing, machines will be able to think and gain the ability to be creative. According to the developers, the new information processing mechanism is very similar to the work of the human brain. There are millions of neurons in the human head that are constantly interacting with each other. essence new technology is that each molecule can have up to three hundred directions of relationships. Thus, thanks to the new technology, machines will be able to solve those tasks that are currently inaccessible to them. According to the researchers, new developments are expected to be applied in the field of diagnosis and treatment of oncological diseases: programmable molecular systems will be introduced into cancer cells and transform them into healthy ones. My opinion about AI in the future. AI has a great future even now AI has achieved a huge breakthrough. Whatever the forecasts for the future, there are already some projects that need attention. This, in particular, is about a project to create an artificial brain called the Blue Brain. The project is being developed by research scientists, representatives of the Federal Polytechnic School (Lausanne). They were able to create a model diagram of the location of synapses in the brain of rats. According to project director Henry Macram, the results were beyond all expectations. It is quite possible that researchers will soon be able to answer many questions that have troubled the minds of scientists so far: will the artificial mind replace the human mind and will it be more highly developed? Is man the closing link in the chain of evolution of the planet? I hope that in the near future we will find answers to these and many other questions.
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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).
Presentation slides
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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 simulate 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 work 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:
Pattern 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
