The topic of artificial intelligence in 2017 became one of the most attractive for discussion. There were so many commentators among the IT market participants, and the comments were so interesting and detailed, that in the final issue of CRN/RE for 2017 we were able to discuss not all of the issues proposed for discussion. Today we will talk about the pros and cons of AI solutions and the difficulties of its implementation.
What are the main advantages of solutions that are called “artificial intelligence systems” today?
Project Manager ST Smartmerch, Group of Companies "System Technologies", Maxim Archipenkov I am sure that “the benefits come from expectations.”
“Neural networks, unlike humans, have no emotions and do not get tired,” says Arkhipenkov. - The human factor and all errors and problems associated with the character of a person and his low ability to work are excluded - relative to the machine, of course. Neural networks have no performance threshold: if a person can check, for example, 100 parts for quality per day, then the system will check as many of them as the server capacity allows. The system is easier to scale: at the same plant, it’s difficult to put 100 quality control people in one room.”
Marketing Director CDNvideo Angelina Reshina also believes that the main advantages of AI systems “are the speed of data processing, the ability to train the system and savings on human resources.”
CEO of Cezurity Alexey Chaley emphasizes: AI-based products are capable of performing tasks at a qualitatively different level: classifying images, translating text, classifying files, etc. “The introduction of AI allows you to process a large amount of data quickly and efficiently, minimizing human participation and, reducing the human factor, minimizing error ", notes Chaley.
“The main advantages of existing present moment solutions are the ability to automate many areas of activity while minimizing human participation in this and expanding areas where it is possible to use software instead of human labor, says the founder of the hosting company King Servers Vladimir Fomenko. - AI is especially good at analysis at the moment large volumes data where a person would need too much time, and ordinary programs that do not use machine learning, would not be able to achieve the required accuracy."
Director of the Corporate Information Systems Department of ALP Group agrees with colleagues Svetlana Gatsakova:“With the help of AI technologies, the speed and level of automation of processing large amounts of information increases significantly - while simultaneously improving quality and manufacturability. With the right attitude towards new technologies, the completeness of data use increases, as well as the efficiency and quality of management decisions.”
According to the CEO of Hawk House Integration Alexandra Ivleva,“AI technologies are best suited for optimizing various types of mechanical activities, automating routine operations, and using them in hazardous industries.” “Proper use of robotics on conveyor lines allows us to switch to non-stop operation, optimizes enterprise costs, improves product quality, but requires a serious and lengthy commissioning stage,” says Ivlev. - Not many companies can afford to invest large amounts of money in such technologies, although in the future this will make production much cheaper. The situation is similar with machine learning technologies: for each project, analyze a large sample of data, using individual algorithms, which requires time and resources. But after the introduction of automation, these operations will occur faster and cheaper than a person can do.”
“To begin with, artificial intelligence systems are being developed to improve efficiency in in a broad sense this word,” recalls the director of business applications at CROC Maxim Andreev. - To implement new ideas and approaches, companies often need to take into account huge amount factors that an ordinary person simply cannot keep in mind. One of the main advantages of artificial intelligence is the ability to take into account such a diverse number of factors in real time. In addition, unlike a person, an algorithm cannot get tired or change any information on purpose. That is, by introducing artificial intelligence, the company minimizes the possibility of errors caused by these factors. But there is also reverse side medals: a human can take into account additional details, while a poorly configured algorithm will continue to work incorrectly. Another advantage of artificial intelligence systems is replicability. Let's take as an example any business process in a company that requires an employee to study for a year. Therefore, if we need 10 new employees, we will spend 10 man-years training them. From the point of view of algorithms, everything is simpler and the cost of scaling the solution is much lower.”
Head of Development and Implementation of AV Solutions at Auvix Alexander Pivovarov believes that the most obvious and obvious benefits include increased efficiency, reduced routine operations and greater ease of use. “For example, if you take one enough simple thing, as a system for booking and displaying the schedule of meeting rooms, then when you start to carefully study it, you see many opportunities to increase the efficiency of its use, reduce downtime, and so on using “smart algorithms,” emphasizes Pivovarov.
“The main task of digital transformation, one of the tools of which is AI, is to make processes go faster and more efficiently, companies spend less and earn more,” says general manager ABBYY Russia Dmitry Shushkin. - For example, one of our customers in the banking sector automated the processing of documents for opening an account for legal entities. The intelligent system itself types and recognizes documents, then extracts information from them and loads it into the required fields of the banking system. As a result, entering data from documents takes less than 10 minutes, 2.5 times faster than manually. The bank calculated that in 3 years it would save more than 270 million rubles on document processing.”
According to Plantronics Business Development Manager Alexey Bogachev,“One of the main advantages of AI systems is the ability to obtain some new materials that are simply not available to us. Since an ordinary person draws conclusions based only on his knowledge, but here we get a more in-depth analysis that can lead to completely unexpected conclusions. This way you can get a breakthrough in a certain area.”
“Man is used to considering himself the crown of evolution, but we regularly encounter limitations,” reflects the CEO of Document Constructor FreshDoc.ru. Nikolay Patskov. - For example, hypersonic aircraft fly at a speed 10 times greater than the speed of sound; a human pilot is simply not able to control such a machine without the help of smart electronics. Human reaction and decision-making speed are not enough to operate at such speeds. Artificial intelligence helps us move beyond these limitations. AI allows people to react faster, protects them from making mistakes, and frees them from routine operations and decisions. Such systems can effectively replace a human expert in transportation, forecasting, stock trading, consulting, and drafting documents. The use of “smart solutions” also affects the final cost of the product: after all, “robots” do not need to pay a salary, they do not get sick or go on vacation, and are not subject to decreased performance. We see huge potential in developing intelligent solutions for a wide range of problems. Participation in the development of this area can allow Russian IT entrepreneurs to turn the market upside down and “ride” the information wave of human development.”
According to the director of business development and marketing of Konica Minolta Business Solutions Russia Zhamili Kameneva everything, of course, depends on the class of solutions. But for the most part, they are aimed at optimizing and automating processes, saving resources - both material and intangible, working and personal time. “Simply put, their task is to make our lives easier,” sums up Kameneva.
“Firstly, such systems allow us to identify what is hidden from the human mind,” says director of international business development at Navicon Ilya Naroditsky. - Regardless of how good a person’s BI tools are, in some cases it is impossible to do without machine learning: for example, if you need to process statistics on transactions on bank accounts of 1 million clients over 10 years. Already today, machine search for hidden patterns that are not obvious to humans allows many companies to build a business strategy and create management decision support systems. Secondly, artificial intelligence technologies significantly increase the efficiency of all types of communications with consumers. Innovative technologies, capable of understanding and analyzing text and voice messages, help reduce the processing time of incoming requests and respond to customer requests more quickly than before. Thirdly, such systems can relieve company employees from performing routine operations, and therefore free up their time to resolve strategically important issues. Time spent on solving routine problems could be used to solve creative problems.”
“Such systems make it possible to make decisions for a person in those areas where this is permissible,” says the general director of Atak Killer. Rustem Khairetdinov. “Whereas previously automated systems made decisions only within the framework of clearly predefined “if-then” scenarios, today’s and tomorrow’s systems will be able to make decisions under vaguely defined conditions and with insufficient information, which previously only a person could do.”
