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Human brain replicated, replaced by big data analytics

Posted: 22 Nov 2013     Print Version  Bookmark and Share

Keywords:open-source algorithm  big data  cognitive computing 

Vinod Khosla, a veteran venture capitalist, has called for improvements that reduce human error by putting more healthcare decisions into the hands of smart systems. Separately, cognitive computing researcher Jeff Hawkins showed advances in applying techniques used in the neocortex to sorting large datasets.

Today's medicine relies on doctors' expert opinions, which are often "based on a series of biases that are more often right than wrong," said Khosla, a serial entrepreneur and co-founder of Sun Microsystems. "I suspect we will need humans out of the loop."

Khosla cited numerous studies quantifying the impact of human errors in diagnosing and treating health issues. He argued for more work on healthcare sensors and analytics, an area where he is currently investing in several start-ups including AliveCor, Ginger.io, Kyron and Quanttus.

"Data science will do more for medicine in the next 10 years than biological science," Khosla told a symposium on cognitive computing at the IBM Almaden Research centre.

He referred to today's digital medicine products as "clumsy toddler steps" that will lead to more sophisticated offerings that empower consumers. "I think change will come from consumer-driven healthcare, and I hope a few role models will cause an avalanche of interest."

Separately, Jeff Hawkins, described his latest product, Grok. It uses a technique employed in the neocortex to track large datasets by creating so-called sparse distributed representations (SDRs).

Grok is based on an SDR algorithm Hawkins's company released as an open-source code. "We don't know how to characterise it mathematically, but I'd argue this is a basic building block of cognitive computing," he said.

The software, released for use on Amazon's cloud service, handles rapid detection and ranking of anomalies in data streams and provides tools to investigate them quickly. Hawkins described the tool as a single sensor—like an ear—attached to a very rudimentary brain "a thousandth the size of a mouse's neocortex."

"We see something unusual—we don't know why it happened or its root cause—that requires a physics model and an understanding of how things are supposed to work" in a given system, he said.

Nevertheless, he claimed the tool is a powerful one that could be applied broadly to big data analytics problems in areas as diverse as finance, web sales, and manufacturing. CEPT Systems in Austria is using the open-source algorithm at the heart of Grok in its work on natural language recognition.

- Rick Merritt
  EE Times





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