New to deep learning? Here are 4 easy lessons from Google

Google employs some of the world’s smartest researchers in deep learning and artificial intelligence, so it’s not a bad idea to listen to what they have to say about the space. One of those researchers, senior research scientist Greg Corrado, spoke at RE:WORK’s Deep Learning Summit on Thursday in San Francisco and gave some advice on when, why and how to use deep learning.

His talk was pragmatic and potentially very useful for folks who have heard about deep learning and how great it is — well, at computer vision, language understanding and speech recognition, at least — and are now wondering whether they should try using it for something. The TL;DR version is “maybe,” but here’s a little more nuanced advice from Corrado’s talk.

(And, of course, if you want to learn even more about deep learning, you can attend Gigaom’s Structure Data conference in March and our inaugural Structure Intelligence conference in September. You can also watch the presentations from our Future of AI meetup, which was held in late 2014.)

Probably the most-useful piece of advice Corrado gave is that deep learning isn’t necessarily the best approach to solving a problem, even if it would offer the best results. Presently, it’s computationally expensive (in all meanings of the word), it often requires a lot of data (more on that later) and probably requires some in-house expertise if you’re building systems yourself.

So while deep learning might ultimately work well on pattern-recognition tasks on structured data — fraud detection, stock-market prediction or analyzing sales pipelines, for example — Corrado said it’s easier to justify in the areas where it’s already widely used. “In machine perception, deep learning is so much better than the second-best approach that it’s hard to argue with,” he explained, while the gap between deep learning and other options is not so great in other applications.

That being said, I found myself in multiple conversations at the event centered around the opportunity to soup up existing enterprise software markets with deep learning and met a few startups trying to do it. In an on-stage interview I did with Baidu’s Andrew Ng (who worked alongside Corrado on the Google Brain project) earlier in the day, he noted how deep learning is currently powering some ad serving at Baidu and suggested that data center operations (something Google is actually exploring) might be a good fit.