An Interview with an Anonymous Data Scientist (2016)

> On two occasions I have been asked, — "Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?" In one case a member of the Upper, and in the other a member of the Lower, House put this question. I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.

Luckily math has developed methods such as error-detecting/error-correcting codes (to insure against small typos/transmission errors), constructive results on continuity and robustness of functions (i.e. we can prove that if the error in the input data is less than some concretely computable delta, the solution will have an error less than epsilon; or we can ensure that the error in the solution is less than some computable epsilon if we can ensure that the error in the input data "is not too large" (i.e. bounded by some computable epsilon) etc.

In this sense I don't consider the question as that absurd.

Eh, pretty disappointing interview. It doesn’t tske a team to utilize gpu computing, it takes one person and I’ve done it. Also, you can’t complain about there being no strong-ai companies and then list accomplishments of strong-ai companies.

I personally don’t like the phrase data scientist but I get it and I get why it’s science as opposed to engineering. I personally like the split between machine learning, BI, and data engineering.

I enjoyed his comments on Tensorflow.

> It’s really bad to use. There’s so much hype around it, but the number of people who are actually using it to build real things that make a difference is probably very low.

I wonder how many data scientists out there are actually developing Tensorflow models for a mission-critical project at work. I'm not. I have used Tensorflow successfully within my personal projects, but I've yet to need it for anything "real."

I think we're just used to computers advancing noticeably on a regular basis: "Is this year's iPhone better enough to justify an upgrade?"

Also, we judge the difficulty of things by our own experience. It took us ~1 billion years to get to the point where we could communicate abstract ideas and play chess. These were once believed to be the challenging problems in AI.

It turned out that chess is easy we're just relatively bad at it.

I work at a tech company and one of the things I have recently noticed is how ML and AI terms are being increasingly used by the business people. The guys who have no technical understanding, these are accountants or marketing guys saying we should ask tech team to design ML to solve these problems. Its as if ML is a thing to through at every kind of imaginable problem and it will be magically solved. I believe a lot of this has to do with PR around this by big tech companies. Take for example, the recent alpha zero vs stock fish PR, it has been spun around by Google in a way as if it was some magic. You hear a lot about how it took just 4 hours and I find it hard to explain to people that 4 hour time is meaningless. It is about how many games it could play in that time. Moreover the match happened between two systems on a different hardware and that is a big difference and also the fact that it used a arbitrary type of time control of, 1 min/move. Again this can make big difference but it is a big struggle to get past this PR fluff. To be clear, I am not denying the advances made by deep mind, I just want people to understand that it has come on back of probably the the world best team of scientists alongside state of the art Google designed hardware and incredible monetary resources of Google.