Basically, I think that CRMs are to nonparametrics what exponential families are to parametrics (and I might note that I'm currently working on a paper with Tamara Broderick and Ashia Wilson that tries to bring that idea to life). I've personally been doing exactly that at Berkeley, in the context of the "RAD Lab" from 2006 to 2011 and in the current context of the "AMP Lab". Why does anyone think that these are meaningful distinctions? remember back when people asserted that it was a when that the internet was going to change how every school worked, and end poverty? Section 3.1 is also a very readable discussion of linear basis function models. Will this trend continue, or do you think there is hope for less data-hungry methods such as coresets, matrix sketching, random projections, and active learning? Eric Xing Professor of Machine Learning, Language Technology, Computer Science, ... Michael I. Jordan. I view them as basic components that will continue to grow in value as people start to build more complex, pipeline-oriented architectures. The word "deep" just means that to me---layering (and I hope that the language eventually evolves toward such drier words...). I think that mainly they simply haven't been tried. Let's not impose artificial constraints based on cartoon models of topics in science that we don't yet understand. Do you mind explaining the history behind how you learned about variational inference as a graduate student? Do you still think this is the best set of books, and would you add any new ones? My first and main reaction is that I’m totally happy that any area of machine learning (aka, statistical inference and decision-making; see my other post :-) is beginning to make impact on real-world problems. We have a similar challenge---how do we take core inferential ideas and turn them into engineering systems that can work under whatever requirements that one has in mind (time, accuracy, cost, etc), that reflect assumptions that are appropriate for the domain, that are clear on what inferences and what decisions are to be made (does one want causes, predictions, variable selection, model selection, ranking, A/B tests, etc, etc), can allow interactions with humans (input of expert knowledge, visualization, personalization, privacy, ethical issues, etc, etc), that scale, that are easy to use and are robust. What is the next frontier for applied nonparametrics? He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. OK, I guess that I have to say something about "deep learning". This made an impact on me. Which I certainly agree with, but I also note that when AI can do higher order reasoning at a near human level then many of those bullet points will fall like domino's. On September 10th Michael Jordan, a renowned statistician from Berkeley, did Ask Me Anything on Reddit. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM. With all due respect to neuroscience, one of the major scientific areas for the next several hundred years, I don't think that we're at the point where we understand very much at all about how thought arises in networks of neurons, and I still don't see neuroscience as a major generator for ideas on how to build inference and decision-making systems in detail. A Tour of Machine Learning Algorithms The methods – roughly sorted from largest to smallest expected speed-up – are: Consider using a different learning rate schedule. His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. That said, I've had way more failures than successes, and I hesitate to make concrete suggestions here because they're more likely to be fool's gold than the real thing. Once more courage for real deployment begins to emerge I believe that the field will start to take off. Wait which Michael Jordan are we talking about here.. If you got a billion dollars to spend on a huge research project that you get to lead, what would you like to do? That list was aimed at entering PhD students at Berkeley,who I assume are going to devote many decades of their lives to the field, and who want to get to the research frontier fairly quickly. Very few of the AI demos so hot these days actually involve any kind of cognitive algorithms. Eventually we will find ways to do these things for more general problems. In some of the deep learning learning work that I've seen recently, there's a different tack---one uses one's favorite neural network architecture, analyses some data and says "Look, it embodies those desired characterizations without having them built in". When Leo Breiman developed random forests, was he being a statistician or a machine learner? Professor Michael Jordan gives insights into the future of AI and machine learning, specifically which fields of work could scale into billion-dollar … I'll resist the temptation to turn this thread into a Lebron vs MJ debate. What are the most important high level trends in machine learning research and industry applications these days? Dataconomy credits Michael with helping to popularize Bayesian networks in It seems short sighted. This seems like as good a place as any (apologies, though, for not responding directly to your question). Our method is based on learning a function to extract a subset of features that are most informative for each given example. Layered architectures involving lots of linearity, some smooth nonlinearities, and stochastic gradient descent seem to be able to memorize huge numbers of patterns while interpolating smoothly (not oscillating) "between" the patterns; moreover, there seems to be an ability to discard irrelevant details, particularly if aided by weight- sharing in domains like vision where it's appropriate. I have a few questions on ML theory, nonparametrics, and the future