# Geoffrey Hinton on Reddit (u/geoffhinton)

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/geoffrey-hinton

Geoffrey Hinton: AI pioneer, 2024 Nobel Prize in Physics.

- Reddit account: [u/geoffhinton](https://www.reddit.com/user/geoffhinton/) (Personal account)
- How we know it's theirs: [The 2014 r/MachineLearning AMA, posted from this account](https://www.reddit.com/r/MachineLearning/comments/2lmo0l/)
- Where they post, in the records we retrieved: r/MachineLearning (60)
- Known activity in the posts we found: 2014 to 2020
- Wikipedia: https://en.wikipedia.org/wiki/Geoffrey_Hinton

## Moments

### u/geoffhinton in r/MachineLearning, Aug 11, 2016 · Behind the scenes

Replying to a redditor:

> How was '[Dropout](https://en.wikipedia.org/wiki/Convolutional_neural_network#Dropout)' conceived? Was there an 'aha' moment?

> There were actually three aha moments. One was in about 2004 when Radford Neal suggested to me that the brain might be big because it was learning a large ensemble of models. I thought this would be a very inefficient use of hardware since the same features would need to be invented separately by different models. Then I realized that the "models" could just be the subset of active neurons. This would allow combinatorially many models and might explain why randomness in spiking was helpful.
>
> Soon after that I went to my bank. The tellers kept changing and I asked one of them why. He said he didn't know but they got moved around a lot. I figured it must be because it would require cooperation between employees to successfully defraud the bank. This made me realize that randomly removing a different subset of neurons on each example would prevent conspiracies and thus reduce overfitting.
>
> I tried this out rather sloppily (I didn't have an adviser) in 2004 and it didn't seem to work any better than keeping the squared weights small so I forgot about it.
>
> Then in 2011, Christos Papadimitriou gave a talk at Toronto in which he said that the whole point of sexual reproduction was to break up complex co-adaptations. He may not have said it quite like that, but that's what I heard. It was clearly the same abstract idea as randomly removing subsets of the neurons. So I went back and tried harder and in collaboration with my grad students we showed that it worked really well.

Full thread: https://www.reddit.com/r/MachineLearning/comments/4w6tsv/comment/d6dgyse/ · 81 points as of Nov 7, 2026

### u/geoffhinton in r/MachineLearning, Aug 11, 2016 · Personal story

Replying to a redditor:

> Oh wow I have an MA in experimental psychology and am super interested in hearing from him about the path he took to get to where he is now. I feel a bit stuck as I try to head into data science as a career.

> I did not like experimental psychology. The kinds of theories they were willing to entertain were hopelessly simple. So I became a carpenter for a year. I wasn't very good at that so I did a PhD in AI. Unfortunately, my idea of AI was a great big neural network that learned everything from data. This was not the received wisdom at the time even though, so far as I can tell, it was what Turing believed in.

Full thread: https://www.reddit.com/r/MachineLearning/comments/4w6tsv/comment/d6dlbsg/ · 109 points as of Nov 7, 2026

### u/geoffhinton in r/MachineLearning, Nov 10, 2014 · Behind the scenes

Replying to a redditor:

> How did you get the idea for the Boltzmann machine?

> Terry Sejnowski had the idea of combining simulated annealing with Hopfield nets. We then figured out that the neurons would have to use the logistic function to make this work. Initially we thought of these stochastic Hopfield nets as just a way of doing search, but about six months later we started working on unsupervised learning for these nets.  I had to give my first research seminar at CMU and I was terrified that I wouldn't have anything good to say. So I worked very hard. Terry always works very hard anyway. I guessed that we should be minimizing the KL divergence between the distribution we wanted to model and the distribution exhibited by the network when it was at thermal equilibrium at a temperature of 1. Terry did the math. This led to such nice derivatives that we knew we were onto something. Also it justified Crick and Mitchison's theory of sleep as unlearning.
>
> A few years later, Peter Brown pointed out that our learning algorithm was actually doing maximum likelihood and I said "What's maximum likelihood?".

Full thread: https://www.reddit.com/r/MachineLearning/comments/2lmo0l/comment/clypbhc/ · 31 points as of Nov 7, 2026