Artificial intelligence ai. What Every Engineer Should Know About Artificial Intelligence? In business and trade

Since the invention of computers, their ability to perform various tasks has continued to grow exponentially. Humans develop the power of computer systems by increasing the number of tasks performed and reducing the size of computers. The main goal of researchers in the field of artificial intelligence is to create computers or machines as intelligent as humans.

The term “artificial intelligence” is coined by John McCarthy, inventor of the Lisp language, founder of functional programming and winner of the Turing Prize for his enormous contribution to the field of artificial intelligence research.

Artificial intelligence is a way to make a computer, computer-controlled robot or program capable of thinking intelligently like a human.

AI research is carried out by studying human intelligence, and then the results of this research are used as the basis for the development of intelligent programs and systems.

AI philosophy

During the operation of powerful computer systems, everyone was asked the question: “Can a machine think and behave the same way as a person? ".

Thus, the development of AI began with the intention to create such intelligence in machines, similar to human.

The main goals of AI

Creation of expert systems - systems that demonstrate intelligent behavior: learn, show, explain and give advice;
Implementation of human intelligence in machines is the creation of a machine capable of understanding, thinking, learning and behaving like a human.

What drives AI development?

Artificial intelligence is a science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, mechanical engineering. One of the main directions of artificial intelligence is the development of computer functions related to human intelligence, such as: reasoning, learning and problem solving.

AI and non-AI program

Programs with and without AI differ in the following properties:

AI applications

AI has become dominant in various areas such as:

Games - AI plays a decisive role in strategy games such as chess, poker, tic-tac-toe, etc., where the computer is able to calculate a large number of all kinds of decisions based on heuristic knowledge.

Natural language processing is the ability to communicate with a computer that understands the natural language that people speak.

Speech recognition - some intelligent systems are able to hear and understand the language in which a person communicates with them. They can handle various accents, slangs, etc.

Handwriting recognition - the software reads text written on paper with a pen or on the screen with a stylus. It can recognize the shapes of letters and convert it to editable text.

Smart robots are robots capable of performing tasks set by humans. They have sensors to detect physical data from the real world, such as light, heat, movement, sound, shock and pressure. They have high performance processors, multiple sensors and huge memory. In addition, they are able to learn from their own mistakes and adapt to the new environment.

The history of AI development

Here is the history of AI development during the 20th century


	Karel Čapek puts on a play in London called Universal Robots, the first use of the word “robot” in English.

	Isaac Asimov, a Columbia University graduate, introduces the term robotics.
	Alan Turing is developing the Turing test for assessing intelligence. Claude Shannon publishes a detailed analysis of the intellectual chess game.
	John McCarthy introduces the term artificial intelligence. Demonstration of the first launch of an AI program at Carnegie Mellon University.
	John McCarthy invents the lisp programming language for AI.
	Danny Bobrow's dissertation at MIT shows that computers can understand natural language quite well.
	Joseph Weizenbaum at MIT is developing Eliza, an interactive assistant who conducts dialogue in English.
	Scientists at Stanford Research Institute have developed Sheki, a motor-powered robot capable of sensing and solving tasks.
	A team of researchers at the University of Edinburgh built Freddy, a famous Scottish robot capable of using eyesight to find and assemble models.
	The first computer-controlled autonomous vehicle, the Stanford Trolley, was built.
	Harold Cohen Designed and Demonstrated Programming, Aaron.
	A chess program that beats the world chess champion Garry Kasparov.
	Interactive robotic pets will become commercially available. MIT depicts Kismet, a robot with a face that expresses emotions. Robot Nomad explores remote areas of Antarctica and finds meteorites.

Artificial intelligence is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is often applied to the project for the development of systems endowed with intelligent processes that are characteristic of humans, such as the ability to reason, generalize, or learn from past experiences. In addition, the definition of the concept of AI (artificial intelligence) is reduced to a description of a set of related technologies and processes, such as, for example, machine learning, virtual agents and expert systems. In simple terms, AI is a crude mapping of neurons in the brain. Signals are transmitted from neuron to neuron and, finally, are output - a numerical, categorical or generative result is obtained. This can be illustrated with the following example. if the system takes a picture of a cat and is trained to recognize whether it is a cat or not, the first layer can identify the general gradients that define the overall shape of the cat. The next layer can identify larger objects such as ears and mouth. The third layer defines smaller objects (such as a mustache). Finally, based on this information, the program will output "yes" or "no" to tell if it is a cat or not. The programmer does not need to "tell" the neurons that these are the functions they should be looking for. The AI \u200b\u200blearned them on its own, exercising on many images (with and without cats).

What is artificial intelligence?

Description of the artificial neuron

An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network. Artificial neurons are elementary units in artificial neural networks. An artificial neuron receives one or more inputs and sums them up to produce an output or activation, representing the action potential of the neuron, which is transmitted along its axon. Usually, each input is analyzed separately and the sum is passed through a non-linear function known as an activation function, or transfer function.

When did AI research start?

