Computer Games

A bot just defeated one of the world's best video gamers


The AI win stunned the gaming community, because bots are generally considered inferior to expert human players. This one from Open AI -- a nonprofit artificial intelligence research firm known mainly for its backing by serial entrepreneur Elon Musk, of Tesla (TSLA) and SpaceX fame -- is a different story, and possibly a cautionary one. Open AI says its mission is to promote "responsible" AI development. Or, as Musk puts it, to ensure that AI doesn't grow unchecked and become the death of humanity. Musk said Saturday via Twitter that AI is "more [of a] risk than North Korea."

Reinforcement learning for complex goals, using TensorFlow


Attention readers: We invite you to access the corresponding Python code and iPython notebooks for this article on GitHub. Reinforcement learning (RL) is about training agents to complete tasks. We typically think of this as being able to accomplish some goal. Take, for example, a robot we might want to train to open a door. Reinforcement learning can be used as a framework for teaching the robot to open the door by allowing it to learn from trial and error.

Google develops computer program capable of learning tasks independently


Google scientists have developed the first computer program capable of learning a wide variety of tasks independently, in what has been hailed as a significant step towards true artificial intelligence. The same program, or "agent" as its creators call it, learnt to play 49 different retro computer games, and came up with its own strategies for winning. In the future, the same approach could be used to power self-driving cars, personal assistants in smartphones or conduct scientific research in fields from climate change to cosmology. The research was carried out by DeepMind, the British company bought by Google last year for £400m, whose stated aim is to build "smart machines". Demis Hassabis, the company's founder said: "This is the first significant rung of the ladder towards proving a general learning system can work.