More on statistical reasoning - Chomsky on "Where AI went wrong"

I have written before about the claim that statistical reasoning is over-hyped in its claim that all the problems in the universe can be solved by statistical reasoning over past data (see also my report about Sethu Raman Keynote in DEBS'12).  

A more blunt claim against statistical reasoning has been made by Noam Chomsky which claims that the fact that AI took a wrong direction by making statistical reasoning its mainstream, and claim that knowledge achieved by statistical reasoning, while having practical usages as "good enough" for various uses, creates shallow knowledge that only approximate the universe and does not create a solid model of the universe.  

An interesting interview with Noam Chomsky was published recently in "the Atlantic".    

Always thought provoking!

I'll write more about correlation vs. causality.  

Additional presentation - talking about AI and EP

To complete the presentations list -- another interesting presentation from RuleML'11 (the first out of two) by 
Alex Artikis and Nenad Stojanovic  talking about AI approaches to situation detection and remaining challenges.

On outside-in vs. inside-out innovation

Today I participated in presenting to some young people from the Israeli Defense Forces, that are going around in research institutes in academia and industry to understand what is innovation, and what is research.   Recently, there has been an article in one of the Israeli newspapers that talked about inside-out vs.outside-in innovation, or where are great ideas coming from?    Many companies are trying to do outside-in innovation, doing market surveys to identify gaps and requirements in order to determine what next to do;  this article claimed that some of the bigger innovations (e.g. Facebook, iPhone and more) are inside-out, somebody comes with great idea out of the blue,  and it is sometimes being used for purposed that the inventor did not imagine.   The outside-in is used to refine, and shape the next generations.   I believe that this observation is true,  one cannot obtain great ideas from customers' requirements, as they typically don't think that they need it until it is there.   

Event processing and Artificial Intelligence - a tutorial in AAAI 2011

Paul Vincent has recently  blogged  about event driven applications within the applications types cited by the Wired magazine in an article talking about further AI applications.  There are other work within the AI community which look at the short and long term impact of AI research on the universe, such as the AAAI presidential panel on AI research.     There is a lot of potential in collaboration between the AI and EP communities,  from the EP side, there are variety of AI techniques that can be used, and from the AI perspective it provides cases in which AI techniques can be specialized in order to be used in practice.      

Recently my colleague Yagil Engel , an AI person we recruited  to investigate applications of  AI techniques for EP,  and myself have been notified that our tutorial proposal for AAAI 2011, a top AI conference, was accepted.  


So according to the plan we'll be talking four hours in August about the relationships between EP and AI, the first two hours are planned to provide a crash course on event processing for the AI audience, while the second half is planned to discuss various EP challenges that relate to various AI areas (planning, learning, uncertainty handling, autonomous systems and more.    The idea is to establish a dialog with more people in the AI community and pursue this line of research further.    The down side: we actually need to prepare now a four hours tutorial.   Will write more about it when it is finished (I am sharing all my public presentations on slideshare).