A cloak-and-dagger tale behind an anticipated result in particle physics
sciencemag.org
sciencemag.org
> A notable early use of a blind analysis in physics was in a measurement of the e/m of the electron, by Dunnington. In this measurement, the e/m was proportional to the angle between the electron source and the detector. Dunnington asked his machinist to arbitrarily choose an angle around 340°. Only when the analysis was complete, and Dunnington was ready to publish a result, did he accurately measurethe hidden angle.
[1] https://www.slac.stanford.edu/econf/C030908/papers/TUIT001.p...
They won't be able to invert a binary tree on white-board, or a hacker-rank tab, in 30-45 minutes.
They're clearly not the smartest ones.
I wonder if this is somehow a scheme to reduce employee turnover in general - to get salaries under control. I doubt it could so be planned. I have no doubt it has some effect in that direction as it adds friction and to the job hunters process.
Its actually how inbelieve Elon got successful .. its his physics training.
Ive met physics students in higher semesters ... and they were down right better programmers too (thry built things they used, they really hacked programs together) while we spent our time trying to ... well i dont know what, but somethig wasnt right. This wasnt CS50 though .. just a German uni.
One of the reasons physicists like programming so much, is that it's the modern duct tape for putting disparate things together, and there's so much stuff out there to use.
Physicists are always among the earliest users of technologies -- vacuum tubes, transistors, and successive generations of computers. At my college, the earliest adopters of personal computers were all physics professors. The one exception was a humanities prof whose kid happened to be a physics major.
The moment you touch the test data you have tainted it because your brain can do a crude search on the space of solutions and reconcile them after the fact. This is why any preprocessing and cleaning is parameterized on the training set and then applied to the data, that also includes the class of models you can consider and is why you never select final models based on their performance on the test set; it becomes biased.
[1] probability of getting a good model.
[2] how much the empirical performance underestimates the expected performance.
[3] the number of models you can select from a particular class of models.
Unluckily for them their online literacy says I'm too stupid to write simulation code them so their loss.
But imagine the FPGA-grunt in those things!