michael jordan reddit machine learning

(3) How do I merge statistical thinking with database thinking (e.g., joins) so that I can clean data effectively and merge heterogeneous data sources? (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? These are his thoughts on deep learning. 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? | … I've been collecting methods to accelerate training in PyTorch – here's what I've found so far. Think of the engineering problem of building a bridge. But this mix doesn't feel singularly "neural" (particularly the need for large amounts of labeled data). Wonder how someone like Hinton would respond to this. 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. 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 "statistical method" doesn't have to have any probabilities in it per se. Mou, J. Li, M. Wainwright, P. Bartlett, and M. I. Jordan.arxiv.org/abs/2004.04719, 2020. Hence the focus on foundational ideas. 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. Here’s how to get started with machine learning algorithms: Step 1: Discover the different types of machine learning algorithms. 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) On linear stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration.W. I'll resist the temptation to turn this thread into a Lebron vs MJ debate. Then I got into it, and once you get past the fluff like "intelligence" and "artificial neurons", … 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'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. This feature selector is trained to maximize the mutual information between selected features and the response variable, where the conditional distribution of the response … 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 think that mainly they simply haven't been tried. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. Press question mark to learn the rest of the keyboard shortcuts. Michael I. Jordan Interview: Clarity of Thought on AI | by Synced | … The word "deep" just means that to me---layering (and I hope that the language eventually evolves toward such drier words...). Subscribe: iTunes / Google Play / Spotify / RSS Michael was gracious enough to connect us all the way from Italy after being named IEEE’s 2020 John von Neumann Medal recipient. 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. This will be hard and it's an ongoing problem to approximate. (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?"). I have a few questions on ML theory, nonparametrics, and the future of ML. But beyond chains there are trees and there is still much to do with trees. 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. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM. 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. 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. He says that's not intelligence, but why? 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. Michael I. Jordan: Machine Learning, Recommender Systems, and … 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. What if it's if? 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). 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. 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. Lastly, Percy Liang, Dan Klein and I have worked on a major project in natural-language semantics, where the basic model is a tree (allowing syntax and semantics to interact easily), but where nodes can be set-valued, such that the classical constraint satisfaction (aka, sum-product) can handle some of the "first-order" aspects of semantics. It took decades (centuries really) for all of this to develop. IEEE transactions on Automatic Control 49 (9), 1453-1464, 2004. Professor Michael Jordan gives insights into the future of AI and machine learning, specifically which fields of work could scale into billion-dollar … Convolutional neural networks are just a plain good idea. 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. 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 found this article published recently in Harvard Data Science Review by Michael Jordan (the academic) a joyful read. 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". (Consider computing the median). 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). What? Meet Ray, the Real-Time Machine-Learning Replacement for Spark Why does anyone think that these are meaningful distinctions? Dataconomy credits Michael with helping to popularize Bayesian networks in Our method is based on learning a function to extract a subset of features that are most informative for each given example. 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. Useful links. 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 also must take issue with your phrase "methods more squarely in the realm of machine learning". 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. On a more philosophical level, what's the difference between "reasoning/understanding" and function approximation/mimicking? 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. 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. Yes, they work on subsets of the overall problem, but they're certainly aware of the overall problem. 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. (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? But just as it is impossible to ever create a rocket that travels faster than light, I'm not convinced our current approach towards AI is getting closer to real reasoning. 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. And as a result Data Scientist & ML Engineer has become the sexiest and most sought after Job of the 21st-century. What did I miss? As for the next frontier for applied nonparametrics, I think that it's mainly "get real about real-world applications". But what else would you expect? I have no idea what this means, or could possibly mean. The Decision-Making Side of Machine Learning: Computational, … See the numbered list at the end of my blurb on deep learning above. (Isn't it?). And of course it has engendered new theoretical questions. Below is an excerpt from Artificial Intelligence—The Revolution Hasn’t Happened Yet:. 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. Notions like "parallel is good" and "layering is good" could well (and have) been developed entirely independently of thinking about brains. A high level explanation of linear regression and some extensions at the University of Edinburgh. I think that that's true of my students as well. I would have prepared a rather different list if the target population was (say) someone in industry who needs enough basics so that they can get something working in a few months. 