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Editor to share with you what are the core of artificial intelligence, I believe that most people do not know much about it, so share this article for your reference, I hope you can learn a lot after reading this article, let's learn about it!

The core of artificial intelligence: 1, computer vision, refers to the ability of computers to recognize objects, scenes and activities from images; 2, machine learning, refers to the computer system does not have to follow explicit program instructions; 3, natural language processing; 4, robot; 5, speech recognition, mainly concerned with automatic and accurate transcription of human speech technology.

The core of artificial intelligence:

1. Computer vision

Computer vision refers to the ability of a computer to recognize objects, scenes and activities from images. Computer vision technology uses a sequence of image processing operations and other techniques to decompose image analysis tasks into small tasks that are easy to manage. For example, some techniques can detect the edges and textures of objects from the image, and classification techniques can be used to determine whether the identified features can represent a class of objects known to the system.

Computer vision has a wide range of applications, including: medical imaging analysis is used to improve disease prediction, diagnosis and treatment; face recognition is used by Facebook to automatically identify people in photos; in security and surveillance areas, it is used to identify suspects; in shopping, consumers can now use smartphones to photograph products to get more purchase choices.

As a related discipline, machine vision generally refers to the visual application in the field of industrial automation. In these applications, computers recognize objects such as producing parts in a highly restricted factory environment, so the goal is simpler than computer vision that seeks to operate in an unrestricted environment. Computer vision is an ongoing research, while machine vision is a "solved problem", which is a subject of system engineering rather than research. Because of the continued expansion of applications, some computer vision startups have attracted hundreds of millions of dollars in venture capital since 2011.

2. Machine learning

Machine learning refers to the ability of a computer system to improve its performance by relying on data instead of following explicit program instructions. Its core is that machine learning automatically discovers patterns from data, and once patterns are found, they can be used for prediction. For example, give the machine learning system a database of credit card transaction information such as transaction time, merchant, location, price and whether the transaction is justified, and the system will learn the patterns that can be used to predict credit card fraud. The more transaction data processed, the more accurate the forecast will be.

Machine learning has a wide range of applications, and it has the potential to improve almost all performance for activities that generate huge data. In addition to fraud screening, these activities include sales forecasts, inventory management, oil and gas exploration, and public health. Machine learning technology also plays an important role in other areas of cognitive technology, such as computer vision, which can improve its ability to identify objects by constantly training and improving visual models in massive images.

Today, machine learning has become one of the hottest research areas in cognitive technology, attracting nearly $1 billion in venture capital between 2011 and 2014. Google also bought Deepmind, a company that studies machine learning technology, for $400 million in 2014.

3. Natural language processing

Natural language processing (NLP) refers to the human-like text processing ability of computers. For example, extract meaning from text, and even independently interpret meaning from texts that are readable, natural in style and grammatically correct. A natural language processing system does not understand the way human beings deal with text, but it can skillfully process text in very complex and mature means. For example, automatically identify all the people and places mentioned in a document; identify the core issues of the document; and extract and table the terms and conditions in a pile of human-readable contracts. These tasks can not be accomplished by traditional text processing software, which can operate only for simple text matching and patterns.

Like computer vision technology, natural language processing combines a variety of technologies that help to achieve goals. A language model is established to predict the probability distribution of language expression, for example, the maximum possibility that a given string of characters or words can express a particular semantics. The selected features can be combined with some elements in the text to identify a paragraph of text, which can distinguish one type of text from other text, such as spam and normal email. Machine learning-driven classification methods will become the criteria for screening, used to determine whether an email is spam.

Because context is so important in understanding the difference between "timeflies" and "fruitflies", the practical application of natural language processing technology is relatively narrow, including analyzing customer feedback on a particular product and service, automatically discovering certain meanings in civil proceedings or government investigations, automatically writing formulaic examples such as corporate revenue and sports, and so on.

4. Robot

The integration of cognitive technologies such as machine vision and automatic planning into tiny but high-performance sensors, brakes and cleverly designed hardware has spawned a new generation of robots that have the ability to work with humans. can flexibly handle different tasks in a variety of unknown environments. For example, drones, "cobots" that can share work for humans in the workshop, and so on.

5. Speech recognition

Speech recognition is mainly concerned with the technology of automatically and accurately transcribing human speech. The technology must face some problems similar to natural language processing, have some difficulties in dealing with different accents, background noise, distinguishing homonyms / synonyms ("buy" and "by" sound the same), and need to keep up with the normal speed of speech. Speech recognition systems use some of the same technologies as natural language processing systems, supplemented by other technologies, such as acoustic models that describe sound and its probability of appearing in specific sequences and languages. The main applications of speech recognition include medical dictation, voice writing, computer system voice control, telephone customer service and so on. Domino customers Pizza, for example, recently launched a mobile APP that allows users to place orders over voice.

The above is all the contents of this article "what is the Core of artificial Intelligence". Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!

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