Companies and other business entities are actively involved in defining data products and applied research every year. Academia has always played a role in creating new methods and solutions/algorithms in the fields of machine learning and artificial intelligence.
However, there is doubt about how powerful and effective such research efforts are.
Is studying AI in academia a waste of time?
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And here we are again with the season four of the Data Science at Home podcast.
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This time we have something for you if you want to help us shape the data science leaders of the future, we have created this the Data Science at Home’s Ambassador Program.
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Ambassadors are volunteers who are passionate about data science and want to give back to our growing community of data science professionals and enthusiasts.
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You will be instrumental in helping us achieve our goal of raising awareness about the critical role of data science in cutting edge technology.
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If you want to learn more about this program, visit the Ambassadors page on our email@example.com.
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Welcome back to another episode of Data Science at Home podcast.
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I’m Francesco, your host.
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For the next 20 minutes or so, I’m podcasting from the regular office of A Methods Technologies based in Belgium.
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In this episode I would like to report some statements from a very respectable individual like Jeremy Howard, creator of Fast AI, also ex President of Kaggle, who said something very recently in an interview that is going to, let’s say, make some people angry or just disappointed or just pissed.
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Well, he basically said that research in the deep learning world is a total waste of time.
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And so this statement in fact is first of all the statement of a person who knows what he’s talking about.
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I respect Jeremy very much and for those who don’t know him, please google his story and you will understand immediately that this is not a charlatan or a person just blubs about things to get noticed on the web.
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And as a matter of fact, he has a point that is definitely undeniable.
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The fact that there is a lot of research in the deep learning world that is, let’s say, useless to say the least, or it’s definitely leading nowhere with respect to the big picture of artificial intelligence.
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It’s worth spending some minutes about this statement and also share my opinion.
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I’ve been a researcher myself, so I think I can contribute if there is a way to contribute to this statement and to give you my personal thoughts.
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Well, there are two things that essentially Mr.
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Jeremy Howard is reporting and he thinks that in fact there is lack of research or lack of interest in these two fields in particular, transfer learning and active learning.
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In his opinion, these are the two most, not the most important, but very important fields that paradoxically nobody is spending time on and on this.
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I’m 100% aboard, 100% on the same page here.
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I do believe transfer learning and active learning are extremely important and definitely would raise the bar and definitely facilitate or improve a lot of things happening in the deep learning world if these two things are improving or if more effort and more time and more resources would be spent around transfer learning and active learning.
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And that’s true in the academic world.
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Not so many people are putting or not so many institutions are putting enough effort on these two things.
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Now, what is transfer learning? Transfer learning is of course we have covered transfer learning a long time ago on this show.
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But it’s essentially a way, a methodology to save some training costs, for example, and move across domains, essentially.
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And so having models that can be tuned and be kind of ready for domains in which they were not thought for example.
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You can have an image classifier that has been trained on general purpose images and then you can do transfer learning to adapt that method or that neural network to.
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Medical images or images in a more narrow sector you would just retrain.
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You would transfer the initial or the first layers of the network and then you would tune or retrain completely the last layers and that could save you a lot of time or a lot of costs or a lot of energy when you need to train.
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A massive neural network of several million parameters or even billions of parameters.
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Not only that, so not only you save energy, you save costs, you save time when it comes to retraining a network.
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But transfer learning also helps.
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You dealing with lack of data in the medical imaging example, definitely general public images are much more available or much easier available than medical images.
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Think about medical images of a particular or a rare disease or disorder, right? So in that case, retraining a network from scratch would definitely lead you nowhere in terms of accuracy or robustness of the model.
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What would help, however, is utilizing or taking advantage of the initial layers of networks trained on the most available images that are out there.
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And that’s why in this case you’re dealing with lack of data in a smart way which is indeed using transfer learning.
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Another important benefit of transfer learning is definitely generalization.
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If you can transfer the learning or what a neural network learned from one sector to another it means that a lot of the inner internal parameters can be reutilized across sectors, across domains and that’s something that leads you towards a better generalization.
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The fact that the network can still perform maybe losing a bit of accuracy in percentage points but it could still perform in a relatively different scenario, relatively different domain energy, lack of data, moving from one domain to another, generalization costs, time are all benefits.
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So the statement of Jeremy Howard is if all these things are there like we get all these things for free once we put effort in transfer learning in the field of transfer why nobody’s putting effort on it, right, which makes sense.
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Another problem is another field that Jeremy Howard thinks or believes is not gaining, is not getting the attention that it deserves is active learning.
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And that’s also something that almost nobody is working on it.
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And that’s true.
