Singular Value Decomposition

An extremely nice tutorial about Singular Value Decomposition shows how you can extract pretty specific information from a bunch of data. I think SVD is very interesting to analyze data from different perspectives, one perspective is the product (how close is it really to another?) and the other perspective is the customer (how close is customer A to customer B?).

The problem starts to occur when people change their preferences. People generally go through phases (well, not all of us, but many!), and this is accompanied by different needs and different preferences. For this reason, A.I. designers need to understand that historical information only has limited value. The temporal trends in such analysis never come up to the surface, but I'm sure that some research is being done in this area, to further contextualize data in the realms of time.

I'm likely to speak at Sogeti Engineering World 2009, yet to be confirmed. My presentation will be about Artificial Intelligence and how it applies to business. Already now, businesses at lower levels are getting more interested in making the most out of their data. They have good knowledge about how their business works and who (in general) their customers are, but they cannot quantify their customer base from different perspectives.

My presentation will make clear how Artificial Intelligence is important to cases like response modeling, online recommendations, retention modeling and it will explain to engineers how they can apply certain techniques (borrowed from libraries) to their own problems at hand.

Where most people think of A.I. as some kind of black magic or silver bullet, I think it's important to realize that it's just juggling with numbers (at the moment). Over the past 50 years, A.I. has expanded into a number of different territories. One territory is more related to our "explicit knowledge" about things, the rule based systems and prolog. The other area is more related to "tacit knowledge", or what we know without being able to tell how we know it. It just works/is.

Neural networks, SVD, Kohonen are more mathematical constructs around the idea of tacit knowledge. We can't really trace it from input -> output, we just know it works. Other languages like Prolog work on the execution of basic rules or truths and demonstrate how the real world would act.

Our minds continuously sway between these two different areas of knowledge. We infer a lot of different information just through observation, sometimes supported by external teachers. But we also judge observations on truths that we have learned, or rules.

Many solutions in A.I. have depended on the combination of different techniques to offer the best solution. One solution that seems to work well now, for example, is spam assassination. SpamAssassin, now an Apache project, is one of the most popular spam-fighting schemes for email servers. It doesn't depend on a single scheme to rule out spam, but combines them as part of a certain model. Each different technique is either restraining or backing up another technique.

The very interesting question here is that in computers, we tend to use either RBS (Rule Based Systems) or other techniques like Neural Networks or Bayesian Belief Networks to solve a certain problem. One system is invoked before the other, as in a type of hierarchy. If we assume that the human brain only has neurons at his disposal, how can all these different techniques be applied in unison at the right time and moment? How do we know which strategy to rely on?