In 1935 the British researcher A.M. Turing described an abstract computing machine that consists of limitless memory and a scanner that moves back and forth through memory, character by character. The scanner reads what it finds, writing down further characters. The actions of the scanner are dictated by an instruction program, which is also stored in memory as symbols. The earliest successful AI program was written in 1951 by Christopher Strachey. In 1952, this program could play checkers with a person, surprising everyone with its ability to predict moves. In 1953, Turing published a classic early article on chess programming.

The difference between artificial intelligence and natural

Intelligence can be defined as the general mental capacity for reasoning, problem solving, and learning. By its general nature, intelligence integrates cognitive functions such as perception, attention, memory, language, or planning. natural intelligence is distinguished by a conscious attitude to the world. Human thinking is always emotionally colored, and it cannot be separated from corporeality. In addition, a person is a social being, therefore society always influences thinking. AI has nothing to do with the emotional sphere and is not socially oriented.

How do human and computer intelligence compare?

It is possible to compare human thinking with artificial intelligence based on several general parameters of the organization of the brain and machine. The activity of a computer, like the brain, includes four stages: encoding, storing, analyzing data and issuing a result. In addition, the human brain and AI can self-learn depending on data obtained from the environment. Also, the human brain and machine intelligence solve problems (or tasks) using certain algorithms.

Do computer programs have an IQ?

No. The IQ indicator is associated with the development of a person's intelligence depending on age. AI in some way exceeds some human abilities, for example, it can keep a huge number of numbers in memory, but this has nothing to do with IQ.

What is a Turing test?

Alan Turing developed an empirical test that shows whether a program is capable of capturing all the nuances of human behavior to such an extent that a person cannot determine with whom he is communicating - with an AI or with a live interlocutor. Turing suggested that an outside observer evaluate the conversation between a person and a machine that answers questions. The judge does not see who exactly is responsible, but he knows that one of the interlocutors is AI. Conversation is limited to only the text channel (computer keyboard and screen), so the result is independent of the machine's ability to display words as human speech. In the event that the program manages to trick the person, it is considered that it coped with the test effectively.

Symbolic approach

The symbolic approach to AI is a collection of all methods of researching artificial intelligence based on high-level symbolic (human-readable) representations of tasks, logic and search. The symbolic approach was widely used in AI research in the 1950s and 1980s. One of the popular forms of the symbolic approach is expert systems that use a combination of specific production rules. Manufacturing rules link symbols into logical links that are similar to the If-Then algorithm. The expert system processes the rules to draw conclusions and determine what additional information it needs, that is, what questions to ask using human-readable characters.

Logical approach

The term "logical approach" implies an appeal to logic, thinking, solving problems using logical steps. Logicians back in the 19th century developed precise notation for all kinds of objects in the world and the relationships between them. By 1965, there were programs that could solve any logical problem (the peak of the popularity of this approach came in the late 1950s and 1970s). Proponents of the logical approach within the framework of logical artificial intelligence hoped to build intelligent systems on such programs (in particular, written in the Prolog language). However, this approach has two limitations. First, it is not easy to take informal knowledge and put it in the formal terms that are required for AI processing. Second, there is a big difference between solving a problem in theory and solving it in practice. Even problems with a few hundred facts can exhaust the computational resources of any computer if it doesn't have any guidance as to which reasoning to use first.

Agent-based approach

An agent is something that acts (from Lat. Agere, "to do"). Of course, all computer programs do something, but computer agents are expected to do more: work autonomously, perceive environmental signals (using special sensors), adapt to change, create goals and fulfill them. A rational agent is one who acts to achieve the best expected result.

Hybrid approach

It is assumed that this approach, which became popular in the late 1980s, works most efficiently, as it is a combination of symbolic and neural models. The hybrid approach increases the cognitive and computational capabilities of the machine.

Artificial intelligence technology market

The market is expected to grow to $ 190.61 billion by 2025, at an annual growth rate of 36.62%. Market growth is driven by factors such as the adoption of cloud applications and services, the emergence of big data and the strong demand for intelligent virtual assistants. However, there are still few experts developing and implementing AI technologies, and this is holding back the market growth. AI systems need integration and technical support for maintenance.

AI processors

Modern AI tasks require powerful processors that can handle huge amounts of data. Processors must have access to large amounts of memory, and the device also needs high-speed data links.

In Russia

At the end of 2018, Russia launched a series of Elbrus-804 servers showing high performance. Each of the computers is equipped with four eight-core processors. With the help of these devices, you can build computing clusters, they allow you to work with applications and databases.

World market

Drivers and market leaders are two corporations - Intel and AMD, manufacturers of the most powerful processors. Intel has traditionally focused on making machines with higher clock speeds, AMD is focused on constantly increasing the number of cores and providing multi-threaded performance.

National Development Concept

Three dozen countries have already approved national strategies for the development of AI. In October 2019, the draft National Strategy for the Development of AI is to be adopted in Russia. It is assumed that a legal regime will be introduced in Moscow to facilitate the development and implementation of AI technologies.

