History of artificial intelligence

Artificial intelligence development started just after World War II, when scientists like Alan Turing explored the possibility of machines being able to “think.” In 1950, Turing published *Computing Machinery and Intelligence*, where he proposed the Turing Test as a method for determining whether a machine was capable of imitating human intelligence. Artificial intelligence attracted a great deal of attention in the 1960s, spawning the first chess-playing programmes and algebraic problem-solving ones. However, the first “winter period” of AI came in the 1970s, where real-world advances did not quite reach the lofty expectations set by many, and the funding of research was reduced.

Interest in AI took over in the 1980s as a result of a combination of the development of algorithms for machine learning and increased computing power. This era is marked by improvements in the realisation of expert systems—which can simulate the decisions of human experts within a particular domain. Starting with the new millennium, a new era of AI had begun, accelerated by developments in the internet, big data, and greater computing power. Breakthroughs in deep learning and neural networks have thus far led to a number of systems now capable of speech and image recognition, underpinning recent work on autonomous cars, personalised medicine, and other applications.

Artificial intelligence is breaking new frames and challenges, finding its place in daily life, and radically changing many spheres: business, medicine, education included. AI history is the way from utopian ideas to real technologies, which inspire scientists and developers to create new things.
Artificial Intelligence has undergone many changes in such a short time since its existence. It is possible to single out six stages in the history of its development.

In the early years of development, encouraged by early successes, a number of researchers including Herbert Simon made optimistic predictions. Simon predicted that “within ten years a digital computer would be the world’s chess champion.” However, when in the mid-1960s a ten-year-old boy defeated a computer at chess and a US Senate report highlighted the limitation of machine translation, progress in AI had slowed significantly. These were considered to be the dark times for AI.

The next one was semantic AI, in which researchers became interested in the psychology of the memory and comprehension mechanisms. By the mid-1970s, methods of semantic knowledge representation started to appear, along with expert systems that made use of skilled knowledge to reproduce thought processes. These systems promised very much, especially in medical diagnosis.

In the 1980s and 1990s, the development of machine learning algorithms and improving technical capabilities resulted in the development of intelligent systems capable of carrying out various tasks such as fingerprint identification and speech recognition. The period was marked by integrating AI into other disciplines for the creation of hybrid systems.

Later in the 1990s, AI began to combine with robotics and a human-machine interface to form something similar to affective computing, which analyses and then reproduces human emotions; this helped in the development of dialogue systems like chatbots.

Since 2010, new opportunities in computing have enabled a marriage of big data with deep learning techniques inspired by artificial neural networks. Advances in speech and image recognition, natural language understanding, and unmanned vehicles are signalling a new AI renaissance.

Artificial intelligence applications

Artificial intelligence technologies have demonstrated great advantages compared to human capabilities in different activities. For example, in 1997, the Deep Blue computer from IBM defeated Garry Kasparov, at the time a world chess champion. In 2016, computer systems defeated the best Go and poker players in the world to manifest their capabilities of processing and analysing huge amounts of data measured in terabytes and petabytes, respectively.

The applications, ranging from recognising speech to identifying faces and fingerprints from millions of others like those used by secretarial typists, use machine learning techniques. The same technologies permit cars to drive themselves and computers to outperform dermatologists in diagnosing melanoma from pictures of moles taken with mobile phones. Military robots and automated assembly lines in factories also make use of the power supplied by artificial intelligence.

In the scientific world, AI has been used to break down the functions of biological macromolecules, including proteins and genomes, according to the order of their components. This separates in silico from historical methods like experiments in vivo—on living organisms—and in vitro—in laboratory conditions.

The applications of self-learning intelligent systems range from industry and banking to insurance, healthcare, and defence. The automation of numerous routine processes transforms professional activity and makes some professions potentially extinct.

Distinction of AI from neural networks and machine learning

Artificial Intelligence, more commonly referred to as AI, is a general field in computer science that addresses the creation of intelligent machines able to continue activities that usually require human intelligence. It covers, but is not limited to, specialised programmes and various technological approaches and solutions. AI makes use of many logical and mathematical algorithms which can be based on neural networks for the purpose of emulating human brain processes.

Neural networks represent a specific kind of computer algorithm, which can be viewed as a mathematical model composed of artificial neurons. Such systems do not require preliminary programming to carry out certain functions. On the contrary, they are capable of learning from previous experience, just like neurons in the human brain create and strengthen their connections during the learning process. Neural networks are tools within AI for the accomplishment of tasks involving recognition or processing of data.

While AI is the general term describing machines that can think and learn like humans, the key subset of AI concerning technologies and algorithms which make programmes learn and improve without human intervention is called machine learning. Such systems analyse input data, find patterns in it, and use this knowledge to process new information and resolve more complicated problems. One of the methods for organising machine learning is called neural networks.

Therefore, if we seek to find an analogy of AI within the human body, the AI will act like the entire functioning of the brain, whereas machine learning will be the analogy to information processing and problem-solving techniques, and neural networks will be structural elements—like neurons—which will perform data processing at an atomic level.

Application of AI in Modern Life

AI has found its place in almost every sphere of life in the modern world, starting from commercial use to medical and up to manufacturing technologies. There exist two main types of artificial intelligence: weak and strong. The weak ones are specialised in narrower tasks, like diagnosis or data analysis, while strong AI is created to solve global complex problems deeper by imitating human intelligence.

Big Data analysis with the use of AI finds high applicability in commerce by enabling big commerce platforms to study consumer behaviour and optimise marketing strategies.

Artificial intelligence manufacturing has had its application in monitoring and coordinating workers’ activities, greatly increasing efficiency and safety in the work process. In the transport sector, AI serves in traffic control, monitoring of road conditions, and development and improvement of unmanned vehicles.

The luxury brands are incorporating AI that will perform deep analysis of customers’ needs and personalise products for them. In healthcare, AI is changing the face of diagnostics, development of drugs, health insurance, and even clinical trials, thus making healthcare services a far more accurate and efficient affair.

The reasons for this technological development are rapid growth in information flows, stepped-up investment in the AI sector, and demands for higher productivity and greater efficiency in all sectors. Artificial intelligence continues to expand its influence, penetrating new areas and transforming traditional approaches to business and everyday activities.

Areas of use of AI

Artificial Intelligence (AI) is infiltrating many aspects of everyday life, transforming traditional industries and creating new opportunities to improve efficiency and accuracy:

1. Medicine and healthcare: AI is used to manage patient data, analyse medical images such as ultrasounds, X-rays and CT scans, and diagnose diseases based on symptoms. Intelligent systems offer treatment options and help individuals lead healthier lifestyles through mobile apps that can monitor heart rate and body temperature.
2. Retail and e-commerce: AI analyses users’ online behaviour to offer personalised recommendations and advertising. This includes promoting products users have viewed in online shops and suggesting similar products based on their interests.
3. Politics: AI has also been used in political campaigns. For instance, during Barack Obama’s campaign, AI tools were employed to optimise where and when to hold speeches based on data-driven analysis.
4. Industry: AI supports production processes by analysing equipment load, forecasting demand, and optimising resource usage to reduce costs.
5. Gaming and education: In gaming, AI creates more realistic opponents and customised game scenarios. In education, it personalises learning plans based on students’ needs and abilities and helps manage educational resources.

The application of AI spans many other fields, including legal services, finance, and urban infrastructure management, emphasising its role as a major driver of modern innovation and technological advancement.

History of artificial intelligence