So pretty much like any major enterprise system from the likes of IBM, SAP, Oracle, ...

So by platform I mean, lets say you train a NERC model using Watson Knowledge Studio. Obviously this model has to be "deployed" somewhere so you can call it using an API. They host it for you and they bill you per API call. Anyone can go create their own entity type system and manually annotate a training dataset. So it's definitely a re-usable platform, you don't need to pay for any IBM consultancy to use it. I found that the NLP offerings have many problems, and that the documentation alone is not enough to help resolve all of them. So eventually, IBM will just tell your employer you're stupid and that's why it's not working as it should and you should pay IBM to come in.

But make no mistake, these are all just standard machine learning tools that have been "packaged" so end-users can use them through a web front end. It is in no way, whatsoever, getting any input from any AI/Neural Network/Database/whatever you want to call it/ thing called "Watson".

I personally think it's disingenuous because when people hear Watson they think Jeopardy and they think that somehow that technology is involved when they use any of the Watson.* products.

The use of the Watson name is a deliberate attempt to take advantage of the Jeopardy game. It's a name that has cachet, and I've seen just enough of the marketing perspective to know that marketing will push very hard to reuse a successful name.

I think federation would be a better term. There was a core set of APIs and hardware that might be called "Watson proper" but each market segment would be handled by a different organization. And then there was the proliferation of odd ball things out of research or little groups looking for growth/stability that get Watson branded.

Sometimes we'd be the first time a team relaizes there is already something doing what they've been building.

Eventually they scrapped the project because it not only took a ton of employee time to talk with IBM's team to get it set up and working, it also cost a significant chunk of money and wasn't as good as what the people who already worked at the company thought they could do themselves.

With all due respect to the people that work at IBM, I just can't imagine IBM's sales and consulting cultures to work well with deploying AI. I don't know firsthand, but from what I've heard and what I would guess anyway, a lot of the people selling Watson and actually on the front lines working with it probably aren't that knowledgeable about AI/ML/whatever. I just don't see how you could determine a project's feasibility or effectiveness without having a sharp conceptual knowledge of the actual AI algorithms and what potentially what kind of data is needed to make them shine.

Suppose a university admissions department offers paper surveys to prospective students at the end of on-campus tours. In an effort to improve admitted student yield (the percentage of students that actually attend the university after being accepted), the university wants to be able to scan these surveys' text digitally and then perform sentiment analysis to determine how excited the student is about attending the university, or more directly, how likely the student is to matriculate. The university doesn't have any people capable of doing this, so they get into contact with Watson.

How much will the salespeople at Watson pry into the questions of the survey, demographics and culture of the school, or the sample size? Will they ask about statistics such as acceptance rate, yield, and which students are most likely to matriculate (based on quantifiable metrics)? Even the type or color of paper and text field sizes on the surveys on could affect the feasibility of the project regarding OCR, or bias the responses toward short answers. I would argue that a lot of knowledge about the project would be necessary before a sales quote or even the feasibility of the project itself could be considered, but would a salesperson know to ask these question? Would they even be incentivized to ask? Would the consultants know that certain questions could make OCR hard or sentiment analysis a wash? Would a statistician be consulted to see if the same or better results could be obtained from simple analysis of GPA, ZIP, and test scores?

I'm sure everybody at Watson is pretty technically competent - and to be sure, I'm sure for most consulting and sales that IBM does, I wouldn't have to make the following qualification. But to be brutally honest, I think the type of people who are familiar enough with AI to be the person you want working at Watson in consulting and sales probably are using those skills as developers and data scientists. And even then, again with all due respect to IBM employees (and I know IBM puts out a lot of great research), those people might not also be at IBM either.

at the beginning there was the one true watson. watson was a way to process, correlate and provide indsight on a corpus of knowledge expressed in natural language. the technology was good but had one major weakness: the knowledge extraction part had a large bulk of manual labor needed to weed out the noise from the relevant part, because to a processing engine each bit of information is equal to every other bit of information. so you needed domain expert to proviede an initial tuning and after that watson was a good solution for the problem statement.

this process however required non technical domain expert to be working closely with the watson analysts at the tuning for an unspecified and quite long amount of time, comopounding the already astronomical costs of the solution itself

now as you might imagine like any other company ibm has a lot divisions - cloud, services, intelligence etc. the watson division due the large research costs and the few clients that were able to afford and make use of the tech was scoring too much quarters in red.

ibm is also a financial company, so they did what they usually do when one division needs padding: they started moving everything remotedly related to intelligence and analytics under the watson moniker, to drive up quarterly reports. this had the side effect that the watson marketing is a clusterfuck of overlapping and unrelated solutions that more pften than not don't even work togheter natively, but are presented as a whole ecosistem.

now, of course anyone trying to make sense of the whole thing is going to be faced with all kind of claims against all kind of slutions, without any idea of what does what.

but originally the only omission from marketing was what watson actually required and how and what it could give back to a company. but the problem is... it's the kind of solution that you have to build to see where it goes. you cannot be sure of the results from the beginning.

Now a year later I’m finally being asked to clarify “what is Watson” so that the decision makers can better understand the techniques being used rather than the fantasies about what was being used that they were encouraged to develop through misleading marketing and consultants ...

Today .NET is a great product ecosystem and a huge success, except for the horribly awkward name.

I don't know if they'll ever regret naming it Watson, but latching onto the AI craze isn't necessarily a losing strategy as long as the products are good and successful. Even if they have nothing to do with AI.