AI Research

The questions of what artificial intelligence is and how it works have been of concern to scientists from different countries for more than a decade. The US state budget spends $ 200 million annually on research. In Russia, over 10 years - from 2007 to 2017 - about 23 billion rubles were allocated. The AI \u200b\u200bresearch support sections will be an important part of the national strategy framework. Soon, new research centers will open in Russia, and the development of innovative software for AI will continue.

AI standardization

The rules and regulations in the field of AI in Russia are in the process of constant revision. It is assumed that at the end of 2019 - beginning of 2020, national standards will be approved, which are now being developed by market leaders. In parallel, the National Standardization Plan for 2020 and beyond is being formed. The world has the standard “Artificial Intelligence. Concept and terminology ”, and in 2019 experts began to develop its Russified version. The document must be approved in 2021.

Impact of artificial intelligence

The introduction of AI is inextricably linked with scientific and technological progress, and the scope of application is expanding every year. We face this every day in our life, when a large retail chain on the Internet recommends a product to us or, only opening a computer, we see an advertisement for a movie that we just wanted to watch. These recommendations are based on algorithms that analyze what the consumer has bought or viewed. Artificial intelligence is behind these algorithms.

Is there a risk to the development of human civilization?

Elon Musk believes that the development of AI can threaten humanity and the results can be worse than the use of nuclear weapons. Stephen Hawking, a British scientist, fears that humans could create artificial intelligence with superintelligence that could harm humans.

Economy and business

The penetration of AI technology into all spheres of the economy will increase the volume of the global market for services and goods by $ 15.7 trillion by 2030. The United States and China are still leaders in terms of all kinds of AI projects. The developed countries - Germany, Japan, Canada, Singapore - are also striving to realize all the possibilities. Many countries whose economies are growing at a moderate pace, such as Italy, India, Malaysia, are developing strengths in specific areas of AI applications.

To the labor market

The global impact of AI on the labor market will follow two scenarios. First, the proliferation of certain technologies will lead to the layoff of a large number of people, since computers will take over many tasks. Secondly, in connection with the development of technical progress, AI specialists will be in great demand in many industries.

AI bias

AI system bias is likely to become an increasingly common problem as artificial intelligence moves out of the labs and into the real world. Researchers fear that without proper training in assessing the data and identifying the potential for bias in the data, vulnerable groups in society could be harmed or denied. Until now, researchers do not have data on whether systems built on the basis of machine learning will threaten humanity.

Applications

Artificial intelligence and its applications are undergoing transformation. The definition of Weak AI ("weak AI") is used when it comes to the implementation of narrow tasks in medical diagnostics, electronic trading platforms, and robot control. Whereas Strong AI ("strong AI") researchers define as intelligence, which is set to global tasks, as if they were set before a person.

Defense and military use

By 2025, global sales of related services, software and hardware will rise to $ 18.82 billion, and the market will grow 14.75% annually. AI is used for data aggregation, bioinformatics, troop training, and the defense sector.

In education

Many schools include AI introductory lessons in computer science curricula, and universities make extensive use of big data technologies. Some programs monitor student behavior, grade tests and essays, recognize spelling mistakes, and provide suggestions for corrections.

There are also online courses on artificial intelligence. For example, at the educational portal.

In business and trade

Over the next five years, leading retailers will have mobile apps that work with digital assistants such as Siri to make shopping easier. AI allows you to make huge amounts of money online. One example is Amazon, which is constantly analyzing consumer behavior and improving algorithms.

Where can you study on the topic #artificial intelligence

In the electric power industry

AI helps to predict the generation and demand for energy resources, reduce losses, and prevent resource theft. In the power industry, the use of AI to analyze statistical data helps to select the most profitable supplier or automate customer service.

In the production area

According to a McKinsey survey of 1,300 CEOs, 20% of businesses are already using AI. Recently, Mosselprom has introduced AI into its packaging workshop. The AI's ability to recognize the image is used. The camera records all the actions of the employee by scanning the barcode on the clothes and sends the data to the computer. The number of transactions performed directly affects the employee's salary.

In brewing

Carlsberg uses machine learning to select yeast and expand its assortment. The technology is implemented on the basis of a digital cloud platform.

In the banking sector

The need for reliable data processing, the development of mobile technologies, the availability of information and the proliferation of open source software make AI a technology in demand in the banking sector. More and more banks are attracting borrowed funds with the help of mobile application development companies. New technologies are improving customer service, and analysts predict that within five years AI in banks will make most of the decisions on their own.

By transport

The development of AI technologies is a driver of the transport industry. Monitoring road conditions, detecting pedestrians or objects in the wrong places, autonomous driving, cloud services in the automotive industry are just a few examples of AI applications in transport.

In logistics

AI capabilities enable companies to more efficiently predict demand and build supply chains at minimal cost. AI helps to reduce the number of used vehicles required for transportation, optimize delivery times, and reduce the operating costs of transport and storage facilities.

In the market for luxury goods and services

Luxury brands have also turned to digital technology to analyze customer needs. One of the challenges faced by developers in this segment is managing and influencing customer emotions. Dior is already adapting AI to manage customer-brand interactions using chatbots. Luxury brands will compete in the future, and the level of personalization they can achieve with AI will be critical.