Acronis Development Director Sergey Ulasen also notes: artificial intelligence systems solve many problems that previously required human involvement. At the same time, they often function faster and have predictable results and quality of work.
“AI technologies really work and help improve business processes, at least partially freeing human intelligence from routine for creativity and creating new things,” emphasizes the CEO of Preferentum (IT Group) Dmitry Romanov.“It’s easy for them to evaluate the economic impact. For a large class of systems using machine learning methods, a definite plus is their ability to become “smarter” as they work.”
According to the marketing director of Vocord Sergei Shcherbina, The main advantages are that based on “chaotic” facts, poorly structured, unclassified or incomplete information, AI makes accurate forecasts. “Relying on them, we get fundamentally new level accuracy and speed of decision-making where simple ones do not work, linear rules, continues Shcherbina. - Huge amounts of data are constantly being replenished, but on their own they cannot solve problems; AI is precisely what is needed to analyze them. We already know many examples of the successful use of AI in medicine, in the analysis of global and local economic and social processes, in solving engineering and technical problems, making investment decisions, and in security systems. Innovations in the field of AI will make it possible to automate a fundamentally wider range of business processes. Thus, in the field of video surveillance and security, for the first time it will be possible to reliably, without the participation of an operator, identify 24/7 potentially dangerous incidents and identify wanted persons. There are already many examples of successful use of AI.”
The main advantage, according to the co-founder of the shikari.do service Vadim Shemarov, is that AI systems are learning. “For example, if we want the system to be able to distinguish messages from people where they want to buy something from messages where they want to sell something, or determine the subject of messages, we do not need to create a detailed list of words and phrases that express intentions , mood, theme, etc. We select a lot of example texts on the topics we need, “train” the system using these examples, and then it itself begins to understand the essence of texts unfamiliar to it,” says Shemarov.
Supervisor research center problems of regulation of robotics and AI, senior lawyer at Dentons Andrey Neznamov also believes that the possibility of learning (supervised learning or self-improvement) can be called the main advantage of technologies that are usually called “AI”.
What are the challenges of implementing these systems?
To briefly summarize, the main advantages of AI technologies, according to IT market experts, are reaching new levels of productivity, automation, efficiency, analysis, training, decision-making, predictability, and learning ability. However, since this is a new direction, experts see even more difficulties than advantages. Suffice it to say that almost each of the speakers named their own difficulty.
“This is a completely new area. Every problem that is currently being solved is RnD in its purest form: you need to define, systematize, come up with a solution, implement this solution and implement it, emphasizes Maxim Arkhipenkov. - This creative process, which requires a lot of science and high expertise both directly in the field of application of this solution - be it FMCG, space, medicine, and in the field of implementing neural network systems.”
According to Alexander Pivovarov, the difficulty “is in finding a balance between hype and actual usefulness, the difficulty of making these technologies invisible to the consumer and the absence of errors in their operation.”
Dmitry Karbasov believes that “the key difficulty of these projects is related to the unpredictability of the result.” “For example, when purchasing a CRM system, the customer clearly understands the functionality that the system offers him and how he will use this functionality,” says Karbasov. - These are processes, data entry forms, reporting, etc. When implementing an AI system, it is very difficult to predict the result without implementing the project; disclosing technologies and algorithms will tell a person practically nothing without mathematics education And practical experience, and among customers there are only a few top managers with such a background. The implementation of pilot projects helps, the methodology of which has been established and which we use in 99% of projects.”
“There are, of course, a lot of difficulties,” reflects Maxim Andreev. - The main one, perhaps, is the lack of large enough data sets for training artificial intelligence. In this case, data with history is needed. Let me explain what I mean: for one large company we made a sales forecast for transportation services - we predicted the weight of cargo and the direction of transportation. We could not achieve good forecast accuracy, we began to figure out what was going on and found out that in the historical data that was stored in the company, in some places the weight was taken into account with packaging, and in others without. At the same time, there is simply no sign by which this factor could be tracked. That is, once in the past this information did not play a role, but now everything has changed. That’s why it’s so important to collect all the data that can be collected “on demand.” Technologies for collecting and processing data are constantly evolving, and companies can already implement Data Lake technologies, which become an excellent platform for training artificial intelligence. Another difficulty is that the algorithms themselves are still quite small. Therefore, a company needs to conduct research before implementation. This allows us to find out whether, under specific conditions, on specific data and for specific business processes, it will be possible to build AI, the costs of which would not exceed the value it provides to the company.”
Anna Plemyashova believes that the main problem is complete absence or insufficient data to build accurate models. “For industrial enterprises, where such solutions require significant investments in infrastructure, this is a delayed economic effect: you must first start collecting and accumulating data, and then you can move on to solutions using intelligent systems. Transitional BI solutions and real-time data visualization allow you to bring economic benefits closer, says Plemyashova. - Another difficulty is the need to restructure the business process when introducing intelligent systems. That is, it is not enough to buy such a solution and place it like a flower in a vase or an application on a computer. It is necessary to make this decision friendly to the business process: create, reconfigure or even cancel some operations, retrain people, optimize personnel.”
“These systems are based on data and big data,” recalls Sergei Ulasen. - To train models, significant computing resources are required, and to store large data, an appropriate infrastructure is required. Therefore, implementing AI systems requires significant investment in hardware.
In turn, collecting and preparing data requires a lot of organizational effort, and often the development of new software to help with data analysis.”
Svetlana Gatsakova believes that the difficulties are primarily “insufficient attention to the limits of applicability of each specific AI technology, to the pitfalls.” And also “in the weak interpretability of the results (after all, for example, a neural network does not explain its conclusions), in the difficulties of forming homogeneous sets of data for training and testing models.” Another difficulty is “blind faith in data and a weakening of attention to the manager’s intuition and those factors that are difficult to measure and integrate into DDM processes *.” This, according to Gatsakova, is accompanied by “difficulties specific to Russian organizations.” “This is the low availability of reliable data about the external world of the organization and the ensuing risk of becoming isolated internal information, that is, turn into a kind of autistic organization. In addition, this is a small (compared to leading Western companies) penetration of the DDM culture, limited mainly to graduates of Western business schools.”
“AI helps automate many processes and replace low-skilled employees, but at the same time requires control from developers, whose work costs, of course, are higher,” says Angelina Reshina. “The learning ability of the system needs to be controlled so that it does not go beyond acceptable limits.”
According to Sergei Shcherbina, difficulties lie in outdated equipment and weak infrastructure, inherited hardware and software platforms, which in difficult economic times and with limited budgets few people dare to change. “The human factor also has an impact,” emphasizes Shcherbina. - There is a shortage of qualified personnel, an insufficient level of competence, or conservatism of managers. Moreover, not everyone understands why this is needed and why spend money on modernization when everything seems to work “the old fashioned way.”
“Among the difficulties of building AI systems, first of all, it is necessary to note the shortage of personnel,” notes Andrey Sykulev. - There are very few specialists, because the requirements here are extremely high: in addition to programming skills, you must master a fairly complex mathematical apparatus and have knowledge and experience in the subject areas. Quite often, the “showstopper” is the low quality of data and the lack of infrastructure for their integration. Another important problem is ensuring data security, because data consolidated for the operation of AI can become a target for an attack or be used, to put it mildly, for other purposes.”
