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Thanks for being honest :) > ...difficulty of finding out what the actual interesting bits are. > Uber's more detailed writeup ... is vague We indeed struggle

by fritzo 9y ago

Thanks for being honest :) > ...difficulty of finding out what the actual interesting bits are. > Uber's more detailed writeup ... is vague We indeed struggled to balance accessibility and depth that blog post. One place to go deeper is the Pyro tutorials http://pyro.ai/examples http://pyro.ai/examples For more technical framing, take a look at Noah's paper [1]. Broadly I think the "interesting bits" are the integration of newer deep neural net methods with older Bayesian methods of flexible probabilistic modeling. [1] Deep Amortized Inference for Probabilistic Programs Daniel Ritchie, Paul Horsfall, Noah D. Goodman https://arxiv.org/abs/1610.05735 https://arxiv.org/abs/1610.05735

ineedasername 9y ago
The paper clarifies a lot, and makes me think you meant something like "generative model" instead of "generative intelligence" The problem with using the later is that it only rarely (maybe a couple dozen over the last few years) appears in conjunction with ML in rigorous research publications. When it does, it is used with extremely precise and well-defined parameters (see GPRS from M.Golumbic) or used in reference to the traditional "home" of generative intelligence at the cross roads of cognitive science and pedagogical theory & methods. There, it is a close cousin and necessary component of "general intelligence." That's why its use here set off alarm bells of "Alert! Marketing speak at work! Gross overstatement of capabilities have been detected!" Because it doesn't fall much short of claiming "general intelligence" as a practical result of & use for Pyro.