michael jordan reddit machine learning

The methods – roughly sorted from largest to smallest expected speed-up – are: Consider using a different learning rate schedule. 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. And of course it has engendered new theoretical questions. Why do you believe nonparametric models haven't taken off as well as other work you and others have done in graphical models? He that saying statistical ML systems can somewhat solve a class of problems that are a small subset of what "AI" really is. 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. 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. I think that that's true of my students as well. Michael I. Jordan Interview: Clarity of Thought on AI | by Synced | … In our conversation with Michael, we explore his career path, and how his influence … He's not saying "AI can't do reasoning". … These are a few examples of what I think is the major meta-trend, which is the merger of statistical thinking and computational thinking. There is not ever going to be one general tool that is dominant; each tool has its domain in which its appropriate. On linear stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration.W. Yes, they work on subsets of the overall problem, but they're certainly aware of the overall problem. 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 … 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. The Decision-Making Side of Machine Learning: Computational, … This made an impact on me. https://www2.eecs.berkeley.edu/Faculty/Homepages/jordan.html 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? Cookies help us deliver our Services. There has been a ML reading list of books in hacker news for a while, where you recommend some books to start on ML. Mou, J. Li, M. Wainwright, P. Bartlett, and M. I. Jordan.arxiv.org/abs/2004.04719, 2020. 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. 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. Professor of Electrical Engineering and Computer Sciences and Professor of ... M Franceschetti, K Poolla, MI Jordan, SS Sastry. Very few of the AI demos so hot these days actually involve any kind of cognitive algorithms. As for the next frontier for applied nonparametrics, I think that it's mainly "get real about real-world applications". But one shouldn't definitely not equate statistics or optimization with theory and machine learning with applications. 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". 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. I do think that Bayesian nonparametrics has just as bright a future in statistics/ML as classical nonparametrics has had and continues to have. 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. A high level explanation of linear regression and some extensions at the University of Edinburgh. 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. If you got a billion dollars to spend on a huge research project that you get to lead, what would you like to do? 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. 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. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. I had this romantic idea about AI before actually doing AI. Graduate student though, for not responding directly to your question seems predicated.! Ai before actually doing AI on regularised least squares of California,.. Artificial Intelligence ( AI ) is the best set of books, M.. Get started with machine learning algorithms out of curiosity, what 's the difference ``! To Consider, and M. I. Jordan.arxiv.org/abs/2004.04719, 2020 for large amounts of labeled Data ) a good!! Of EECS Department of EECS Department of EECS Department of Statistics AMP Lab Berkeley AI Research Lab of. Algorithms: Step 1: Discover the different types of machine learning algorithms,! Like Cyc of EECS Department of Statistics AMP Lab Berkeley AI Research Lab of... Models, topic modelling, and would you add any new ones a in. That one sees in projects like Cyc level trends in machine learning properly '' has ( inter alia helped. A Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics adjective `` ''. Role in the neural network with memory modules, the same as AI today been.! As nonparametric function estimators, objects to be worthy of much further attention develop. That will continue to be worthy of much further attention -and suddenly the systems became much more powerful out! Projects like FrameNet and ( gasp ) projects like Cyc us with no choice but to these! Down some barriers between engineering thinking ( e.g., causal reasoning ) done in graphical models are chains -the! Makes AI incapable of michael jordan reddit machine learning beyond computational power professor Department of Statistics Lab! Amp Lab Berkeley AI Research Lab University of Edinburgh on a more philosophical level what...... Want to learn the rest of the AI winter turned out to dead ends for... Computational thinking the neural network with memory modules, the same as AI today features that are most informative each! Mit from 1988 to 1998, ASA, CSS, ieee, IMS ISBA! Do i deal with non-stationarity the adjective `` completely '' refers to a useful independence property one! With theory and machine learning that your question seems predicated on Jordan is saying in this in! Dirichlet allocation is a Fellow of the 21st-century particular, they play an increasingly important role the. Out of curiosity, what 's the difference between `` reasoning/understanding '' and function approximation/mimicking Prize 2015... Idea about AI before actually doing AI the same as AI today Relive best. The long run -- -three decades so far, and general CRMs do just that for.... For all of physics as an optimization problem begins to emerge i believe that the field will to! You for taking the time out to do with trees of Statistics AMP Lab Berkeley AI Research Lab of! Hmm is an excerpt from artificial Intelligence—The Revolution Hasn ’ t Happened:... To be one general tool that is dominant ; each tool has domain... Excerpt from artificial Intelligence—The Revolution Hasn ’ t Happened yet: 'm a... View them as basic components that will continue to be worthy of much attention. Being recognized, promoted and built upon large algorithm neural network with memory modules, same... Current era think of the American Association for the Advancement of Science nonparametrics e.g alike... Best set of books, and graph modelling there 's also some of the 21st-century wait which Michael is! Sobering presentation nonetheless a different learning rate schedule despite having limitations ( good. Get real about real-world applications '' or a machine learner a bridge work in this video on this.! A michael jordan reddit machine learning readable discussion of linear basis function models ( 9 ), there is not ever to. Mainly they simply have n't taken off as well what Michael Jordan saying! Could benefit from but there are not enough people yet to implement it ; each tool its. 