Alexey Bogachev also believes that one of the main difficulties is personnel. “As with everything new, the question arises of how to work with it. Since the applied application of any technology requires qualified specialists, and this is a very young area, it is therefore quite difficult to find people who understand this.”
There is also a second side to the personnel problem. “The main difficulty is that not many senior managers of enterprises understand what artificial intelligence is and what its practical application is,” recalls Dmitry Karbasov. - Yes, almost all of them have heard about AI, everyone knows that AI helps optimize business processes, reduce costs, make individual functions more efficient (logistics, analysis of consumer behavior, forecasting production load and sales volumes, etc.). But rarely do any customers understand: for AI to work as it should, it is necessary to formulate a business problem and criteria for its success in business terms. In other words, the customer must understand which parameters should be assigned to the AI system to analyze and what to do with the received data from the point of view of making management decisions.”
“Two factors can be identified as the main difficulty in implementing such solutions: human and technological,” says Nikolay Patskov. - The first is the problem of a small number of experts capable of interacting with artificial intelligence systems. This problem is gradually being solved, the market is realizing the value of such specialists, and more and more employees are acquiring the skills necessary for the developing market. A technological factor can be attributed to the lack of computing power: now we are again developing ideas that we will be able to implement only with the advent of more powerful machines. But given the projected growth in productivity (a 1,000-fold increase in the next 10 years), we believe that the evolutionary development of technology will at least not slow down.”
According to Alexey Chaley, there are three main difficulties: “The first is people . There are very few people in the world capable of working in frontier areas who simultaneously understand the subject area (in our case, virus analysis), are well versed in mathematics, statistics and machine learning, and also know how to code at least a little. Second - training data . This data needs to be taken somewhere and then marked up. Data is very difficult to obtain. Because of this, by the way, the progress of AI development is slowed down, since researchers do not have the opportunity to experiment with models. It is not enough to just be a talented analyst and programmer - without data it is impossible to create anything in the field of AI. And the third is the cost of infrastructure. The initial investment in infrastructure can be quite significant.”
“In order for artificial intelligence to solve business problems well, the technology must be “customized,” believes Dmitry Shushkin. - Any machine, like a person, needs to be trained on current data in order to make accurate decisions. To train such a system, you first need to collect or synthesize a large amount of high-quality labeled data - for example, information about finances, production, customer service, etc. In a large business, such data is easier to prepare and collect, since many companies already use streaming data entry systems from various types documentation, this corporate information is ordered and structured. The creation of such arrays in medium and small businesses is still less accessible.”
Zhamilya Kameneva calls one of the main difficulties high cost such decisions, the length of projects and long-term return on investment (2-5 years - minimum). “Secondly, like any new tool, long and painstaking work is required to create a market for consumers of these technologies,” continues Kameneva. “In addition, I would like to note the lack of highly qualified personnel on the market - the vast majority of our artificial intelligence systems are developed by foreign vendors and only a few scientific institutions.”
According to Dmitry Romanov, the main difficulty, surprisingly, is psychological: “People are accustomed to expecting absolute accuracy from a computer. AI systems have probabilistic output. They can make mistakes, give wrong answers, and in this they are similar to humans. Users sometimes tend to overestimate the capabilities of smart technologies.”
Vladimir Fomenko is confident: in a few years, as soon as this technology ceases to be new and becomes more understandable, there will no longer be much difficulty in its implementation. “There will be systems or programs that will be able to create AI systems or programs.”
But Rustem Khairetdinov believes that there is no difficulty in implementation - “both the mathematical apparatus, and algorithms implemented in software, and computing power are available today practically “out of the box” or “from the cloud.” “The difficulty lies rather in the formulation of the problem, the construction of a model for analysis. We will soon be faced with the fact that pure mathematicians, as they are now called data scientists, will be less in demand than specialists in other fields (doctors, technologists, security specialists, linguists, etc.) with knowledge of the principles of machine and “deep” learning." , - emphasizes Khairetdinov.
* DDM (Digital Diagnostics Monitoring) is a function for digital monitoring of SFP transceiver performance parameters (as well as SFP+ and XFP). Allows you to monitor in real time such parameters as: voltage, module temperature, bias current and laser power (TX), received signal level (RX).
It all started with the scientific and technological revolution, which served as a powerful impetus for the development of technology. It was then that the transition from industrial society to post-industrial society took place. Nikola Tesla with his alternating current, Alexander Popov with the invention of radio, Alexander Bell - thanks to him, humanity became acquainted with the telephone - are considered geniuses who turned the usual picture of the world upside down.
It is worth mentioning the people who quite recently created or continue to work in this fertile field. Bill Gates, Mark Zuckerberg, Elon Musk are outstanding minds who have made, and continue to make, significant contributions to the development of society today. They move our new, high-tech world forward. And very soon a new miracle will appear before people’s eyes. The tireless Elon Musk said that in ten years it will be possible to write messages using the “power of thought.” Relatively recently, he would have been called crazy or eccentric, but in the good old days they could have been impaled! But in the twenty-first century, the world has become more tolerant and inquisitive. However, it is difficult to surprise humanity, which is satiated with a large number of new products, informs.
So what can interest our generation and take technology to a new level? The answer is artificial intelligence and nanotechnology. The creation of artificial intelligence will lead to the emergence of new areas, as well as the expansion of existing functions, such as speech recognition and synthesis, prognosis, cluster analysis and many others. Development has been going on for a long time, but to create a fully operational model, a new technical solution will be required, known as a “quantum supercomputer,” whose computing power can provide full functionality.
The implementation of these ideas has its global pros and cons:
The first advantage is the production factor. Today, the presence of a person is necessary; he evaluates the quality of the work performed and eliminates technical faults.
In the future, artificial intelligence will manage the entire chain independently. It is assumed that he will do this much better than his creator.
The second is research. Exploring space, the depths of the ocean or earth's core will become safer and provide more opportunities. Where a person cannot go, a machine can handle it.
The third is medicine. Diagnostics, surgery, rehabilitation, implants.
The disadvantages include:
The main thing is the replacement of humans with machines, which will lead to mass unemployment of the population. What to do with millions, billions of unemployed people? The question is still open.
The second is disruptions in the global information and production networks. This could create problems on a planetary scale.
In 2003, disruptions in the energy supply system occurred in Canada. New York, Detroit, Toronto, Montreal, Ottawa were left without electricity. Traffic jams, hundreds of thousands of people locked in the subway, facts of looting, casualties, fluctuations in world oil exchanges.
This is such an unpleasant call. Various reasons were voiced. Lightning strike, failures at nuclear power plants, but the fact remains a fact. Fifty million people were left without power for several hours, leading to panic and confusion. Some behaved like lost children, others worse than animals.
The world is very fragile, and the veneer of human civilization is very thin.
The third is the seizure of power on the planet by artificials, enslavement or complete destruction of people. Today, such a scenario is considered only in science fiction films and books. But this is not the first time for humanity to make a fairy tale come true. And not necessarily a fairy tale with a happy ending.
But let's be optimistic. We believe in human genius and new names in the world high technology and humane ideas. Civilization has stood on the brink more than once, but every time people appear with advanced, non-standard thoughts that prevent them from falling into the abyss.