In public administration

The state apparatus of many countries is not yet ready for the challenges that are hidden in AI technologies. Experts predict that many of the existing government structures and processes that have evolved over the past several centuries are likely to become irrelevant in the near future.

In forensics

Different AI approaches are used to identify criminals in public places. In some countries, like Holland, the police use AI to investigate complex crimes. Digital forensics is an emerging science that requires the mining of huge volumes of highly complex data sets.

In the judicial system

Developments in the field of artificial intelligence will help radically change the judicial system, make it fairer and free from corruption. China was one of the first AI in the judicial system. It can be assumed that over time, robotic judges will be able to operate on big data from the repositories of public services. Machine intelligence analyzes a huge amount of data, and it does not experience emotion like a human judge. AI can have a huge impact on information processing and statistics collection, as well as predict possible violations based on data analysis.

In sports

The use of AI in sports has become commonplace in recent years. Sports teams (baseball, soccer, etc.) analyze individual player performance data by considering different factors in matchmaking. AI can predict the future potential of players by analyzing game technique, physical condition and other data, as well as estimate their market value.

In healthcare medicine

This area of \u200b\u200bapplication is developing rapidly. AI is used in disease diagnosis, clinical research, drug development and health insurance. In addition, there is now a boom in investing in numerous medical applications and devices.

Analysis of citizens' behavior

Monitoring the behavior of citizens is widely used in the field of security, including tracking behavior on sites (in social networks) and in instant messengers. For example, in 2018, Chinese scientists managed to identify 20 thousand potential suicides and provide them with psychological assistance. In March 2018, Vladimir Putin ordered to step up the actions of state bodies to combat the negative impact of destructive movements on social networks.

In the development of culture

AI algorithms are starting to generate works of art that are difficult to distinguish from those created by humans. AI offers creative people a variety of tools to make their visions come true. Right now, the understanding of the role of the artist in a broad sense is changing, since AI provides a lot of new methods, but also poses many new questions for humanity.

Painting

Art has long been considered the exclusive sphere of human creativity. But it turns out that machines can do a lot more creatively than humans can imagine. In October 2018, Christie’s sold the first AI painting for $ 432,500. A generative adversarial network algorithm was used that analyzed 15,000 portraits created between the 15th and 20th centuries.

Music

Several music programs have been developed that use AI to create music. As in other areas, AI in this case also mimics a mental task. A notable feature is the ability of the AI \u200b\u200balgorithm to learn from the information it receives, such as computer-assisted technology that is able to listen to and follow a human performer. AI also drives so-called interactive composition technology, in which a computer composes music in response to a live musician performing. In early 2019, Warner Music signed the first ever contract with a performer - the Endel algorithm. Under the terms of the contract, the Endel neural network will release 20 unique albums during the year.

The photo

AI is rapidly changing the way we think about photography. In just a couple of years, most advances in this area will be AI-driven, not optics or sensors as they used to. For the first time, advances in photography technology will not be associated with physics and will create a completely new way of photographing. Even now, the neural network recognizes the slightest changes in face modeling in photo editors.

Video: face swap

In 2015, Facebook began testing DeepFace technology on the site. In 2017, Reddit user DeepFakes came up with an algorithm to create realistic face swap videos using neural networks and machine learning.

Media and literature

In 2016, AI Google, after analyzing 11,000 unpublished books, began writing its first literary works. Researchers at Facebook AI Research in 2017 came up with a neural network system that can write poetry on any topic. In November 2015, the direction of preparation of automatic texts was opened by the Russian company Yandex.

Go games, poker, chess

In 2016, an AI beat a human in Go (a game with more than 10,100 variations). In chess, the supercomputer defeated the human player because of the ability to store in memory the moves ever played by people and program new ones 10 steps ahead. Bots are now playing poker, although it used to be thought that it was almost impossible to teach a computer to play this card game. Every year, developers are improving algorithms more and more.

Face recognition

Face recognition technology is used for both photo and video streams. Neural networks build a vector, or "digital", face template, then these templates are compared within the system. She finds anchor points on the face that determine individual characteristics. The algorithm for calculating the characteristics is different for each of the systems and is the main secret of the developers.

For the further development and application of AI, it is necessary to train first of all a person

Sergey Shirkin

Dean of the Faculty of Artificial Intelligence

Artificial intelligence technologies in the form in which they are applied now have existed for about 5-10 years, but in order to apply them, oddly enough, a large number of people are required. Accordingly, the main costs in the field of artificial intelligence are the costs of specialists. Moreover, almost all basic artificial intelligence technologies (libraries, frameworks, algorithms) are free and are in the public domain. At one time, finding specialists in machine learning was almost impossible. But now, largely thanks to the development of MOOC (Massive Open Online Course, massive open online course), there are more of them. Higher educational institutions also supply specialists, but they often have to complete their studies on online courses.

