Probably best to get the kids out of the room before you play this one. Lots of heavy breathing by Brigette. And you can read the backstory here. (And a bit more here, if you’re so inclined.) But to business … The Starting Point … and the FIRST Illustrative Text String I had previously worked… Continue reading Brigette Bardot Says It All (An Exercise in the 1D Cluster Variation Method)
Category: Stat Mech – Free Energy
CORTECONs and AGI: Reaching Latent Layer Equilibrium
The most important thing in building an AGI is the ability to repeated bring the latent layer to equilibrium. This is the fundamental capability that has been missing in previous neural networks. The lack of a dynamic process to continuously reach a free energy minimum is why we have not had, until now, a robust… Continue reading CORTECONs and AGI: Reaching Latent Layer Equilibrium
1D CVM Object Instance Attributes: wLeft Details
We illustrate how the specific values for a single object-oriented instance attribute are determined. We do this for the specific case of the “wLeft” instance attribute for the Node object in the 1-D cluster variation method (1D CVM) code. The same considerations will apply when we progress to the 2D CVM code. The specific values… Continue reading 1D CVM Object Instance Attributes: wLeft Details
Variational Free Energy: Getting-Started Guide and Resource Compendium
Many of you who have followed the evolution of this variational inference discussion (over the past ten blogposts), may be wondering where to start. This would be particularly true for readers who are not necessarily familiar with the variational-anything literature, and would like to begin with the easiest, most intuitive-explanatory articles possible, and then gently… Continue reading Variational Free Energy: Getting-Started Guide and Resource Compendium
Variational Free Energy and Active Inference: Pt 5
The End of This Story This blogpost brings us to the end of a five-part series on variational free energy and active inference. Essentially, we’ve focused only on that first part – on variational free energy. Specifically, we’ve been after Karl Friston’s Eqn. 2.7 in his 2013 paper, “Life as We Know It,” and similarly… Continue reading Variational Free Energy and Active Inference: Pt 5
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
Variational Free Energy and Active Inference: Pt 3
When we left off in our last post, we’d determined that Friston (2013) and Friston et al. (2015) reversed the typical P and Q notation that was commonly used for the Kullback-Leibler divergence. Just as a refresher, we’re posting those last two images again. The following Figure 1 was originally Figure 5 in last week’s… Continue reading Variational Free Energy and Active Inference: Pt 3
Variational Free Energy and Active Inference: Pt 2
Our intention with this post is to cover not only the notion, but the notation, used by Karl Friston in his 2013 paper, “Life as We Know It.” (Actually, we’re addressing a very small notational subset – albeit one that needs to be treated with great care and caution.) To do this, we’re also discussing… Continue reading Variational Free Energy and Active Inference: Pt 2
Variational Free Energy and Active Inference: Pt 1
Overarching Story Line This new blogpost series, on variational free energy and active inference, presents tutorial-level studies centered on the free energy equation (Eq. 2.7) of Karl Friston’s 2013 paper, “Life as We Know It.” Specifically, we’re focused on the free energy equation shown in Figure 1 below. Over this blogpost series, we will reinforce… Continue reading Variational Free Energy and Active Inference: Pt 1
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