Eight years ago a machine learning algorithm learned to identify a cat —and it stunned the world. A few years later AI could accurately translate languages and take down world champion Go players. Now, machine learning has begun to excel at complex multiplayer video games like Starcraft and Dota 2 and subtle games like poker. AI, it would appear, is improving fast.
But how fast is fast, and what’s driving the pace? While better computer chips are key, AI research organization OpenAI thinks we should measure the pace of improvement of the actual machine learning algorithms too.
In a blog post and paper —authored by OpenAI’s Danny Hernandez and Tom Brown and published on the arXiv, an open repository for pre-print (or not-yet-peer-reviewed) studies—the researchers say they’ve begun tracking a new measure for machine learning efficiency (that is, doing more with less). Using this measure, they show AI has been getting more efficient at a wicked pace.
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