AI technologies

Artificial intelligence

As a science (like biology, history and so on) it has appeared in the middle of the 20th century with publications, the first neural network computer and self-learning program to play checkers.

Since that time scientists have been developing computer systems for solving intellectual tasks – from chess-playing to automotive driving.

Machine learning, deep learning, computer vision and natural language processing are AI technologies, training computers to accomplish different kinds of tasks by using different methods and algorithms (processing large amounts of data and recognizing patterns in the data for example).

Machine learning

Machine learning is a subset of AI, it's based on self-learning algorithms. Learning computers to solve tasks, loading information into the machine's "memory" and setting goals. ML methods: teaching with a teacher sets a specific goal, tests a hypothesis, or confirms a pattern. Learning without a teacher – the unknown result of intelligent data processing, the computer finds patterns by itself, learns to think like a person.

Machine learning is used to automate business operations – user identification, collection and analysis of customer data, and structuring a set of parsed data used to train algorithms.

Deep learning

Deep learning is a part of machine learning. It's the new stage (inspired by the structure of a human brain) in the evolution of neural networks. It can process large amounts of data, its algorithms use complex multi-layered neural networks.

Common uses are image and speech recognition.

Computer vision

Object detection on images and videos is the main task of computer vision technology. It consists of searching and detecting rectangles' sizes and coordinates.

This task has been solved without an artificial neural network for a long time. For example,

face recognition appeared in 2001, but those methods could find objects in only one perspective.

The other computer vision technologies are object tracking, semantic segmentation, depth and distance assessment, etc.

Deep learning

Using natural languages creates synergy between machines and humans.

Modern language models learn to make texts, speech similar to humans.

Speech recognition for example is commonly used in education platforms, software for search engines. The most successful example of implementing speech recognition are software products for call centers.