I'm not even convinced Watson is a platform. My impression is that it's just a consulting division of the company that deploys teams to build solutions that are in some way related to AI, with each solution or implementation potentially being completely unique from the ground up. Perhaps someone from IBM can correct me though.
I'm currently sitting in a meeting about implementing the Watson Enterprise Search product in my company and that is more or less the impression I've gotten. They sell it as a platform that is easy to customize and then once you're in they bill you tons of hours to help you because the system is indecipherable and poorly documented.
They sell it as a platform that is easy to customize and then once you're in they bill you tons of hours to help you because the system is indecipherable and poorly documented.
So pretty much like any major enterprise system from the likes of IBM, SAP, Oracle, ...
Sounds like every IBM product: WebSphere, Tivoli, WSSR, RAD, fuck even AIX. Many of those can be replaced with open source tools at a fraction of the cost and at a huge increase in performance.
Watson is a brand name. Specifically It's the Machine Learning brand name. Watson Developer Cloud is the product suite and it's just a set of pre-trained classical, machine learning, and deep learning based APIs for a variety of tasks. NLU(UIMA) text identification, NLC(Fuzzy String Matching), Visual Recognition, Tone Analysis(VADER), Discovery (Document Database + NLU + Knowledge Graph), Speech (STT/TTS), Text Translation (Literal not Semantic), Assistant (Conversational State Engine with embedded linguistic neural net). We're ahead in some aspects and a bit behind in others. There is also a generic Machine Learning Service which allows you to train Classical or Deep Learning algorithms and push them to a rest endpoint for production use. Ultimately the "Watson" from jeopardy was sliced up and pieces stuffed into various products. Anything with a smattering of AI/ML gets the Watson brand on it. I personally hate the Watson commercials as people who don't know anything about the subject think Watson is this singular sentient entity. Those who do know about AI/ML know we have the same general tech as everyone else. One benefit we do have though is petabytes of training data and expertise in just about every line of business on the planet.
I only know specifically about the NLP stuff, e.g. Natural Language Understanding (AlchemyLanguage), Natural Language Classification (it's just a multi-label text classifier) and Watson Knowledge Studio (Basically allows you to create your own named entity recognition classifier (NERC), also supports relations and co-reference resolution. You manually hand-annotate examples through a Web UI).
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.
> 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 used to work for IBM, on a backend service used by various Watson (and non Watson) branded projects.
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.
At my previous firm I worked with a pre-sales engineer who was formerly at IBM Watson before working at the firm. This is essentially it. Implementations of Watson were no different than doing an ERP project.
I knew people who had their division pay for Watson as a way to get their business AI and data science needs fulfilled without hiring developers outside their price range.
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.
