> Generative adversarial networks, or GANs, are a conceptual advance that allow reinforcement learning problems to be solved automatically. They mark a step toward the longstanding goal of artificial general intelligence while also harnessing the power of parallel processing so that a program can train itself by playing millions of games against itself. At a conceptual level, GANs link prediction with generative models.<p>What? Every sentence here is so wrong I have a hard time seeing what kind of misunderstanding would lead to this.<p>GAN's are a conceptual advance of generative models (i.e. models that can generate more, similar data). Reinforcement learning is a separate field. Parallel processing is ubiquitous, and has nothing to do with GANs or reinforcement learning (they are both usually pretty parallellized). Self-play sounds like they wanted to talk about the alphago/alphazero papers? And GANs are infamously not really predictive/discriminative. If anything, they thoroughly disconnected predicition from generative models.
"Generalized adversarial networks, or GANs, are a conceptual advance that allow reinforcement learning problems to be solved automatically." -<p>"Generalized" :D
Also the description is nonsense. This has nothing to do with reinforcement learning.
Makes me wonder about the rest.
I’m sorely missing Maximum Likelihood Estimation (MLE). It’s a statistical technique that goes back to Gauss and Laplace but was popularized by Fisher. In AI/ML it’s often referred to as “minimizing cross-entropy loss”, but this is just a misappropriation / reinvention of the wheel. The math is the same and MLE is a much more sane theoretical framework.
> 2. John Tukey (1977). Exploratory Data Analysis.<p>> This book has been hugely influential and is a fun read that can be digested in one sitting.<p>Wow. The PDF is over 700 pages. That seems fairly impressive for single-sitting digestion.
Out of the 10 papers I am able to download 3 of them freely.<p>- For the papers I am quoted 26EUR - 39EUR<p>- For the books I am quoted 129EUR - 133EUR<p>This is audacious. Some of these papers are form the 70ies. And I highly doubt that the authors get any royalties from those sales.
How have they attributed GANs and Deep Learning to Statistics? I thought Goodfellow was doing an AI PhD and that Hinton is a biologically inspired / neuroscience fellow?