<story><title>Ideas in statistics that have powered AI</title><url>https://news.columbia.edu/news/top-10-ideas-statistics-ai</url></story><parent_chain></parent_chain><comment><author>317070</author><text>&amp;gt; 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.&lt;p&gt;What? Every sentence here is so wrong I have a hard time seeing what kind of misunderstanding would lead to this.&lt;p&gt;GAN&amp;#x27;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&amp;#x2F;alphazero papers? And GANs are infamously not really predictive&amp;#x2F;discriminative. If anything, they thoroughly disconnected predicition from generative models.</text></comment>
<story><title>Ideas in statistics that have powered AI</title><url>https://news.columbia.edu/news/top-10-ideas-statistics-ai</url></story><parent_chain></parent_chain><comment><author>bjornsing</author><text>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&amp;#x2F;ML it’s often referred to as “minimizing cross-entropy loss”, but this is just a misappropriation &amp;#x2F; reinvention of the wheel. The math is the same and MLE is a much more sane theoretical framework.</text></comment>