EdTech Considers “Lessons Learned” Regarding Big Data & Analytics in the Medical Industry

In January of 2011, IBM Watson made its debut on Jeopardy; since then the Watson technology platform has been applied to education, customer engagement, financial services, IoT, and the medical industry. IBM made bold claims regarding the business and scientific value Watson’s artificial intelligence (AI) would provide.

Since then, IBM third quarter revenue has declined $390 million ($18.76B versus $19.15B in the third quarter of the previous year). Cognitive Solutions, the business segment which includes the highly-touted Watson, was one of IBM’s poorest performing segments.

In an effort to expand into medical data and medical AI, IBM acquired Phytel in 2015. Following the acquisition, IBM was forced to downsize the company segment; some laid-off engineers spoke up about the platform masking real difficulties in turning AI into a profitable business model despite Watson’s powerful AI under the hood. An anonymous engineer likened it to ‘having great shoes but not knowing how to walk’.

MD Anderson, the University of Texas cancer center, was aiming for their moonshot; using IBM Watson for analytics applications focused on eradicating cancer. As of February 2017, the partnership had fallen apart, costing MD Anderson more than $62MM without meeting their goals.

“AI isn’t an amorphous black hole that sucks in unstructured data to produce insights. A solid data pipeline and a domain-specific understanding of the AI business problem at hand is table minimum”

Open source “cognitive computing” has made great strides since Watson’s debut on Jeopardy. Claudia Perlich, professor and data scientist with 6 years of experience at the IBM Watson Research Center confirmed that,

“In the data-science community, the sense is that whatever Watson can do, you can probably get as freeware somewhere, or possibly build yourself with your own knowledge”

Thomas H. Davenport, author of recent book, The AI Advantage: How to Put the Artificial Intelligence Revolution to Work (Management on the Cutting Edge), cuts through the hype of the AI craze. Davenport explains how businesses can “put artificial intelligence to work now, in the real world”.

Engineers from Phytel make it clear that smaller, flexible companies are winning bids for AI projects from their customers: “Smaller companies are eating us alive, they’re better, faster, cheaper.”

Originally published at https://sightlinedata.com.