Now artificial intelligence may well recognize that a person is planning to change jobs, and can offer him appropriate online courses, many of which can be taken with only a smartphone. And this means that you can study even while on the road - for example, on the way to work. One of the first such projects was the online resource Coursera, but later many similar educational projects appeared, each of which occupies a specific niche in online education.

You need to understand that AI, like any program, is primarily a code, that is, a text formatted in a certain way. This code needs development, maintenance and improvement. Unfortunately, this does not happen by itself, the code cannot "come to life" without a programmer. Therefore, all fears about the omnipotence of AI are groundless. Programs are created for strictly defined tasks, they do not have feelings and aspirations like a person, they do not perform actions that the programmer has not laid in them.

We can say that in our time, AI has only individual skills of a person, although it can outstrip the average person in the speed of their application. True, it takes many hours of effort of thousands of programmers to develop each such skill. The greatest thing that AI is capable of so far is to automate some physical and mental operations, thereby freeing people from routine.

Does the use of AI carry any risks? Rather, now there is a risk of not seeing the possibility of using artificial intelligence technologies. Many companies are aware of this and are trying to develop in several directions at once, hoping that some of them may "shoot". The example of online stores is indicative: now only those who realized the need to use AI, when it was not yet in a trend, remained afloat, although it was quite possible to “save money” and not invite the necessary mathematicians-programmers for some unknown reason.

Artificial Intelligence Development Perspective

Computers can now do a lot of things that only humans could previously do: play chess, recognize letters of the alphabet, check spelling, grammar, recognize faces, dictate, speak, win game shows, and more. But skeptics persist. As soon as one succeeds in automating another human ability, skeptics say that this is just another computer program, and not an example of self-learning AI. AI technologies are just finding widespread use and have huge growth potential in all areas. Over time, humanity will create more and more powerful computers, which will increasingly improve in the development of AI.

Is the purpose of AI to put the human mind into a computer?

There is only a rough understanding of how the human brain works. So far, not all properties of the mind can be imitated using AI.

Will AI be able to reach the human level of intelligence?

Scientists are striving to ensure that AI can solve even more diverse problems. But it is too early to talk about reaching the level of human intelligence, since thinking is not limited to only one algorithms.

When can artificial intelligence reach the level of human thinking?

At this stage of accumulation and analysis of information, which is now achieved by humanity, AI is far from human thinking. However, in the future, there may be breakthrough ideas that will influence a sharp leap in the development of AI.

Can a computer become an intelligent machine?

A part of any complex machine is a computer system, and here it is possible to speak only of intelligent computer systems. The computer itself has no intelligence.

Is there a connection between speed and the development of intelligence in computers?

No, speed is only responsible for some of the properties of intelligence. The speed of processing and analyzing information alone is not enough for intelligence to appear.

Is it possible to create a children's machine that can develop through reading and self-learning?

This has been discussed by researchers for almost a century. The idea will probably come true someday. Today, AI programs do not process or use as much information as children can.

How are computability and computational complexity related to AI?

Computational complexity theory focuses on classifying computational problems according to their inherent complexity and linking these classes to each other. A computational problem is a problem solved by a computer. The computation task is solvable by mechanical application of mathematical steps such as an algorithm.

Conclusion

Artificial intelligence has already had a huge impact on the development of our world, which was impossible to predict a century ago. Smart phone networks route calls more efficiently than any human operator. Cars are built in unmanned factories by automated robots. Artificial intelligence is integrated into the most common household items, such as a vacuum cleaner. The mechanisms of AI are not fully understood, but experts predict that the development of AI will get even closer to the development of the human brain in the coming years.

Artificial Intelligence (AI, English Artificial intelligence, AI) - the science and technology of creating intelligent machines, especially intelligent computer programs. AI is related to the similar goal of using computers to understand human intelligence, but is not necessarily limited to biologically plausible methods.

What is artificial intelligence

(J. McCarthy) AI designs machines that behave intelligently
(Britannica) AI - the ability of digital computers to solve problems commonly associated with highly intelligent human capabilities
(Feigenbaum) AI - develops intelligent computer systems with capabilities that we traditionally associate with the human mind: language understanding, learning, reasoning ability, problem solving, etc.
(Elaine Rich) AI is the science of how to teach computers to do something in which a person is more successful at the moment

Intelligence (from Latin intellectus - sensation, perception, understanding, understanding, concept, reason), or mind - the quality of the psyche, consisting of the ability to adapt to new situations, the ability to learn and remember based on experience, understanding and applying abstract concepts and using their knowledge for environmental management. Intelligence is the general ability to learn and solve difficulties, which unites all human cognitive abilities: sensation, perception, memory, representation, thinking, imagination.

In the early 1980s. computational scientists Barr and Feigenbaum have proposed the following definition of artificial intelligence (AI):

Later, a number of algorithms and software systems began to be attributed to AI, the distinctive feature of which is that they can solve some problems in the same way as a person thinking about their solution would do.

The main properties of AI are language understanding, learning and the ability to think and, importantly, to act.

AI is a complex of related technologies and processes that develop efficiently and rapidly, for example:

natural language processing
expert systems
virtual agents (chat bots and virtual assistants)
recommendation systems.

