A few weeks ago, I went to the Lihue-based Kaua’i Farmer’s Market for locally-grown fresh veggies. And, of course, I swung by the Kaua’i Master Gardeners booth. Now, I’d visited with these guys before. And yes, they knew that I taught AI at Northwestern. But still, my jaw dropped when the first question that one… Continue reading The Future of AI: Part 0 (Prelude) – Reductio ad Absurdum
Category: Artificial Intelligence
Variational Free Energy and Active Inference: Pt 4
Today, we interpret the q(Psi | r) and p(Psi, s, a, r | m) in Friston’s (2013) “Life as We Know It” (Eqn. 2.7) and Friston et al. (2015) “Knowing One’s Place” (Eqn. 3.2). This discussion moves forward from where we left off in the previous post, identifying how Friston’s notation builds on Beal’s (2003)… Continue reading Variational Free Energy and Active Inference: Pt 4
Kullback-Leibler, Etc. – Part 3 of 3: The Annotated Resources List
I thought it would be (relatively) straightforward to wrap this up. Over the past several posts in this series, we’ve discussed the Kullback-Leibler (K-L) divergence and free energy. In particular, we’ve described free energy as the “universal solvent” for artificial intelligence and machine learning methods. This next (and last) post in this series was intended… Continue reading Kullback-Leibler, Etc. – Part 3 of 3: The Annotated Resources List
The Kullback-Leibler Divergence, Free Energy, and All Things Variational – Part 2 of 3
Free energy is the universal solvent of AI (artificial intelligence). It is the single underlying rule or principle that makes AI possible. Actually, that’s a simplification. There are THREE key things that underlie AI – whether we’re talking deep learning or variational methods. These are: Free energy – which we’ll discuss in this post, Latent… Continue reading The Kullback-Leibler Divergence, Free Energy, and All Things Variational – Part 2 of 3