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What is the difference between artificial intelligence and neural networks?

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Artificial intelligence (AI) and artificial neural networks (ANN) are two exciting and intertwined fields in computer science. There are, however, several differences between the two that are worth knowing about. The key difference is that neural networks are a stepping stone in the search for artificial intelligence. Artificial intelligence is a vast field that has the goal of creating intelligent machines, something that has been achieved many times depending on how you define intelligence. Despite the fact that we have computers that can win at "Jeopardy" and beat chess champions, the goal of AI is generally seen as a quest for general intelligence, or intelligence that can be applied to diverse and unrelated situational problems.


Microsoft's AI writes code by looting other software

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Artificial intelligence has taught itself to create its own encryption and produced its own universal'language'. A neural network, called DeepCoder, developed by Microsoft and University of Cambridge computer scientists, has learnt how to write programs without a prior knowledge of code. First reported by the New Scientist, the system works by taking lines of code from existing programs and combining them. The system is only able to produce short, five-line, pieces of code at present but this has been enough to test it against real-world problems used by trainee developers. "We have found several problems in real online programming challenges that can be solved with a program in our language," the research paper says.


How to Teach Artificial Intelligence Some Common Sense

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Five years ago, the coders at DeepMind, a London-based artificial intelligence company, watched excitedly as an AI taught itself to play a classic arcade game. They'd used the hot technique of the day, deep learning, on a seemingly whimsical task: mastering Breakout,1 the Atari game in which you bounce a ball at a wall of bricks, trying to make each one vanish. Deep learning is self-education for machines; you feed an AI huge amounts of data, and eventually it begins to discern patterns all by itself. In this case, the data was the activity on the screen--blocky pixels representing the bricks, the ball, and the player's paddle. The DeepMind AI, a so-called neural network made up of layered algorithms, wasn't programmed with any knowledge about how Breakout works, its rules, its goals, or even how to play it.


Top Artificial Intelligence Influencers To Follow in 2020 MarkTechPost

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Yoshua Bengio: Yoshua Bengio OCFRSC (born 1964 in Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning.[1][2][3] He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning.[4] He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA). Geoffrey Hinton: Geoffrey Everest HintonCCFRSFRSC[11] (born 6 December 1947) is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.


Top Artificial Intelligence Influencers To Follow in 2020 MarkTechPost

#artificialintelligence

Yoshua Bengio: Yoshua Bengio OCFRSC (born 1964 in Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning.[1][2][3] He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning.[4] He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA). Geoffrey Hinton: Geoffrey Everest HintonCCFRSFRSC[11] (born 6 December 1947) is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.