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Saturday, July 23, 2016

Following Watson Facing Cybercrime

Continuing to follow Watson to determine what the ideal application is.  Is it mostly about big and volatile knowledge, appropriately indexed?   Leveraged with machine learning.  To find appropriate cases for reapplication?  That's what I have seen so far as part of application proposals.   Not AI ... but certainly a form of 'Practical Intelligence'.    And maintaining that knowledge via focused learning.

Example, Recently in  Wired:

" ... IBM announced that Watson is taking its cognitive learning chops to the cloud, where it’ll apply them to analyzing, identifying, and (hopefully) preventing cybersecurity threats. But first, it’s going to have to learn. Fast. .... 

There are already plenty of computer-enhanced approaches to combating cybercrime, most of which involve identifying outliers or abnormalities—like when a user logs a few too many failed password attempts—and determining whether those constitute some sort of threat.

Collecting and analyzing this type of data can and does work. It’s not ideal, though. First, there’s simply too much of it; according to a recent IBM report, the average organization sees over 200,000 pieces of security event data every single day. There’s simply no way to keep up with it all. And while solutions like MIT’s recent AI2 can trim down the number of incidents a human researcher needs to sift through, there’s still the fact that the data points being considered are only a small part of the picture.  ... " 

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