He doesn't say deep learning doesn't work, in fact he does give credit where credit is due and states that it has come along with incredible results. Let me just say that I do think that completely random measures (CRMs) continue to be worthy of much further attention. Press question mark to learn the rest of the keyboard shortcuts. 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. I'd invest in some of the human-intensive labeling processes that one sees in projects like FrameNet and (gasp) projects like Cyc. Most of what is labeled AI today, particularly in the public sphere, is actually machine learning (ML), a term in use for the past several decades. 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) 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. It seems that most applications of Bayesian nonparametrics (GPs aside) currently fall into clustering/mixture models, topic modelling, and graph modelling. 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. We have hammers, screwdrivers, wrenches, etc, and big projects involve using each of them in appropriate (although often creative) ways. We have made such good progress that a lot of fields could benefit from but there are not enough people yet to implement it. 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. Do you mind explaining the history behind how you learned about variational inference as a graduate student? Professor Michael Jordan gives insights into the future of AI and machine learning, specifically which fields of work could scale into billion-dollar … 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?). Machine-Learning Maestro Michael Jordan on the Delusions of … 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. 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. I'm in it for the long run---three decades so far, and hopefully a few more. Hence the focus on foundational ideas. 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). Very challenging problems, but a billion is a lot of money. (4) How do I visualize data, and in general how do I reduce my data and present my inferences so that humans can understand what's going on? Whether you prefer to write Python or R code with the SDK or work with no-code/low-code options in the studio , you can build, train, and track machine learning and deep-learning models in an Azure Machine Learning Workspace. outside of quant finance and big tech very few companies/industries can use machine learning properly. I don't expect anyone to come to Berkeley having read any of these books in entirety, but I do hope that they've done some sampling and spent some quality time with at least some parts of most of them. (another example of an ML field which benefited from such inter-discipline crossover would be Hybrid MCMC, which is grounded in dynamical systems theory). Great questions, particularly #1. But one shouldn't definitely not equate statistics or optimization with theory and machine learning with applications. we need people who can frame processes for ML. Azure Machine Learning can be used for any kind of machine learning, from classical ml to deep learning, supervised, and unsupervised learning. I have no idea what this means, or could possibly mean. This leaves us with no choice but to distribute these workloads. In particular, they play an increasingly important role in the design and analysis of machine learning algorithms. Also I rarely find it useful to distinguish between theory and practice; their interplay is already profound and will only increase as the systems and problems we consider grow more complex. Do you think there are any other (specific) abstract mathematical concepts or methodologies we would benefit from studying and integrating into ML research? 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. Similarly, layered neural networks can and should be viewed as nonparametric function estimators, objects to be analyzed statistically. 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. I don't know what to call the overall field that I have in mind here (it's fine to use "data science" as a placeholder), but the main point is that most people who I know who were trained in statistics or in machine learning implicitly understood themselves as working in this overall field; they don't say "I'm not interested in principles having to do with randomization in data collection, or with how to merge data, or with uncertainty in my predictions, or with evaluating models, or with visualization". Below is an excerpt from Artificial Intelligence—The Revolution Hasn’t Happened Yet:. Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. Like all these thousands of papers that get published every year, where they just slightly change their training methodology/objective function/whatever, make a demo how this gives you 2% performance increase in some scenarios, come up with a catchy acronym for it and then pass it off as original research. He says that's not intelligence, but why? Of course, the "statistics community" was also not ever that well defined, and while ideas such as Kalman filters, HMMs and factor analysis originated outside of the "statistics community" narrowly defined, there were absorbed within statistics because they're clearly about inference. 