Herself’s Artificial Intelligence

We’ve all been to various .edu sites to the engineering courses only to find a Power Point Presentation with little information.

Stanford Engineering’s Machine Learning Course is an exception.

You have access to video of all the lectures, transcripts of all the lectures and class notes. I started working through it last week, brushing up on old topics and filling in gaps in my knowledge. Of all the online artificial intelligence and machine learning courses online, this is the best I’ve seen so far. The math is very low level, programming minimal, you’ll find it a very easy to work through introduction to machine learning.

Artificial Intelligence | Machine Learning
Instructor: Ng, Andrew

This course provides a broad introduction to machine learning and statistical pattern recognition.

Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control.
The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
Students are expected to have the following background:

Prerequisites: – Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.
- Familiarity with the basic probability theory. (Stat 116 is sufficient but not necessary.)
- Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.)

MIT also has a class available for Machine Learning, no lectures, but there are pdf notes.

I’ve been catching up reading AI papers on my Sony Reader, it’s very nice to have all these books and papers with you all the time, and I ran across a paper on 3d creatures that evolve and battle. The software is open source and there is extensive information explaining the project.

Evolving Virtual Creatures

I evolve virtual creatures. That is, instead of designing the creatures myself, I let Darwinian evolution do the work for me. Starting with a bunch of completely random creatures, I evaluate them, select the “best” (where the definition of “best” depends on what you are trying to obtain: distance covered, outcome of a fight, etc.), allow these “good” creatures to “reproduce” (that is, generate new creatures based on recombination and mutations of these “good” creatures), and start this cycle again with the new population. After a while, increasingly efficient behaviours appear spontaneously.

Both the morphology (body) and behaviour (neural network) of the creatures are fully evolved: I only specify the evaluation function (that is, how to determine the “score” of a creature), and evolution takes care of the design, slowly turning random creatures into highly efficient machines.

This research builds upon the seminal work of Karl Sims; in fact, this was the first successful reimplementation of Karl Sims’ system. I improved the genetic encoding and the developmental system. I also chose to use standard, McCulloch-Pitts neurons instead of the complex functional neurons used by Sims (which makes the problem harder, but more interesting: creatures can’t rely on complex neurons to directly provide useful behaviours, they need come up with their own coordinated neural systems).

I also managed to evolve “fighting” creatures: creatures evolve to smash each other out. I believe this is a first (previous attempts used simplistic forms of “combat”).