Can this silly meme die already? Maybe it's understandable coming from an economist who values education for no other reason than it's economic effects, but it's strange coming from someone who clearly understands the value of personal development.
That is, for example, why it is possible to find people presumably seriously suggesting to:
3. Flashcard the Deep Learning Book (4-6m)
4. Flashcard ~100 papers in a niche (2m)
I mean, it's clear to me that the language deployed in the article is ostensibly about teaching yourself to do machine learning research when what it's really discussing is how to get hired by one of the companies that are curently paying six-figure salaries for machine learning engineers etc.
Or I'm just old and cynical. Wait, let me find my false teeth so I can chew that over.
However, there is a lot more to AI than high-school maths and I don't just meean -more maths. I mean knowledge, lore if you like. It's a field with a long history, stretching back to the 1930's even (before it was actually named as "AI" in Dartmouth, in the 1950's). A lot of very capable people have worked on AI for a very long time and have actually advanced their respective sub-fields each with leaps and bounds and it's not very sensible to expect new leaps while being completely clueless of what has been achived before. You can't stand on the shoulders of giants if you don't know that there are giants and that they have shoulders you can stand on.
Unfortunately, most people who enter the field today know nothing of all that, or even that there was an "all that" before 2012 (if they even know what happened in 2012; and to be honest, one wouldn't understand what 2012 means if one doesn't know what came before). So on the one hand they are not capable of making leaps and on the other hand they don't even know what a leap would look like. And probably think that a "leap" is a 10% improvement of the state of the art for a standard classification benchmark.
I agree with you though that what is needed to make leaps in AI is curiosity. Lots and lots of curiosity. Vast amounts of curiosity. Curiosity of the kind that you only find in people who are a bit zbouked in the head. Or just people who have a lot of time in their hands, to study whatever their fancy tells them to.
So- not the kind of person who flashcards The Deep Learning Book, if nothing else because that means the person doesn't have the time to, you know, actually read the damn book well enough to grokk it.
I mean seriously, what the fuck is it with the bloody flashcards?
I know I’m just speaking from my own experience and what works for me doesn’t necessarily work for everybody. But my claim isn't that everyone should do as I did, my claim is that you're wrong that a self-taught ML researcher would necessarily only be able to make superficial contributions because they are bad at math.
MIT has takes 3,000 students, Canadian universities take 30,000 students. (Remember Canada has 30 million people and US has 300 million.)
- https://web.mit.edu/facts/enrollment.html
- https://www.univcan.ca/universities/facts-and-stats/enrolmen...
But I'm not sure what that has to do with buying expensive formal education credentials.
That may be the case for small colleges with high tutors-to-student ratios, but it's not the reality in many of the behemoth colleges / universities that feed warm bodies into jobs.
I've seen first hand classes with hundreds and hundreds of students, where everything worked like an assembly line. Standardized tests with zero feedback, mentors were student TAs, a class or two above you, and they had been assigned to tens of students themselves - while correcting hundreds of homework / problem sets on the side.
When you go to school like that, it can quickly feel like you're just another name on a list, with some avg. grade on the side.
And it's only going to get worse with the ever-rising number of enrolled students.
If it's easy to see that a piece of output (a paper, code library, machine learning model, whatever) or a job candidate is great, then the credentials behind it don't matter much. However if it's challenging to evaluate quality, then people will shift to looking at secondary signals such as credentials, price, etc.
As to why a lot of economists go with the signaling model of education, well, it might just say something about their field and how much they got out of their own educations.
https://en.wikipedia.org/wiki/The_Case_Against_Education#Rev...
Bryan Caplan back and forth with Noah Smith on the book: https://www.econlib.org/archives/2015/04/educational_sig_1.h...
Bryan Caplan back and forth with Bill Dickens on the book: https://www.econlib.org/archives/2010/08/education_and_s.htm...