Jason Dorrier, writing for SingularityHub:
AI, it would appear, is improving fast. But how fast is fast, and what’s driving the pace? OpenAI’s Danny Hernandez and Tom Brown 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.
Why track algorithmic efficiency? The authors say that three inputs drive progress in machine learning: available computing power, data, and algorithmic innovation. In an earlier paper, OpenAI showed the latest headline-grabbing AIs require a rather shocking amount of computing power to train, and that the required resources are growing at a torrid pace.
Computing power is easier to track, but improvements in algorithms are a bit more slippery.
Read more on SingularityHub.