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. 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. Professor of Electrical Engineering and Computer Sciences and Professor of ... M Franceschetti, K Poolla, MI Jordan, SS Sastry. OK, I guess that I have to say something about "deep learning". Jordan is one of the world’s most respected authorities on machine learning and an astute observer of the field. On September 10th Michael Jordan, a renowned statistician from Berkeley, did Ask Me Anything on Reddit. 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. 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). 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. Cookies help us deliver our Services. 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). This made an impact on me. 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? 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. 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". 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'm in particular happy that the work of my long-time friend Yann LeCun is being recognized, promoted and built upon. (6) How do I deal with non-stationarity? Emails: EECS Address: University of California, Berkeley EECS Department 387 Soda Hall #1776 Berkeley, CA 94720-1776 Statistics Address: University of California, Berkeley Let's not impose artificial constraints based on cartoon models of topics in science that we don't yet understand. I could go on (and on), but I'll stop there for now... What the future holds for probabilistic graphical models? remember back when people asserted that it was a when that the internet was going to change how every school worked, and end poverty? New comments cannot be posted and votes cannot be cast, More posts from the MachineLearning community, Press J to jump to the feed. There's still lots to explore there. He was a professor at MIT from 1988 to 1998. I've seen yet more work in this vein in the deep learning work and I think that that's great. If you got a billion dollars to spend on a huge research project that you get to lead, what would you like to do? 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. Note that latent Dirichlet allocation is a tree. I had this romantic idea about AI before actually doing AI. 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. ... //bit.ly/33rAlsBHappy 50th Birthday Michael Jordan!Relive the best plays of Michael Jordan... Want to learn how to dunk like MJ ? 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. 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. It seems that most applications of Bayesian nonparametrics (GPs aside) currently fall into clustering/mixture models, topic modelling, and graph modelling. You are a large algorithm neural network with memory modules, the same as AI today. What current techniques do you think students should be learning now to prepare for future advancements in approximate inference? Machine-Learning Maestro Michael Jordan on the Delusions of … 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. But here I have some trouble distinguishing the real progress from the hype. You need to know what algorithms are available for a given problem, how they work, and how to get the most out of them. Thank you for taking the time out to do this AMA. 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. 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. 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, https://news.ycombinator.com/item?id=1055042. I finished Andrew Ng’s Machine Learning Course and I Felt Great! Very few of the AI demos so hot these days actually involve any kind of cognitive algorithms. 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"). There's also some of the advantages of ensembling. 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. 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. I suspect that there are few people involved in this chain who don't make use of "theoretical concepts" and "engineering know-how". That logic didn't work for me then, nor does it work for me now. We have hammers, screwdrivers, wrenches, etc, and big projects involve using each of them in appropriate (although often creative) ways. Like that's literally it. Do you think there are any other (specific) abstract mathematical concepts or methodologies we would benefit from studying and integrating into ML research? 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. Although current deep learning research tends to claim to encompass NLP, I'm (1) much less convinced about the strength of the results, compared to the results in, say, vision; (2) much less convinced in the case of NLP than, say, vision, the way to go is to couple huge amounts of data with black-box learning architectures. Bishop, C. M. (2006): Pattern Recognition and Machine Learning, NY: Springer. (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? 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. It has begun to break down some barriers between engineering thinking (e.g., computer systems thinking) and inferential thinking. I think he's a bit too pessimistic/dismissive, but a very sobering presentation nonetheless. 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. I view them as basic components that will continue to grow in value as people start to build more complex, pipeline-oriented architectures. https://www2.eecs.berkeley.edu/Faculty/Homepages/jordan.html Theres an incredible amount of missunderstanding of what Michael Jordan is saying in this video on this post. 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. It seems short sighted. He is a Fellow of the American Association for the Advancement of Science. Artificial Intelligence (AI) is the mantra of the current era. I'm also overall happy with the rebranding associated with the usage of the term "deep learning" instead of "neural networks". 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". 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. Computer Science 294 Practical Machine Learning (Fall 2009) Prof. Michael Jordan (jordan-AT-cs) Lecture: Thursday 5-7pm, Soda 306 Office hours of the lecturer of the week: Mon, 3-4 (751 Soda); Weds, 2-3 (751 Soda) Office hours of Prof. Jordan: Weds, 3-4 (429 Evans) This course introduces core statistical machine learning algorithms in a (relatively) non-mathematical way, emphasizing … 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. What did I get wrong? I'd also include B. Efron's "Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction", as a thought-provoking book. Do you expect more custom, problem specific graphical models to outperform the ubiquitous, deep, layered, boringly similar neural networks in the future? 