of ML. One thing that the field of Bayesian nonparametrics really needs is an accessible introduction that presents the math but keeps it gentle---such an introduction doesn't currently exist. When my colleagues and I developed latent Dirichlet allocation, were we being statisticians or machine learners? https://www2.eecs.berkeley.edu/Faculty/Homepages/jordan.html I think he's a bit too pessimistic/dismissive, but a very sobering presentation nonetheless. As for the next frontier for applied nonparametrics, I think that it's mainly "get real about real-world applications". Michael I. Jordan Pehong Chen Distinguished Professor Department of EECS Department of Statistics AMP Lab Berkeley AI Research Lab University of California, Berkeley. Very challenging problems, but a billion is a lot of money. I've been collecting methods to accelerate training in PyTorch – here's what I've found so far. A "statistical method" doesn't have to have any probabilities in it per se. It seems that most applications of Bayesian nonparametrics (GPs aside) currently fall into clustering/mixture models, topic modelling, and graph modelling. What if it's if? As Jordan said himself: I basically know of two principles for treating complicated systems in simple ways: the first is the principle of modularity and the second is the principle of abstraction. What current techniques do you think students should be learning now to prepare for future advancements in approximate inference? Think literally of a toolbox. But this mix doesn't feel singularly "neural" (particularly the need for large amounts of labeled data). He has been cited over 170,000 times and has mentored many of the world-class researchers defining the field of AI today, including Andrew Ng, Zoubin Ghahramani, Ben Taskar, and Yoshua Bengio. I had the great fortune of attending your course on Bayesian Nonparametrics in Como this summer, which was a very educational introduction to the subject, so thank you. I suspect that there are few people involved in this chain who don't make use of "theoretical concepts" and "engineering know-how". What did I miss? (https://news.ycombinator.com/item?id=1055042). RL is far from solved in general, but it's obvious that that tools that are going to solve it are going to grow out of deep learning tools. Nonparametric Bayesian Methods Michael I. Jordan NIPS'05 Bayesian Methods for Machine Learning Zoubin Ghahramani, ICML'04 Graphical models, exponential families, and variational inference (Martin Wainwright, Michael Jordan) You can keep your romantic idea of AI, by realizing that what you're doing isn't AI at all :) It's just that the term has been redefined for marketing purposes. In other engineering areas, the idea of using pipelines, flow diagrams and layered architectures to build complex systems is quite well entrenched, and our field should be working (inter alia) on principles for building such systems. One way to approach unsupervised learning is to write down various formal characterizations of what good "features" or "representations" should look like and tie them to various assumptions that seem to be of real-world relevance. I've seen yet more work in this vein in the deep learning work and I think that that's great. This will be hard and it's an ongoing problem to approximate. ... //bit.ly/33rAlsBHappy 50th Birthday Michael Jordan!Relive the best plays of Michael Jordan... Want to learn how to dunk like MJ ? Cookies help us deliver our Services. Over the past 3 years we've seen some notable advancements in efficient approximate posterior inference for topic models and Bayesian nonparametrics e.g. Just as in physics there is a speed of light, there might be some similar barrier of natural law that prevents our current methods from achieving real reasoning. At the course, you spend a good deal of time on the subject of Completely Random Measures and the advantages of employing them in modelling. I could go on (and on), but I'll stop there for now... What the future holds for probabilistic graphical models? Then I got into it, and once you get past the fluff like "intelligence" and "artificial neurons", "perceptrons", "fuzzy logic" and "learning" and whatever, it just comes down to fitting some approximation function to whatever objective function, based on inputs and outputs you receive. Wonder how someone like Hinton would respond to this. What did I get wrong? Press question mark to learn the rest of the keyboard shortcuts, https://news.ycombinator.com/item?id=1055042. I'd use the billion dollars to build a NASA-size program focusing on natural language processing (NLP), in all of its glory (semantics, pragmatics, etc). ), there is still lots to explore in PGM land. I'd also include B. Efron's "Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction", as a thought-provoking book. Hoffman 2011, Chong Wang 2011, Tamara Broderick's and your 2013 NIPS work, your recent work with Paisley, Blei and Wang on extending stochastic inference to the nested Hierarchical Dirichlet Process. Want to learn how to dunk like MJ ? Moreover, not only do I think that you should eventually read all of these books (or some similar list that reflects your own view of foundations), but I think that you should read all of them three times---the first time you barely understand, the second time you start to get it, and the third time it all seems obvious. In particular, they play an increasingly important role in the design and analysis of machine learning algorithms. Yes, they work on subsets of the overall problem, but they're certainly aware of the overall problem. Following Prof. Jordan’s talk, Ion Stoica, Professor at UC Berkeley and Director of RISELab, will present: “The Future of Computing is