Unsupervised sentiment neuron • Hacker News Board

Re: Unsupervised sentiment neuron

#51

Post by [deleted] » Thu, Apr 06, 2017, 6:18 PM UTC

visarga wrote: post #48

phkahler wrote: post #27

Earlier quoted context omitted.

Would that be why people raised in dysfunctional or abusive families have very deep rooted issues? Everything learned later in life seems more fluid, but some of those problematic attitudes or personality traits are extremely hard to change. Seems consistent with those parts being build in lower layers with a decades long training experience. And yet at a higher level things can be learned and changed fairly easily.

Could be possible. For example, when a child is deprived of any human and humane contact, they end up with a very very impoverished linguistic system as well as delayed development of all cognitive abilities. Could be because the lack the unsupervised training phase.

[deleted]

Re: Unsupervised sentiment neuron

#52

Post by argonaut » Thu, Apr 06, 2017, 6:23 PM UTC

rspeer wrote: post #24

emcq wrote: post #20

Earlier quoted context omitted.

The technique of training a model on a lot of data for a long time and then leveraging its sophisticated representation only learning the last layer(s) on small datasets to create accurate models is common practice.

It's a good idea, but it doesn't seem very common so far. This is what my NLP company (Luminoso) does -- we train a domain-general model of word meanings on a lot of data, then do the last step on the probably-small amount of specific data you actually have. Even customers who are knowledgeable about machine learning usually haven't heard of the idea before. They've been assuming that the only way to do NLP is to get…

My ML background is primarily undergraduate work, and even then it was extremely common.

Re: Unsupervised sentiment neuron

#53

Post by anonymfus » Thu, Apr 06, 2017, 6:23 PM UTC

This article is not accessible. It puts all textual examples into images and ever has some absolutely unnecessary animation. Please fix it.

Re: Unsupervised sentiment neuron

#54

Post by visarga » Thu, Apr 06, 2017, 6:31 PM UTC

YCode wrote: post #2

The synthetic text they generated was surprisingly realistic, despite being generic. If I were perusing a dozen reviews I probably wouldn't have spotted the AI-generated ones in the crowd.

That's a good observation. The code and trained model for this paper is on Github. It could be used to make a fake review generator. Dangerous.

Re: Unsupervised sentiment neuron

#55

Post by visarga » Thu, Apr 06, 2017, 6:32 PM UTC

happycube wrote: post #45

beaconstudios wrote: post #34

Earlier quoted context omitted.

at the moment, AI short stories are derivative, so it's unlikely. They may well be better than the average, if trained on highly regarded works, but they're not completely novel.

At the moment RNN's can't remember context, so they can make stuff that looks correct, but only on the surface. I think that'll change, eventually...

We need some kind of hierarchical approach, and/or memory.

Re: Unsupervised sentiment neuron

#56

Post by chrisfosterelli » Thu, Apr 06, 2017, 6:32 PM UTC

ericjang wrote: post #38

Why are people being so critical about this work? Sure, the blog post provides a simplified picture about what the system is actually capable of, but it's still helpful for a non-ML audience to get a better understanding of the high-level motivation behind the work. The OpenAI folks are trying to educate the broader public as well, not just ML/AI researchers. Imagine if this discovery were made by some undergraduate…

I personally think they did great. They targeted the blog post at a more general audience so most people can follow and get an overhead view of the idea, and then put two giant buttons for "View code" and "Read paper" right at the top of the blog post for those who want more technical writing and working code.

Re: Unsupervised sentiment neuron

#57

Post by visarga » Thu, Apr 06, 2017, 6:33 PM UTC

teraflop wrote: post #29

gallerdude wrote: post #21

The neural network is savage enough to learn "I would have given it zero stars, but that was not an option." Are we humans that predictable?

The training data consisted of 82 million reviews, so I'm sure that phrase (or slight variants) occurred hundreds of thousands of times.

Could be checked by counting n-grams to see just how much it differs from other reviews.

Re: Unsupervised sentiment neuron

#58

Post by changoplatanero » Thu, Apr 06, 2017, 6:35 PM UTC

What's the easiest way to make a text heatmap like the ones in their blog?

Re: Unsupervised sentiment neuron

#59

Post by visarga » Thu, Apr 06, 2017, 6:36 PM UTC

sushirain wrote: post #30

Very interesting. I wonder if they tried to predict part-of-speech tags.

That would probably work. Karpathy's character based RNN could detect semantic meaning in text and code. http://karpathy.github.io/2015/05/21/rnn-effectiveness/

Re: Unsupervised sentiment neuron

#60

Post by emcq » Thu, Apr 06, 2017, 6:42 PM UTC

kleiba wrote: post #32

emcq wrote: post #23

It's very difficult to understand what the contributions are here. From what I've read so far this feels more of a proposal for future research or a press release than advancing the state of the art. * Using large models trained on lots of data to provide the foundation for sample efficient smaller models is common. * Transfer learning, fine tuning, character RNNs is common. Were there any insights learned that give…

* advancing the state of the art It says right at the top: "we get 91.8% accuracy versus the previous best of 90.2%" on a standard sentiment corpus. In addition, their method needs less training data than previous approaches. * Were there any insights learned that give a deeper understanding of these phenomena? The main appeal lies in the fact that a model trained on a (1) different and (2) very general task basicall…

If the main contribution here is the quality of the model and its interesting and powerful representation of text, I hope OpenAI does something distruptively different and releases the weights and trained model.

The accidental sentiment neuron is a function of the model, distribution of the input dataset, and the optimizer finding nice saddle points. Insight into these foundational components would make these results amazing. It sounded like training on other datasets doesn't have the same sentiment properties, which provides a lever to explore these concepts more.

At the moment it feels like the Google cat neuron. It attracted a lot of intrigue but the individual contribution from that in terms of research was more on the infrastructure side, and few people seem to refer back to that publication at this point.

That said OpenAIs mission in itself doesn't necessarily require novel research. For example, the gym is fostering a competitive atmosphere for the community to work on RL which hopefully leads to more progress in the field.

Training a model for a month is difficult and if it has captured interesting phenomena it seems in the interest of the community to release the weights and model. It would be hard for the community to reproduce this without a month of compute and 83M Amazon reviews.