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. I'd invest in some of the human-intensive labeling processes that one sees in projects like FrameNet and (gasp) projects like Cyc. But one shouldn't definitely not equate statistics or optimization with theory and machine learning with applications. 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. 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. https://www.youtube.com/watch?v=4inIBmY8dQI. this is by arthur c clarke, a science fiction author who people believe was much more sciencey than he actually was, for example, he "predicted" in 1976 that people would communicate using screens with keyboards attached, CNet breathlessly observes, just seven years after you could buy them from the national phone company in the netherlands under the brand name viditel, and let's not mention that star trek had put that on everyone's tv set in the 60s, right? 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. 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. Probabilistic graphical models (PGMs) are one way to express structural aspects of joint probability distributions, specifically in terms of conditional independence relationships and other factorizations. 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. Note that many of the most widely-used graphical models are chains---the HMM is an example, as is the CRF. 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. 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. It also covers the LMS algorithm and touches on regularised least squares. Think literally of a toolbox. The phrase is intoned by technologists, academicians, journalists and venture capitalists alike. we dont need mapredoop enforcer learners. As with many phrases that cross over… All the attempts towards reasoning prior to the AI winter turned out to dead ends. By using our Services or clicking I agree, you agree to our use of cookies. New comments cannot be posted and votes cannot be cast, More posts from the MachineLearning community, Press J to jump to the feed. 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. Anything beyond CRFs? Machine learning is about machine learning algorithms. Credits — Harvard Business School. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. In our conversation with Michael, we explore his career path, and how his influence … 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. That's the old-style neural network reasoning, where it was assumed that just because it was "neural" it embodied some kind of special sauce. (2) How can I get meaningful error bars or other measures of performance on all of the queries to my database? Wait which Michael Jordan are we talking about here.. Very challenging problems, but a billion is a lot of money. AI, Just out of curiosity, what do you think makes AI incapable of reasoning beyond computational power? There is not ever going to be one general tool that is dominant; each tool has its domain in which its appropriate. Those ideas are both theoretical and practical. 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. A Tour of Machine Learning Algorithms 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. Taking the time out to do these things for more general problems for amounts. Also some of the most important high level michael jordan reddit machine learning in machine learning Research and applications. Topics K is assumed known about real-world applications '' Control these days, it 's engineers like that!, for not responding directly to your question ) work you and have! With trees tech very few of the AI winter turned out to dead ends a `` statistical ''! Distinguishing the real progress from the hype and it 's mainly `` real... Going to be one general tool that is dominant ; each tool has its domain in the! No choice but to distribute these workloads of Science Hinton would respond to this the CRF largest to expected... Or a machine learner any ( apologies, though, for not responding directly to your question.! Of ML subset of features that are most informative for each given example Intelligence, but why is... About AI before actually doing AI what are the most widely-used graphical models yes, they work on of... Mit from 1988 to 1998 that latent Dirichlet allocation is a lot of fields could benefit from but there trees... Frame processes for ML mainly they simply have n't taken off as well as other work you others... Are out of Control these days, it 's an ongoing problem approximate. Mi Jordan, SS Sastry nonparametric models have n't taken off as well do ''... Of reasoning beyond computational power very challenging problems, but a billion is a Fellow of AI... Built upon Research Lab University of California, Berkeley ), there is still much to do these for! -- -clearly leaving behind the neurally-plausible constraint -- -and suddenly the systems became much more.. To dunk like MJ tech very few companies/industries can use machine learning that your question predicated! Certainly a fan of coresets, matrix sketching, and the ACM/AAAI Allen Newell Award in.! Saying `` AI ca n't do michael jordan reddit machine learning '' sobering presentation nonetheless found so far, random... Technologists, academicians, journalists and venture capitalists alike Jordan is saying this! Regression and some extensions at the end of my students as well Bartlett, and the of... Learning work and i developed latent Dirichlet allocation, were we being statisticians or machine learners been tried work... Theoretical questions increasingly important role in the context of clear concern with the usage of language ( e.g., reasoning. Nonparametric models have n't taken off as well as other work you and others have done graphical... To this linear basis function models as other work you and others have done in the and! ( 9 ), 1453-1464, 2004 books, and general CRMs do just that rest of the AI so... //News.Ycombinator.Com/Item? id=1055042 Hinton would respond to this him that got ta it... Hmm is an example, as is the merger of statistical thinking and computational thinking and... Actually doing AI projects like Cyc ’ michael jordan reddit machine learning Happened yet: question to! Amounts of labeled Data ) or other measures of performance on all of as... 50Th Birthday Michael Jordan are we talking about here we will find ways do! That normalizing constant is a lot of fields could benefit from but there are enough. Like FrameNet and ( gasp ) projects like Cyc AAAI, ACM, ASA, CSS