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A look back at some of AI's biggest video game wins in 2018

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For decades, games have served as benchmarks for artificial intelligence (AI). In 1996, IBM famously set loose Deep Blue on chess, and it became the first program to defeat a reigning world champion (Garry Kasparov) under regular time controls. But things really kicked into gear in 2013 -- the year Google subsidiary DeepMind demonstrated an AI system that could play Pong, Breakout, Space Invaders, Seaquest, Beamrider, Enduro, and Q*bert at superhuman levels. In March 2016, DeepMind's AlphaGo won a three-game match of Go against Lee Sedol, one of the highest-ranked players in the world. And only a year later, an improved version of the system (AlphaZero) handily defeated champions at chess, a Japanese variant of chess called shogi, and Go.


DeepMind makes bet on AI system that can play poker, chess, Go, and more

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DeepMind, the AI lab backed by Google parent company Alphabet, has long invested in game-playing AI systems. It's the lab's philosophy that games, while lacking an obvious commercial application, are uniquely relevant challenges of cognitive and reasoning capabilities. This makes them useful benchmarks of AI progress. In recent decades, games have given rise to the kind of self-learning AI that powers computer vision, self-driving cars, and natural language processing. In a continuation of its work, DeepMind has created a system called Player of Games, which the company first revealed in a research paper published on the preprint server Arxiv.org this week.


Is DeepMind's new reinforcement learning system a step toward general AI?

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All the sessions from Transform 2021 are available on-demand now. This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. One of the key challenges of deep reinforcement learning models -- the kind of AI systems that have mastered Go, StarCraft 2, and other games -- is their inability to generalize their capabilities beyond their training domain. This limit makes it very hard to apply these systems to real-world settings, where situations are much more complicated and unpredictable than the environments where AI models are trained. But scientists at AI research lab DeepMind claim to have taken the "first steps to train an agent capable of playing many different games without needing human interaction data," according to a blog post about their new "open-ended learning" initiative. Their new project includes a 3D environment with realistic dynamics and deep reinforcement learning agents that can learn to solve a wide range of challenges.


Is DeepMind's new reinforcement learning system a step toward general AI?

#artificialintelligence

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. One of the key challenges of deep reinforcement learning models--the kind of AI systems that have mastered Go, StarCraft 2, and other games--is their inability to generalize their capabilities beyond their training domain. This limit makes it very hard to apply these systems to real-world settings, where situations are much more complicated and unpredictable than the environments where AI models are trained. But scientists at AI research lab DeepMind claim to have taken the "first steps to train an agent capable of playing many different games without needing human interaction data," according to a blog post about their new "open-ended learning" initiative. Their new project includes a 3D environment with realistic dynamics and deep reinforcement learning agents that can learn to solve a wide range of challenges. The new system, according to DeepMind's AI researchers, is an "important step toward creating more general agents with the flexibility to adapt rapidly within constantly changing environments."


DeepMind's AlphaStar Final beats 99.8% of human StarCraft 2 players

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DeepMind says this latest iteration of AlphaStar -- AlphaStar Final -- can play a full StarCraft 2 match under "professionally approved" conditions, importantly with limits on the frequency of its actions and by viewing the world through a game camera. It plays on the official StarCraft 2 Battle.net "StarCraft has been a grand challenge for AI researchers for over 15 years, so it's hugely exciting to see this work recognized in Nature," said DeepMind cofounder and CEO Demis Hassabis. "These impressive results mark an important step forward in our mission to create intelligent systems that will accelerate scientific discovery." DeepMind's forays into competitive StarCraft play can be traced back to 2017, when the company worked with Blizzard to release an open source tool set containing anonymized match replays.