How to Use Watson
August 17, 2015
While there are many possibilities for cognitive computing, what makes an idea a reality is its feasibility and real life application. The Platform explores “The Real Trouble With Cognitive Computing” and the troubles IBM had (has) trying to figure out what they are going to do with the supercomputer they made. The article explains that before Watson became a Jeopardy celebrity, the IBM folks came up 8,000 potential experiments for Watson to do, but only 20 percent of them.
The range is small due to many factors, including bug testing, gauging progress with fuzzy outputs, playing around with algorithmic interactions, testing in isolation, and more. This leads to the “messy” way to develop the experiments. Ideally, developers would have a big knowledge model and be able to query it, but that option does not exist. The messy way involves keeping data sources intact, natural language processing, machine learning, and knowledge representation, and then distributed on an infrastructure.
Here is another key point that makes clear sense:
“The big issue with the Watson development cycle too is that teams are not just solving problems for one particular area. Rather, they have to create generalizable applications, which means what might be good for healthcare, for instance, might not be a good fit—and in fact even be damaging to—an area like financial services. The push and pull and tradeoff of the development cycle is therefore always hindered by this—and is the key barrier for companies any smaller than an IBM, Google, Microsoft, and other giants.”
This is exactly correct! Engineering is not the same as healthcare and it not all computer algorithms transfer over to different industries. One thing to keep in mind is that you can apply different methods from other industries and come up with new methods or solutions.