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Thank you for the perspective. Now when we talk machine learning are we talking: L. Pachter and B. Sturmfels. Algebraic Statistics for Computational Biology. C

Thank you for the perspective. Now when we talk machine learning are we talking: L. Pachter and B. Sturmfels. Algebraic Statistics for Computational Biology. Cambridge University Press 2005. G. Pistone, E. Riccomango, H. P. Wynn. Algebraic Statistics. CRC Press, 2001. Drton, Mathias, Sturmfels, Bernd, Sullivant, Seth. Lectures on Algebraic Statistics, Springer 2009. Or more like: Watanabe, Sumio. Algebraic Geometry and Statistical Learning Theory, Cambridge University Press 2009. My understanding (I do not do AI or machine learning) that AI is distinct from these more mathematical analytic perspectives. Finally, might we argue that generally AI/ML is more easily suited to data that's already high quality eg. CERN data, trade data, drug trial data as opposed to unconstrained data eg. Find the buses in these 1MM jpegs?

kk58 7y ago
Pure CS based AI approaches are primarily for Image, Text, and maybe graphs and control. The domains are called computer vision, natural language processing, graph learning and reinforcement learning Structured Data like tables, time series etc the techniques are still from statistics. Regression for example is the workhorse for numerical prediction problems I think a lot of people are missing the point about leaps AI has made because they aren't aware of NLP or CV or reinforcement learning. So "AI" mentioned above is stunningly good for buses in 1MM image and reasonably good drug trial, cern data. The business models required for making AI business successful haven't been invented yet. Good AI model will be Deep stack : example would be something like precision agriculture where you'd use AI for designing rice then use iot and earth observation to locate right acreages and monitor growth and adjust nutrient at crop level and get dramatically great output with least wastage and highest nutritional content. Most AI companies are still started by ex CS folks who in general arent aware of deep technical opportunities in other disciplines. I think this will change soon very fast due to ubiquity of deep learning training material, libraries and research papers.

kk58 7y ago

Pure CS based AI approaches are primarily for Image, Text, and maybe graphs and control. The domains are called computer vision, natural language processing, graph learning and reinforcement learning Structured Data like tables, time series etc the techniques are still from statistics. Regression for example is the workhorse for numerical prediction problems I think a lot of people are missing the point about leaps AI has made because they aren't aware of NLP or CV or reinforcement learning. So "AI" mentioned above is stunningly good for buses in 1MM image and reasonably good drug trial, cern data. The business models required for making AI business successful haven't been invented yet. Good AI model will be Deep stack : example would be something like precision agriculture where you'd use AI for designing rice then use iot and earth observation to locate right acreages and monitor growth and adjust nutrient at crop level and get dramatically great output with least wastage and highest nutritional content. Most AI companies are still started by ex CS folks who in general arent aware of deep technical opportunities in other disciplines. I think this will change soon very fast due to ubiquity of deep learning training material, libraries and research papers.