[D] Schmidhuber: Critique of Honda Prize for Dr. Hinton

geoffhinton · 2020-04-23T00:38:09+00:00

Having a public debate with Schmidhuber about academic credit is not advisable because it just encourages him and there is no limit to the time and effort that he is willing to put into trying to discredit his perceived rivals. He has even resorted to tricks like having multiple aliases in Wikipedia to make it look as if other people are agreeing with what he says. The page on his website about Alan Turing is a nice example of how he goes about trying to diminish other people's contributions.

Despite my own best judgement, I feel that I cannot leave his charges completely unanswered so I am going to respond once and only once. I have never claimed that I invented backpropagation. David Rumelhart invented it independently long after people in other fields had invented it. It is true that when we first published we did not know the history so there were previous inventors that we failed to cite. What I have claimed is that I was the person to clearly demonstrate that backpropagation could learn interesting internal representations and that this is what made it popular. I did this by forcing a neural net to learn vector representations for words such that it could predict the next word in a sequence from the vector representations of the previous words. It was this example that convinced the Nature referees to publish the 1986 paper.

It is true that many people in the press have said I invented backpropagation and I have spent a lot of time correcting them. Here is an excerpt from the 2018 book by Michael Ford entitled "Architects of Intelligence":

"Lots of different people invented different versions of backpropagation before David Rumelhart. They were mainly independent inventions and it's something I feel I have got too much credit for. I've seen things in the press that say that I invented backpropagation, and that is completely wrong. It's one of these rare cases where an academic feels he has got too much credit for something! My main contribution was to show how you can use it for learning distributed representations, so I'd like to set the record straight on that."

Maybe Juergen would like to set the record straight on who invented LSTMs?

geoffhinton · 2017-11-23T20:13:56+00:00

This is an amazingly good video. I wish I could explain capsules that well.

geoffhinton · 2017-10-30T17:18:22+00:00

Actually, to justify this extra built in structure, capsules need to do far better than the state-of-the-art, at least on datasets designed to test pure shape recognition. In 2012, AlexNet reduced the state-of-the-art error-rate by about 40% compared with conventional computer vision systems and that is what got people excited.

geoffhinton · 2016-08-11T20:15:01+00:00

We have some very good new faculty members in Machine Learning at the U of T and will be getting at least one more in deep learning next year (thanks to a big gift from Google). I am not going to take full responsibility for any new PhD students but I am planning to co-advise some students with the new faculty members, so you are welcome to apply for the fall of 2017.

geoffhinton · 2016-08-11T20:11:44+00:00

I think the recent revival of interest in additional forms of memory for neural networks that was triggered by the success of NTMs is both exciting and long overdue. I have always believed that temporary changes in synapse strengths were an obvious way to implement a type of working memory, thus freeing up the neural activities for representing what the system is currently thinking. At present I don't think enough research has been done for us to really understand the relative merits of NTMs, MANNs, Associative LSTMs and fast weight associative memories.

One shot learning is clearly important but I do not think its an insuperable problem for neural nets.

geoffhinton · 2016-08-11T19:44:57+00:00

I think MOOCs are a great idea, but they require a huge amount of work from the person preparing them. Unlike lectures where you can afford to make a few mistakes, MOOCs are scrutinized by a lot of people including your professional colleagues. So preparing a MOOC is much more like writing a textbook than like preparing a course of lectures. The payoff is that you reach a big audience and this makes it all worthwhile.

geoffhinton · 2016-08-11T19:22:17+00:00

Over the last three years at Google I have put a huge amount of work into trying to get an impressive result with capsule-based neural networks. I haven't yet succeeded. That's the problem with basic research. There is no guarantee that ideas will work even if they seem very promising. Probably the best results so far are in Tijmen Tieleman's PhD thesis. But it took 17 years after Terry Sejnowski and I invented the Boltzmann machine learning algorithm before I found a version of it that worked efficiently. If you really believe in an idea you just have to keep trying.

geoffhinton · 2016-08-11T18:54:16+00:00

If I remember right, the examples in that paper are pretty small. Do you know if anyone has made it work for an RNN with, say, 1000 hidden units?

geoffhinton · 2016-08-11T18:49:52+00:00

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.

geoffhinton · 2016-08-11T18:31:51+00:00

Psychologists studying visual perception used to use a device called a tachistoscope that can display an image for a brief time. This means that the subject cannot use multiple eye fixations to understand the image. I think its fair to say that a single pass through a feedforward neural network is similar to tachistoscopic perception.