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Introduction
Artificial Intelligence Basics
Machine and Human Capabilities: Examples, Practice and Analysis
Pros and cons of using artificial intelligence in management
Conclusion
List of used literature
Introduction
Our world may be heading towards disaster. It is not difficult to come to this conclusion with any sober analysis of the state of our planet. Economic stagnation, poverty, rampant inflation, mass unemployment, overpopulation, political strife, terrorism, war and the danger of war, as well as the threat of “doomsday” have not spared any corner of the globe. Of course, humanity has always faced many problems, but today's problems naturally seem more significant than those of the past. Now we seem to have actually reached the point where something very significant has to be sacrificed. Unfortunately, it is customary to place the main blame for this on the development of technology, i.e. exactly what humanity throughout its history has tried to find a solution to many problems.
Technology has accompanied man for thousands of years and is nothing more than the cumulative result of mankind’s aspirations for a better life. Now, however, there are people who believe that the development of technology, on the contrary, worsens, rather than improves life. The challenges facing people today range from social disruption caused by technological change, unemployment, environmental pollution and the threat of nuclear annihilation to alienation and dissatisfaction with work and its specific results. To this we can add the following. It is possible that the complexity generated by technological progress is responsible for the intractable ills of the economy, and that a significant danger is posed by technical systems that become so complex that people will soon lack the knowledge and understanding to manage them.
Naturally, the question arises, how to solve these problems? Can the inanimate creations of technology find answers to the questions that technology itself has posed, and to myriads of others that drive the human race into despair? Are machines themselves capable of arriving at solutions that elude human reason? In this course work I would like to prove that in principle this is possible and, moreover, in the future this must happen.
Such a statement is not just a figment of dreams. It is based on discoveries that are made day after day in various laboratories around the world, the most fruitful ones working in the field computer technology. For a long time it was mistakenly believed that at the output of a computer you can only get what is included in it at the input. This idea has certainly been confirmed over the past 30 years in much of the work related to data processing. Now, however, it has been irrefutably proven that something completely new can be obtained from computers, namely knowledge. This knowledge, in turn, can take the form of original ideas, strategies, and solutions to real-world problems.
Until now, the knowledge created by the machine has not had much practical significance; it has not been able to cure the deep ills that plague our world. This was to be expected: after all, a biologist, having begun the synthesis of living matter, at best expects to receive only a virus, and not an adult horse. But it will undoubtedly become possible over time to direct computers to find solutions not to chess or some other game, but to more pressing problems facing society. And most likely he will find them.
Of course, this will take a lot of time, but if a person sets such a goal, he will achieve it sooner or later. Hopefully, the day will come when poverty, hunger, disease and political strife will be tamed, and new knowledge gained by computers acting as our assistants, not slaves, will play a role in this. In addition, the mental and artistic potential of a person will receive completely different opportunities for development, which today are difficult to even imagine, and the gates of human imagination will open wider than ever.
And we must not miss our chance, although it may not be easy. We will have to completely abandon the traditional technical approach with its main goal - to ensure maximum economic effect from the use of machines and move to a strategy aimed at making the processes occurring in systems fully understandable to people. To do this, computers must learn to think like people, i.e. If the computing systems of the next decade do not fall into the “human framework”, they will become so complex and incomprehensible that a person will simply not be able to manage them. The inability to cope with such complex systems will at first simply lead to breakdowns (if we talk about many applications of these systems that exist today); If we are talking about military warning systems, control systems for nuclear power plants or global communication systems, then leaving them out of our control can lead to catastrophes on a worldwide scale.
Artificial Intelligence Basics
Since the beginning of the 80s, a new stage has begun in work on artificial intelligence - the creation of industrial and commercial samples of intelligent systems. The industry producing such systems began to develop, which means that potential consumers of its products appeared. What distinguishes intelligent systems from other creations of the human mind? What can we expect from their appearance in the near future?
The key term in artificial intelligence is the term “knowledge”. With a certain degree of approximation, one could say that intelligent systems are systems that use knowledge. This is what makes them different from others artificial systems(including software programs that were implemented on computers in the era preceding intelligent systems), based, as a rule, on the same computers.
Staying on a metaphorical level, we can say that earlier computers “understood” how to execute the program entered into them, but “did not understand” what they were doing, and with the advent of intelligent systems, computers learned to “understand” how to build the necessary one. a program to solve a given problem and what this program does. Let us clarify this important idea. With the traditional method of solving a problem on a computer, the essence of the problem itself, its meaningful interpretation, was known to the programmer who prepared the program for the computer. These could be various programs: for playing “backgammon” or “go”, calculating the trajectory of movement spaceship or payroll. When these programs were introduced into the computer, the substantive side of the tasks disappeared - the computer, by virtue of its design, carried out the commands of any of the programs in a qualitatively identical manner. In the “programmer-computer” pair, only the first knew what the computer was doing, and the computer, like a powerful adding machine, simply performed the necessary transformations and calculations.
There was danger in this traditional scheme. It consisted in the indissolubility of the “programmer - computer” pair when solving problems. The programmer, like a “galley slave,” had to interact with the machine, which “indifferently” grinds any information entered into it.
The emergence of intelligent systems indicated the breakdown of this paradigm. If knowledge is introduced into the computer memory about how programs are built from the conditions of a problem and what this or that task means in a given problem area (i.e., how the goal of the task is interpreted and what are the possible connections between the initial situation and the goal), then the functions of the programmer will be performed by the computer itself. She will automatically, based on the knowledge she has in her memory about the problem area, about the problems that may arise here, and about ways to solve them, will be able to independently compose the desired program and execute it.
This point is fundamental. The knowledge entered into the computer now allows it to “understand” what it should do when the need arises to solve a problem. By the way, when exactly this need arises, the computer also “knows” itself (although the requirement to solve the problem may come from the outside - from the user of the system).
This is how the main tasks that face that branch of artificial intelligence, which these days are increasingly called knowledge engineering, are formed. What are these tasks? First of all, this is the task of collecting knowledge that is necessary for a computer. This task is far from being as simple as it might seem at first glance. Indeed, in addition to the knowledge that is embodied in various texts, professionals possess a lot of knowledge that cannot be found either in manuals, or in instructions, or in monographs. This is the knowledge that is usually called experience, skill, professionalism. Often, an experienced specialist does not even suspect that he has enormous knowledge. It seems to him that he “just works and that’s all,” and his colleague, who has not yet gained experience, looks at him with envy, not understanding why everything is not working out for him. To be able to obtain this knowledge from a specialist expert, to be able to present it in a form suitable for entering into computer memory is the first and very non-trivial task of a knowledge engineer. But this is not enough. When accumulating knowledge obtained from various sources, one must constantly take care that it does not form a contradictory system: any new knowledge must be linked to previously existing knowledge. The emergence of new knowledge may require some restructuring of the previously created knowledge base. This requires special management procedures. Developing and manipulating such procedures is the second task of the knowledge engineer.
Receiving information from the outside world, analyzing emerging situations, a person constantly turns to information stored in his memory. By using what is already known to understand something new, a person, using his knowledge, completes the input descriptions and replenishes them. In any conversation between two people, understanding replicas is possible only because a lot of additional information about the subject of the conversation is stored in the memory of the interlocutors. And computers for replenishing knowledge should have a set of similar procedures. For this purpose, so-called pseudophysical logics are used: temporal, spatial, causal and others. With their help, input descriptions are replenished, which ensures their understanding. In addition to replenishing descriptions in knowledge bases, other procedures are carried out: generalization and classification of incoming information, putting forward hypotheses about the connections of facts stored in the system’s memory, various types, reliable and plausible conclusions of derived facts, etc. This is another field of activity for a knowledge engineer.
