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!! 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