Is IBM Watson a 'Joke'?

"Watson" is not a thing, it's a brand.

IBM have built several, individually successful pieces of technology, all branded as "Watson", and used the impression that they are a single entity to market it as far smarter than it actually is.

Under the hood "Watson" technologies seem to fall into 3 categories:

- The very successful publicity stunt on Jeopardy. The technology here was good, although from what I understood from tech talks given by IBM, it was a bit more brute force than you might think.

- Custom built software, often for medical and legal purposes, that is effective, if limited in scope, and comes with a hefty consulting and support contract. You're essentially buying a data science team at IBM.

- Productised machine learning APIs, that aren't particularly great, and are very isolated in their abilities.

(1) is fun but not useful to anyone really, (2) is useful, but expensive and no better than what you'd get from most other consultancies, or might be worse knowing IBM's consulting practices, and (3) is too limited to build the core of a business on, so will likely only ever be used to provide a step up until companies move technologies in house.

Having worked on a project that tried to implement "Watson" in one of our use cases, I can say with some certainty that it is worse than a joke.

>“If you call IBM Watson a joke you call the hundreds of companies and startups that have built on it a joke.”

This quote would be damnation by faint praise if it were not meant in earnest.

In all this analysis we seem to be glossing over the fact that what IBM is selling in their Watson platform is not what played Jeopardy. The original point of criticism is that "Watson" in its current offering is little more than marketing hyped technology that is quickly becoming a widely available commodity. That criticism stands true and this article seems to agree that Watson is a joke.

"As the M.D. Anderson fiasco illustrates, IBM fell into the trap of over-promising and under-delivering. “IBM claimed in 2013 that ‘a new era of computing has emerged’ and gave Forbes the impression that Watson ‘now tackles clinical trials’ and would be in use with patients in just a matter of months,” Freedman noted.

As to whether Watson will ever be useful in clinical situations? “This is hard,” opined Stephen Kraus, a partner at Bessemer Venture Partners. “It’s not happening today, and it might not be happening in five years. And it’s not going to replace doctors.”

And that's the crux of the problem with Watson. IBM routinely promises that 'cognitive computing' (CC) can solve literally any data-based or science problem in your business' enterprise. But once you look closer at their methods, you find nothing special; CC is the same old bag of data mining tricks everyone has been using for decades. IBM's real problem: Watson brings nothing new to the table.

At the Fortune 100 big pharma where I work we've gone through several rounds of evaluating IBM Watson's box of tricks for the value it might add to drug discovery in the chemical or clinical spaces. And each time we've taken a pass. IBM just hasn't shown that it has a track record of material successes in cutting edge AI, beyond what's available most anywhere.

In particular, the plate tectonics of AI shifted with the advent of deep learning, which came along after IBM Watson won Jeopardy, and which still seems absent from their AI repertoire. Until CC demonstrates "competitive competency" in cutting edge AI and not more marketing jabber, I expect IBM Watson will continue to underwhelm.

I think that, slowly but surely, companies are realizing that IBM's technology is no better than freely (or much cheaper) available alternatives, no matter how many enterprise-y buzzwords they wrap it up in.

Chamath Palihapitiya is doing nothing for the perception of VC's as a bunch of arrogant loud mouths.

Arrogant because they confuse past success with actual skill and loud mouths because they spend so much time pumping their copy cat investments to get people to focus on their "me too" efforts rather than their competitors' "me too" efforts.

AI as a service is not scalable enough as it takes people being experts across AI and application domain. AI as a product is probably too challenging to get right but it is tempting to use as a sales tool. Utter these initials and you got a conversation going. Mission accomplished. Cost externalized: Company reputation.