Google DeepMind: The Rise of Global AI Dominance in 2021

Google DeepMind: The Rise of Global AI Dominance in 2021
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DeepMind began working on AI technology by teaching it how to play old video games from the 70s and 80s

DeepMind is an artificial intelligence company founded in London in 2010. The company is dedicated to accelerating the industry with an interdisciplinary approach. It brings together new ideas and advances in machine learning, neuroscience, engineering, mathematics, simulation and computing infrastructure. This all started when DeepMind's founders began working on AI technology by teaching it how to play old video games from the 70s and 80s. Acquired by Google in 2014, Deepmind's research covers everything from climate change to healthcare and finance. In partnership with The European Bioinformatics Institute (EMBL-EBI), DeepMind launched its AlphaFoldprotein structure database in 2021.

Intelligence allows DeepMind to learn, imagine, cooperate, create, communicate and so much more. By better understanding different aspects of intelligence, one can use this knowledge as inspiration to build novel computer systems that learn to find solutions to difficult problems on their own.

Determined to create AI agents that could better address the limitations of human intelligence, Deepmind created XLand. In addition to having a bigger range of possible games to work on, these agents will now be able to deal with entirely new situations and challenge themselves with new games and tasks, such as ones they have never seen before.

The DeepMind AI agents are represented by 3D virtual avatars that reside in a multiplayer online environment meant to mimic the physical world. Players' surroundings are assessed through RGB images, and they learn how to interact with different games and genres.

According to DeepMind, in contrast to traditional, top-down approaches, the agents make frequent attempts at self-betterment through trial and error, exploring different states in search of satisfying outcomes. The systems displayed a wide range of patterns rather than highly optimized and specific patterns for specific tasks.

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