Browsed by
Category: A Resource

2-D Cluster Variation Method: Code V&V

2-D Cluster Variation Method: Code V&V

New Code (Not Released Yet): V&V the Code Before We Play:   Well, my darling, as you gathered from last week’s post, the world has shifted. Up until now, when we were talking about having a new free energy function to use inside a neural network, we had to do “Gedankenexperiments” (German for “thought experiments”). Now, though, there’s working code – and I so LOVE seeing the numbers and graphs come out; teasing it, playing with it … stroking it…

Read More Read More

Statistical Mechanics, the Future of AI, and Personal Stories

Statistical Mechanics, the Future of AI, and Personal Stories

Statistical Mechanics and Personal Stories (On the Same Page!)   Yikes! It’s Thursday morning already. I haven’t written to you for three weeks. That’s long enough that I have to pause and search my memory for my username to get into the website. Thanksgiving was lovely. The Thursday after that was grading, all day – and for several days before and after. By now, I (and most of you) have had a few days of recovery, from what has been…

Read More Read More

Third Stage Boost – Part 2: Implications of Neuromorphic Computing

Third Stage Boost – Part 2: Implications of Neuromorphic Computing

Neuromorphic Computing: Statistical Mechanics & Criticality   Last week, I suggested that we were on the verge of something new, and referenced an article by von Bubnoff: A brain built from atomic switches [that] can learn, together with the follow-on article Brain Built on Switches. The key innovation described in this article was a silver mesh, as shown in the following figure. This mesh is a “network of microscopically thin intersecting silver wires,” grown via a combination of electrochemical and…

Read More Read More

Third Stage Boost: Statistical Mechanics and Neuromorphic Computing – Part 1

Third Stage Boost: Statistical Mechanics and Neuromorphic Computing – Part 1

Next-Generation Neural Network Architectures: More Brain-Like   Three generations of artificial intelligence.. The third generation is emerging … right about … now. That’s what is shown in this figure, presented in log-time scale. Brief history of AI in log-time scale The first generation of AI, symbolic AI, began conceptually around 1954, and lasted until 1986; 32 years. On the log-time scale shown in the figure above, this entire era takes place under the first curve; the black bell-shaped curve on…

Read More Read More

Machine Learning: Multistage Boost Process

Machine Learning: Multistage Boost Process

Three Stages to Orbital Altitude in Machine Learning Several years ago, Regina Dugan (then Director of DARPA) gave a talk in which she showed a clip of epic NASA launch fails. Not just one, but many fails. The theme was that we had to risk failure in order to succeed with innovation. This YouTube vid of rocket launch failures isn’t the exact clip that she showed (the “action” doesn’t kick in for about a minute), but it’s pretty close. For…

Read More Read More

Neg-Log-Sum-Exponent-Neg-Energy – That’s the Easy Part!

Neg-Log-Sum-Exponent-Neg-Energy – That’s the Easy Part!

The Surprising (Hidden) “Gotcha” in This Energy Equation: A couple of days ago, I was doing one of my regular weekly online “Synch” sessions with my Deep Learning students. In a sort of “Beware, here there be dragons!” moment, I showed them this energy equation from the Hinton et al. (2012) Nature review paper on acoustic speech modeling: One of my students pointed out, “That equation looks kind of simple.” Well, he’s right. And I kind of bungled the answer,…

Read More Read More

Neural Network Architectures: Determining the Number of Hidden Nodes

Neural Network Architectures: Determining the Number of Hidden Nodes

Figuring Out the Number of Hidden Nodes: Then and Now   One of the most demanding questions in developing neural networks (of any size or complexity) is determining the architecture: number of layers, nodes-per-layer, and other factors. This was an important question in the late 1980’s and early 1990’s, when neural networks first emerged. Deciding on the network architecture details is even more challenging today. In this post, we’re going to look at some strategies for deciding on the number…

Read More Read More

Selecting a Neural Network Transfer Function: Classic vs. Current

Selecting a Neural Network Transfer Function: Classic vs. Current

Neural Network Transfer Functions: Sigmoid, Tanh, and ReLU   Making it or breaking it with neural networks: how to make smart choices.     Why We Weren’t Getting Convergence   This last week, in working with a very simple and straightforward XOR neural network, a lot of my students were having convergence problems. The most likely reason? Very likely, it’s been my choice for the transfer function. I had given them a very simple network. (Lots of them are still…

Read More Read More

Notational Frenzy

Notational Frenzy

When the Subtle Art of Mathematical Notation Defeats You (and How to Fight Back)   A couple of years ago, I was teaching Time Series and Forecasting for the first time. I didn’t know the subject – at all – but that didn’t bother me. Hey, it was mathematics, right? Anything that’s mathematical, I can eat for lunch, and then want some dessert-equations afterwards. First week, introducing the subject. That went fine. Second week, Simple Exponential Smoothing (SES). That’s simple….

Read More Read More

Seven Essential Machine Learning Equations: A Cribsheet (Really, the Précis)

Seven Essential Machine Learning Equations: A Cribsheet (Really, the Précis)

Making Machine Learning As Simple As Possible Albert Einstein is credited with saying, Everything should be made as simple as possible, but not simpler. Machine learning is not simple. In fact, once you get beyond the simple “building blocks” approach of stacking things higher and deeper (sometimes made all too easy with advanced deep learning packages), you are in the midst of some complex stuff. However, it does not need to be more complex than it has to be.  …

Read More Read More