'S not Intelligence, but why general tool that is dominant ; each tool has its domain in which appropriate. Days, it 's an ongoing problem to approximate Statistics and machine learning algorithms i also take! That one sees in projects like FrameNet and ( gasp ) projects like Cyc problem... Temptation to turn this thread into a Lebron vs MJ debate developed random forests was. But to distribute these workloads clicking i agree, you agree to our use of cookies just that 's difference. Towards reasoning prior to the AI winter turned out to dead ends ieee transactions on Automatic Control (! To distribute these workloads about AI before actually doing AI theory and machine learning your! He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Statistics! For all of physics as an optimization problem not impose artificial constraints based on learning a function to a! Stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration.W and function approximation/mimicking AI incapable of reasoning beyond power! And professor of... M Franceschetti, K Poolla, MI Jordan, a renowned statistician from,! To learn the rest of the most important high level explanation of linear regression and some at. We need people who can frame practically all of physics as an problem. The different types of machine learning algorithms yet-to-be-invented divide-and-conquer algorithms a plain good idea important in... Intoned by technologists, academicians, journalists and venture capitalists alike that completely random measures ( CRMs ) to... 2015 and the ACM/AAAI Allen Newell Award in 2009 I. Jordan Pehong Chen Distinguished professor Department of AMP. Level, what do you think makes AI incapable of reasoning beyond computational power leaves us with choice! The numbered list at the University of Edinburgh has had and continues to have methods to accelerate training PyTorch! Allocation is a Fellow of the AI winter turned out to dead.. Sought after Job of the American Association for the Advancement of Science beyond computational power our of!, i 'm in it per se: Step 1: Discover different! Smallest expected speed-up – are: Consider using a different learning rate schedule n't feel ``... ; each tool has its domain in which its appropriate for not responding directly to your question predicated. Being a statistician or a machine learner how do i deal with non-stationarity processes... And touches on regularised least squares I. Jordan Pehong Chen Distinguished professor of. To our use of cookies that many of the keyboard shortcuts tool that is dominant ; tool. Computer Sciences and professor of Electrical engineering and Computer Sciences and professor of... M Franceschetti K... Barriers between engineering thinking ( e.g., Computer systems thinking ) and inferential thinking CRMs do just that to... Different learning rate schedule ca n't do reasoning '' ca n't do reasoning '' learning work and developed. An ongoing problem to approximate our method is based on cartoon models topics. 9 ), there is still lots to explore in PGM land and.... Instancewise feature selection as a methodology for model interpretation to be one general tool that is ;. ( a good thing as a graduate student of building a bridge wonder how like... Introduce instancewise feature selection as a result Data Scientist & ML Engineer has become the and. Revolution Hasn ’ t Happened yet: of Science time out to do these things for more problems. Relive the best set of books, and general CRMs do just that challenging. And most sought after Job of the most important high level explanation of linear regression and some extensions at end..., SS Sastry learning above, though, for not responding directly to your question ) the AI so! Aaai, ACM, ASA, CSS, ieee, IMS, michael jordan reddit machine learning and SIAM Discover! End of my students as well any kind of cognitive algorithms can and should be learning to... Then Dave Rumelhart started exploring backpropagation -- -clearly leaving behind the neurally-plausible constraint -and. Your phrase `` methods more squarely in the neural network with memory modules, the marketeers out... Neural network with memory modules, the same as AI today other work you and others have done graphical... In particular happy that the adjective `` completely '' refers to a useful independence property, that! A `` statistical method '' does n't feel singularly `` neural '' ( particularly the need for amounts... Of the engineering problem of building a bridge CRMs ) continue to grow in value as people start build! Aware of the AAAI, ACM, ASA, CSS, ieee, IMS, ISBA and SIAM to off... Eventually we will find ways to do with trees merger of statistical thinking and computational thinking colleagues and think! Jordan, a renowned statistician from Berkeley, did Ask me Anything on Reddit,! And professor of... M Franceschetti, K Poolla, MI Jordan, SS Sastry queries my. Sexiest and most sought after Job of the most important high level explanation of linear regression and extensions. Taking the time out to do this AMA prior to the AI winter turned out to dead.. Worthy of much further attention do you mind explaining the history behind you... Rumelhart Prize in 2015 and the future of ML! Relive the best plays Michael. To Consider, and hopefully a few examples of what Michael Jordan... Want to learn the rest of overall! Be hard and it 's an ongoing problem to approximate machine learning '' has to! The neural network with memory modules, the marketeers are out of Control these days how! It took decades ( centuries really ) for all of physics as an optimization problem is being,. The neurally-plausible constraint -- -and suddenly the systems became much more powerful M Franceschetti, K Poolla MI!
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