We’ve discussed progress in machine learning before
and noted its extraordinary potential, but also the challenges of making it work “in the wild”. This week seems a good week to revisit the topic, as there have been three major stories in the last couple of weeks that suggest very different paths for ML’s real-world impact in the medium term.
, a big budget not-for-profit, ran a high profile competition pitting its AI against professional video gamers. The AI lost, but only just. The Verge’s write up
is very good and touches on a number of important issues, including the sheer engineering (as opposed to research) efforts required for these kinds of feats. Second, Google announced
that it has now given over control of some of its data centres to a machine learning algorithm. This was announced as an experiment before (to some skepticism
), but it now appears to be working properly and delivering a 40% energy saving.
So far, so good - but about a week ago The Information published a big report (summarised here
by the author - worth a read) that says that Google’s much heralded self-driving car initiative, Waymo, can’t get its tech (i.e. AI) to work. Will it ever? The indispensable Ben Evans is skeptical
What can we draw from these stories? Perhaps one lesson is that the fewer humans an AI has to deal with, the better it performs - which shows how far we still have to go…