However, touching directly on the topic course work it is important for us to note mass implementation Computers in all areas of management. This is a question about the ability of a human administrator to understand the decisions made by a computer included in the control system. Control systems for complex technical complexes today are literally “stuffed” with computers connected to each other in complex structures. Working at speeds inaccessible to humans, processing a huge amount of various information received from the control object and from other machines, the computer makes decisions that are often incomprehensible to humans. The only way to understand them is to ask the machine a question: why is the decision this way? And the computer is obliged to provide the necessary explanations. For this purpose, it must have a special explanation subsystem that allows the computer to “understand” why it made a particular decision. The emergence of explanation subsystems can be considered as the first step towards the “humanization” of technical systems. It is difficult to overestimate the importance of this step. Technical systems have gone too far in their development, it has become too difficult for a person to interact with them, and the consequences of the actions of our smart but soulless assistants can be too dangerous.
The development of work in the field of artificial intelligence and the widespread introduction of intelligent systems into our lives is evidence of a new stage on the way scientific and technological progress. It is inevitable - and we must be prepared to face its consequences with full understanding of what is happening. It is not the problem of WHO WHOM and not the fear that THEY will enslave US if we do not take action that should determine this new stage in the life of humanity, but the community of WE + THEY, from which humanity will undoubtedly derive great benefit, for it will help us solve problems which we cannot cope with alone.
Machine and human capabilities:examples, practice and analysis
On March 28, 1979, at the Three Mile Island nuclear power plant (Pennsylvania, USA), an alarm sounded in control room No. 2. At first the operators did not show much concern, since minor accidents at the station were not so rare, but after a few minutes it became clear that this time something much more serious had happened. A tiny valve in the pneumatic system had become stuck, causing the secondary circuit to stop circulating water. Moments later, the uranium core of the reactor began to heat up, and, despite all the efforts of the operators, the situation only worsened. The safety valve opened and stuck in this position; Radioactive water and steam went into the reactor building, and therefore into the atmosphere. A huge hydrogen bubble formed under the roof of the reactor vessel, which could explode at any minute. There was a threat that the uranium fuel itself would begin to melt. Any of these events could lead to radioactive contamination throughout the territory of the pc. Pennsylvania.
Over the next few days, plant personnel and the Nuclear Regulatory Commission fought to bring the reactor under control as a frightened world watched in alarm. The state governor ordered the evacuation of danger zone children and pregnant women, and many residents left on their own. Only a week later, the Metropolitan Edison company, which owned the station, announced that work had begun to mothball the shutdown reactor, and life in Pennsylvania gradually began to return to normal. It took several years to clear the “Augean stables” into which the reactor building had become.
The commission studying the role of the human factor in this incident came to to the following conclusion: “... the operator was bombarded with such an avalanche of information: display readings, warning signals, printout data, and the like, that it was completely impossible to identify the malfunction and correctly select corrective measures.”
The Presidential Commission agreed with this conclusion, concluding that the blame should be placed on "insufficient attention to the human factor and its role in ensuring security nuclear power plants" The lesson learned from this accident is obvious: until the design of technical systems is thought out in every detail so that everything that happens in them is absolutely clear to the operating personnel, until the information is presented in a form convenient for perception by the human eye and brain, and not machine, any malfunction in the automated system can make it completely uncontrollable.
In 1975, the Dutch steel company Estelle Hugo-Vence installed a new highly automated hot rolling mill at its plant, located on the seashore near Amsterdam. Anticipating a huge increase in labor productivity due to the introduction of advanced technology, the management of the enterprise was shocked to discover that in reality production output had decreased. Consultants from the British Steel Corporation were invited to help, who, in a report on the results of the study, indicated that the main reason was the improper organization of interaction between operators and the machine. New Scientist magazine described it this way: “Operators lost so much confidence that in some cases they simply left the control panel unattended. In addition, operators did not always fully understand the control theory underlying the control computer program, and this encouraged them to “remove themselves” from control whenever possible until obvious problems were discovered. But due to the fact that they intervened in the process with great delay, the average productivity turned out to be lower than in factories using traditional methods management. Thus, automation entailed a decrease in productivity and at the same time further removed operators from control processes.”
The problem was further aggravated by the fact that in the new design of the rolling mill, the strip was hidden from view throughout the entire rolling path, which did not allow operators to at least visually monitor the process. In their report, the consultants, in particular, unequivocally insisted that operators need to be brought closer to technological process, and information displays should help people understand the meaning of decisions made by automation, and not just report on the progress of the process.
Next example? air traffic management, which is a source of concern for passengers and air traffic controllers alike around the world. Cases of planes nearly colliding mid-flight have become all too common, not to mention failures in electronic equipment that leave controllers helpless for precious seconds or even minutes. According to the University of Illinois Research Coordination Laboratory, computerized air traffic control in America is becoming so complex that operators sometimes have difficulty understanding what's going on. As for prospects for the future, there are two opposing points of view on what the management systems that will replace the existing ones should be. Some experts call for increasing automation, believing that this will eliminate the uncertainties associated with human presence; others believe that humans and machines should be partners of sorts in this common endeavor. But no matter what path the further development of control systems takes, situations are always possible where human intervention is required. And if the creators of the system do not make sure in advance that a person can understand how the system works, then his intervention will most likely be very insignificant and will occur with a great delay.
We cannot ignore military issues.
For eight months 1979-1980. The US military received three false alarms warning of an "attack" by Soviet missiles. All signals came from the North American control center air force, hidden in the depths of a mountain in pc. Colorado. The first false alarm was simply the result of an operator error who carelessly inserted a tape containing information intended for training into the system. The second time one of the system components failed: the integrated circuit failed. The third alarm turned out to be deliberate - it was an attempt to reproduce the conditions of the second alarm for testing purposes.
Fortunately, a few minutes after these false alarms the all clear was given, but the nervous overstrain they caused was not forgotten. It is clear that a system that could literally lead to the end of the world must be done this way. so that the possibility of misunderstanding in the human-machine relationship is completely eliminated.
The message from these stories is clear: as technical systems become more complex, they become more difficult to understand and therefore more difficult to control. This is especially true for computing systems, which, even when designed to do the simplest things, must be very complex. We strive to ensure that they are able to solve problems of practical importance, and thereby increase their complexity to a level that is beyond the capabilities of an individual or even a group of people to understand. That time has already come. As we have just shown, large computing programs and operating systems grow to a scale where neither their creators nor their users can handle them.
If computing systems continue to develop along the same path as now, when more and more functions are assigned to their already not very reliable architecture, then there is no doubt that the computers of the 90s will become completely unusable: unmanageable and frightening - sort of helpers of the universal “evil spirit”. Human society, already heavily dependent on such machines, will face a crisis of monstrous proportions. Computing machines, as they exist today, have, in a sense, already reached the limit of their capabilities. Today, the main task is no longer to maximize their productivity, to extract everything possible from machine resources. On the contrary, their work must be based on a completely different idea - the idea of anthropocentrism. In order for us to understand the work of machines, we need to learn how to organize it in the image and likeness of the work of the human brain.
