# Yoshua Bengio on Reddit (u/yoshua_bengio)

Part of Scientists on Reddit, researched with Autumn (https://www.autumn.ai) from public sources. Updated 2026-11-07. Interactive version: https://www.autumn.ai/resources/scientists-on-reddit/yoshua-bengio

Yoshua Bengio: AI pioneer, Turing Award winner.

- Reddit account: [u/yoshua_bengio](https://www.reddit.com/user/yoshua_bengio/) (Personal account)
- How we know it's theirs: [The 2014 r/MachineLearning AMA, verified from this account](https://www.reddit.com/r/MachineLearning/comments/1ysry1/)
- Where they post, in the records we retrieved: r/MachineLearning (65)
- Known activity in the posts we found: 2014 to 2021
- Wikipedia: https://en.wikipedia.org/wiki/Yoshua_Bengio

## Moments

### u/yoshua_bengio in r/MachineLearning, Feb 26, 2014 · Behind the scenes

Replying to a redditor:

> We have seen deep learning work really well for image/video/sound. Do you foresee it working for text classification as well? Most papers that have tried text/document classification using deep learning have not done better than the conventional SVM/Bayes. What are your thoughts…

> I predict that deep learning will have a big impact in natural language processing. It has already had an impact, in part due to an old idea of mine (from NIPS'2000 and a 2003 paper in JMLR): represent words by a learned vector of attributes, learned so as to model the probability distribution of sequences of words in natural language text. The current challenge is to learn distributed representations for sequences of words, phrases and sentences. Look at the work of Richard Socher, which is pretty impressive. Look at the work of Tomas Mikolov, who beat the state of the art in language models using recurrent networks and who found that these distributed representations magically capture some form of analogical relationships between words. For example, if you take the representation for Italy minus the representation for Rome, plus the representation for Paris, you get something close to the representation for France: Italy - Rome + Paris = France. Similarly, you get that King - Man + Woman = Queen, and so on. Since the model was not trained explicitly to do these things, this is really amazing.

Full thread: https://www.reddit.com/r/MachineLearning/comments/1ysry1/comment/cfpje92/ · 10 points as of Nov 7, 2026

### u/yoshua_bengio in r/MachineLearning, Feb 27, 2014 · Behind the scenes

Replying to a redditor:

> Is there attempts to apply neural nets to the task of machine translation? When do you think NN based approaches replace statistical methods in commercially deployed MT systems? I mean in speech recognition(all major industry players) and vision(Google, Baidu) tasks NNs are…

> Briefly, since neural nets already beat n-grams on language modeling, you can first use them to replace the language-modeling part of MT. Then you can use them to replace the translation table (after all it's just another table of conditional probabilities). Other fun stuff is going on. The most exciting and ambitious approaches would completely scrap the current MT pipeline and learn to do end-to-end MT purely with a deep model. The interesting aspect of this is that the output is structured (it is a joint distribution over sequences of words), not a simple point-wise prediction (because there are many translations that are appropriate for a given source sentence).

(Excerpt.)
Full thread: https://www.reddit.com/r/MachineLearning/comments/1ysry1/comment/cfq7fs9/ · 5 points as of Nov 7, 2026