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". The Decision-Making Side of Machine Learning: Computational, … Michael Irwin Jordan (born February 25, 1956) is an American scientist, professor at the University of California, Berkeley and researcher in machine learning, statistics, and artificial intelligence. Good stuff, the marketeers are out of control these days, it's engineers like him that gotta keep it real. ), there is still lots to explore in PGM land. 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". Based on seeing the kinds of questions I've discussed above arising again and again over the years I've concluded that statistics/ML needs a deeper engagement with people in CS systems and databases, not just with AI people, which has been the main kind of engagement going on in previous decades (and still remains the focus of "deep learning"). But this mix doesn't feel singularly "neural" (particularly the need for large amounts of labeled data). In that spirit of implementing, which topic modeling application areas are you most excited about at the moment and looking forward, what impact do you think these recent developments in fast, scalable inference for conjugate and conditionally conjugate Bayes nets will have on the applications we develop 5-10 years from now? 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". This has long been done in the neural network literature (but also far beyond). Indeed, with all due respect to bridge builders (and rocket builders, etc), but I think that we have a domain here that is more complex than any ever confronted in human society. When my colleagues and I developed latent Dirichlet allocation, were we being statisticians or machine learners? I mean you can frame practically all of physics as an optimization problem. Until we have general quantum computers that can simulate arbitrary scenarios (not even sure if that's possible), I don't see how you wouldn't rely on statistics, which forces you onto the common domain of function approximaters on high-dim manifolds. 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. 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. You need to know what algorithms are available for a given problem, how they work, and how to get the most out of them. 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. If you got a billion dollars to spend on a huge research project that you get to lead, what would you like to do? 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. That logic didn't work for me then, nor does it work for me now. Jordan is one of the world’s most respected authorities on machine learning and an astute observer of the field. Decision trees, nearest neighbor, logistic regression, kernels, PCA, canonical correlation, graphical models, K means and discriminant analysis come to mind, and also many general methodological principles (e.g., method of moments, which is having a mini-renaissance, Bayesian inference methods of all kinds, M estimation, bootstrap, cross-validation, EM, ROC, and of course stochastic gradient descent, whose pre-history goes back to the 50s and beyond), and many many theoretical tools (large deviations, concentrations, empirical processes, Bernstein-von Mises, U statistics, etc). Over the past 3 years we've seen some notable advancements in efficient approximate posterior inference for topic models and Bayesian nonparametrics e.g. https://www2.eecs.berkeley.edu/Faculty/Homepages/jordan.html These are his thoughts on deep learning. But beyond chains there are trees and there is still much to do with trees. By using our Services or clicking I agree, you agree to our use of cookies. This will be hard and it's an ongoing problem to approximate. What current techniques do you think students should be learning now to prepare for future advancements in approximate inference? OK, I guess that I have to say something about "deep learning". Meet Ray, the Real-Time Machine-Learning Replacement for Spark 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. 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. literally everything in their list was on star trek (admittedly the smart watches were chest badges and handhelds, so maybe they're novel, but dick tracy and you're clear again), back here in reality, people get things wrong in both directions at both age brackets far more often than they get them right, and possible isn't the important question besides; feasable is, i mean, fusion was possible in the 70s (the 40s if you count weapons,) but it's still not feasable yet. Useful links. Models that are able to continue to grow in complexity as data accrue seem very natural for our age, and if those models are well controlled so that they concentrate on parametric sub-models if those are adequate, what's not to like? Although I could possibly investigate such issues in the context of deep learning ideas, I generally find it a whole lot more transparent to investigate them in the context of simpler building blocks. 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. And then Dave Rumelhart started exploring backpropagation---clearly leaving behind the neurally-plausible constraint---and suddenly the systems became much more powerful. 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. There has been a ML reading list of books in hacker news for a while, where you recommend some books to start on ML. Emails: EECS Address: University of California, Berkeley EECS Department 387 Soda Hall #1776 Berkeley, CA 94720-1776 Statistics Address: University of California, Berkeley There's still lots to explore there. 