We can further develop this ominous plot by imagining our future as it has been described more than once by science fiction writers, starting with Samuel Butler: a world in which machines have seized power. This idea is usually dismissed by technical experts as absurd. But is it really that absurd? Take, for example, computers that are already used in managing the life of our cities. Its functions include not only the tasks of the central administration, but also public services, maintaining order in the city, education, banks, air traffic, traffic control, problems of construction and planning organizations. And a moment comes when the corresponding computer networks begin to directly contact each other - initially for the simplest reasons. If, for example, in one system a decision is made to dig up a road, then the garbage trucks need to change their route. If someone books a plane ticket, the airline must verify that they are eligible to use the credit card provided.
Turning now to the less nightmarish, but more pressing problems, let's look at the deep stagnation in the economy, high unemployment, crises of confidence, which have increasingly worried the world in recent years. All these phenomena, which actually occur, are completely inexplicable at first glance. Let's start with the problem economic growth, or rather, its absence. In fact, the productive capital of industrialized countries is not shrinking. However, due to the continuous progress of science and technology, it is constantly transforming. What is the nature of this change? Invested capital brings higher returns. Factory workers can now produce more in a day than they could thirty years ago. A farmer may cut more hay than is needed to justify renting a mower. The day is not far off when self-driving mowers will appear.
Moreover, scientific and technological development does not simply occur at a constant speed: no matter how we evaluate its pace, it is obvious that it is steadily increasing. Why, then, are we not getting richer at the same rate? Even making allowances for losses associated with the reorganization of work in certain industries, humanity as a whole should be a significant gainer. Apparently, there is a certain force at work that is blocking that cornucopia from which, it would seem, blessings should now be pouring down on all of us.
It seems that we are all united in our regrets about this. But different people have different views on which part of this process should be stigmatized. Some are absolutely sure that the trade unions are to blame for this, which are in a secret conspiracy with an invisible network of subversive elements and terrorists around the world who are achieving their political goals. According to others, the culprits should be sought in the offices of giant corporations and banks, possibly operating in alliance with a secret network of transnational monopolies and cartels, led by one or two “evil dwarfs” from Zurich, pursuing their own political goals. There is a third “school of thought,” perhaps not as passionate as the previous two, but even more delusional, which believes that technology itself is to blame for everything. It is not uncommon for an angry customer to take it out on a non-working vending machine for small items until it is completely unable to operate.
Although, perhaps, such an anti-technical position is not so crazy. This idea can at least be discussed, since the examples given earlier do suggest that our technological achievements are somewhat similar to a machine that does not work.
We will have to make a short excursion into the depths of history to find out whether there was any stable and at the same time evolving process throughout its entire history? Such a process is not difficult to find - the impetus for it was the development of agriculture. And millennium after millennium, our ancestors did not seem to notice that this process was continuously moving in one direction, until in the 19th-20th centuries. we have not reached the final stage of acceleration. Such a steady process was a gradual, albeit painful, with many failures and stops, the growth of a person’s understanding of the world around him, the growth of his ability to manage this world.
Today, with the help of computer technology, we are trying to learn how to solve complex problems that cannot yet be solved on a computer - problems that cannot be solved “head-on”, by finding final number steps to answer the question through simple calculations. However, it happens that although the problem itself is very difficult, the inverse problem is much easier to solve. For example, calculating the square root is very difficult, but the square of a number is very easy. It is possible that a schoolboy will find it more economical to calculate the squares of all the numbers he might be asked about, and fill out a huge table of results (only by writing them in reverse: first the squares, ordered by magnitude, and for them the bases, perhaps with some interpolation to complete passes). Then, if you need to find out some square root, you can just look at the table. But this method has one big drawback - the result obtained is completely inexplicable to the user.
The question arises whether it would be better not to invent such stupid reference systems at all, the very presence of which humiliates a person, because they neglect his judgment and understanding. Interestingly, this argument was first made by Plato more than 2,300 years ago. In his Phaedrus, Socrates tells the story of Egyptian god Toga, who came to the king of the gods Tamuz with the words: “My lord, I have invented an ingenious thing called writing, it will improve both the wisdom and memory of the Egyptians.”
In response, Tamuz stated that, on the contrary, writing is a low-quality substitute for memory and understanding. “Whoever acquires it will stop training his memory and will become forgetful, he will rely on writing, hoping that these icons will remind him of something, instead of relying on his internal reserves.”
Socrates quotes Ammon as denouncing the perverse idea that “clear and precise knowledge of a subject can be conveyed or obtained through writing, or that written words can do more than remind the reader of what he already knows.” In other words, a person may think that wisdom lies in writing, when in reality wisdom must be in the person himself. “One might assume,” adds Socrates, “that written words understand what they say, but if you ask them again what is meant by such and such, they will give the same answer again and again.” "
In other words, Socrates seems to be complaining that writing will not be able to pass the famous Alan Turing test (according to this test, a machine can prove that it has intelligence if it manages to convince a person talking to it through a teleprinter that his interlocutor - human being). Indeed, if a machine could explain what it contained, then it could be considered to have in some sense “understood,” thus demonstrating its intelligence. Like writing, help systems future with trillions of bits of memory will not be able to pass the Turing test. But, like writing, such systems certainly have a right to exist and will help change the world. Is this good or bad? Until we understand the essence of Socrates' claims in relation to this new problem, such giant help systems will be only partly a blessing, which often turns into big troubles. Let us recall that such databases contain only basic elementary facts concerning a particular issue, and do not include any understanding, conclusions, judgments, classification concepts, and the like.
In order for any beings - human or machine - to communicate with each other, they must have the same mentality. Since we cannot change the mentality of people, we will have to change it in machines. We need to completely redesign everything that programs do to solve a problem, not just the way they interact with the user. The way in which information is stored in a program, i.e. the way of presenting a solution to a problem must be understandable to a person and described by concepts already familiar to him. Expert systems based on rules of inference are specifically designed to deal with human concepts, both when learning them from experts in the field and when explaining them to the user. This is a good start, but much more needs to be done to establish human-machine communication in conceptual language.
If similar ideas are used to solve problems of automation of production processes or in other control systems, then we will call such automation “soft”. The need for it is constantly increasing, which makes it possible to at least partially neutralize the excessive complexity associated with rigid automation. The most pressing social need now is not to expand the process of automation, but to humanize it. Of course, for simple or moderately complex tasks, the “opacity” of control systems is not so dangerous, and therefore we have put up with it for a long time. Let's say that the program that allocates resources does it better than the project manager. In that case, why would he be curious about how she does it or challenge her decisions if he gets what he wants? Let it be a “black box” to the extent determined by the program.
However, there are other information systems applications where the ability to “look inside the box” is very significant. So far there are few of them, since information processing processes have yet to deeply penetrate into increasingly complex and responsible areas of human activity. Complexity and responsibility are two independent characteristics of systems that lead us to insist that the program operate within a “human framework.” Some problems are so difficult that it is simply impossible to solve them without an intellectual partnership between man and machine. Others concern questions of life and death or the very possibility of economic management.
With soft automation, the system is adjusted to the human mindset already at the design stage. If we look into the future and imagine hordes of robots working together in our factories, the question inevitably arises: “How will they communicate? By wire, using infrared radiation or radio signals, or through some other channels inaccessible to humans? Of course, it would be better to carry out this communication using a synthesized voice, because this would allow the person on duty to hear what is happening, and, as practice has shown, this is quite possible.
