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>You've actually tried that? Even in a STEM field? So you are speaking from first hand knowledge? Me? no, have you? I don't have a degree at all. I've worke
by lsc 13y ago
>You've actually tried that? Even in a STEM field? So you are speaking from first hand knowledge?
Me? no, have you? I don't have a degree at all. I've worked with a few PhDs, though, and I have several family members in the 'failed academic' class who still putter about teaching a class here or there but mostly work in industry.
I don't think they could be classified as failures by any reasonable measure, well, except for the failure to make tenure.
I mean, if you measure success by job stability, then yeah, academics are super rich, and working in industry is a failure... but that's not how I see things.
More to the point, if it was really that big of a deal? you could just leave it off your resume. It's certainly better than a felony in that regard.
- graycat 13y ago> have you? Yes. Been there. Done that. Have the T-shirt. > More to the point, if it was really that big of a deal? you could just leave it off your resume. It's certainly better than a felony in that regard. A time gap on a resume is about as bad as a felony conviction, really, for looking for a job, worse. A period as a prof, in a slot that would require a Ph.D., does say "I've got a Ph.D.". If leave off the prof slot, then that is another gap on the resume. Such gaps are resume fraud and into the circular file, that is, as bad as a felony conviction. Actually, a felony conviction is likely easier to hide during a job application than a Ph.D. No, I don't have a felony conviction. Indeed have never been arrested or convicted of anything except minor traffic violations. What I said is true: A Ph.D. in a STEM field (maybe no one would resent a Ph.D. in art history) outside of academics or some industrial research lab concentrating on publicity or luster is a job killer. At one point, with a Ph.D., I sent 1000 resume copies and got back only silence or laughs. Earlier in my career, before my Ph.D., once I sent a few resumes and in two weeks went on 7 interviews and got 5 offers. Yes, one of my dissertation advisors is President of one of Stanford, MIT, CMU, Cal-Tech, or Georgia Tech; no help. The Chair of my oral exam is a Member, US National Academy of Engineering. No help. Math SAT 768. GRE Math 800. No help. One reason I'm an entrepreneur is that due to my Ph.D., in engineering from some applied math from a famous, world class research university, I'm unemployable at anything better than stocking shelves at Wal-Mart. Literally. No joke. Period. 'Data mining?' 'Big data'. 'Machine learning'? I have some of the very best background in such topics. Still, just unemployable. Period. Computing? One of the best backgrounds, long at IBM Research where I won an award for my software, algorithm, and architecture contribution to IBM's leading artificial intelligence language. Published in computer science, mathematical statistics, and artificial intelligence. Long background in practical software. Right: Not in C++, Ruby, or Python. So, unemployable. So I'm doing a startup. There my Ph.D. is crucial -- the core of the work is some secret sauce I did from some original research I did that very much needed my Ph.D. Net, it's much easier for me to be the CEO of my own company, and likely get a few million in venture funding, and be wealthy if the company does much of anything, than just to get a job that will let me buy a house. Literally. In case my business is successful, mayhe Ph.D. will have paid off, but not for a job. "Paid off" needs some grains of salt since the delays cost me house, wife, family, etc. Right: Thanksgiving was eating alone. No, I won't be sending any children to college, much less a Ph.D. Actually early in my career a Ph.D. seemed like it would help for non-academic jobs, but that was a boom time around DC with work heavily for the US DoD. Once the Cold War was over, that work went away. A good Ph.D. in a STEM field sets one apart in a way that means that they might be able to get a job stocking shelves at Wal-Mart. Sorry 'bout that.
- nnq 13y agoOff-topic, but really, what is "IBM's leading artificial intelligence language"?
- PeterisP 13y agoProbably Prolog, as IBM's Watson tech seems to use it - but yeah, Prolog skills are a very specialized niche that's used very, very rarely in industry.
- graycat 13y agoAt its time, KnowledgeTool. It's a forward chaining expert system language using the Forgy RETE algorithm and is a pre-processor to PL/I. It was marketed as for artificial intelligence but, really, as it was created by our group at Yorktown Heights, was mostly intended to automate monitoring and system management for server farms and networks. One of my contributions was architecture, algorithms, and code for rule-subroutines. The core of the work was to understand the PL/I stack of dynamic descendancy and to exploit it to keep the state of the user's rule subroutine calls that had yet to return while calling a run-time package with the RETE algorithm and then return to the user's code with it's state intact. It was a fast idea one afternoon and then a long night typing. When I got some sleep and back to the office, our programmer trying to finish the implementation of rule subroutines was done that day instead of some weeks, and the result was much cleaner. In addition, I cooked up some new ideas for anomaly detection in data from the envisioned server farms and networks. It's an ambitious approach to behavioral monitoring (try to say what is normal and declare anything else as an anomaly and potential problem to be investigated -- as in the old Herve Debar post at SANS, such monitoring has had false alarm rate too high, and I solved that problem) where a user gets a knob to turn to select false alarm rate exactly in advance. The work was ambitious when I did it because it can use quite a lot of data; now the cost of so much data is so low that it is from trivial down to no concern. As applied probability, it's a statistical hypothesis test that is both multivariate (use more information and, thus, get higher detection rate of real problems) and distribution-free (make no assumptions about probability distributions) -- maybe still the only example of a test with these two properties. The paper was published in Information Sciences. I had some data from a cluster at Allstate and showed with that data that the false alarm rate calculation was correct. And with some additional simulated data, showed that the detector was darned sensitive (intuitively) to anything different. Basically could have a checkerboard with normal performance on the white squares and anomalies on the black squares, and the detector will figure this out, including if the checkerboard is in 20 dimensions. Due to such checkerboard examples, data in fewer dimensions would be useless; so, in this case, the multidimensional aspect was crucial. While such a checkerboard is unlikely in practice, the practical point is that the detector is adaptive to quite bizarre normal performance. Indeed, we should expect that the multidimensional geometric region of normal performance will, often in practice, be essentially a fractal -- my work does well approximating fractals. Now that there is so much data on real-time performance from high end server farms and networks, my work should be deployed, but I doubt that it has been.