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Comment by distant_hat | Hacker News Reader

Slightly over a year back, we were contacted by IBM to try out Watson under some program for startups. Starting out, they asked us to give them a try. We gave them a NLP related task and they were confident they would be able to do a good job of it. After multiple meetings over the next 2 months, they barely produced any useful output. In the meantime, an intern with us got a pretty decent solution using just Python. Our management did get invited to lunches and meetings for selling them some more products. During one of such invitations, since our CEO couldn't make it, I was sent. I was surprised by how shoddy their presentation was given that I thought they might not be doing a good job on the problem we gave them but they should be slick on the selling side. Their presentation seemed like it was out of 2005 or something.

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This is a common pattern everywhere unfortunately. Snake oil salesmen and their victims.

I have daily skirmishes at my company with our CTO and his subordinates who constantly want to believe snake oil salesmen because they’re looking for simple ways outs of hard problems. It’s really sad.

For some it seems easier to -_trust_ a dude with a power point presentation and no real experience over good engineers telling the truth and trying to help them for real.

I now think it’s madness for anyone to be in charge of anything if they don’t have relevant experience in the relevant domain. Nice people or not, they’re susceptible to bullshit.

> I now think it’s madness for anyone to be in charge of anything if they don’t have relevant experience in the relevant domain.

This.

In support of that idea, here's C.A.R. Hoare, from 'The Emperor's Old Clothes', the 1980 Turing Award lecture:

'At last, there breezed into my office the most senior manager of all, a general manager of our parent company, Andrew St. Johnston. I was surprised that he had even heard of me. "You know what went wrong?" he shouted--he always shouted-- "You let your programmers do things which you yourself do not understand." I stared in astonishment. He was obviously out of touch with present day realities. How could one person ever understand the whole of a modem software product like the Elliott 503 Mark II software system? I realized later that he was absolutely right; he had diagnosed the true cause of the problem and he had planted the seed of its later solution.'

What was the solution here? To learn the Elliott 503 Mark II more deeply?

Given the speaker, I'm guessing the suggestion is that the incident sowed the seed for him to develop Hoare Logic[0]

[0] https://en.wikipedia.org/wiki/Hoare_logic

Oversimplifying, but quoting from the speech, that particular challenge was overcome as follows: "Thus we muddled through by common sense and compromise to something approaching success."

He then discusses how the problems there led to his thinking on CSP, and, eventually, formal methods.

But, go read the speech, it's much more wonderful than any summary thereof: http://zoo.cs.yale.edu/classes/cs422/2010/bib/hoare81emperor...

You know, it would be very profitable for someone to profess an expertise in X, reach out to various companies to help them use X to solve their problems, then simultaneously gain experience in X while patenting the heck out of any problems solved with X.

You’re describing a person who has a huge amount of expertise in sales.

But a person like that can very lucratively sell actual products that already exist, so why would they want to sell this mysterious X?

Because the person is actually a business that:

a) employs enough scientists who know what they're doing and lawyers who know what they're doing to file relevant and defensible patents; and

b) has enough entrenched relationships that they can pay for a sales force what knows what they're doing; but

c) doesn't employ enough engineers who know what they're doing to develop actual competitive products around those patentable ideas.

but products that actually exist are X-1 not X

That's roughly how I broke in to technical work, modulo the patents. 25 years later I actually do feel at home in particular domains. But I was a young college dropout in the early 90s, and while I never outright misrepresented my experience, I did tell myself that nobody else had any idea how that stuff worked either. (Which I tend to believe more now than I did then, but for different reasons...)

As programming and systems engineering slowly formalizes, it seems to me that's getting harder to do. I see fewer self-taught folks in their early-mid 20s these days at least, although I could be observing my personal bias from aging. Anyone else have an idea?

This is exactly what IBM and Oracle etc have been doing for years.

What do you think of your CEO now :)?

Well, he was pretty good, he let us take the decisions completely so we never paid anything for it other than in time. Moreover, we weren't flush with cash or anything so they wouldn't have made much anyway.

Wow, I have been in this exact situation as an intern

This sounds like this is a common problem for Watson's go-to market strategy.

Python is included in the most recent versions of Watson Studio ML. Were Jupyter notebooks included when you originally tried the tasks? I’m curious why the included python didn’t work and the standalone did.

That's like saying book A is in Spanish and book B is also in Spanish, so why did you like book B better? Python is just a language, what you do with it is what matters. Sounds like they built something completely distinct from Watson and it did the job.

And that’s fair. I’m always curious with how people are using the same or similar tools that my company uses. It’s good to hear common struggles or differences in workflows.

I suspect it's not that the included Watson Python didn't or wouldn't work but rather that the Watson-Python costs money (via paying for Watson) and Python-Python does not.

Given your perspective, a more important question might be what underlying algorithms are being used? I’m only a rookie data scientist but knowing that (for example) a random forest outperformed a neural net in this case, or even just a set of heuristics, is solid information. These can be built in Python, R, even C depending on the application, developer experience and a bunch of other stuff.

I'm looking at it from a couple of levels to support my data scientists. They're primarily python-based but we are adding newer data scientists who have experience with R. Algorithmically, we built a neural network for phenotyping.

Python written by someone new to the industry and eager to please and make a place for themselves VS Python written by body shop consultants who work at a company famous for failing projects that go well over budget and often simply don't work.

The intern probably had direct access to the data and access to the people with domain knowledge inside the company.

The engineers at IBM were probably separated from the problem by many layers of bureaucracy (meetings, project managers, technical specs).

Even working inside a company, without access to the right data and the right people you aren’t going to get anywhere.

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