Monday, November 9, 2009

How hard was the exam?

Please post constructive comments here....

Saturday, October 31, 2009

Past, Present and Future? of AI

I just read this article (http://www.techradar.com/news/world-of-tech/future-tech/the-past-present-and-future-of-ai-643838) about AI. It's a good read, but it focuses on the popular idea of AI from its humble beginnings. Nothing in here about planning, preference representation, or relational Bayes' nets. The article does at least acknowledge the disconnect between "Hollywood" AI and real AI research--something lacking in most mainstream coverage of AI.

Tuesday, October 20, 2009

Lisp type philosophy catching on fast

I have noticed this new trend where the advantages of pure functional programming are filtering into newer languages in the main stream. One specific pivot point is concurrency and scalability. The paradigm of how immutability==high concurrency==scalability is being replicated in multiple ways, first map reduce now this. Its high time the goodness of lisp like languages was not restricted due to its esoteric coding style ...
http://blog.bestinclass.dk/index.php/2009/09/scala-vs-clojure-lets-get-down-to-business/

Thursday, October 15, 2009

Human-Aware AI: Robots that "show emotion"

This great talk on TED by David Hanson talks about robots and robot faces that interface with humans and are able to show, among other things, empathy.

Monday, October 5, 2009

Stanford's Autonomous Helicopter Project

Here's a link to Stanford's autonomous helicopter project, part of which Andrew Ng showcased at IJCAI 2009 during his Computers & Thought Award lecture.


Remember, this helicopter is completely autonomous :)

Thursday, September 10, 2009

Google code jam, easy AI problem!!?

This is a good problem in google code jam... Relates to AI kind of...
1)You know your initial state completely
2) Environment observability is only local(next rooom), but it is static
3) Actions are deterministic...
http://code.google.com/codejam/contest/dashboard?c=32003#s=p1

The problem boils down to this:
"given your actions can you determine the configuration of your environment, in this case a maze..."
Here is the python solution... Kinda had fun time solving it.. took about 1.5 hours to figure the problem and solve it... Have fun!

Next on the reading list (for classes next week)

Once we are done with Bryce el al paper on belief-space planning, we will turn to atomic models for
decision-theoretic search--aka Markov Decision Processes.

The primary reading will be the following paper:

http://www.cs.washington.edu/research/jair/abstracts/boutilier99a.html

You should read until the end of Section 3 for now (section 4 is factored representations--we will discuss
that later)

I will also provide you with the relevant chapter from R&N.

Rao