Hierarchical Temporal Memory

In April of 2008, Jeff Hawkins founder of Numenta presented their biology-inspired Hierarchical Temporal Memory as keynote speaker at the 2008 RSA Conference.

This link points to the start page. You will have to register to watch the 45 minute talk online, but the keynote talks are free.

Posted under companies, intelligence, research, technology

This post was written by admin on September 2, 2008

Tags: ,

The Rise of the Robots

Microprocessor developers are acknowledging that the advances in microprocessor technology may soon enable artificial intelligence systems to be as or more intelligent than human beings.

At the Intel Developer Forum, Justin Rattner, Intel chief technology officer announced:

The industry has taken much greater strides than anyone ever imagined 40 years ago.

There is speculation that we may be approaching an inflection point where the rate of technology advancements is accelerating at an exponential rate, and machines could even overtake humans in their ability to reason, in the not so distant future.

Full story here… The Star Online written by M. Madhavan

Posted under companies, intelligence, microprocessors, technology

This post was written by admin on September 2, 2008

Tags: , , , ,

Intelligent Instruments

Intelligent Robotic Arm

The LEGO Mindstorm NXT robotics system is an excellent testbed for research in machine learning and artificial intelligence. At Knuthlab Robotics at the University at Albany, we are developing intelligent instruments using LEGOs.

Our first instrument is a robotic arm that is designed to locate a characterize a white circle on a black background using the LEGO light sensor. It relies on Bayesian inference, which is implemented using a technique called Nested Sampling, which was developed by John Skilling. This software allows the robot to learn the characteristics of the circle using the light sensor data that it has collected. The real advance here is the inquiry engine, which uses Bayesian adaptive exploration to decide which measurements to take next. It does this by considering all the possible measurements that it could take, and computes the expected gain in information from each possible measurement. It then chooses to take the measurement with the greatest expected information gain. The process then repeats as the robot learns about the circle.

The system is easily generalized to solving other problems, such as exploring rooms, interpreting people’s emotions, and doing real science.

We recently presented our research at the MaxEnt 2007 workshop in Saratoga Springs NY. Below are links to a video of the talk, my slides, and our research paper.

Video: Designing Intelligent Instruments, K.H. Knuth

Slides: Designing Intelligent Instruments, K.H. Knuth

Research Paper:
Knuth K.H., Erner P.M., Frasso S. 2007. Designing intelligent instruments. K.H. Knuth, A. Caticha, J.L. Center, A. Giffin, C.C. Rodriguez (eds.), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Saratoga Springs, NY, USA, 2007, AIP Conference Proceedings 954, American Institute of Physics, Melville NY, In press.

Posted under intelligence, mindstorms, research

This post was written by admin on August 31, 2008

Tags: , , , ,