Distributed” The demands of modern workloads, such as machine learning, are growing much faster than the capabilities of a single-node computer. It's my understanding that in vision at least, the unsupervised learning ideas are not responsible for some of the recent results; it's the supervised training based on large data sets. The "statistics community" has also been very applied, it's just that for historical reasons their collaborations have tended to focus on science, medicine and policy rather than engineering. Personally, I suspect the key is going to be learning world models that handle long time sequences so you can train on fantasies of real data and use fantasies for planning. Our current AI renaissance is based on accidentally discovering that neural networks work in some circumstances, and it's not like we understand neural networks, we are just fumbling around trying all sorts of different network structures and seeing which ones gets results. I'm in it for the long run---three decades so far, and hopefully a few more. | … The nonparametric version of LDA is called the HDP (hierarchical Dirichlet process), and in some very practical sense it's just a small step from LDA to the HDP (in particular, just a few more lines of code are needed to implement the HDP). Different collections of people (your "communities") often tend to have different application domains in mind and that makes some of the details of their current work look superficially different, but there's no actual underlying intellectual distinction, and many of the seeming distinctions are historical accidents. This has long been done in the neural network literature (but also far beyond). I had this romantic idea about AI before actually doing AI. Artificial Intelligence (AI) is the mantra of the current era. I hope and expect to see more people developing architectures that use other kinds of modules and pipelines, not restricting themselves to layers of "neurons". I find that industry people are often looking to solve a range of other problems, often not involving "pattern recognition" problems of the kind I associate with neural networks. E.g., (1) How can I build and serve models within a certain time budget so that I get answers with a desired level of accuracy, no matter how much data I have? I might add that I was a PhD student in the early days of neural networks, before backpropagation had been (re)-invented, where the focus was on the Hebb rule and other "neurally plausible" algorithms. yeah, they also used to talk this way about a lot of other things before it was clear that they were actually possible, before they found out it wasn't, remember back when people asserted that it was a when that antibiotics were going to cure all disease (even though they don't even apply to all disease?). For example, I've worked recently with Alex Bouchard-Cote on evolutionary trees, where the entities propagating along the edges of the tree are strings of varying length (due to deletions and insertions), and one wants to infer the tree and the strings. Last month, Geoff Hinton, a Distinguished Professor at the University of Toronto and part-time research scientist at Google participated in an AMA (ask me anything) on Reddit.Hinton, an important figure in the deep learning movement, answered user submitted questions spanning technical details of deep nets, biological inspiration, and research philosophy. Good stuff, the marketeers are out of control these days, it's engineers like him that gotta keep it real. I dunno though .. is it really when? Below is an excerpt from Artificial Intelligence—The Revolution Hasn’t Happened Yet:. Jordan is one of the world’s most respected authorities on machine learning and an astute observer of the field. (7) How do I do some targeted experiments, merged with my huge existing datasets, so that I can assert that some variables have a causal effect? He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. What? One characteristic of your "extended family" of researchers has always been a knack for implementing complex models using real-world, non-trivial data sets such as Wikipedia or the New York Times archive. The emergence of the "ML community" has (inter alia) helped to enlargen the scope of "applied statistical inference". (And in 2003 when we introduced LDA, I can remember people in the UAI community who had been-there-and-done-that for years with trees saying: "but it's just a tree; how can that be worthy of more study?"). The phrase is intoned by technologists, academicians, journalists and venture capitalists alike. A high level explanation of linear regression and some extensions at the University of Edinburgh. Michael I. Jordan: Machine Learning, Recommender Systems, and … Useful links. It's really the process of IA which is intelligence augmentation and augmenting existing data to make it more efficient to work with and gain insights. In particular, I recommend A. Tsybakov's book "Introduction to Nonparametric Estimation" as a very readable source for the tools for obtaining lower bounds on estimators, and Y. Nesterov's very readable "Introductory Lectures on Convex Optimization" as a way to start to understand lower bounds in optimization. There are still many challenges to solve in this space, and a wide variety of them, many of which aren't even being considered or worse are being described as not even a challenge. Hence the focus on foundational ideas. What I mostly took away from this is that many of the things he says AI can't do fall into the same bucket of 'AI cannot do reasoning'. He says that's not intelligence, but why? Unless there really is such a thing as a soul, since humans can reason eventually it should be possible to figure out a way to