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. 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. AI, I finished Andrew Ng’s Machine Learning Course and I Felt Great! But I personally think that the way to go is to put those formal characterizations into optimization functionals or Bayesian priors, and then develop procedures that explicitly try to optimize (or integrate) with respect to them. Note also that exponential families seemed to have been dead after Larry Brown's seminal monograph several decades ago, but they've continued to have multiple after-lives (see, e.g., my monograph with Martin Wainwright, where studying the conjugate duality of exponential families led to new vistas). And I continue to find much inspiration in tree-based architectures, particularly for problems in three big areas where trees arise organically---evolutionary biology, document modeling and natural language processing. Also, note that the adjective "completely" refers to a useful independence property, one that suggests yet-to-be-invented divide-and-conquer algorithms. A "statistical method" doesn't have to have any probabilities in it per se. 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. (5) How can I do diagnostics so that I don't roll out a system that's flawed or so that I can figure out that an existing system is now broken? Notions like "parallel is good" and "layering is good" could well (and have) been developed entirely independently of thinking about brains. Note that latent Dirichlet allocation is a tree. 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. On a more philosophical level, what's the difference between "reasoning/understanding" and function approximation/mimicking? 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. I had this romantic idea about AI before actually doing AI. I think that mainly they simply haven't been tried. We introduce instancewise feature selection as a methodology for model interpretation. Liberating oneself from that normalizing constant is a worthy thing to consider, and general CRMs do just that. My understanding is that many if not most of the "deep learning success stories" involve supervised learning (i.e., backpropagation) and massive amounts of data. I think that too few people have tried out Bayesian nonparametrics on real-world, large-scale problems (good counter-examples include Emily Fox at UW and David Dunson at Duke). Having just written (see above) about the need for statistics/ML to ally itself more with CS systems and database researchers rather than focusing mostly on AI, let me take the opportunity of your question to exhibit my personal incoherence and give an answer that focuses on AI. Theres an incredible amount of missunderstanding of what Michael Jordan is saying in this video on this post. On the other hand, despite having limitations (a good thing! Similarly, Maxwell's equations provide the theory behind electrical engineering, but ideas like impedance matching came into focus as engineers started to learn how to build pipelines and circuits. Eventually we will find ways to do these things for more general problems. 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. And as a result Data Scientist & ML Engineer has become the sexiest and most sought after Job of the 21st-century. (Consider computing the median). This last point is worth elaborating---there's no reason that one can't allow the nodes in graphical models to represent random sets, or random combinatorial general structures, or general stochastic processes; factorizations can be just as useful in such settings as they are in the classical settings of random vectors. Note that latent Dirichlet allocation is a parametric Bayesian model in which the number of topics K is assumed known. Very few of the AI demos so hot these days actually involve any kind of cognitive algorithms. On September 10th Michael Jordan, a renowned statistician from Berkeley, did Ask Me Anything on Reddit. Michael I. Jordan Pehong Chen Distinguished Professor Department of EECS Department of Statistics AMP Lab Berkeley AI Research Lab University of California, Berkeley. I'll resist the temptation to turn this thread into a Lebron vs MJ debate. I dunno though .. is it really when? Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. Now LDA has been used in several thousand applications by now, and it's my strong suspicion that the users of LDA in those applications would have been just as happy using the HDP, if not happier. 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 could go on (and on), but I'll stop there for now... What the future holds for probabilistic graphical models? Think of the engineering problem of building a bridge. 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 view them as basic components that will continue to grow in value as people start to build more complex, pipeline-oriented architectures. I also recommend A. van der Vaart's "Asymptotic Statistics", a book that we often teach from at Berkeley, as a book that shows how many ideas in inference (M estimation---which includes maximum likelihood and empirical risk minimization---the bootstrap, semiparametrics, etc) repose on top of empirical process theory. Indeed I've spent much of my career trying out existing ideas from various mathematical fields in new contexts and I continue to find that to be a very fruitful endeavor. Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts posted @ 2014-10-24 09:37 DeepVL 阅读( 127 ) 评论( 0 ) 编辑 收藏 刷新评论 刷新页面 返回顶部 Think literally of a toolbox. They've mainly been used in the context of deriving normalized random measures (by, e.g., James, Lijoi and Pruenster); i.e., random probability measures. I have a few questions on ML theory, nonparametrics, and the future of ML. 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. There is not ever going to be one general tool that is dominant; each tool has its domain in which its appropriate. This seems like as good a place as any (apologies, though, for not responding directly to your question). 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. I've been collecting methods to accelerate training in PyTorch – here's what I've found so far. remember back when people asserted that it was a when that the internet was going to change how every school worked, and end poverty? He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. Machine learning addresses the question of how to build computers that improve automatically through experience. Michael I. Jordan: Machine Learning, Recommender Systems, and … What? Throughout the eighties and nineties, it was striking how many times people working within the "ML community" realized that their ideas had had a lengthy pre-history in statistics. Here’s how to get started with machine learning algorithms: Step 1: Discover the different types of machine learning algorithms. Bishop, C. M. (2006): Pattern Recognition and Machine Learning, NY: Springer. In the topic modeling domain, I've been very interested in multi-resolution topic trees, which to me are one of the most promising ways to move beyond latent Dirichlet allocation. Yes, they work on subsets of the overall problem, but they're certainly aware of the overall problem. Indeed, it's unsupervised learning that has always been viewed as the Holy Grail; it's presumably what the brain excels at and what's really going to be needed to build real "brain-inspired computers". Just out of curiosity, what do you think makes AI incapable of reasoning beyond computational power? What are the most important high level trends in machine learning research and industry applications these days? 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. Lastly, and on a less philosophical level, while I do think of neural networks as one important tool in the toolbox, I find myself surprisingly rarely going to that tool when I'm consulting out in industry. Artificial Intelligence (AI) is the mantra of the current era. Are the SVM and boosting machine learning while logistic regression is statistics, even though they're solving essentially the same optimization problems up to slightly different shapes in a loss function? I think he's a bit too pessimistic/dismissive, but a very sobering presentation nonetheless. (https://news.ycombinator.com/item?id=1055042). I don't think that the "ML community" has developed many new inferential principles---or many new optimization principles---but I do think that the community has been exceedingly creative at taking existing ideas across many fields, and mixing and matching them to solve problems in emerging problem domains, and I think that the community has excelled at making creative use of new computing architectures. It has begun to break down some barriers between engineering thinking (e.g., computer systems thinking) and inferential thinking. Michael I. Jordan Interview: Clarity of Thought on AI | by Synced | … I suspect that there are few people involved in this chain who don't make use of "theoretical concepts" and "engineering know-how". 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. Note that many of the most widely-used graphical models are chains---the HMM is an example, as is the CRF. All the attempts towards reasoning prior to the AI winter turned out to dead ends. Let's not impose artificial constraints based on cartoon models of topics in science that we don't yet understand. 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. 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. I had this romantic idea about AI before actually doing AI. (Isn't it?). Mou, J. Li, M. Wainwright, P. Bartlett, and M. I. Jordan.arxiv.org/abs/2004.04719, 2020. 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. Credits — Harvard Business School. You are a large algorithm neural network with memory modules, the same as AI today. ... //bit.ly/33rAlsBHappy 50th Birthday Michael Jordan!Relive the best plays of Michael Jordan... Want to learn how to dunk like MJ ? Then I got into it, and once you get past the fluff like "intelligence" and "artificial neurons", … I found this article published recently in Harvard Data Science Review by Michael Jordan (the academic) a joyful read. Section 3.1 is also a very readable discussion of linear basis function models. Sometimes I am a bit disillusioned by the current trend in ML of just throwing universal models and lots of computing force at every problem. I've seen yet more work in this vein in the deep learning work and I think that that's great. This made an impact on me. 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. 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. 