OK fine but there was _some_ non trivial technology behind the system that won the Jeopardy game. What I'm asking is whether what seemed like the state of the art NLP system at the time got eclipsed by newer and better systems or whether the whole thing was never really a state of the art NLP system to begin with.
The problem with Watson is that they don't have a business case. IBM sent salespeople to big customers with big problems and tried to find things to fix. In some cases, they did. But most of the time, the insurance companies, government agencies, etc they do business with scratched their heads and didn't do anything. Learning new things about their data might be seen as a threat to whatever enterprise bullshit they do!
The problem is you have companies like Google, Microsoft, Amazon, Apple, Facebook, etc have problems that this tech solves. It's easier to come up with a product with a problem that you understand. I can ask Google Photos or Siri to show me pictures of my dog in the snow in 2015, and they do. So I give Google & Apple money to store my crap. Google and Facebook use AI with all of the data they hoover up to peddle products to me. My grandparents get ads for depends, I get ads for drones, Google and Facebook make $.
Now, companies like Amazon, Microsoft and Google can go to companies that were prospected by IBM with solutions. Microsoft is minting money with ATP, because enterprise security teams suck. Amazon is selling creepy facial recognition to people, because people see it on TV, have a Ring doorbell, and want the capability. Google is selling GIS solutions, etc based on work done on maps.
> Learning new things about their data might be seen as a threat to whatever enterprise bullshit they do!
No, the problem was that Watson would be unable to help you learn anything new about enterprise data. IBM didn't even have a plausible, non-trivial proof of concept to trot out six years ago.
State of the art NLP system isn't a very meaningful term. You can't really say that Watson was "better" than Google's NLP systems at the time because they were solving different problems.
In general, what they did was not trivial, but it also wasn't revolutionary. Some of it was novel, but novel in the sense that it applied specifically to the problem they were trying to solve. It had little impact on the technology behind what IBM eventually tried to sell as Watson.
Anybody with a bucket of money could have built the same thing at the time. The impressive part was IBM figuring out that it would be worth spending a bucket of money on.
