What are the classifications of artificial intelligence?

The classification of artificial intelligence includes cognitive AI, machine learning AI and deep learning. Artificial intelligence is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence.

#The operating environment of this article: windows10 system, thinkpad t480 computer.

Artificial Intelligence (Artificial Intelligence), the English abbreviation is AI. It is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence.

1. Cognitive AI (cognitive AI)

Cognitive computing is the most popular branch of artificial intelligence, responsible for all interactions that feel "human-like". Cognitive AI must be able to easily handle complexity and ambiguity, while also continuously learning from the experience of data mining, NLP (natural language processing) and intelligent automation.

People are now increasingly inclined to think of cognitive AI as a mixture of the best decisions made by artificial intelligence and the decisions of human workers to oversee more difficult or uncertain events. This can help expand the applicability of AI and generate faster, more reliable answers.

2. Machine Learning AI (Machine Learning AI)

Machine Learning (ML) AI is the kind of artificial intelligence that can autonomously drive your Tesla on the highway. It's still at the cutting edge of computer science, but is expected to have a huge impact on the everyday workplace in the future. Machine learning is about finding some "patterns" in big data, and then using these patterns to predict results without too much human explanation, and these patterns cannot be seen in ordinary statistical analysis.

3. Deep Learning

If machine learning is cutting-edge, then deep learning is cutting-edge. This is the kind of AI you'd send to a trivia quiz. It combines analysis of big data and unsupervised algorithms. Its applications often revolve around large unlabeled data sets that need to be structured into interconnected clusters. This inspiration for deep learning comes entirely from the neural networks in our brains, so it can be appropriately called artificial neural networks.

Deep learning is the basis of many modern speech and image recognition methods and allows for greater accuracy over time than non-learning methods previously offered.

Hope that in the future, deep learningAI can autonomously answer customer inquiries and complete orders through chat or email. Or they can help marketing by suggesting new products and specifications based on their vast pools of data. Or maybe one day they could become full-service assistants in the workplace, completely blurring the lines between robots and humans.

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