Tackling a complex problem (still relevant today) at an early age, getting great results and describing the solution clearly/concisely.
My master thesis was ~60 pages long, and was probably about 1/1000 as useful as this one.
¯\_(ツ)_/¯
Comparing your journey to others’ is pointless.
There is a very small number of truly gifted people who end up discovering things on their own at a young age. Most gifted people who discover something do so with the benefit of a mentor who can work with them to refine their talent into skill.
My masters thesis sucked largely because I tried to do it on my own and didn't even pick an advisor until I was almost done. At that point all they could do with my mess was to say "well, this is a decent descriptive paper and we need more descriptive papers in the field," and then give me proofreading comments. I didn't have a damn clue what I was doing, the end product was mediocre, and I didn't learn nearly as much as I could have. I'm not an outstanding talent by any means, but not seeking out mentorship in school is one of my only career-related regrets.
The fact of the matter is that that some people are deprived of mentorship, either through bad personal decision-making or through bad academic infrastructure. These people have a much harder road to expertise and success than the people who were mentored.
My experience, mostly in grad school, was that anyone editing my work wanted more verbiage. If you only needed a short, one-sentence paragraph to say something, it just wasn’t accepted. There had to be more.
Jeff Dean is an uncommonly good communicator. But he also benefited from being allowed, perhaps even encouraged, to prioritize effective and concise communication.
Most people aren’t so lucky, and end up learning that this type of concision will not go over well. People presume you’re writing like a know-it-all, or that you didn’t do due diligence on prior work.
I _never_ got that feedback. My mentors all emphasized economy of language and nobody cared how "thick" my thesis was.
This is a pretty amusing story about verbiage.
Back in the old days, you would send a manuscript/research article to colleagues/friends by _snail-mail_ to get their feedback. You'd wait a month, and maybe they would mail a 'red-inked' copy of your manuscript back to you.
My Ph.D. advisor sent out a draft to a colleague who was famous for being harsh with the red-ink.
After a month, my advisor receives the manuscript in the mail.
* He turns to page 1. No red ink!
* He turns to page 2. STILL no red ink! [He must looove the paper]
* Keeps turning pages (no red ink!!).
* On page 10--in red ink--is written, "Start here."
So this puts the reviewer in a situation with misaligned incentives. They might prefer to tell you to prioritize concise communication, but believe the risk is high that such a thing will get vetoed by the committee for Dilberty reasons, and thus their feedback gets optimized for what the committee will superficially think.
When the committee is mostly attentive professors, this isn’t so bad and everybody is aligned on short, to-the-point style.
But my experience is that this is hardly true. Maybe one committee member will be an attentive technical authority, sometimes only your advisor. The others will be deans or directors of various sorts who view it as an administrative chore to even have to sign off, and probably farm that review out to grad students or adjuncts, who are far more likely to take a capricious point of view about e.g. heavy literature review or conclusion sections.
That shouldn't add too much. No more than a few pages. It would still concise but then also a scientific work.
Also, I had to be submit 3 identical hard-bind copies of that bullshit report.
I had a friend who's advisor made them make everything longer the way you describe, theirs was in excess of 100 pages. (IIRC this advisor had suggested that while the guidelines say ~50 pages this was the bare minimum sufficient for a pass).
I guess it depends a lot on your advisor.
I value conciseness dearly, and prefer quality over quantity in scientific writing, i.e. I would accept incredibly short theses, if the content is sufficiently presented (reproducible and comprehensive), and most of all, contains a valuable contribution.
The reason I typically have to request "more verbiage" and an own section on the state of the art, is because I need to force my students to confront their sitcom ideas with the history of "what has been done before, and what the actual current problems are".
Unfortunately, the approaches of most students are neither new nor particularly interesting in this regard.
It's strange to expect an undergrad to do new and interesting work when they haven't even finished their basic education in the field. Solve problems that are easy but not important enough for professional academics, sure. Do an application of a standard idea in a specific environment (like porting an pp to Android), sure. But not new approaches to the field.
One thing most people don't get is that Dean is basically a computer scientist with expertise in compiler optimizations, and TF is basically an attempt at turning neural network speedups into problems related to compiler optimization.
