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UC Berkeley researchers teach computers to be curious
When you played through Super Mario Bros. or Doom for the very first time, chances are you didn't try to speedrun the entire game but instead started exploring -- this despite not really knowing what to expect around the next corner. It's that same sense of curiosity, the desire to screw around in a digital landscape just to see what happens, that a team of researchers at UC Berkeley have imparted into their computer algorithm. And it could drastically advance the field of artificial intelligence.
AIs fight to the death in 'Doom' contest next month
Google DeepMind took a leap forward last year when its artificial intelligence agent mastered 49 Atari 2600 games. The learning system, or "deep Q-network" (DQN), that DeepMind designed achieved this mastery through general experience, rather than specific programming for each game. This milestone is just one step along a grander path toward the general-purpose "smart machine": an AI that can master any task with minimal input. DeepMind's work in this field is groundbreaking, and it's helping advance the field in ways you might not expect. Wojciech Jaśkowski is an assistant professor at the Institute of Computing Science (ICS) at Poznan University of Technology, Poland. After reading about DeepMind's feat in the scientific journal Nature, he began to think about the possibilities. If an agent could learn Atari 2600 with our current levels of knowledge, why not push the envelope -- why not try a 3D game? Jaskowski settled on the 1993 first-person shooter Doom. It has low power requirements and, more important, it's open source. He assembled a team of university students from ICS with the aim of building a platform that would facilitate testing AI agents.
