Computer vision

Computer vision refers to the ability of a computer to recognize objects, scenes, and activities from images. Computer vision techniques use sequences made up of image processing operations and other techniques to break down image analysis tasks into manageable chunks. For example, some techniques are able to detect edges and textures of objects from an image, and classification techniques can be used to determine whether recognized features are representative of a class of objects known to the system.

Machine learning

Machine learning refers to the ability of a computer system to improve its performance without having to follow explicit program instructions, relying solely on data. At its core, machine learning is the automatic discovery of patterns from data, which, once discovered, can be used to make predictions. For example, give a machine learning system a database of credit card transaction information such as transaction time, merchant, location, price, and whether the transaction is legitimate, and the system will learn patterns that can be used to predict credit card fraud. The more transaction data that is processed, the more accurate the prediction will be.

Robotics

The integration of cognitive technologies such as machine vision and automated planning into extremely small but high-performance sensors, brakes, and cleverly designed hardware has given rise to a new generation of robots that have the ability to work alongside humans and are flexible enough to maneuver through a variety of unknown environments to handle different tasks in unknown environments. Examples include drones, ?cobots? that can share the workload for humans on the shop floor, and more.

Speech Recognition

Speech Recognition is primarily concerned with technology that automatically and accurately transcribes human speech. The technology has to face problems similar to those of natural language processing, with difficulties in the handling of different accents, background noise, distinguishing homophones/anagrams (?buy? and ?by? sound the same), and the need to have a working speed to keep up with the normal rate of speech. Speech recognition systems use some of the same techniques as natural language processing systems, supplemented by other techniques such as acoustic models that describe sounds and the probability of their occurrence in particular sequences and languages. The main applications of speech recognition include medical dictation, speech writing, voice control of computer systems, and telephone customer service. Domino抯 Pizza, for example, recently launched a mobile app that allows users to place orders by voice.

Natural Language Processing

Natural Language Processing refers to the ability that computers have to process human-like text. For example, extracting meaning from text, or even autonomously deciphering meaning from text that is readable, natural in style, and grammatically correct. A natural language processing system does not understand the way humans process text, but it can process it skillfully with great sophistication and sophistication. For example, it can automatically identify all the people and places mentioned in a document; it can recognize the core issues of a document; and it can extract and tabulate the terms and conditions of a human-readable contract.

About the five core technologies of AI, Aoto has shared with you here. If you have a strong interest in big data engineering, I hope this article can help you. If you still want to know more about data analyst, big data engineer tips and materials, you can click on other articles on this site to learn.

The above is what I shared with you about the five core technologies of artificial intelligence, more information can be concerned about the Global Green Ivy to share more dry goods