I'd like to thank my undergrad university for hosting my undergrad thesis for 25 years with only 1-2 URL changes. Some interesting details include: Latex2Html held up, mostly, for 25 years and several URL changes. The underlying topic is still relevant (training the weight coefficients of a binary classifier to maximize performance) to my work today, even if I didn't understand gradient descent or softmax at the time.
Kudos to University of Minnesota (@UMNews) Honors Program. Earlier this year, I asked Prof. Vipin Kumar, my advisor for this work, if he still had a copy, since I had lost my copy. He didn't, but checked with the Honors Program and eventually got a very nice response saying: "Jeff and Prof. Kumar, Here is a pdf copy of the thesis in question. We digitized our physical library about 8-10 years ago and no longer keep hard copies of anything. Hope this is what you are looking for."
Lots of good work with neural networks was done back then:
A learning algorithm for Boltzmann machines
DH Ackley, GE Hinton, TJ Sejnowski - Cognitive science, 1985
Learning representations by back-propagating errors
DE Rumelhart, GE Hinton, RJ Williams - nature, 1986
Phoneme recognition using time-delay neural networks
A Waibel, T Hanazawa, G Hinton, K Shikano, KJ Lang - Readings
in speech recognition, 1990The interest in NNs was ignited (in part) by this double volume collection of essays called "Parallel Distributed Processing" edited by Rumelhart and McClelland.
Dean even cites them. And, if you read the contributors, it contains many (though not all) of the heavy hitters.
Reading back on it, it will sound very familiar. All the amazing breakthroughs: object recognition, handwriting recognition etc all seemed to be there. But all that rapid progress just seemed to stop. There was this quantum leap and then you were back to grinding out for even 0.1% improvement.
For those who stuck through the second winter, things obviously paid off.
The intro essay is online:
Then when the data explosion started during the 00s, it laid the groundwork for the NN comeback.
My entire career has consisted of reimplementing bits and pieces of things I've previously built all the way back to high school and then reimplementing whatever was new on the previous round in the next one.
https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/lin...
https://github.com/apple/darwin-xnu/blob/master/libsyscall/m...
Also, not all intelligent American kids can or want to go to elite schools, even if they are academically qualified. In the U.S., you often hear stories of kids turning down really good schools for ones they felt were a better "fit" (financially, culturally, etc.). And unlike the rest of the world, elite colleges in the U.S. are often private and expensive. Despite need-blind admissions, not everyone can afford them without going into heavy debt. (many middle-class parents make just enough money for their kid to not qualify for substantial financial aid).
So kids go to schools they can afford.
One of my college professors (who attended Princeton and MIT) once told me that in his observation, the top 5 percentile students in (good) state schools aren't that different from the kids who went to Princeton or MIT. I didn't believe him at the time, but having worked with different folks over the years, my experience inclines me to believe that there's some truth in that observation.
Owing to its population and economy, the U.S. has a large enough talent pool that the top percentile students at large, well-funded state schools (of which UMN is an example) are plenty smart. If you were to meet the really smart top-5-percentile kids from such state colleges (I have), you'd have no doubt that many of them could have attended MIT or CMU.
To be sure, good colleges can give you a headstart in life -- but it's what you do with that advantage that counts.
--
Examples of smart computer folk who went to decent, but non-elite schools for undergrad:
Doug Crockford (Javascript), SFSU
JJ Allaire (ColdFusion, Rstudio, etc.), Macalester College
Ward Cunningham (Wikis), Purdue
Rich Hickey (Clojure), SUNY Empire State (though he did go to Berklee College of Music)
John Carmack (Doom, Quake), U. Missouri Kansas City
Sergey Brin (Google), U. Maryland College Park (before Stanford)
Larry Page (Google), U. Michigan (before Stanford)
Dave Cutler (VMS, Windows NT), Olivet College
Bram Cohen (BitTorrent), U at Buffalo
Ryan Dahl (Node.js), UCSD, then U Rochester
Larry Wall (Perl), Seattle Pacific U (before Berkeley)
Alan Kay (Smalltalk, windowing GUIs), U Colorado, then U Utah.
Brendan Eich (Javascript, Mozilla), Santa Clara U (before UIUC)
> To be sure, good colleges can give you a headstart in life -- but it's what you do with that advantage that counts.
I just graduated undergrad from a state school (Rank #49 in CS) but I'm still pretty skeptical of this fact.