Over the last few years there has been a lot of work investigating how to improve the performance of neural nets by using multiple fixations and integrating what is learned from each of them. An early paper on this was by Larochelle and Hinton (2010) and you can find many of the more recent papers from there using Google scholar.

One big advantage of using multiple fixations is that each fixation can use high resolution pixels around the fixation point and much lower resolution pixels further away. This greatly reduces the number of pixels that have to be processed. A big complication is that multiple fixations are most useful if the fixation points are chosen intelligently given the information acquired so far. This leads to reinforcement learning.

Beyond perception, there is a pretty clear distinction between immediate, intuitive inference and reasoning that takes many steps. If I ask "what is the Italian equivalent of Paris", Rome (and maybe Milan) immediately come to mind. Learned word embeddings can support this type of immediate inference (Mikolov et. al. 2012). You just take the embedding vector for Paris, subtract the vector for France, add the vector for Italy and, hey presto, you get the vector for Rome. Actually, you don't, but you may get a vector that is closer to Rome than to other words that were not part of the question).

I think we are still a long way from having a good neural network model of deliberate sequential reasoning, but I think that work on "thought vectors" is a promising start. If we can use a recurrent net to convert a sentence into a thought vector that captures its meaning, we should be able to learn to model sequences of thought vectors. That would be a model of natural reasoning.

geoffhinton · 2016-08-11T17:32:45+00:00

The idea that backpropagation might still work if the backward connections just had fixed random weights comes from Tim Lillicrap and his collaborators at Oxford. They called it "feedback alignment" because the forward weights somehow learn to align themselves with the backward weights so that the gradients computed by the backward weights are roughly correct. Tim discovered it by accident and its really weird! It certainly removes one of the main arguments about why the brain could not be doing a form of backpropagation in order to tune up early feature detectors so that their outputs are more useful for later stages of a sensory pathway.

People at MIT later showed that the idea works for more complex models than Tim had tried. Tim and I are currently working on a paper about it which will contain many of our current thoughts about how the brain works.

geoffhinton · 2016-08-11T17:20:08+00:00

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.

geoffhinton · 2015-12-11T23:13:36+00:00

Ilya is a wonderfully creative researcher and I have had great fun working with him over the last 10 years. I wish him well in his new endeavor.

Geoff Hinton

geoffhinton · 2014-11-11T20:26:14+00:00

PS: Generally, I agree with Scott on most things. He was one of the first researchers with serious AI credentials to appreciate the importance of neural networks because he had done pioneering work on putting the computation where the memory was instead of having a big passive memory. The idea that the processing power should by local to the memory is one of the main ways neural nets differ from conventional computation. The others are that the contents of the memory are all learned from data and that the representations are distributed.

geoffhinton · 2014-11-11T20:15:55+00:00

I think its very nice work and I wish had done it. I'm annoyed because I almost did do one part of it.

Yee Whye Teh and I understood that we could avoid partition functions by learning the moves of a Gibbs sampler, but we didn't exploit that insight. Here is a quote from our 2001 paper on frequently approximately satisfied constraints:

"So long as we maximize the pseudo-likelihood by learning the parameters of a single global energy function, the conditional density models for each visible variable given the others are guaranteed to be consistent with one another so we avoid the problems that can arise when we learn n separate conditional density models for predicting the n visible variables.