AI methods: NLP, CV, Data Science

Natural Language (NLP) Speech Technology

texts: recognize, automatically translate
speech: recognize, generate

find, track, classify, identify objects
extract data from images
analyze the information received

It is applied for

object recognition
descriptions of the content of images and videos
gesture and handwriting recognition
intelligent image processing

extract knowledge
find patterns in data
predict

Use methods

Statisticians
Econometrics
Machine learning, Deep learning

National Strategy for the Development of Artificial Intelligence

Main article: National Strategy for the Development of Artificial Intelligence

AI Research

Main article: Research in the field of artificial intelligence

AI standardization

Artificial Intelligence in Healthcare

2019

3 main trends in artificial intelligence in 4 minutes

Rosstandart approved the first standards in the field of AI

The Federal Agency for Technical Regulation and Metrology (Rosstandart) approved in December 2019 the first national standards in the field of artificial intelligence - GOST R 58776-2019 “Means for monitoring behavior and predicting people's intentions. Terms and definitions "and GOST R 58777-2019" Air transport. Airports. Technical means of inspection. Methodology for Determining Quality Indicators for Recognizing Illegal Investments Using Shadow X-ray Images ”.

The standard is designed to ensure effective communication of intelligent robotic systems (including unmanned vehicles) with humans. The interaction of intelligent systems consists in predicting the intentions of each other and determining further actions based on this forecast. Behavior prediction can also be used to identify people with criminal intent.

The second adopted standard, GOST R 58777-2019, establishes uniform requirements for systems and algorithms for recognizing illegal contents of baggage and hand baggage using X-ray images. The standard will also improve the reliability of test results for systems and algorithms.

Terminological standard “Artificial intelligence. Concepts and terminology ”is fundamental to the entire family of international normative and technical documents in the field of artificial intelligence. In addition to terms and definitions, this document contains conceptual approaches and principles for constructing systems with elements, a description of the relationship of AI with other end-to-end technologies, as well as basic principles and framework approaches to the regulatory and technical regulation of artificial intelligence.

Following the meeting of the ISO / IEC profile subcommittee in Dublin, ISO / IEC experts supported the proposal of the delegation from Russia on the simultaneous development of a terminological standard in the field of AI not only in English, but also in Russian. The document is expected to be approved in early 2021.

The development of products and services based on artificial intelligence requires an unambiguous interpretation of the concepts used by all market participants. The terminology standard will unify the "language" in which developers, customers and the professional community communicate, classify such properties of AI-based products as "security", "reproducibility", "reliability" and "confidentiality". Common terminology will also become an important factor for the development of artificial intelligence technologies within the framework of the National Technology Initiative - more than 80% of companies in the NTI perimeter use AI algorithms. In addition, the ISO / IEC decision will strengthen the authority and expand the influence of Russian experts in the further development of international standards.

During the meeting, ISO / IEC experts also supported the development of a draft international document Information Technology - Artificial Intelligence (AI) - Overview of Computational Approaches for AI Systems, in which Russia acts as a co-editor. The document provides an overview of the state of the art in artificial intelligence systems, describing the main characteristics of the systems, algorithms and approaches, as well as examples of specialized applications in the field of AI. The development of this draft document will be carried out by a specially created within the subcommittee working group 5 "Computational approaches and computational characteristics of artificial intelligence systems" (SC 42 Working Group 5 "Computational approaches and computational characteristics of AI systems").

As part of its work at the international level, the delegation from Russia managed to achieve a number of landmark decisions that will have a long-term effect on the development of artificial intelligence technologies in the country. The development of the Russian-language version of the standard, even from such an early phase, is a guarantee of synchronization with the international field, and the development of the ISO / IEC subcommittee and the initiation of international documents with Russian co-editors are the foundation for further promoting the interests of Russian developers abroad, ”commented.

Artificial intelligence technologies are in wide demand in various sectors of the digital economy. Among the main factors hindering their full-scale practical use is the underdevelopment of the regulatory framework. At the same time, it is the well-developed regulatory and technical base that ensures the specified quality of technology application and the corresponding economic effect.

In the area of \u200b\u200bartificial intelligence, the Cyber-Physical Systems TC based on RVC is developing a number of national standards, the approval of which is scheduled for late 2019 - early 2020. In addition, together with market players, work is underway to form the National Standardization Plan (NSS) for 2020 and beyond. TC "Cyber-physical systems" is open to proposals for the development of documents from interested organizations.

2018: Development of standards in the field of quantum communications, AI and a smart city

The Technical Committee "Cyber-physical systems" based on RVC, together with the Regional Engineering Center "SafeNet", on December 6, 2018, began developing a set of standards for the markets of the National Technology Initiative (NTI) and the digital economy. By March 2019, it is planned to develop technical standardization documents in the field of quantum communications, and, according to RVC. More details.

