Simon’s Chess Factor

Sometime in 2014 or 2015, I was chatting with a friend, a guy with a Ph.D. in biomedical engineering. He knew about my background in AI and robotics, and was curious about Uber’s and Google’s self-driving car efforts. He asked if I was optimistic about prospect of autonomous cars. By autonomous, he meant: a taxi … Continue reading Simon’s Chess Factor

Unboxing Rose

Little G wanted to open the box. The sun wasn't up yet. The whoosh of the first morning tubecar had shaken her awake in her hiding place under the bridge. The noise was so deep that it wasn't so much sound as a feeling. Taking rich people up to San Fran or wherever they went, … Continue reading Unboxing Rose

PLASTK version 0.1

In a departure from my recent blog themes, I'd like to get back to AI and machine learning today to announce the first development release of PLASTK: The Python Learning Agent Software Toolkit. From the PLASTK README.txt file: PLASTK is a Python class library for building and experimenting with learning agents, i.e. software programs that … Continue reading PLASTK version 0.1

DARPA Urban Challenge has Started

The national qualifying event (NQE) forDARPA' s Urban Challenge started today in Victorville, CA. The Urban Challenge is the current incarnation of the DARPA Grand Challenge autonomous car competition. Unlike the previous challenge, which was an off-road race, the current challenge takes place on city streets with traffic. The Austin Robot Technology team (ART)  made … Continue reading DARPA Urban Challenge has Started

Great UT Computer Sciences Movie

I just learned about the new promotional video for the UT Austin Computer Sciences department. It gives some great shots of the UT campus, and a nice promo of the department, and the study of Computer Science generally. Highlights: The hilarious man-on-the-street interviews asking "what is an algorithm," and appearances by my fellow UT AI-Lab/Neural-Networks … Continue reading Great UT Computer Sciences Movie

Machine Learning Blogs

I've started reading a couple of interesting blogs on machine learning: Machine Learning (Theory) by John Langford, and Machine Learning Thoughts by Olivier Bousquet. Both are theory-oriented but also seem to cover philosophical issues and meta-issues about ML, CS, and science in general.