Job placement program for top students in Stanford's online AI class

Prof Thrun mentioned in the last office hours that there were about 1650 people with "perfect scores" and he seemed a little thunderstruck by that and said he would admit these folks into Stanford if he could, because they are (paraphrasing, from memory)"Stanford quality".

What struck me was how much importance he gave to this metric, which isn't that hard to game on an online offering.

I know a few people (who shall remain nameless) who collaborate and check each others answers and so on before submission, in direct violation of the Stanford policy (and have 100s or close to it), and so probably have received this mail, whereas more "deserving" (note quotes) people who honestly work through the course material may not because they have, say, a 85% or 90% score.

That said, my key takeaway from this is that professors are very impressed by perfect scores irrespective of how you got them. There must be something magical about that row of 100s. Once you set up a grading/ranking system, it is psychologically very hard not to admire people who end up at the top.

I am personally a little dubious that the people with the highest scores would make the best pool of employees, especially given that this is an online course without the programming component, but what do I know?

I wrote Java code for most of the algorithms in AIMA as a side project a few years ago [1], and after I read an online post by Peter Norvig saying a few of his students had tried and failed a few times (to implement the code in Java- Common Lisp code existed and the Python version was in its infancy), I sent him the code and this became the "official" java distribution for AIMA ( though I don't maintain it anymore- the immensely talented Ciaran O'Reilly of the Stanford Research Institute does) and no one ever invited me to Stanford or offered me a cool AI job[2], sob! :-P.

No I am not bitter I tell you, not even the teeniest bit :-p [3]

I wonder how this signalling will play into the upcoming courses? If there are tangential real world benefits to be gained by attempting a "perfect" score, then you can expect a lot more game playing wrt scores and exams.

[1] More about how Peter Norvig shredded my initial code etc here http://news.ycombinator.com/item?id=2405277

[2] though eventually, after a lot more work, it did lead to my working on good ML/robotics etc projects from Bangalore, which is a hard thing to do in the Great Outsourcing Wasteland.

[3] I am really not bitter.

I wrote the code for the hell of it, not to get a job. AIMA was my introduction to the fascinating field of AI. It is a great, great book and it has a lot more material than is covered in the course.

I once did want to go to Stanford and learn from the great profs there, but now in a "mountain comes to Mohammed" fashion, Stanford is coming to me. I don't care about the credentialling - I just want to learn. I took the AI online course and enjoyed Peter's and Sebastian's teaching immensely. Fwiw I should have a high 90's score, (I didn't add it all up) but nowhere near a perfect score.

I'm not sure why there's a down-vote here, because that was exactly the question I had. There has been a system of professors referring their top students to their colleagues for years, and on a small scale I don't see much issue. However, with these large online classes, these professors can stand to be gatekeepers to an incredibly massive - and valuable - source of leads. It doesn't help when there is no true transparency to the process, and a desire for the school to recoup costs - we assume the professor would just send the top 1000 over regardless of source, but what's to stop them from only referring the names of students who, say, enroll for credit and pay the university? Or those who pay some sort of a separate "consideration fee"? I can't put my finger on why, but I'm a little unnerved by this...

My gut feeling is that they weighted specific questions.

I'm sure it's not perfect, and there are exceptions. But I'm willing to be there will be a high correlation between high scores and passion.

Unfortunately the "difficulty" of the ML class assignments was not in the material application of the things learned in the lecture but instead in the gotchas of Matlab/Octave vectorization. Most of the assignments were in the format of: "Here's a forumla that applies to the element, generalize it to the matrix so there's no for-loops." Which, while challenging for those not accustomed to thinking that way, was not a challenge of applying what one learned from the lectures.

The course was awesome for what it was: a chance for the average programmer to get their feet wet in ML, but the fact that it was hosted by Stanford seems to lead to a somewhat humorous irony that it was pretty accessible whereas Stanford's real-life rigor is supposed to be nothing of the sort.