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Active learning is very important because it allows to help a neural network or a machine learning model train on data that first of all are not there but they can be or they will be annotated by human beings and so there is kind of a human in the loop while we train such models for some domains for some particular use cases having a human in the loop is extremely important and again I refer to the medical sector one more time because we want humans to be in the loop during training especially at the beginning when we don’t really know if that particular machine learning model is going to perform and when it’s going to be ready to replace almost entirely for example a medical doctor it’s a lot of responsibility that we are putting in the hands so to speak of an artificial intelligence so we would better keep the human in the loop during training which is one of the most critical aspects of building a machine learning model the training part is in my opinion much more important than designing for example the topology because in the training there is a loss that is involved for example the quality of the data and the unpredictable results that the network can give you especially at the beginning of the training process and then all of a sudden or smoothly the network starts changing behavior according to the data that we feed the network with this statement of course opens it’s kind of provocative I read it across these lines I think it’s a provocation because Jeremy Howard is a very intelligent person and in my opinion he didn’t throw it just for the sake of getting on the news but he definitely was provocative with that statement and I think or at least that’s how I’ve interpreted this interview there is a problem in academic research and industry there is not enough bound between the two unfortunately and that’s something that we know is the case and it’s not something that has been appeared only in the last few years it’s something that has always been like that more or less in every country in every continent so in my opinion academic research and industry are not necessarily bound in fact I would add unfortunately but some other times it’s quite impossible to bind these two worlds or fill that gap between these two very diverse words that have a very different objectives in fact the fact that there’s no active research in transfer learning and active learning is probably because academia doesn’t have that need that in fact is for example felt by the industry the industrial world in the industry there is a need for transfer learning and active learning don’t forget that many companies out there kind of reinvent the wheel all the time for that particular use case and so we have seen this over and over again in different factors they take a neural network from an online repository and they tweak and tune it and change the topology and tailor to their needs to the need of the business use case.
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Be it fintech.
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Be it healthcare.
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Tech or automotive.
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So in my opinion, and that’s kind of a consequence of what Jeremy Howard is saying, nobody is bleeding for that problem in academia.
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Nobody is bleeding for lack of transfer learning and active learning.
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And that’s, my opinion, is the reason why these two fields are not really trendy in the academic world.
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Also, I have to say something about academia.
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I’ve been a researcher myself, I also published on internationally peer reviewed papers back in the days and probably my point of view is a bit outdated now, though I don’t believe that.
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But academia produces something that has to be publishable in a way that is usually novel, that improves on some state of the art, even not necessarily in a critical way.
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We have seen micro improvements on everything.
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We have a research group that publishes something claims that improved zero point something percent over the state of the art.
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That’s the currency that academia takes and considers for publications.
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At least that was the currency in my days, which is several years ago, it’s not decades.
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But more importantly, academia needs something that is scientifically sound, right, and can be explained with the tools that are pertinent to academics.
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They try to explain something that can be represented with a formula, a method that is sound, that can be proved as you prove ethereum, right? That’s the currency, that’s the language academia wants to talk, which is not necessarily the same language, in fact, it’s never the same language that the industrial world wants to speak.
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Now, there have been several attempts to explain deep learning with more consolidated methods or in a mathematically rigorous fashion and they all failed.
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To the best of my knowledge.
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We have been trying to, for example, compare or explain deep learning with thermodynamics or theoretical physics.
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Then some people, even with equilibria game theory, maybe I’m going to make an episode about all these things.
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But there have been used a lot of theoretical concepts to borrowed from other disciplines, usually physics, to explain deep learning.
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And the problem of deep learning is that many deep learning concepts are not fully mathematically rigorous as one can say, for example for physics or for abstract mathematics, for pure mathematics.
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There’s no formula that tells you if the data is in this shape and the topology of the network is in this other shape, then you’re going to get this as a formula.
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And that’s a problem because that’s what academia wants.
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And deep learning is definitely not that deep learning can offer everything, but a way to be formalized regardless of the attempts that have been of course by the community.
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And we have to be very grateful for these research groups that have tried their best to make deep learning to formalize the concept behind deep learning function optimization, for example, has been seen as an energy minimization problem in physics.
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So we can name many scenarios in which we take some mature or very consolidated theory from academia and we try to utilize it to explain deep learning and neural networks.
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That’s a matter of fact.
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Now, behind the statement of Jeremy Howard, of course, I don’t really have, I’m not in the position to say who’s right, who’s wrong.
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It’s definitely provocative.
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It’s definitely something that gives you food for thought and a debate should be open about this, if we will, but it doesn’t have to raise an eyebrow in that respect, in my opinion, because we have been doing this for several decades now.
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I see an analogy with, for example, computer programming.
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Now, academia taught me the basic constructs, the data structures and algorithms courses that I had.
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Another one on the theory of compilers, the pumping Llama or whatever other concept, academic concept you might think of.
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But at the end of the day, the actual programming language constructs were left to, let’s say, my passion, my time.
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It was not something that was taught in academia and it should stay like that.
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So I see an analogy there with deep learning and academic research.
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In deep learning.
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I agree with Jeremy that indeed there is some sort of incompatibility with what is the objective of the academic world with respect to what the objective of deep learning models would be.
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But I would not say it’s a total waste of time, that’s for sure.
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If you ask me, is researching deploying a waste of time? I would say not always.
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I would not say no, because it is sometimes, but not always.
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Many of the concepts related to, for example, function optimization are mostly coming from academic effort.
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Some other micro optimizations on network topology, I’ve seen dozens and dozens of papers where they literally slightly change the topology of a network and they boom, they have a new paper.
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Well, these are truly experimental and probably, yes, doing academic research around this very narrow concept would be a waste of time, in my opinion, but not entirely.
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I would definitely keep in mind the intrinsic gap, let’s say, between academia and industry.
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That, in my opinion, is definitely here to stay.
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Well, that’s it for today.
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Thank you very much for listening.
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I’ll speak with you next time.
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You’ve been listening to Data Science at home podcast.
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