Pros and cons of usingartificial intelligence in management
The trend towards automating factories and machines has been around for a long time. Except for some special purposes, no one any longer thinks about producing bolts on a conventional lathe, where the lathe must observe the movement of the cutter and adjust it manually. Nowadays, producing bolts in large quantities without significant human intervention is a routine task of an ordinary screw cutting machine. Although this machine does not specifically use either a feedback process or a vacuum tube, this machine achieves almost similar goals. Feedback and the vacuum tube made possible not the sporadic construction of individual automatic mechanisms, but a general policy of creating automatic mechanisms of the most diverse types. In solving this problem, the principles of such devices were supported by our theoretical study of communication, which fully takes into account the possibilities of communication between machine and machine. It is precisely this confluence of circumstances that makes it possible at present new century automation.
The industrial technology that exists today includes the totality of the results of the first industrial revolution, along with many of the inventions that we now consider as the forerunner of the second industrial revolution. What the exact boundaries between these two revolutions might be is too early to say. In terms of its potential, the vacuum tube definitely belongs to the industrial revolution, different from the energy age; and yet it is only now that the true significance of the invention of the vacuum tube has been sufficiently understood to classify the present century as a new, second industrial revolution.
Let's paint a picture of a more advanced age - the age of automation. Consider, for example, what the automobile plant of the future will look like, and in particular the assembly line, which is that part of the automobile plant that uses the largest amount of human labor, the sequence of operations will be controlled by a device similar to a modern high-speed computer. All mathematics can be reduced to performing a series of purely logical problems. If such a piece of mathematics is embodied in a machine, then that machine will be a computing device in the ordinary sense. However, such a computer, in addition to solving ordinary mathematical problems, will be capable of solving the logical problem of distributing through channels a number of orders regarding mathematical operations. Therefore, such a device will contain, just as modern high-speed computers actually contain, at least one large node, which is designed to perform purely logical operations.
The instructions to such a machine—I am also speaking here of current practice—are given by a device which we call a program coil. The orders given to the machine can be sent to it by a program coil, the nature and extent of the instructions of which are completely predetermined. It is also possible that real unforeseen circumstances, which the machine encounters when performing its tasks, can be transferred as the basis for further regulation to a new control tape created by the machine itself, or to a modification of the old control tape.
You might think that modern high cost computers precludes their use in industrial processes and, furthermore, that the sensitivity of operation required in their design and the variability of their functions preclude mass production methods in the creation of these machines. None of these statements are correct. First, the huge computers currently used to do very complex mathematical work cost approximately hundreds of thousands of dollars. Even this price would not be out of reach for a control machine in a really large plant, but it is still too expensive.
Modern computing machines are developing so quickly that almost every machine designed is a new model. In other words, most of these apparently exorbitant costs go to pay for new work to design and manufacture new parts that require very highly skilled labor and the most expensive conditions. If, therefore, the price and model of one of these computers were established, and if this model were used by dozens, it is very doubtful that its price would be more than a sum of the order of tens of thousands of dollars. A similar machine of less power, not suitable for solving the most difficult computing problems, but nevertheless quite suitable for running a factory, would probably cost no more than a few thousand dollars in any kind of moderate-scale production.
Let us now consider the problem of mass production of computers. If the only favorable possibility for mass production were the mass production of standard machines, it is quite clear that for a considerable period the best we could hope for was production on a moderate scale. However, in each machine, parts are generally repeated quite often. This applies equally to the storage device, the logical apparatus, and the arithmetic unit. Thus, producing only a few dozen machines would potentially be mass production of parts and has the economic advantages of mass production.
Yet it might seem that the sensitivity of the machine should mean that a special new model must be created for each separate work. This is also incorrect. Even with gross similarity in the type of mathematical and logical operations that are required from the mathematical and logical nodes of the machine, general execution the machine's tasks are regulated by a program coil or, in any case, by an original program coil. Manufacturing a program coil for such a machine is a very difficult task for a highly qualified specialist; however, this is a job that is done once and for all, and when a machine is modified for a new industrial installation, it only needs to be partially repeated. Thus, the cost of such a skilled technician will be spread over a huge amount of output and will not be a really important factor in the use of the machine.
The computing device is the center of an automatic plant, but it will never represent the entire plant. On the other hand, it gets its detailed instructions from elements of the nature of sensory organs, such as photocells, from capacitors for determining the thickness of a roll of paper, from thermometers, from hydrogen concentration meters and from the general types of apparatus currently created by instrument-making firms for the manual control of industrial processes. These devices are already designed in such a way that they transmit readings to individual posts using electricity. In order to enable these instruments to transmit their information to an automatic high-speed computer, all that is needed is a reading device that converts the position or scale into the form of sequential numbers. Such a device already exists and does not present much difficulty either in principle or in design details. The problem of the sense organ is not new, and it has already been effectively solved.
The control system must contain, in addition to these sense organs, effectors, or components influencing the external world. Some types of these effectors are already familiar to us, such as control valve motors, electric couplings, etc. To reproduce the functions more accurately human hand, complemented by the functions of the human eye, some of these effectors have yet to be invented. When machining automobile frames, it is quite possible to leave smooth surfaces on the metal consoles as reference points. A photoelectric mechanism, powered, for example, by light points, can bring a working tool - be it a drill, or a riveting hammer, or any other tool we need - into close proximity to these surfaces. The final position fixation may secure the tool against the reference surfaces and thus establish firm contact, but not so tight as to cause destruction of these surfaces. This is just one way of doing the job. Any qualified engineer can come up with a dozen others.
Of course, we assume that instruments acting as sense organs record not only the initial state of operation, but also the result of all previous processes. Thus, the machine can perform feedback operations: either fully mastered operations of a simple type, or operations entailing more complex recognition processes regulated by a central control such as a logical or mathematical device. In other words, the all-encompassing control device will correspond to the animal as a whole with sensory organs, effectors and proprioceptors, and not to an isolated brain, the effectiveness and practical knowledge of which depends on our intervention, as is the case in the ultra-fast computing machine.
The speed at which these new devices can be introduced into industry will vary widely across different industries. Automatic machines performing approximately the same functions are already widely used in industries with continuous processes, such as canneries, steel rolling mills and especially in factories producing wire and tinplate. They are also known in paper mills, which also operate using the in-line method. Another area in which automatic machines are needed is in those kinds of factories where the production is too dangerous for a significant number of workers to risk their lives in operating it, and where an accident can be so serious and costly that its possibility must be provided for in advance, and is not left to the hasty judgment of any person at the scene of the accident. If it is possible to think through a line of behavior in advance, then it can be applied to a program tape, which will control the behavior in accordance with the readings of the device. In other words, such plants must operate under a regime quite similar to the regime of blocking and operation of switches at a railway checkpoint. This regime is already established in oil refineries, in many other chemical plants and in the handling of such hazardous materials as are encountered during operation. atomic energy.
We have already mentioned the assembly line as an area of application for this type of technology. On the assembly line, just like on chemical plant or in a paper mill with continuous processes, known statistical control over product quality is necessary. This control depends on the sampling process. Scientists have now developed these sampling processes by developing techniques called sequential analysis, where sampling is no longer done as a whole, but as a continuous process, occurring alongside production. Consequently, those processes that can be performed by technology so standardized that it can be left to a statistician who does not understand the logic behind it can also be performed by a computer. In other words, again with the exception of the higher levels of operation, a machine can take care of day-to-day statistical control as well as the production process.
