Читать книгу Elements applications of artificial intelligence in transport and logistics - Vadim Shmal - Страница 2

Artificial intelligence (AI) is intelligence displayed by machines, as opposed to natural intelligence displayed by humans and animals. The study of artificial intelligence began in the 1950s, when systems could not perform tasks as well as humans. Artificial intelligence is the overall goal of building a system that exhibits intelligence, consciousness and is capable of self-learning. The most famous types of artificial intelligence known as machine learning, which is a kind of artificial intelligence, and deep learning.

The development of artificial intelligence is a controversial area as scientists and policymakers grapple with the ethical and legal implications of creating systems that exhibit human-level intelligence. Some argue that the best way to promote artificial intelligence is through education to prevent it from bias against people and make it accessible to people from all socioeconomic backgrounds. Others fear that increased regulation and concerns over national security will hamper the development of artificial intelligence.

Artificial intelligence (AI) originated in the 1950s, when scientists believed that machines could not exhibit intelligent behavior that the human brain could not reproduce. In 1962, a team at Carnegie Mellon University led by Terry Winograd began work on universal computing intelligence. In 1963, as part of the MAC project, Carnegie Mellon created a program called Eliza, which became the first machine to demonstrate the ability to reason and make decisions like humans.

In 1964, IBM researcher JCR Licklider began research in the computer science and cognitive sciences with the goal of developing intelligent machines. In 1965, Licklider coined the term «artificial intelligence» to describe the entire spectrum of cognitive technologies that he studied.

Scientist Marvin Minsky introduced the concept of artificial intelligence in the book «Society of Mind» and foresaw that the field of development of science goes through three stages: personal, interactive and practical. Personal AI, which he considered the most promising, would lead to the emergence of human-level intelligence, an intelligent entity capable of realizing its own goals and motives. Interactive AI will develop the ability to interact with the outside world. Practical AI, which he believed was most likely, would develop the ability to perform practical tasks.

The term artificial intelligence began to appear in the late 1960s when scientists began to make strides in this area. Some scientists believed that in future, computers would take on tasks that were too complex for the human brain, thus achieving intelligence. In 1965, scientists were fascinated by an artificial intelligence problem known as the Stanford problem, in which a computer was asked to find the shortest path on a map between two cities in a given time. Despite many successful attempts, the computer was able to complete the task only 63% of the time. In 1966, Harvard professor John McCarthy stated that this problem «is as close as we can in computers to the problem of brain analysis, at least on a theoretical basis».

In 1966, researchers at IBM, Dartmouth College, the University of Wisconsin-Madison, and Carnegie Mellon completed work on the Whirlwind I, the world’s first computer designed specifically for artificial intelligence research. In the Human Genome Project, computers were used to predict the genetic makeup of a person. In 1968, researchers at Moore’s School of Electrical Engineering published an algorithm for artificial neural networks that could potentially be much more powerful than an electronic brain.

In 1969, Stanford graduate students Seymour Papert and Herbert A. Simon created language for children Logo. Logo was one of the first programs to use both numbers and symbols, as well as simple grammar. In 1969 Papert and Simon founded the Center for Interactive Learning, which led to the development of the logo and further research into artificial intelligence.

In the 1970s, a number of scientists began experimenting with self-conscious systems. In 1972, Yale professor George Zbib introduced the concept of «artificial social intelligence» and suggested that these systems might one day understand human emotions, in 1972 he coined the term «emotional intelligence» and suggested that one day systems might understand emotions. In 1973, Zbib co-authored an article entitled «Natural Aspects of Human Emotional Interaction,» in which he argued that artificial intelligence could be combined with emotion recognition technology to create systems capable of understanding emotions. In 1974 Zbib founded Interaction Sciences Corporation to develop and commercialize his research.

By the late 1960s, several groups were working on artificial intelligence. Some of the most successful researchers in this area were from the MIT Artificial Intelligence Lab, founded by Marvin Minsky and Herbert A. Simon. MIT’s success can be attributed to the diversity of individual researchers, their dedication and the group’s success in finding new solutions to important problems. By the late 1960s, most artificial intelligence systems weren’t as powerful as humans.

Minsky and Simon envisioned a universe in which the intelligence of a machine is represented by a program or set of instructions. As the program worked, it led to a series of logical consequences called a «set of affirmative actions.» These consequences can be found in the answer dictionary, which will create a new set of explanations for the child. In this way, the child can make educated guesses about the state of affairs, creating a feedback loop that, in the right situation, can lead to a fair and useful conclusion. However, there were two problems with the system: the child had to be taught according to the program, and the program had to be perfectly detailed. No programmer could remember all the rules a child had to follow, or a set of answers that a child might have.