Impact of artificial intelligence

Risk to the development of human civilization

Impact on the economy and business

Impact of artificial intelligence technologies on the economy and business

Impact on the labor market

Artificial intelligence bias

At the heart of everything that is AI practice (machine translation, speech recognition, natural language processing, computer vision, driving automation, and more) is deep learning. This is a subset of machine learning, characterized by the use of neural network models, which can be said to mimic the work of the brain, so they can hardly be attributed to AI. Any neural network model is trained on large datasets, thus it acquires some "skills", but how it uses them is not clear for the creators, which ultimately becomes one of the most important problems for many deep learning applications. The reason is that such a model works with images formally, without any understanding of what it does. Is such an AI system and can we trust systems built on the basis of machine learning? The significance of answering the last question goes beyond scientific laboratories. Therefore, the attention of the media to the phenomenon called AI bias has noticeably sharpened. It can be translated as “AI bias” or “AI bias”. More details.

Artificial intelligence technology market

AI market in Russia

AI world market

AI Applications

The spheres of AI application are quite wide and cover both technologies familiar to the ear and emerging new directions that are far from mass use, in other words, this is the whole range of solutions, from vacuum cleaners to space stations. All their diversity can be divided according to the criterion of key points of development.

AI is not a monolithic domain. Moreover, some technological areas of AI appear as new sub-sectors of the economy and separate entities, while serving most areas of the economy.

The development of the use of AI leads to the adaptation of technologies in classical sectors of the economy along the entire value chain and transforms them, leading to the algorithmicization of almost all functionality, from logistics to company management.

Use of AI for defense and military purposes

Use in education

Using AI in business

AI in the fight against fraud

On July 11, 2019, it became known that in just two years, artificial intelligence and machine learning will be used to combat fraud three times more often than in July 2019. This data was obtained in a joint study by SAS and the Association of Certified Fraud Examiners (ACFE). As of July 2019, such anti-fraud tools are already used in 13% of the organizations that took part in the survey, and another 25% said they plan to implement them within the next year or two. More details.

AI in the power industry

At the design level: improved forecasting of generation and demand for energy resources, assessment of the reliability of power generating equipment, automation of increasing generation in response to a surge in demand.
At the production level: optimization of preventive maintenance of equipment, increasing generation efficiency, reducing losses, preventing theft of energy resources.
At the promotion level: time-of-day pricing optimization and dynamic billing.
At the service delivery level: automatic selection of the most profitable supplier, detailed consumption statistics, automated customer service, energy optimization based on customer habits and behavior.

AI in manufacturing

At the design level: improve the efficiency of new product development, automated supplier assessment and analysis of parts and components requirements.
At the production level: improving the task execution process, automating assembly lines, reducing errors, reducing the delivery time of raw materials.
At the promotion level: predicting the volume of support and maintenance services, pricing management.
At the level of service delivery: improving the planning of routes of the vehicle fleet, demand for resources of the vehicle fleet, improving the quality of training of service engineers.

AI in banks

AI in transport

The auto industry is on the verge of a revolution: 5 challenges of the era of self-driving

AI in logistics

AI in the judiciary

Developments in the field of artificial intelligence will help radically change the judicial system, make it fairer and free from corruption schemes. This opinion was expressed in the summer of 2017 by Vladimir Krylov, Doctor of Technical Sciences, technical consultant at Artezio.

The scientist believes that the solutions already existing in the field of AI can be successfully applied in various spheres of the economy and public life. The expert points out that AI is successfully used in medicine, but in the future it can completely change the judicial system.

“Looking through the news reports on developments in the field of AI every day, one is amazed at the inexhaustible imagination and fruitfulness of researchers and developers in this field. Reports of scientific research are constantly interspersed with publications of new products breaking into the market and reports of amazing results from the use of AI in various fields. If we talk about the expected events, accompanied by a noticeable hype in the media, in which AI will again become the hero of the news, then I probably will not dare to make technological predictions. I can assume that the next event will be the emergence of somewhere extremely competent court in the form of artificial intelligence, fair and incorruptible. This will happen, apparently, in 2020-2025. And the processes that will take place in this court will lead to unexpected reflections and the desire of many people to transfer to AI most of the processes of managing human society. "

The scientist recognizes the use of artificial intelligence in the judicial system as a "logical step" to develop legislative equality and fairness. The machine mind is not subject to corruption and emotions, it can strictly adhere to the legal framework and make decisions taking into account many factors, including the data that characterize the parties to the dispute. By analogy with the medical field, robotic judges can operate on big data from the repositories of public services. It can be assumed that machine intelligence will be able to quickly process data and take into account significantly more factors than a human judge.

Experts-psychologists, however, believe that the absence of an emotional component when considering court cases will negatively affect the quality of the decision. The verdict of the engine court may be too straightforward, failing to take into account the importance of people's feelings and mood.

Music

Painting

In 2015, the Google team tested neural networks for the ability to create images on their own. Then artificial intelligence was trained on the example of a large number of different pictures. However, when the car was "asked" to portray something on its own, it turned out that it interprets the world around us in a somewhat strange way. For example, for the task of drawing dumbbells, the developers received an image in which the metal was connected by human hands. This was probably due to the fact that at the training stage, the analyzed pictures with dumbbells contained hands, and the neural network misinterpreted this.