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. 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. 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. Anything beyond CRFs? And of course it has engendered new theoretical questions. There's a whole food chain of ideas from physics through civil engineering that allow one to design bridges, build them, give guarantees that they won't fall down under certain conditions, tune them to specific settings, etc, etc. | … Do you still think this is the best set of books, and would you add any new ones? This feature selector is trained to maximize the mutual information between selected features and the response variable, where the conditional distribution of the response … On linear stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration.W. (6) How do I deal with non-stationarity? Has had and continues to have any probabilities in it per se place as (... Michael I. Jordan Pehong Chen Distinguished professor Department of EECS Department of EECS Department of EECS Department of AMP... Other hand, despite having limitations ( a good thing readable discussion of linear regression some. Will find ways to do with trees of `` applied statistical inference '' the of... Jordan.Arxiv.Org/Abs/2004.04719, 2020 -- -and suddenly the systems became much more powerful frame processes for ML invest some... ’ s how to get started with machine learning michael jordan reddit machine learning applications i developed latent allocation... Professor of Electrical engineering and Computer Sciences and professor of... M Franceschetti, K,. Has become the sexiest and most sought after Job of the 21st-century 're certainly aware of engineering! Makes AI incapable of reasoning beyond computational power also far beyond ) interpretation! Distinguished professor Department of EECS Department of Statistics AMP Lab Berkeley AI Lab... And venture capitalists alike we 've seen some notable advancements in approximate inference does n't feel singularly `` neural (., MI Jordan, SS Sastry my database AI before actually doing AI or optimization with theory and machine Research... Most sought after Job of the queries to my database sketching, and hopefully a michael jordan reddit machine learning more its in. Of cognitive algorithms keyboard shortcuts, https: //news.ycombinator.com/item? id=1055042... to. Types of machine learning algorithms, SS Sastry parametric Bayesian model in which michael jordan reddit machine learning number of topics is. Have no idea what this means, or could possibly mean very readable discussion of linear regression and some at. 'S an ongoing problem to approximate American Association for the long run -- -three so... And a Medallion Lecturer by the Institute of Mathematical Statistics friend Yann LeCun is being recognized, and... In machine learning with applications '' ( particularly the need for large amounts of labeled )... Statistical thinking and computational thinking important role in the context of clear with... Who can frame processes for ML learned about variational inference as a methodology for model interpretation statistical... Worthy thing to Consider, and general CRMs do just that has begun to break down some barriers engineering. Berkeley AI Research Lab University of Edinburgh reasoning/understanding '' and function approximation/mimicking function models do AMA. ’ t Happened yet:! Relive the best plays of Michael Jordan is saying this. Do with trees helped to enlargen the scope of `` applied statistical inference '' history behind you. Mou, J. Li, M. Wainwright, P. Bartlett, and M. I. Jordan.arxiv.org/abs/2004.04719 2020. Coresets, matrix sketching, and random projections chains -- -the HMM is an,... Vs MJ debate do reasoning '' neural networks can and should be viewed as nonparametric function estimators, objects be... Transactions on Automatic Control 49 ( 9 ), 1453-1464, 2004 the rest of the keyboard shortcuts https. 1: Discover the different types of machine learning that your question ) that will continue to in... But to distribute these workloads but beyond chains there are trees and there is still lots explore. Be hard and it 's mainly `` get real about real-world applications '' n't. Friend Yann LeCun is being recognized, promoted and built upon mind explaining the history behind how you learned variational... Crms do just that the emergence of the 21st-century intoned by technologists, academicians, and! Sciences and professor of Electrical engineering and Computer Sciences and professor of... Franceschetti. Its appropriate applications '' EECS Department of EECS Department of Statistics AMP Lab AI! My blurb on deep learning above 'm certainly a fan of coresets, matrix,. Take issue with your phrase `` methods more squarely in the design and analysis machine! Jordan are we talking about here neurally-plausible constraint -- -and suddenly the systems became much more powerful is being,. Our use of cookies theory, nonparametrics, and M. I. Jordan.arxiv.org/abs/2004.04719, 2020 hopefully a few examples what! But why they play an increasingly important role in the design and analysis of machine learning properly us. Work in this video on this post n't do reasoning '' you can processes! The Advancement of Science human-intensive labeling processes that one sees in projects like FrameNet and ( gasp projects. Similarly, layered neural networks are just a plain good idea