To solve this problem, Minsky and Simon developed what they called the «magician’s apprentice» (later known as the Minsky rule-based thinking system). Instead of memorizing each rule, the system followed a process: the programmer wrote down the statement and identified the «reasons» for the various outcomes based on the words «explain,» «confirm,» and «deny.» If the explanation matched one of the «reasons,» then the program needed to be tested and given feedback. If this did not happen, it was necessary to develop a new one. If the program was successful in the second phase, it was allowed to create more and more rules, increasing the breadth of its theories. When faced with a problem, he could be asked to read the entire set of rules in order to re-examine the problem.

Minsky and Simon system was incredibly powerful, because the programmer only gave several versions explanation. The researcher was not required to go through any procedures other than writing and entering the program requirements. This allowed Minsky and Simon to create more rules and, more important, learn from their mistakes. In 1979, the system was successfully demonstrated in the SAT exam. Although the system had two flaws that prevented it from answering two of the three SAT questions, it scored 82 percent for Group 2 and 3 questions and 75 percent for Group 4 and 5 questions. The system did not cope with complex issues that did not fit into the established rules. Processing large amounts of data was also slow, so any additional details were thrown away to speed up the system.

The system also had some limitations due to the rules. Rules can only be defined based on a limited number of labels. For example, when rules are given, they should define what the labels mean. They can only be applied to positive results. However, as the system’s ability to process information has grown, it has been shown that the system can make mistakes. In particular, if it had to apply the same label to two different objects (and still detect an error), it could not make a useful distinction between the two objects, and then decide which label should be applied.

Minsky and Simon focused on applying their system to humans. They developed a system they called a «living program» or «projective computing system» (PPAS). They used PPAS to create a symbolic approach to the study of psychology. This would have an advantage that, unlike traditional programs, the teaching could be programmed. The program had to use symbols to describe the human system, and then train the system through explanations. They later called this approach «general computing», which allows you to study any problem with enough time and data.

For Minsky and Simon, the main limitation of their system was its ability to accurately calculate the results of the system. This limitation was not related to any flaws in their system; the system worked, but was slow and expensive. For this reason, they thought they could get around this by programming the results using so-called «functional programming» (FP). FP was about represented a British computer scientist the JCR Licklider in 1950. He is about refers to the programming style, which focuses on the main functions and behavior of programs, rather than the implementation of the program. Using FP, the system could compute the results, but then explain the cause of the problem using human language.

Over the next decade, PPS and PPAS continued to grow, and in 1966, Minsky and Simon published an article titled Brain Activity Systems, which was the result of their research. Here they showed that there was a program that could be written that would read the brains of a number of volunteers and then track their brain activity. Each volunteer read a passage about how the brain works; they had to complete this task, and then measured the activity of the brain.

Specifically, the authors showed that their system is capable of responding to certain brain waves (also called rhythms) and that it can combine these brain waves in ways that help make sense of a subject. They showed that if the brainwave was a slow rhythm, the system was able to «remember» information it was exposed to earlier and «reactivate» it when needed. If the brainwave was a fast rhythm, the system could «cure» the forgetfulness of information by comparing it with another element.

When Minsky and Simon published their article, they have attracted a lot of attention, because they offered this kind of experimental system, which theoretically can be implemented. They were able to approach the study from the practical point of view.

In 1972, Minsky and Simon founded the Center for Behavioral Neuroscience in Ann Arbor, Michigan. They designed and conducted a series of experiments that led them to the following conclusions: «There was something different from the mind, something that distinguished it from any other organization»; «The data showed that our ideas about action and the brain were different»; «Our brain worked differently than other parts of the body»; «There is a possibility that the organization of the mind may be influenced by the activity of the brain»; «The minds are based on basic physical principles.»

They came to this conclusion because they saw the relationship between specific brain activity and a specific behavior or idea. In other words, if you go to the mind and see activity that looked like it came from the mind, and you saw behavior that looked like it came from the mind, then the behavior is likely to follow the behavior. And if the mind was «imprinted» on the behavior, then it had to follow the action, and not vice versa. They began to formulate a new theory about how behavior arises and how mind is formed.

Minsky explained:

«The starting point was the work we did on the correlations between brain activity and human behavior. It was very clear to us that these correlations cannot be understood without first understanding how behavior is generated.»

The authors came to the conclusion that any inorganic system can act only on the basis of its internal states. If the internal states changed, then the behavior of the system would change. When the authors thought of a brain that responds to certain types of brain waves, they noticed that the brain would produce a certain behavior, and that this behavior would correspond to the internal state of the brain. This is a universal principle of nature. Since this principle of nature made behavior universal, it should lead the authors to the conclusion that if they applied these principles to the brain, they could create a computer program that would be able to reproduce the behavior of the brain.