On February 26, 2016 in San Francisco, at a special auction, Google representatives raised about $ 98 thousand from psychedelic paintings written by artificial intelligence. These funds were donated to charity. One of the most successful pictures of the car is presented below.

A painting by Google's artificial intelligence.

Artificial intelligence is not the future, artificial intelligence is the present.

Hearing, speaking, vision and predictive intuition are based on the use of both networks (CNN and RNN), as well as natural language processing (NLP) technologies, which complement each other. Similar technologies are used in Alexa, Siri, Google Now, Cortana, and other smart voice assistants.

What programs are used to create AI?

There are dozens of AI frameworks out there, but this list includes only the top ones.

KERAS

It is an open source Python-based neural network library that can be run under Microsoft CNTK (Cognitive Toolkit), Tensorflow, and many other frameworks.

KERAS is best for beginners.

TENSORFLOW

Tensorflow is the most prominent AI development framework that uses machine learning techniques such as neural networks.

Tensorflow was developed by the Google Brain team, it is this framework that is responsible for the auto-completion of phrases in the text field of the Google search engine, as well as the AI \u200b\u200bof Google applications.

SONNET

Created by the Google DeepMind team, Sonnet is a library that runs on top of TensorFlow to build complex deep learning neural networks. SONNET is best suited for AI research and development and is very challenging for beginners.

CNTK (Microsoft Cognitive Toolkit)

Formerly known as CNTK, the Microsoft Cognitive Toolkit aims to train algorithms to think like the human brain. It has speed, scalability, quality and compatibility with C ++ and Python. Microsoft uses it for AI features in Skype, Cortana, and Bing.

Microsoft CNTK allows users to combine popular deep learning models such as DNN, CNN, and RNN.

PYTORCH

Pytorch is an open source machine learning library for Python based on Torch that uses natural language processing (NLP) technologies.

DL4J (Deeplearning4j)

Deeplearning4j is an open source library for AI development using deep learning techniques. Written specifically for Java and JVM (Java Virtual Machine).

DL4J is powered by its own numerical computation library and can run on both the CPU and GPU.

There are many more different environments for developing artificial intelligence. Just briefly mention ONNX, a deep learning platform that was jointly developed by Facebook and Microsoft, and several others: H2O, DSSTNE, Theano, DeepDetect, ConvNetJS, ACT-R, Caffe, and CaffeOnSpark.

MXNET

Apache MXNET is a deep learning software framework for deploying neural networks. It has a scalable learning model that supports multiple programming languages \u200b\u200bfor AI development: Go, R, Scala, Perl, C ++, Python, Julia, Matlab, JavaScript, and is an open source project.

MXNET is used to deploy neural networks on shared hosting services such as AWS and Microsoft Azure.

Where is artificial intelligence used?

Intelligent systems are used in various fields and areas. They can be found in voice assistants, trading robots, military developments, and so on. Let's go over the most important ones.

Voice assistants

AI-powered voice assistants such as Siri, Google Now, Alexa, Bixby, and Cortana. They listen to what the user is saying to convert the speech into a machine-readable vector, after which a response vector is produced, which is spoken by the voice assistant using Natural Language Processing (NLP).

Smart assistants

Autodesk Eva is a great example of a smart assistant that uses CNN and NLP to interact with customers in real time.

The smart assistant, modeled in 3D, can communicate with the client in real time and simulate the corresponding facial expressions.

Unmanned vehicles

Self-driving cars use radar, LIDAR (light detector and distance finder), GPS and a camera to create 3D models of approaching vehicles. All of this data is combined to determine the location of the vehicle with very high accuracy. The driver is AI, which analyzes all incoming information from the sensors.

Face recognition

The development of artificial intelligence based on CNN has made it possible to implement a facial recognition system.

Recently, China has begun using a face recognition system using CCTV cameras throughout the city, imposing fines for traffic violations. Alibaba stores in China use face and image recognition for invoicing.

Load balancing

Load balancing on roads, transport systems, servers, and so on.

Language translators

Google translator is a good example. It has two modules: an encoder and a decoder. The encoder takes input sentences from speech or text and then translates them into a vector, which is the same format for input from all languages.

The decoder module takes this vector as input and then generates text or speech in the target language. Language recognition is done using RNN, speech output is done using NLP.

Search and analysis of images

Image search and analysis is used to check for plagiarism,
search for people, for SEO purposes, search for offensive content on social networks.

Optimized for best results

Deepmind modules have been trained to play Chess, Go, Dota 2, Starfield 2.

These modules have played hundreds of years of games in just a few weeks of training, leading the AI \u200b\u200bto defeat the best players in the world.

Of course, these are not all areas of AI application. As the technology and capabilities of AI develop, the scope of application of intelligent systems will only expand.

If the trend of technological development continues or accelerates, I am afraid that we will have time to catch the era when computers will become smarter than people, and all services, systems and tools will be connected to a centralized system under the control of artificial intelligence.