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April, 2008:

Super Crunchers: predictive analytics is not enough

Ian Ayres, the author of Super Crunchers, gave a keynote at Fair Isaac’s Interact conference in San Francisco this morning.   He made a number of interesting points related to his thesis that intuitive decision making is doomed.   I found his points on random trials much more interesting, however.

In one of his examples on “The End of Intuition”, a computer program using six variables did a better job of predicting Supreme Court decisions than a team of experts.  He focused on the fact that the program “discovered” that one justice would most likely vote against an appeal if it was labeled a liberal decision.    By discovered we mean that a decision tree for this justice’s vote had a top level decision as to whether the decision was liberal, in which case the program had no further concern for any other information.  (more…)

A Common Upper Ontology for Advanced Placement tests

I have previously written about the lack of a common upper ontology in the semantic web and commercial software markets (e.g., business rules).  For example, the lack of understanding of time limits the intelligence and ease of use of software in business process management (BPM) and complex event processing (CEP).  The lack of understanding of money limits the intelligence and utility of business rules management systems (BRMS) in financial services and the capital markets.   And, more fundamentally, understanding time and money (among other things, such as location, which includes distance) requires a core understanding of amounts.

The core principle here is that software needs to have a common core of understanding that makes sense to most people and across almost every application.  These are the concepts of Pareto’s 80/20 Principle.  A concept like building could easily be out, but concepts like money and time (and whatever it takes to really understand money and time) are in.  Location, including distance, is in.  Luminousity could be out, but probably not if color is in.  Charge and current could be out, but not if electricity or magnetism is in.  The cutoff is less scientific than practical, but what is in has to be deeply consistent and completely rational (i.e., logically rigorous).[2] (more…)

Real AI for Games

Dave Mark’s post on Why Not More Simulation in Game AI? and the comments it elicited are right on the money about the correlation between lifespan and intelligence of supposedly intelligent adversaries in first person shooter (FPS) games.  It is extremely refreshing to hear advanced gamers agreeing that more intelligent, longer-lived characters would keep a game more interesting and engaging than current FPS.  This is exactly consistent with my experience with one of my employers who delivers intelligent agents for the military.  The military calls them “computer generated forces” (CGFs).  The idea is that these things need to be smart and human enough to constitute a meaningful adversary for training purposes (i.e., “serious games”).  Our agents fly fixed wing and rotary wing aircraft or animate special operations forces (SOFs) on the ground.  (They even talk – with humans – over the radio.  I love that part.  It makes them seem so human.) (more…)

The Semantic Arms Race: Facebook vs. Google

As I discussed in Over $100m in 12 months backs natural language for the semantic web, Radar Networks’ Twine is one of the more interesting semantic web startups.  Their founder, Nova Spivak, is funded by Vulcan and others to provide “interest-driven [social] networking”.  I’ve been participating in the beta program at modest bandwidth for a while.  Generally, Nova’s statements about where they are and where they are going are fully supported by what I have experienced.  There are obvious weaknesses that they are improving.  Overall, the strategy of gradually bootstrapping functionality and content by controlling the ramp up in users from a clearly alpha stage implementation to what is still not quite beta (in my view) seems perfect. 

Recently, Nova recorded a few minute video in which he makes three short-term predictions: (more…)

Adaptive Decision Management

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In this article I hope you learn the future of predictive analytics in decision management and how tighter integration between rules and learning are being developed that will  adaptively improve diagnostic capabilities, especially in maximizing profitability and detecting adversarial conduct, such as fraud, money laundering and terrorism.

Business Intelligence

Visualizing business performance is obviously important, but improving business performance is even more important.  A good view of operations, such as this nice dashboard[1], helps management see the forest (and, with good drill-down, some interesting trees). 

With good visualization, management can gain insights into how to improve business processes, but if the view does include a focus on outcomes, improvement in operational decision making will be relatively slow in coming.

Whether or not you use business intelligence software to produce your reports or present dashboards, however, you can improve your operational decision management by applying statistics and other predictive analytic techniques to discover hidden correlations between what you know before a decision and what you learn afterwards to improve your decision making over time.  (more…)

Cyc is more than encyclopedic

I had the pleasure of visiting with some fine folks at Cycorp in Austin, Texas recently.  Cycorp is interesting for many reasons, but chiefly because they have expended more effort developing a deeper model of common world knowledge than any other group on the planet.  They are different from current semantic web startups.  Unlike Metaweb‘s Freebase, for example, Cycorp is defining the common sense logic of the world, not just populating databases (which is an unjust simplification of what Freebase is doing, but is proportionally fair when comparing their ontological schemata to Cyc’s knowledge).  Not only does Cyc have the largest and most practical ontology on earth, they have almost incomprehensible numbers of formulas[1] describing the world.   (more…)