IBM Watson - Rising AI Star or Problem Child?
There’s no doubt that IBM have come a long way since Deep Blue beat Gary Kasparov at chess in 1997. The decision to move to a self-learning platform that processed natural language and to challenge the established champions of the game show “Jeopardy!” with a new platform “Watson” was a brave move.
The first attempt allowed Watson 500 clues from previous programmes as a primer, before optimistically setting it against human competitors. The results were disappointing, Watson was hopelessly slow at responding and got only 15% of the questions correct which compared poorly to the 95% accuracy of the human players. Not deterred, the IBM team set about improving the platform and algorithms bearing fruit in February 2010, when Watson could beat human players on a regular basis, even without a connection to the internet.
Since then, IBM has invested over $1 billion to launch the Watson Group which now has 2 000 employees.Watson Group will develop three new cloud-delivered services: Watson Discovery Advisor, Watson Engagement Advisor, and Watson Explorer.
Watson Discovery Advisor will focus on research and development projects in pharmaceutical industry, publishing, and biotechnology
Watson Engagement Advisor will focus on self-service applications using insights on the basis of natural language questions posed by business users, and
Watson Explorer will focus on helping enterprise users uncover and share data-driven insights based on federated search.
The volume of medical data is expected to double every 73 days by 2020, obviously no human brain can possibly keep up, the case for AI and computers in healthcare is a compelling one. On the basis of its rapid advancement the Watson platform has seen quick adoption, currently having more than 15 000 clients and partners, with more than 230 hospitals using IBM Watson oncology tools.
However, more recent headlines have been less than flattering for IBM. In July, the healthcare news publication “Stat” published a report claiming "internal IBM documents" showed the Watson supercomputer often spit out erroneous cancer treatment advice and that company medical specialists and customers identified "multiple examples of unsafe and incorrect treatment recommendations," even as IBM was promoting its AI technology.
Stat cited several slide decks it had obtained from a presentation made by IBM Watson Health's deputy chief health officer in 2016. The slides mostly blamed problems on the training of Watson by IBM engineers and staff at the Memorial Sloan Kettering Cancer Center (MSKCC).
Separately, an article by the “Wall Street Journal” claimed Watson Health had not made progress in bringing AI to healthcare.
Late last year it was announced that the head of Watson Health, Deborah DiSanzo was leaving her role, and the division has struggled to integrate different technologies from other businesses it has acquired, laying off employees in the process.
There are more reports that Watson’s revenues have been disappointing and that IBM are struggling to maintain momentum.
So, is Watson on the up or has it hit a brick wall?
The answer probably lies in some of the wisdom generated by the research company Gartner and particularly in one of its favourite diagrams that charts the adoption of any new technology or product, the “Hype Cycle”.
The diagram provides a graphical representation of the maturity of emerging technologies through five phases;
Technology Trigger - a potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest trigger significant publicity. Often no usable products exist and commercial viability is unproven.
Peak of Inflated Expectations - early publicity produces a number of success stories—often accompanied by scores of failures. Some companies take action; most don't.
Trough of Disillusionment - interest wanes as experiments and implementations fail to deliver. Producers of the technology shake out or fail. Investment continues only if the surviving providers improve their products to the satisfaction of early adopters.
Slope of Enlightenment - more instances of how the technology can benefit the enterprise start to crystallise and become more widely understood. Second- and third-generation products appear from technology providers. More enterprises fund pilots; conservative companies remain cautious.
Plateau of Productivity - mainstream adoption starts to take off. Criteria for assessing provider viability are more clearly defined. The technology's broad market applicability and relevance are clearly paying off. If the technology has more than a niche market then it will continue to grow.
Where you would place IBM Watson on the Hype Cycle is open to debate, but in my opinion it’s currently hovering in the “Trough of Disillusionment”, that shouldn’t be seen to detract from its future promise.