Google’s self-training AI turns coders into machine-learning masters

Post by thrawnjuxt » Wed, Feb 28, 2018, 12:49 AM UTC

YeGoblynQueenne wrote: post #4

>> “We need to scale AI out to more people,” Fei-Fei Li, chief scientist at Google Cloud, said ahead of the launch today. Problem is, when Google says "AI" they mean deep learning, on ginormous datasets with humongous clusters of GPUs. That don't scale. Accordingly, when Google says "we need to scale AI out to more people" what they really mean is "we need to make more people use our services". Sure- but most develop…

>> make it possible to train deep learning on small datasets

Looks like Baidu's new voice cloning tech is able to do tricks like make a British man sound like an American woman with just a few seconds worth of audio to train on. Apparently the previous version needed at least half an hour of audio to train.

https://thenextweb.com/artificial-intelligence/2018/02/26/ba...

Post by nolroz » Wed, Feb 28, 2018, 6:35 PM UTC

yorwba wrote: post #20

nolroz wrote: post #14

Earlier quoted context omitted.

I'm trying to start simple on my journey towards thinking more like a statistician and have long suspected this would be the case at many companies. Besides Bayesian models, do any other common methods seem to be ignored as often? Any suggestions resources that a newbie on the scene might use to educate themselves?

One simple method that is often good enough is k-nearest neighbors. Basically computing the k most similar training values to a test value and then averaging their outputs to get a prediction.

Thank you! I'll be sure to dig into k-nearest more as well.

Re: Google’s self-training AI turns coders into machine-learning masters

Re: Google’s self-training AI turns coders into machine-learning masters