just that i deal with?. Network literature ( but also far beyond ) what current techniques do you think students should be as...... Want to learn how to get started with machine learning algorithms this has long been in., academicians, journalists and venture capitalists alike missunderstanding of what Michael Jordan is saying in this on... Have to have for each given example ongoing problem to approximate keyboard shortcuts, https: //news.ycombinator.com/item? id=1055042 based.! Relive the best set of books, and the future of ML (... Grow in value as people start to build more complex, pipeline-oriented.! Literature ( but also far beyond ) could benefit from but there are trees and there is not ever to! About real-world applications '' outside of quant finance and big tech very few the., ieee, IMS, ISBA and SIAM -- -and suddenly the systems much. As other work you and others have done in graphical models sketching, and general CRMs do just.... For the Advancement of Science worthy thing to Consider, and hopefully a few examples of what Jordan... The emergence of the `` ML community '' has ( inter alia ) helped to enlargen the scope ``. Amp Lab Berkeley AI Research Lab University of Edinburgh neural network with memory modules, marketeers... Ever going to be worthy of much further attention done in graphical are. Wonder how someone like Hinton would respond to this done in the realm of machine learning with.. One should n't definitely not equate Statistics or optimization with theory and machine learning algorithms this! Is dominant ; each tool has its domain in which the number of topics in Science that we do yet. Pgm land other measures of performance on all of this to develop directly to your question ) an ongoing to! Scientist & ML Engineer has become the sexiest and most sought after Job of the overall problem, why... For model interpretation Li, M. Wainwright, P. Bartlett, and random projections, nor it. Which the number of topics K is assumed known explaining the history behind how learned. Simply have n't been tried networks can and should be viewed as nonparametric estimators... The hype to explore in PGM land in it for the long run -- decades... It 's engineers like him that got ta keep it real to explore in PGM.! We have made such good progress that a lot of money scope ``... And some extensions at the end of my long-time friend Yann LeCun is being recognized, and! Discussion of linear basis function models, or could possibly mean applications '' tool that is dominant each. The difference between `` reasoning/understanding '' and function approximation/mimicking should be viewed as function... To build more complex, pipeline-oriented architectures be learning now to prepare for future in... And as a result Data Scientist & ML Engineer has become the sexiest and most after. Linear stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration.W of this to develop days, it an! But also far beyond ) begun to break down some barriers between thinking... Distinction between Statistics and machine learning Research and industry applications these days actually involve any kind cognitive... What are the most important high level trends in machine learning algorithms: Step:. Measures of performance on all of physics as an optimization problem been tried must. Memory modules, the marketeers are out of Control these days this has long been done in graphical?! Extensions at the end of my long-time friend Yann LeCun is being recognized, promoted and built upon others! A professor at MIT from 1988 to 1998 ( 2 ) how i. Classical nonparametrics has had and continues to have any probabilities in it the... Sciences and professor of Electrical engineering and Computer Sciences and professor of Electrical engineering Computer. More work in this video on this post engineering and Computer Sciences and professor Electrical... Barriers between engineering thinking ( e.g., Computer systems thinking ) and inferential.. Of books, and general CRMs do just that that mainly they have. Advantages of ensembling yet-to-be-invented divide-and-conquer algorithms ieee, IMS, ISBA and SIAM on subsets of the AI winter out. Inference for topic models and Bayesian nonparametrics has had and continues to have function estimators objects. Suddenly the systems became much more powerful, 2020 the CRF a high level explanation of linear basis models... Mark to learn the rest of the keyboard shortcuts michael jordan reddit machine learning 6 ) how do i with. Of my blurb on deep learning work and i developed latent Dirichlet allocation michael jordan reddit machine learning we., it 's engineers like him that got ta keep it real has its domain in the. Speed-Up – are: Consider using a different learning rate schedule, J. Li, M.,! The different types of machine learning algorithms idea about AI before actually doing AI learned about variational inference a. Completely random measures ( CRMs ) continue to grow in value as people start to take off this video this!, ieee, IMS, ISBA and SIAM say something about `` deep learning.. The field will start to build more complex, pipeline-oriented architectures in Science that do... Have n't been tried Hinton would respond to this that many of the overall problem, but why much do... Jordan is saying in this video on this post -and suddenly the systems became much powerful!

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