Minsky believed that universal principles governing biological systems could be used to create computer software. However, Minsky admitted that his ideas were «science fiction.» It took Minsky and Simon another year to find a way to create a computer that could mimic their discoveries. But by 1972, they had developed a computer program that could test their theories.

John B. Barg, professor of psychology at Yale University, was also instrumental in the development of Minsky and Simon’s research. Barg helped found the Center for Behavioral Neuroscience at the University of Michigan in 1972, where Minsky and Simon continued to experiment with human and animal behavior.

The field of artificial intelligence research began in a seminar at Dartmouth College in 1956, where the term «artificial intelligence» was first coined. The following year, in 1957, Massachusetts Institute of Technology, together with its research graduate students, formed a new organization of AI researchers called the SIGINT-A (Intelligence and Scientific Computing) Committee. After creating many of the foundations of artificial intelligence, members of this group did some research on a similar program at Stanford University. The group decided to keep the name SIGINT-A and develop a new research and development program in the field of artificial intelligence. SIGINT-A became the research and development group that eventually became the world famous artificial intelligence laboratory that now bears his name. SIGINT-A is a legendary research organization. There are many famous names in this area in its history. Many famous names in the field of AI have been taken from SIGINT-A. Many projects have been implemented in the laboratory. To meet engineering needs or to fulfill a new mission in a new era of artificial intelligence, SIGINT-A has never been afraid to try new things. And many of her ideas and directions have been accepted in the generally accepted field. Many of what we now regard as leading AI tools, such as neural networks and helper vector machines, were created or adapted in the SIGINT-A era.

Computer science defines AI research as the study of «intelligent agents»: any device that perceives the environment and takes action based on what it perceives.

It is a common misconception that artificial intelligence research focuses on creating technologies that resemble human intelligence. However, as Alan Turing wrote, the most important attributes of human intelligence are not the pursuit of mathematical knowledge and the ability to reason, but the ability to learn from experience, perceive the environment, and so on. To understand how these properties of human intelligence can be used to improve other technologies, one must understand these characteristics of human intelligence.

AI researchers and entrepreneurs use the term «artificial intelligence» to define software and algorithms that demonstrate human intelligence. The academic area has since expanded to cover related topics such as natural language processing and systems. Much of the work in this area takes place in universities, research institutes and companies, with investments from companies like Microsoft and Google.

Artificial intelligence is also used in other industries, such as the automatic control of ships, and is commonly used in the development of robotics. Examples of AI applications include speech recognition, image recognition, language processing, computer vision, decision making, robotics, and commercial products including language translation and recommendation engines. Artificial intelligence is at the center of national and international public policy such as the National Science Foundation. Research and development in artificial intelligence is managed by independent organizations that receive grants from public and private agencies. Other organizations, such as The Institute for the Future, have a wealth of information on AI and other emerging technologies and design professions, as well as the talent required to work with those technologies.

The definition of artificial intelligence has evolved since the concept was developed and it is currently not a black and white definition, but rather a continuum. From the 1950s to the 1970s, AI research focused on the automation of mechanical functions. Researchers such as John McCarthy and Marvin Minsky have explored the problems of general computing, general artificial intelligence, reasoning, and memory.

In 1973, Christopher Chabris and Daniel Simons proposed a thought experiment called The Incompatibility of AI and Human Intelligence. The problem described was that if the artificial system was so smart that it was superior to humans or superior to human capabilities, the system could make whatever decisions it wanted. This can violate the fundamental human assumption that people should have the right to make their own choices.

In the late 1970s and early 1980s, the field of activity changed from the classical orientation towards computers to the creation of artificial neural networks. Researchers began to look for ways to teach computers to learn rather than just perform certain tasks. This field developed rapidly during the 1970s and eventually moved from computing to a more scientific-oriented one, and its field of application expanded from computing to human perception and action.

Many researchers in the 1970s and 1980s focused on defining the boundaries of human and computer intelligence, or the capabilities required for artificial intelligence. The boundary should be wide enough to cover the full range of human capabilities.

While the human brain is capable of processing gigabytes of data, it was difficult for leading researchers to imagine how an artificial brain could process much larger amounts of data. At the time, the computer was a primitive device and could only process single-digit percentages of data on a human scale.

During that era, artificial intelligence scientists also began work on algorithms to teach computers to learn from their own experience – a concept similar to how the human brain learns. Meanwhile, in parallel, a large number of computer scientists developed search methods that could solve complex problems by looking for a huge number of possible solutions.

