Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

November 16, 2011

Big Data Just Got Smaller: New Approach to Find Information

Filed under: Artificial Intelligence,Graphs — Patrick Durusau @ 8:18 pm

Big Data Just Got Smaller: New Approach to Find Information

From the post:

San Diego, CA – Artificial intelligence vendor ai-one will unveil a new approach to graphically represent knowledge at the SuperData conference in San Diego on Wednesday November 16, 2011. The discovery, named ai-Fingerprint, is a significant breakthrough because it allows computers to understand the meaning of language much like a person. Unlike other technologies, ai-Fingerprints compresses knowledge in way that can work on any kind of device, in any language and shows how clusters of information relate to each other. This enables almost any developer to use off-the-shelf and open-source tools to build systems like Apple’s SIRI and IBM Watson.

Ondrej Florian, ai-one’s VP of Core Technology invented ai-Fingerprints as a way to find information by comparing the differences, similarities and intersections of information on multiple websites. The approach is dynamic so that the ai-Fingerprint transforms as the source information changes. For example, the shape for a Twitter feed adapts with the conversation. This enables someone to see new information evolve and immediately understand its significance.

“The big idea is that we use artificial intelligence to identify clusters and show how each cluster relates to another,” said Florian. “Our approach enables computers to compare ai-Fingerprints across many documents to find hidden patterns and interesting relationships.”

The ai-Fingerprint is the collection of all the keywords and their associations identified by ai-one’s Topic-Mapper tool. Each keyword and its associations is a coordinate – much like what you would find on a map. The combination of these keywords and associations forms a graph that encapsulates the entire meaning of the document. (emphasis added)

The line “…encapsulates the entire meaning of the document.” goes a bit far.

Whose “entire meaning” of the document? What documents and who were they tested against? Can it understand the tariff portion of phone bill? (Which I rather doubt has a meaning other than the total.)

There have been computational approaches to knowledge before and there will be others that follow this one. Makes for good press and gets all the pundits exercised but that is about all. Will prove useful in some cases but that doesn’t mean it is a truly generalized solution.

Did want to bring it to your attention for whatever use you can make of it in the long term and in the short term something to annoy your cubicle neighbour.

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