Over the two weeks I listened to Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart by Ian Ayres during my commute back and forth from Dallas. The book is very interesting. Most of what I got out of the book directly was inspiration and ideas on ways to use number crunching in everyday life. Much of what the book is ultimately about is prediction – trying to look into the future to gain some advantage. I think prediction is a really interesting subject, whatever side you’re on (whether you believe in predicability or randomness).

One of the biggest points Super Crunchers trying to make is that people are computing scenarios all the time, trying to predict what will happen – this applies to betting, the movie box office, retail sales and all sorts of other things. The author basically says you can be on one of two ends. You can either be a luddite who is afraid to engage with numbers and computation, in which case you will be put at disadvantage. Or you can be enlightened and use number crunching for your benefit and take advantage of it’s uses. You will be on one end or the other – you might as well make a conscious choice. Those who embrace number crunching stand to benefit from it.

A few examples from the book:

• A wine collector uses past data to predict which wines will be good based on weather and other current data.
• Someone creates a website to predict the prices of seats on airplane flights.
• A company creates an algorithm to help predict revenues and suggest changes to improve profitability of movies.

So for me the book Super Crunchers was really interesting. I liked the audio version, but I think it would still be a worthwhile endeavor to actually read the book. I would give it a rating of 4 out of 5.

My wife and I just got back from a very restful trip out to the Shepherd of the Ozarks Camp in Arkansas.  It was really wonderful.  I scheduled next to nothing, made some new friends and caught up on sleep.  I finished two books while I was there – The Design of Everyday Things, which I’m reading for a class this semester, and On Intelligence, which I read just for fun.  I felt as though The Design of Everyday Things would be best rewritten as a brochure about design – there were some really great points, but the book did not need to be 272 pages.

On Intelligence, however, was wonderful.  The book is by Jeff Hawkins and Sandra Blakeslee.  Jeff Hawkins started Palm, among other things, and is a neuroscientist along with his many other technology ventures.  He talks about a general idea of brain function, specifically dealing with the neo-cortex, but casts the whole thing in a technological light.  His discoveries have serious implications for intelligence in computer systems.  He outlines a new system, better than neural networks and has a company called Nuementa which is trying to implement that system.  I won’t be using his work this semester in grad school, but I am using neural networks to do some basic pattern recognition.  I wish I had more time to research his technology.  If you want to know more about Jeff Hawkins, you might check out his talk at Stanford.

I also met a wonder retired professor that has become a friend and I look forward to talking more.  He’s been very helpful so far in providing some much needed guidance to a career path that seems a little ambiguous at times.  All in all, it was a really wonderful trip.

So, over the past year and a half I’ve been looking into Neural Networks in Flash.  Neural Networks are essentially used in “Artificial Intelligence” and Pattern Recognition.  So I consulted with one of my best friends who is an embedded systems engineer and had him explain to me how they work (as he went to school for programming/etc and took a class of AI).  Well, about a week or two ago I finally got everything working and in a format that I can show to people.

So the video you’re about to see, should you choose to watch it, is just showing the program “training” on some Xs and Os and figuring out the distinguishing features between the two, then when it’s done training I draw some new (never before seen) Xs and Os and it tries to tell me which one is which.  As you’ll see, it was successful, but it isn’t always successful in both training correctly or the recognition afterwards.  I still have some tweaking to do.

Neural Network in Flash by Philip Likens

Best,
Philip Likens
Texas based Interactive Artist and Web Design Instructor