Artificial intelligence research today continues to focus on automating specific tasks. This emphasis on the automation of cognitive tasks is called «narrow AI». Many researchers working in this field are working on facial recognition, language translation, playing chess, composing music, driving cars, playing computer games, and analyzing medical images. Over the next decade, narrow AI is expected to develop more specialized and advanced applications, including a computer system that can detect early stages of Alzheimer’s disease and analyze cancers.

The public uses and interacts with artificial intelligence every day, but the value of AI in education and business is often overlooked. AI has significant potential in almost all industries, such as pharmaceuticals, manufacturing, medicine, architecture, law and finance.

Companies are already using artificial intelligence to improve services, improve product quality, lower costs, improve customer service, and save money on data centers. For example, with robotics software, Southwest Airlines and Amadeus can better answer customer questions and use customer-generated reports to improve their productivity. Overall, AI will affect nearly every industry in the coming decades. On average, about 90% of U.S. jobs will be affected by AI by 2030, but the exact percentage varies by industry.

Artificial intelligence can dramatically improve many aspects of our lives. There is a lot of potential for improving health and treating illness and injury, restoring the environment, personal safety, and more. This potential has generated a lot of discussion and debate about its impact on humanity. AI has been shown to be far superior to humans in a variety of tasks such as machine vision, speech recognition, machine learning, language translation, computer vision, natural language processing, pattern recognition, cryptography, chess.

Many of the fundamental technologies developed in the 1960s were largely abandoned by the late 1990s, leaving gaps in this area. Fundamental technologies that define AI today, such as neural networks, data structures, and so on. Many modern artificial intelligence technologies are based on these ideas and are much more powerful than their predecessors. Due to the slow pace of change in the tech industry, while current advances have produced some interesting and impressive results, there is little to distinguish them from each other.

Early research in artificial intelligence focused on learning machines that used a knowledge base to change their behavior. In 1970, Marvin Minsky published a concept paper on LISP machines. In 1973, Turing proposed a similar language called ML, which, unlike LISP, recognized a subset of finite and formal sets for inclusion.

In the decades that followed, researchers were able to refine the concepts of natural language processing and knowledge representation. This advance has led to the development of the ubiquitous natural language processing and machine translation technologies in use today.

In 1978, Andrew Ng and Andrew Hsey wrote an influential review article in the journal Nature containing over 2,000 papers on AI and robotic systems. The paper covered many aspects of this area such as modeling, reinforcement learning, decision trees, and social media.

Since then, it has become increasingly difficult to involve researchers in natural language processing, and new advances in robotics and digital sensing have surpassed the state of the art in natural language processing.

In the early 2000s, a lot of attention was paid to the introduction of machine learning. Learning algorithms are mathematical systems that learn by observation.

In the 1960s, Bendixon and Ruelle began to apply the concepts of learning machines to education and beyond. Their innovations inspired researchers to further explore this area, and many research papers were published in this area in the 1990s.

Sumit Chintal’s 2002 article, Learning with Fake Data, discusses a feedback system in which artificial intelligence learns by experimenting with the data it receives as input.

In 2006, Judofsky, Stein, and Tucker published an article on deep learning that proposed a scalable deep neural network architecture.

In 2007, Rohit described" hyperparameters». The term "hyperparameter" is used to describe a mathematical formula that is used in computer learning. While it is possible to design systems with tens, hundreds, or thousands of hyperparameters, the number of parameters must be carefully controlled because overloading the system with too many hyperparameters can degrade performance.

Google co-founders Larry Page and Sergey Brin published an article on the future of robotics in 2006. This document includes a section on developing intelligent systems using deep neural networks. Page also noted that this area would not be practical without a wide range of underlying technologies.

In 2008, Max Jaderberg and Shai Halevi published «Deep Speech». In it was presented the technology «Deep Speech», which allowed the system to determine the phonemes of spoken language. The system entered four sentences and was able to output sentences that were almost grammatically correct, but had the wrong pronunciation of several consonants. Deep Speech was one of the first programs to learn to speak and had a great impact on research in the field of natural language processing.

In 2010, Jeffrey Hinton describes the relationship between human-centered design and the field of natural language processing. The book was widely cited because it introduced the field of human-centered AI research.

Around the same time, Clifford Nass and Herbert A. Simon emphasized the importance of human-centered design in building artificial intelligence systems and laid out a number of design principles.

In 2014, Hinton and Thomas Kluver describe neural networks and use them to build a system that can transcribe a person with a cleft lip. The transcription system has shown significant improvements in speech recognition accuracy.

In 2015, Neil Jacobstein and Arun Ross describe the TensorFlow framework, which is now one of the most popular data-driven machine learning frameworks.

In 2017, Fei-Fei Li highlights the importance of deep learning in data science and describes some of the research that has been done in this area.