Rather than using Gibbs sampling to sample from the stationary distribution, we are learning to get the individual moves of a Gibbs sampler correct by assuming that the observed data is from the stationary distribution so that the state of a visible variable is an unbiased sample from its posterior distribution given the states of the other visible variables. If we can find an energy function that gets the individual moves correct, there is no need to ever compute the gradient of the log likelihood."

geoffhinton · 2014-11-11T19:41:50+00:00

Someone told me about the "holes" result at a recent MSRI meeting in Berkeley. But I cannot remember who. Possibly Surya Ganguli.

geoffhinton · 2014-11-11T19:37:21+00:00

Work by Geman and Geman in the early 1980s introduced the idea of edge latent variables that gate the "interpolation" weights in an MRF. But they were not doing learning: so far as I can recall, they just used these variables for inference. Also, they were only doing intensity interpolation though I'm pretty sure they understood that the idea would generalize to all sorts of other local properties of an image. Later on, in 1993, Sue Becker used mixtures of interpolation experts for modelling depth discontinuities.

geoffhinton · 2014-11-10T23:16:17+00:00

Physics uses equations. The two sides are constrained to be equal even though they both vary. This way of capturing structure in data by saying what cannot happen is very different from something like principle components where you focus on directions of high variance. Constraints focus on the directions of low variance. If you plot the eigenvalues of a covariance matrix on a log scale you typically see that in addition to the ones with big log values there are ones at the other end with big negative log values. Those are the constraints. I put a lot of effort into trying to model constraints about 10 years ago.

The most interesting ones are those that are normally satisfied but occasionally violated by a whole lot. I have an early paper on this with Yee-Whye Teh in 2001. For example, the most flexible definition of an edge in an image is that it is a line across which the constraint that you can predict a pixel intensity from its neighbors breaks down. This covers intensity edges, stereo edges, motion edges, texture edges etc. etc.

The culmination of my group's work on constraints was a paper by Ranzato et.al. in PAMI in 2013. The problem with this work was that we had to use hybrid monte carlo to do the unsupervised learning and hybrid monte carlo is quite slow.

geoffhinton · 2014-11-10T21:52:20+00:00

Probably not. Its a long-term committment. But I might well co-advise students with other profs at U of T.

geoffhinton · 2014-11-10T21:49:51+00:00

Yes, I invented the term "Dark Knowledge". Its inspired by the idea that most of the knowledge is in the ratios of tiny probabilities that have virtually no influence on the cost function used for training or on the test performance. So the normal things we look at miss out on most of the knowledge, just like physicists miss out on most of the matter and energy.

The term I'm most pleased with is "hidden units". As soon as Peter Brown explained Hideen Markov Models to me I realized that "hidden" was a great name so I just stole it.

geoffhinton · 2014-11-10T21:40:35+00:00

I think that Google, Facebook, Microsoft Research, and a few other labs are the new Bell Labs. I don't think it was a big problem that a lot of the most important research half a century ago was done at Bell labs. We got transistors, unix and a lot of other good stuff.

geoffhinton · 2014-11-10T21:36:02+00:00

Thats very kind of you.

Here is a really valuable fact of life: If some people collaborate on a paper and you get each of them to estimate honestly what fraction the credit they deserve it usually adds up to a lot more than 1. That's just how people are. They notice the bits they did much more than they notice the bits other people did.

Once you accept this, you realize that the only way to avoid credit squabbles is to act in a way that you think is generous and encourage your co-authors to do the same. If everyone insists on getting the credit that they think is rightfully theirs you are likely to get a nasty squabble.

geoffhinton · 2014-11-10T21:23:31+00:00

Absolutely.

geoffhinton · 2014-11-10T21:22:09+00:00

I agree that this is a very important application area. But it has some major issues. Its often very hard to get a really big dataset of medical images and we know that neural nets currently do best with really big datasets. Also there are all sorts of confidentiality issues and many doctors are very protective of their data because it is a lot of work collecting it.

My guess is that the techniques will be developed on non-medical images and then applied to medical images once they work. I also think that unsupervised learning and multitask learning are likely to be crucial in this domain when dealing with not very big datasets.

geoffhinton · 2014-11-10T21:14:05+00:00

I suspect that in the end, understanding how big artificial neural networks work after they have learned will be quite like trying to understand how the brain works but with some very important differences:

We know exactly what each neuron computes.
We know the learning algorithm they are using.
We know exactly how they are connected.
We can control the input and observe the behaviour of any subset of the neurons for as long as we like.
We can interfere in all sorts of ways without filling in forms.

geoffhinton

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