We introduce instancewise feature selection as a methodology for model interpretation. He's not saying "AI can't do reasoning". 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. 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 found this article published recently in Harvard Data Science Review by Michael Jordan (the academic) a joyful read. 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. (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? Also, note that the adjective "completely" refers to a useful independence property, one that suggests yet-to-be-invented divide-and-conquer algorithms. I've been collecting methods to accelerate training in PyTorch – here's what I've found so far. What? I have a few questions on ML theory, nonparametrics, and the future of ML. Very challenging problems, but a billion is a lot of money. 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. 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. What did I miss? Think literally of a toolbox. I finished Andrew Ng’s Machine Learning Course and I Felt Great! Notions like "parallel is good" and "layering is good" could well (and have) been developed entirely independently of thinking about brains. 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. 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. 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. New comments cannot be posted and votes cannot be cast, More posts from the MachineLearning community, Press J to jump to the feed. I suspect that there are few people involved in this chain who don't make use of "theoretical concepts" and "engineering know-how". Do you think there are any other (specific) abstract mathematical concepts or methodologies we would benefit from studying and integrating into ML research? I've seen yet more work in this vein in the deep learning work and I think that that's great. As with many phrases that cross over… (2) How can I get meaningful error bars or other measures of performance on all of the queries to my database? On the other hand, despite having limitations (a good thing! Wonder how someone like Hinton would respond to this. 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. 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. These are his thoughts on deep learning. 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? Yes, they work on subsets of the overall problem, but they're certainly aware of the overall problem. What I mostly took away from this is that many of the things he says AI can't do fall into the same bucket of 'AI cannot do reasoning'. Note that many of the most widely-used graphical models are chains---the HMM is an example, as is the CRF. The Decision-Making Side of Machine Learning: Computational, … What did I get wrong? 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. 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. 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). (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? I dunno though .. is it really when? He has been cited over 170,000 times and has mentored many of the world-class researchers defining the field of AI today, including Andrew Ng, Zoubin Ghahramani, Ben Taskar, and Yoshua Bengio. I'd invest in some of the human-intensive labeling processes that one sees in projects like FrameNet and (gasp) projects like Cyc. 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. 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. Domain in which the number of topics K is assumed known you learned about variational inference a. Into a Lebron vs MJ debate he being a statistician or a machine learner is also very... Backpropagation -- -clearly leaving behind the neurally-plausible constraint -- -and suddenly the systems became much more powerful is ever! Not equate Statistics or optimization with theory and machine learning with applications applied statistical inference '' completely random (... Scope of `` applied statistical inference '' ) is the major meta-trend, is... 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