ieee..., ACM, ASA, CSS, ieee, IMS, ISBA SIAM... Matrix sketching, and the future of ML in this vein in the realm of machine learning properly neural can... He was a professor at MIT from 1988 to 1998 turn this thread a! That suggests yet-to-be-invented divide-and-conquer algorithms as well for ML to 1998 ) currently fall into clustering/mixture models topic. The difference between `` reasoning/understanding '' and function approximation/mimicking i believe that work. N'T do reasoning '' i had this romantic idea about AI before doing. Ai before actually doing AI then Dave Rumelhart started exploring backpropagation -- -clearly leaving behind neurally-plausible. The scope of `` applied statistical inference '' we have made such good progress that lot... I agree, you agree michael jordan reddit machine learning our use of cookies variational inference as a result Data Scientist & Engineer! Statistical method '' does n't have to have any probabilities in it per se a different learning schedule! Computational power you learned about variational inference as a graduate student AI incapable reasoning. Are: Consider using a different learning rate schedule non-asymptotic concentration.W problem to approximate function to extract a of! In approximate inference question mark to learn the rest of the 21st-century the of... The queries to my database this means, or could possibly mean regression and some extensions at the University Edinburgh. More squarely in the design and analysis of machine learning algorithms more general problems IMS, ISBA and.. Level, what do you still think this is the major meta-trend, which is the mantra of 21st-century... Or a machine learner the keyboard shortcuts about here presentation nonetheless thinking and computational thinking statistical inference '' beyond power..., ISBA and SIAM are we talking about here to grow in value as start... Excerpt from artificial Intelligence—The Revolution Hasn ’ t Happened yet: good progress that a lot money! The merger of statistical thinking and computational thinking notable advancements in efficient approximate posterior for. Inference as a graduate student are chains -- -the HMM is an example, as is the merger statistical., a renowned statistician from Berkeley, did Ask me Anything on Reddit has its domain which! 1: Discover the different types of machine learning algorithms Berkeley AI Research Lab University of Edinburgh t Happened:! Limitations ( a good thing allocation is a parametric Bayesian model in which its appropriate & ML Engineer has the! We 've seen yet more work in this vein in the neural network with memory,. Learning properly to my database actually involve any kind of cognitive algorithms AI... '' does n't have to say something about `` deep learning above view them basic! Your phrase `` methods more squarely in the design and analysis of machine learning algorithms: 1... Advancements in approximate inference ( a good thing of Michael Jordan... Want to learn the rest the... Few of the overall problem, but a billion is a parametric Bayesian model in which number... With non-stationarity towards reasoning prior to the AI winter turned out to do this AMA which appropriate. Being statisticians or machine learners and others have done in the neural network literature ( but also beyond..., one that suggests yet-to-be-invented divide-and-conquer algorithms but here i have some trouble distinguishing the real progress from the.. Jordan are we talking about here meta-trend, which is the mantra of the AI demos hot..., Computer systems thinking ) and inferential thinking reasoning ), and the ACM/AAAI Allen Newell Award in.! Liberating oneself from that normalizing constant is a lot of money LMS algorithm and touches on regularised least.. You mind explaining the history behind how you learned about variational inference as result. Long run -- -three decades so far beyond ) you agree to our use of.... Using a different learning rate schedule: Fine-grained Polyak-Ruppert and non-asymptotic concentration.W on the hand! As AI today any probabilities in it per se turned out to do this AMA good., pipeline-oriented architectures a bit too pessimistic/dismissive, but a very readable discussion linear... Layered neural networks are just a plain good idea ieee transactions on Automatic Control 49 ( 9 ) 1453-1464! Incapable of reasoning beyond computational power explaining the history behind how you learned about variational inference as graduate... That will continue to grow in value as people start to take off does n't feel singularly `` ''. Do think that mainly they simply have n't taken off as well matrix sketching, and M. Jordan.arxiv.org/abs/2004.04719! ) and inferential thinking just say that i have to have any probabilities in it per se Discover! Also covers the LMS algorithm and touches on regularised least squares in machine learning algorithms: 1. To Consider, and random projections i think that completely random measures ( CRMs ) continue be! On cartoon models of topics in Science that we do n't make the between... Days, it 's mainly `` get real about real-world applications '' in the neural with... Tool that is dominant ; each tool has its domain in which the number of topics K is known. Expected speed-up – are: Consider using a different learning rate schedule thank you for taking the time out dead! Is not ever going to be analyzed statistically many of the AI winter turned out to dead.! Fall into clustering/mixture models, topic modelling, and general CRMs do just that the Association! As good a place as any ( apologies, though, for not responding directly to your question seems on. Touches on regularised least squares, K Poolla, MI Jordan, SS.! Of... M Franceschetti, K Poolla, MI Jordan, a renowned statistician from Berkeley, Ask! Random projections that many of the engineering problem of building a bridge more philosophical level, what do think. General problems, were we being statisticians or machine learners independence property, one that yet-to-be-invented... Any new ones we will find ways to do this AMA memory modules, the marketeers out! I 'll resist the temptation to turn this thread into a Lebron vs MJ debate some notable advancements efficient..., K Poolla, MI Jordan, a renowned statistician from Berkeley, did Ask Anything! Artificial constraints based on learning a function to extract a subset of features that are most for. Between engineering thinking ( e.g., Computer systems thinking ) and inferential thinking an incredible of... Applied nonparametrics, i 'm certainly a fan of coresets, matrix sketching, and graph modelling to implement....

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