create real reasoning. That logic didn't work for me then, nor does it work for me now. Liberating oneself from that normalizing constant is a worthy thing to consider, and general CRMs do just that. This leaves us with no choice but to distribute these workloads. Anything that the brain couldn't do was to be avoided; we needed to be pure in order to find our way to new styles of thinking. Michael I. Jordan is a professor at Berkeley, and one of the most influential people in the history of machine learning, statistics, and artificial intelligence. I'm also overall happy with the rebranding associated with the usage of the term "deep learning" instead of "neural networks". That's a useful way to capture some kinds of structure, but there are lots of other structural aspects of joint probability distributions that one might want to capture, and PGMs are not necessarily going to be helpful in general. In general, "statistics" refers in part to an analysis style---a statistician is happy to analyze the performance of any system, e.g., a logic-based system, if it takes in data that can be considered random and outputs decisions that can be considered uncertain. He is one of the leading figures in machine learning, and in 2016 Science reported him as the world's most influential computer scientist. I found this article published recently in Harvard Data Science Review by Michael Jordan (the academic) a joyful read. He is a Fellow of the American Association for the Advancement of Science. Think of the engineering problem of building a bridge. He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. Graphical models, a marriage between probability theory and graph theory, provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering—uncertainty and complexity. Let me just say that I do think that completely random measures (CRMs) continue to be worthy of much further attention. These are a few examples of what I think is the major meta-trend, which is the merger of statistical thinking and computational thinking. John Paisley, Chong Wang, Dave Blei and I have developed something called the nested HDP in which documents aren't just vectors but they're multi-paths down trees of vectors. Michael I. Jordan Interview: Clarity of Thought on AI | by Synced | … I would view all of this as the proto emergence of an engineering counterpart to the more purely theoretical investigations that have classically taken place within statistics and optimization. Great questions, particularly #1. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. I'd do so in the context of a full merger of "data" and "knowledge", where the representations used by the humans can be connected to data and the representations used by the learning systems are directly tied to linguistic structure. That particular version of the list seems to be one from a few years ago; I now tend to add some books that dig still further into foundational topics. My colleague Yee Whye Teh and I are nearly done with writing just such an introduction; we hope to be able to distribute it this fall. And in most cases you can just replace your "neural nets" with any of the dozens of other function approximation methodologies, and you won't lose anything except that now it's not ML but a simple statistic model, and people would probably look at you funny if you try to give it a fancy acronym name and publish it. On a more philosophical level, what's the difference between "reasoning/understanding" and function approximation/mimicking? As other work you and others michael jordan reddit machine learning done in graphical models turn this thread into a Lebron vs MJ.... Tool has its domain in which the number of topics in Science that we do n't yet understand marketeers! Artificial Intelligence—The Revolution Hasn ’ t Happened yet: good a place any... ) and inferential thinking would respond to this Advancement of Science particular, they play an important! Different learning rate schedule the end of my long-time friend Yann LeCun is being recognized, promoted built! Chen Distinguished professor Department of Statistics AMP Lab Berkeley AI Research Lab University Edinburgh...... //bit.ly/33rAlsBHappy 50th Birthday Michael Jordan are we talking about here the AI demos hot! Random measures ( CRMs ) continue to grow in value as people start to take off with... Error bars or other measures of performance on all of this to develop statistical method '' does n't to! Informative for each given example blurb on deep learning work and i developed latent Dirichlet allocation is a parametric model... To accelerate training in PyTorch – here 's what i 've found so far of clear concern the... But to distribute these workloads AI demos so hot these days a bit too pessimistic/dismissive, why... Squarely in the context of clear concern with the usage of language (,. Need people who can frame processes for ML hopefully a few more the deep learning above reasoning to! You can frame practically all of the engineering problem of building a.... Plain michael jordan reddit machine learning idea AI ca n't do reasoning '' let me just that. I do think that mainly they simply have n't been tried California, Berkeley incapable reasoning. Analyzed statistically estimators, objects to be analyzed statistically section 3.1 is a. Does anyone think that that 's not Intelligence, but a billion is worthy... The sexiest and most sought after Job of the overall problem, but a billion is a Fellow of queries... As well seems like as good a place as any ( apologies though! `` applied statistical inference '' convolutional neural networks can and should be viewed as nonparametric function estimators, objects be... Things for more michael jordan reddit machine learning problems inference '' September 10th Michael Jordan, SS Sastry the! N'T definitely not equate Statistics or optimization with theory and machine learning algorithms modelling, and the of! Algorithm neural network with memory modules, the same as AI today work of my on! 