By knowledge engineering is meant a combination of methods, models and techniques aimed at the formation of systems designed to search for solutions to problems based on existing knowledge. In fact, this term refers to methodology, theory and technology, covering the methods of analysis, production, processing and presentation of knowledge.
The essence of artificial intelligence lies in the scientific analysis and automation of the intellectual functions inherent in man. Moreover, common to most problems is the complexity of their machine implementation. The study of AI made it possible to verify that behind the solution of problems lies the need for expert knowledge, that is, the creation of a system that can not only memorize, but also analyze and use the knowledge of experts in the future; it can be used for practical purposes.
History of the term
Knowledge engineering and the development of intelligent information systems, in particular expert systems, are closely related.
At the Stanford University in the USA in the 60-70s, under the leadership of E. Feigenbaum, the DENDRAL system was developed, a little later - MYCIN. Both systems received the title of expert because of their ability to accumulate in computer memory and use expert knowledge to solve problems. This field of technology received the term "knowledge engineering" from the message of Professor E. Feigenbaum, who became the creator of expert systems.
The approaches
Knowledge engineering is based on two approaches: knowledge transformation and model building.
- The transformation of knowledge. The process of changing expertise and the transition from expert knowledge to their software implementation. It was based on the development of Knowledge Based Systems. The format for representing knowledge is the rules. The disadvantages are the impossibility of presenting implicit knowledge and different types of knowledge in an adequate form, the difficulty of reflecting a large number of rules.
- Building models. AI creation is considered a form of simulation; building a computer model designed to solve problems in a specific field along with experts. The model is not able to imitate the activities of an expert at a cognitive level, but allows to obtain a similar result.
Models and methods of knowledge engineering are aimed at the development of computer systems, the main purpose of which is to obtain the knowledge of specialists and their subsequent organization for the most effective use.
Artificial Intelligence, Neural Networks, and Machine Learning: What is the Difference?
One of the ways to implement artificial intelligence is a neural network.
Machine learning is an area of AI development aimed at studying methods for constructing self-learning algorithms. The need for this arises in the absence of a clear solution to a specific problem. In such a situation, it is more profitable to develop a mechanism that can create a method for finding a solution, rather than looking for it.
By the frequently encountered term “deep” (“deep”) learning is meant machine learning algorithms, which require a large amount of computing resources. The concept in most cases is associated with neural networks.
There are two types of artificial intelligence: narrowly targeted, or weak, and general, or strong. The action of the weak is aimed at finding a solution to a narrow list of tasks. The most prominent representatives of narrowly targeted AI are the voice assistants Google Assistant, Siri and Alice. The ability of a strong AI, on the contrary, allows him to perform almost any human task. Today, general artificial intelligence is considered utopian: its implementation is impossible.
Machine learning
Machine learning refers to methods in the field of artificial intelligence used to create a machine that can learn from experience. By the learning process is meant the processing by a machine of huge amounts of data and the search for patterns in them.
The concepts of Machine learning and Data science, despite their similarities, nevertheless differ and each cope with its tasks. Both tools are part of artificial intelligence.
Machine learning, which is one of the sections of AI, is the algorithms on the basis of which the computer is able to draw conclusions without adhering to hard-set rules. The machine is looking for patterns in complex tasks with a large number of parameters, finding more accurate answers, unlike the human brain. The result of the method is accurate prediction.
Data science
The science of how to analyze data and extract valuable knowledge and information from it (data mining). It communicates with machine learning and the science of thinking, with technologies for interacting with large volumes of data. The work of Data science allows you to analyze data and find the right approach for the subsequent sorting, processing, retrieval and retrieval of information.
For example, there is information about the financial expenses of an enterprise and information from counterparties related only by the time and date of operations and interim banking information. A deep machine analysis of intermediate data allows you to determine the most costly counterparty.
Neural networks
Neural networks, being not a separate tool, but one of the types of machine learning, are able to simulate the work of the human brain using artificial neurons. Their action is aimed at solving the problem and self-training based on the experience gained with minimizing errors.
Machine Learning Objectives
The main goal of machine learning is considered to be partial or full automation of the search for solutions to various analytical problems. For this reason, machine learning should give the most accurate forecasts based on the data received. The result of machine learning is predicting and memorizing the result with the possibility of subsequent playback and selection of one of the best options.
Types of Machine Learning
Classification of training based on the presence of a teacher occurs in three categories:
- With the teacher. It is used when the use of knowledge implies training the machine to recognize signals and objects.
- Without a teacher. The principle of operation is based on algorithms that detect the similarities and differences of objects, anomalies, followed by recognition of what is considered to be dissimilar or unusual.
- With reinforcements. They are used if the machine must correctly perform tasks in an external environment with many possible solutions.
By the type of algorithms used, they are divided into:
- Classical training. Learning algorithms developed over half a century ago for statistical offices and carefully studied over time. Used to solve problems associated with working with data.
- Deep learning and neural networks. A modern approach to machine learning. Neural networks are used when generation or recognition of video and images, machine translation, complex decision-making and analysis processes are required.
In knowledge engineering, ensembles of models that combine several different approaches are possible.
The benefits of machine learning
With a competent combination of different types and algorithms of machine learning, automation of routine processes in business is possible. The creative part - negotiating, concluding contracts, drafting and implementing strategies - is left to the people. Such a separation is important, since a person, unlike a machine, is able to think outside the box.
AI Creation Issues
In the context of the creation of AI, there are two problems of creating artificial intelligence:
- The legitimacy of recognizing a person as a self-organizing consciousness and free will and, accordingly, for recognizing artificial intelligence as reasonable requires the same;
- Comparison of artificial intelligence with the human mind and its abilities, which does not take into account the individual characteristics of all systems and entails their discrimination due to the meaninglessness of their activities.
The problems of creating artificial intelligence lie, among other things, in the formation of images and figurative memory. Figurative chains in people are formed associatively, in contrast to machine operation; in contrast to the human mind, the computer searches for specific folders and files, and does not select chains of associative links. Artificial intelligence in knowledge engineering uses a specific database and is not able to experiment.
The second problem is machine learning languages. Text translation by translation programs is often carried out automatically, and the final result is represented by a set of words. Correct translation requires an understanding of the meaning of the sentence, which is difficult to implement AI.
The lack of manifestations of will in artificial intelligence is also considered a problem on the way to its creation. Simply put, the computer has no personal desires, in contrast to the capacities and capabilities for complex calculations.
Modern artificial intelligence systems do not have incentives for further existence and improvement. Most AIs are motivated only by a task posed by a person and the need for its implementation. In theory, this can be influenced by creating feedback between the computer and the person and improving the computer’s self-learning system.
The primitiveness of artificially created neural networks. Today, they are characterized by advantages identical to the human brain: their training is based on personal experience, they are able to draw conclusions and extract the most important from the information received. At the same time, intelligent systems are not able to duplicate all the functions of the human brain. The intelligence inherent in modern neural networks does not exceed the intelligence of an animal.
The minimum effectiveness of AI for military purposes. The creators of robotic machines based on artificial intelligence face the problem of the inability of AI to learn, automatically recognize and correctly analyze the received information in real time.