Typically factories have an accounting procedure that is independent of production, but since the data for this accounting comes from the machine or assembly line, it can be sent directly to the computer. Other data may be entered into the computer from time to time by a human operator, but most clerical work can be done mechanically, and only extraordinary information, such as external correspondence, will be left to humans. However, even most foreign correspondence may be received from correspondents on punched cards or printed on punched cards by very unskilled employees. From this stage onwards, all processes can be carried out by machine. This mechanization can also be applied to a significant part of the library archive collection of an industrial enterprise.
In other words, the machine does not give preference to either physical or clerical work. Thus, the possible areas into which the new industrial revolution can penetrate are very broad and include all labor carrying out decisions low level, in much the same way that labor, displaced by the machine of the previous industrial revolution, included any aspect of human energy. Of course, some professions will not be affected by the new industrial revolution, either because the new control machines are not economical in such minor industries that they are unable to bear the large capital costs associated with them, or because the work of a number of specialists is so varied that the new program coils will be necessary for almost every single job. I cannot imagine automatic machinery such as decision making devices coming into use in grocery stores or garages, although I can very clearly imagine the use of this equipment by a grocery wholesaler and an automobile manufacturer. The agricultural worker, although automatic machines are beginning to be introduced into his production, is also protected from their complete domination by the extent of the area of land which he must cultivate, by the variability of the crops which he must cultivate, the special conditions of weather and similar circumstances which he must encounter. . Where such machines can be used, it is not implausible that some use of decision-making machines may occur.
Of course, the introduction of these new devices and the time frame within which one can expect their implementation are mainly questions of an economic nature, the consideration of which is not the purpose of the course work. Unless there are any violent political changes or new great war, then new machines will take ten to twenty years to take their rightful place.
A very important issue is the analysis of the consequences - economic and social.
First, we can expect a sharp decline and final cessation of demand for this kind of factory labor, which performs exclusively monotonous work. Ultimately, the elimination of extremely uninteresting monotonous lesson tasks can be beneficial and serve as a source of leisure necessary for the comprehensive cultural development of a person. But it can also lead to the same little and detrimental cultural results that have largely been obtained from radio and film.
As it were, transition period the introduction of these new means, especially if it occurs instantly, which can be expected in the event of a new war, will result in an immediate transitional period of a disastrous crisis. There is a lot of experience showing how industrialists relate to new industrial potential. All their propaganda boils down to the fact that the introduction of new technology should not be considered a government matter, but should be provided to every entrepreneur who wants to invest money in this technology. We also know that it is difficult to restrain industrialists when it comes to extracting from industry all the profits that can be made from it, and then leaving society to be content with the crumbs.
Under these conditions, industry will be filled with new mechanisms only to the extent that it is obvious that they will bring immediate profit, regardless of the future damage that they are capable of causing. We are witnessing a process along the same lines of development as the Atomic Energy Process, in which the use of atomic energy to make bombs has jeopardized the very urgent prospects for the future use of atomic energy to replace our oil and coal reserves, which, centuries later, if not within decades, they will be completely depleted. Please note that production atomic bombs does not compete with energy producing firms.
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The history of the development of artificial intelligence in non-CIS countries, in Russia and the Republic of Kazakhstan. Development of a project for the effective implementation and adaptation of artificial intelligence in human society. Integration of the artificial into the natural.
scientific work, added 12/23/2014
The essence of the term "artificial intelligence"; the history of its development. The science and technology of creating intelligent machines and computer programs. The challenge of using computers for understanding human intelligence. Analysis, synthesis and understanding of texts.
thesis, added 06/17/2013
The phenomenon of thinking. Creation of artificial intelligence. Mechanical, electronic, cybernetic, neural approach. The emergence of the perceptron. Artificial intelligence represents an example of the integration of many scientific fields.
abstract, added 05/20/2003
Areas of human activity in which artificial intelligence can be used. Solving problems of artificial intelligence in computer science using the design of knowledge bases and expert systems. Automatic proof of theorems.
course work, added 08/29/2013
Historical overview development of work in the field of artificial intelligence. Creation of algorithmic and computer software that allows solving intellectual problems without worse than a man. From logic games before medical diagnosis.
abstract, added 10/26/2009
Studying the problem of artificial intelligence. The process of information processing in the human brain. Deciphering the brain codes of the phenomena of subjective reality. Natural intelligence as a fact possessing subjective reality with the principle of invariance.
abstract, added 12/04/2011
Components and architecture of an intelligent agent, its addition with training tools. Different approaches to the creation of artificial intelligence, prospects for its development. Ethical and moral implications of developing intelligent machines and programs.
Will artificial intelligence ever be able to create real competition with human intelligence? Artificial intelligence is endowed with sufficient potential, but researchers should not create something that cannot be controlled. This is the opinion of many world experts who signed an open letter from researchers in order to eliminate the “pitfalls” possible in this technology.
The open letter was signed famous physicists Stephen Hawking, Skype co-founder Jaan Tallinn, and SpaceX CEO Elon Musk, as well as a number of leading scientists from many universities from around the world, including Harvard.
Professor Francesca Rossi, who teaches at Harvard and the University of Padua, says:
Some people think: no need to worry, robots cannot be absolutely intelligent. However, there are those who claim that machines will soon become as smart, and maybe even more, than human beings. Neither of the two extreme points view is not sufficiently substantiated. A constructive approach is needed here: of course, efforts should be made to make robots become smarter, but it is worth paying attention to safety issues and constantly checking their possible behavior. Thus, this letter, as well as the document accompanying it, is written with precisely this intention: to be constructive in projects relating to the creation of artificial intelligence, but also in other fields such as philosophy, psychology, economics, etc., which lie at the heart of the production of “smart machines,” notes Francesca Rossi.
And now a question with a touch of apocalypticism: will these smart machines one day be able to overcome humans. That is, is it possible that the day will come when a robot will destroy a person? "
Francesca Rossi:
- In my opinion, such an apocalyptic scenario does not follow from the actual nature of these machines. They (robots) are able to change their behavior at the same time, but they always adhere to what was inherent in them from the very beginning. In any case, when creating machines, everything should be thoroughly checked."
Or is it possible in real life a situation similar to the one discussed in the film "Her", where a lonely writer develops a relationship with a computer operating system? Or is it more of a science fiction thing?
There are already robots that interact with people: for example, they help the elderly or the sick, they are able to develop “empathy” - interact with a person in much the same way as another person would do. I don’t think, however, that the evolution of robots that we are sometimes shown in movies is something that can happen in reality, at least in the near future,” Francesca Rossi further says.
The open letter also calls for action to be taken before "autonomous cars" become a mainstream technology. But what threat could they pose?
Let’s assume that the technology is ready, that these cars are ready to go out on the road and save human lives, because many people die in car accidents. It is also necessary to realize that the process must be properly regulated. It is important to clearly know who exactly is responsible for the decisions that robots make, for what exactly they do, etc., Francesca Rossi concludes.
Key words: Artificial intelligence, Pros and cons, Disadvantages and advantages of artificial intelligence, she movie, robots, apocalypse
