Artificial intelligence (AI) is a branch of information technology aimed at creating computer systems capable of imitating human thinking processes. AI systems are already capable of recognizing text and speech, analyzing large databases, making predictions and identifying patterns.
The operating principle of AI is to process large volumes of data intended for training and their further analysis in order to identify matches, relationships and patterns. Based on the results of such analysis, AI draws conclusions and forms forecasts for the further development of events and processes.
For example, by remembering a huge number of different images, the recognition program learns to identify objects, and chatbots are getting better at conducting dialogues with people, constantly receiving new examples of real text chats.
AI programming is based on three cognitive areas: learning, reasoning and self-correction.
Education. During this process, algorithms are created to solve specific problems. AI collects massive amounts of data and, step by step, carries out the instructions it receives to turn that data into useful information.
Reflections. This branch of AI programming teaches a program to choose from an available set of algorithms exactly the one that will lead to achieving the best result.
Self-correction. AI programmed for self-correction is capable of analyzing the result obtained and, based on its own experience, correcting the involved algorithms to obtain more accurate results and the most reliable forecasts.
Here are some examples of the use of AI in different types of technologies.
Robotization. The design and production of modern robots is impossible without the use of AI. More and more industries and sciences are using robots to perform tasks that are difficult or impossible for humans to perform. Robots are used underwater and in space, on assembly lines and for the manufacture of very precise and complex parts. The ability of robots to independently select the best algorithm to solve a given problem significantly saves time, as well as energy and material resources.
Automation. Automation elements have long been widely used in a large number of different enterprises and industries. Adding AI technologies to them can significantly increase the list of tasks that can be automated. A very important point is that AI, by collecting and analyzing data coming from automated lines, is able to independently draw conclusions, respond to changes in the parameters of production processes and adjust them.
Machine learning. This technology teaches a computer to operate without programming. Let’s look at the existing types of machine learning algorithms:
- Machine learning with a teacher. Labeled data sets are analyzed so that patterns can be identified and used in the future to label new data sets.
- Unsupervised learning. Data sets are not labeled or sorted by any criteria.
- Learning with reinforcement by signals from the interaction environment. The data sets are not labeled, but there is feedback to the AI system after a specified number of actions have been completed.
Driverless cars. Autonomous vehicles acquire and improve automated vehicle-driving skills by combining technologies such as deep learning, image recognition and computer vision. Such cars are capable of analyzing road signs and markings, as well as responding to rapid changes in the situation while driving.
Machine vision. With the help of this technology, machines are able to “see” and analyze the surrounding space. Visual information is captured using a video camera, an analog-to-digital converter, and digital signal processing. Using machine vision, software systems can analyze technical, medical and other images, check the quality of finished products in production, and perform other tasks that require processing of any visual component.
Natural language processing (NLP). Human language is processed by a computer program for the purpose of recognition, transformation into text, translation into other languages, and detection of spam.