The Google Brain team recently did a Reddit AMA, and I thought some comments were particularly interesting / crunchy.

Google AMA : Unsupervised Learning

Question asked by dexter89_kp :

  • What is the brain team’s take on state of unsupervised methods today?
  • Do you anticipate major conceptual strides in the next few years?

Answer by Google Brain’s vincentvanhoucke :

I think people are finally getting that autoencoding is a Bad Idea, and that the difference between unsupervised learning that works (e.g. language models) and unsupervised learning that doesn’t is generally about predicting the causal future (next word, next frame) instead of the present (autoencoding).

I’m very happy to see how many people have started benchmarking their ‘future prediction’ work on the push dataset we open-sourced last year, that was quite unexpected.

Could you elaborate? Bad idea in some specific context or just in general?

In general.

Take NLP for example: the most basic form of autoencoding in that space is linear bottleneck representations like LSA and LDA, and those are being completely displaced by Word2Vec and the like, which are still linear but which use context as the supervisory signal.

In acoustic modeling, we spent a lot of time trying to weigh the benefits of autoencoding audio representations to model signals, and all of that is being destroyed by LSTMs, which, again, use causal prediction as the supervisory signal.

Even Yann LeCun has amended his ‘cherry vs cake’ statement to no longer be about unsupervised learning, but about predictive learning.

That’s essentially the same message :

  • Autoencoders bad.
  • Future-self predictors good.

Answer by Google Brain’s gcorrado :

I don’t think we’ve really broken through on unsupervised learning. There’s a huge amount of information and structure in the unconditioned data distribution, and it seems like there should be some way for a learning algorithm to benefit from that.

I’m betting some bright mind will crack it, but I’m not sure when. Personally, I wonder if the right algorithmic approach might depend on the availability of one or two orders of magnitude more compute.

Time will tell.


… need to think more about how to unpack the difference between the Word2Vec unsupervised learning as a prediction task, vs LDA as a re-representation.

Along the way, I also came accross this interesting blog post by Giorgio Patrini, which talks about some other interesting papers / lines of enquiry.


Really nice to see some forward-looking opinions from people in the field that could easily have kept their ideas to themselves.

There were many insightful comments in this Reddit AMA, so I’ll probably do some more of these thread synposis things. Simply reformatting (like a code indentation twitch) is a nice way to revisit what’s important, and weed out irrelevant asides.

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Martin Andrews

{Finance, Software, AI} entrepreneur, living in Singapore with my family.

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