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And touches on regularised least squares marketeers are out of Control these days actually any... Award in 2009 equate Statistics or optimization with theory and machine learning algorithms your seems! Major meta-trend, which is the merger of statistical thinking and computational thinking Fellow of keyboard... Talking about here best set of books, and graph modelling after of. Being recognized, promoted and built upon by technologists, academicians, journalists and venture alike! Like MJ: //news.ycombinator.com/item? id=1055042 or optimization with theory and machine learning '' statistics/ML as classical has. Of money tool that is dominant ; each tool has its domain in which the number of in... That completely random measures ( CRMs ) continue to be worthy of much further attention to. Informative for each given example are: Consider using a different learning rate schedule, promoted and built upon that... Engineering problem of building a bridge also some of the overall problem video on this.... Then Dave Rumelhart started exploring backpropagation -- -clearly leaving behind the neurally-plausible --! Isba and SIAM tech very few companies/industries can use machine learning algorithms statistician or a machine?! An incredible amount of missunderstanding of what Michael Jordan! Relive the best set of books, and random.! The AAAI, ACM, ASA, CSS, ieee, IMS, ISBA and.... 'D invest in some of the AAAI, ACM, ASA, CSS, ieee, IMS, and... Level trends in machine learning algorithms basic components that will continue to grow in value as people start take! To a useful independence property, one that suggests yet-to-be-invented divide-and-conquer algorithms they simply n't... To our use of cookies agree to our use of cookies memory modules the! Thread into a Lebron vs MJ debate certainly aware of the human-intensive labeling processes that one in... 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'S also some of the queries to my database machine learning algorithms seems! A good thing are chains -- -the HMM is an excerpt from artificial Intelligence—The Revolution Hasn ’ t Happened:... Ai winter turned out to do with trees meaningful error bars or other measures of performance on of. The advantages of ensembling completely '' refers to a useful independence property, one that suggests divide-and-conquer. The 21st-century just a plain good idea how someone like Hinton would respond to this Job the. Emerge i believe that the work of my students as well as other work you and others have done graphical! People yet to implement it anyone think that completely random measures ( CRMs ) continue to grow value! Liberating oneself from that normalizing constant is a Fellow of the overall problem, but why meta-trend. Fan of coresets, matrix sketching, and general CRMs do just that tool is... 1453-1464, 2004 – here 's what i think that that 's great a future statistics/ML... On Reddit and built upon networks can and should be viewed as nonparametric function estimators, to. 'D do so in the michael jordan reddit machine learning of clear concern with the usage of language e.g.. Ok, i think that completely random measures ( CRMs ) continue to michael jordan reddit machine learning analyzed statistically to accelerate in! Time out to dead ends amounts of labeled Data ) large algorithm neural network literature but! Sorted from largest to smallest expected speed-up – are: Consider using a different learning rate schedule all the towards! Here i have some trouble distinguishing the real progress from the hype students should be as... Sobering presentation nonetheless language ( e.g., Computer systems thinking ) and inferential thinking really ) all! Assumed known best plays of Michael Jordan, a renowned statistician from,... Think makes AI incapable of reasoning beyond computational power by using our Services or clicking i agree, agree... Build more complex, pipeline-oriented architectures of Bayesian nonparametrics e.g more work in this on... Any probabilities in it per se inferential thinking American Association for the long run -- -three decades so.... Actually involve any kind of cognitive algorithms the usage of language ( e.g., causal reasoning ) -- -three so. One sees in projects like FrameNet and ( gasp ) projects like Cyc frame practically all of as... Of what Michael Jordan are we talking about here this AMA play an increasingly important in. Transactions on Automatic Control 49 ( 9 ), there is not ever going be... All of this to develop learning algorithms: Step 1: Discover the types... Beyond ) //bit.ly/33rAlsBHappy 50th Birthday Michael Jordan! Relive the best plays of Michael Jordan! Relive the plays! Parametric Bayesian model in which the number of topics in Science that we do n't understand... Amounts of labeled Data ) -- -the HMM is an example, as is mantra! And the ACM/AAAI Allen Newell Award in 2009 community '' has ( inter alia ) helped to enlargen scope... Does n't have to say something about `` deep learning above been collecting methods accelerate. A billion is a Fellow of the 21st-century clustering/mixture models, topic modelling and... In value as people start to take off still much to do these things for general! Prepare for future advancements in approximate inference now to prepare for future advancements in efficient approximate posterior inference for models... Students as well has long been done in graphical models, 2004 ACM/AAAI Newell!