Earlier quoted context omitted.
>I have zero trust in IBM to market their ML products correctly so that proper checks are maintained. That's a problem, yes, but it's not a new one. Vendor management has been around for decades, centuries, millennia maybe. If I contract out part of my job, it's still my responsibility to make sure the contractors are doing their job right. "But their marketing said..." or "but their sales guys said..." is not an exc…
The difference is like:like vs like:unlike, and seems to be one of the more dangerous ML application challenges. If I as a medical provider hire a remote vendor, who has medical teams in India look over initial results to flag issues, those humans will fail in human ways. I can anticipate that: I'm a human. If I use a similar ML product, it's very difficult for me to anticipate (or even understand) the ways it which…
I design and deploy customized automation systems for customers, and it's part of the standard process that we run the automation side-by-side with the old process for several months in order to learn the new failure methods and synchronize the process. Yes, for a few months we're duplicating the machine's work, but without the machine we'd be doing the work anyway. And no one is going to die if my automation fails, but we still do this anyway. It's crazy to think anyone would believe they didn't need to do side-by-side verification no matter what sales and marketing told them.
I don't know enough about Watson or IBM sales to say if Watson is good or bad, but I'm not trying to defend Watson or IBM. Watson may very well be a complete failure. But that aside, it's not the only failure in this story. No one should expect to implement a new tool and never verify if it's working correctly.