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My story as a self-taught AI researcher

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Re: My story as a self-taught AI researcher

#111

Post by TrackerFF »

exdsq wrote: post #14

Survivorship bias or reality: 3 months learning FastAI, 3-12 months personal projects and consulting, 2 months flashcards of ~100 papers, 6 months to publish a paper What does he mean by ‘paper’? A Medium post? NeurIPS?

It could be that he's an exceptionally smart and driven guy, that just happens to pick up things really fast; I've seen those IRL myself, but not in only a year or two.

But yeah, going from 6 months of programming experience with C, to a Deep Learning internship - that sounds a bit far stretched.

Re: My story as a self-taught AI researcher

#112

Post by randomdata »

ynx wrote: post #89
jshowa3 wrote: post #81

I don't know why people think getting a credential does nothing or that people "copy and paste" the assignments. Sure it may be possible, but what prevents people from copying and pasting public git repos? Either way, this whole focus on "portfolios are everything and credentials are meaningless" spits in the face of all the work I did to get my university education. And it didn't involve "copying assignments". And y…

yeah, as someone who had a partial education, I totally get the value of a degree, so whenever someone says "higher education is useless" I read it as "However successful I am now, I wasn't the kind of person who would succeed in school then"

Whenever I hear someone say "higher education is useless" I hear it in reference to the "common wisdom" that higher education increases incomes. In which case they are absolutely correct. Higher education is useless in achieving that. Incomes have held stagnant for many decades, even as more and more of the population attain higher levels of scholastic achievement. Mathematically, incomes cannot not remain stagnant if more money is earned as a result of attaining higher education.

I'm not sure I have heard anyone claim that "higher education is useless" in general. Education is never useless in general.

Re: My story as a self-taught AI researcher

#113

Post by rumanator »

JamesBarney wrote: post #66
rumanator wrote: post #54

Earlier quoted context omitted.

> Mind providing some concrete examples? It's like the difference between, say, applied and pure sciences. One is focused on developing and studying new algorithms, while the other is focused on using algorithms developed by someone else in practical applications. To put it differently, it's like physics vs engineering. A physicist might develop new structural analysis methods, while the engineer would use those meth…

I understand the separation between physics. But most structural analysis methods are discovered by professors of structural engineering and not physicists(and much of it is empirical). But I was asking because I was specifically looking for concrete examples in deep learning.

> I understand the separation between physics. But most structural analysis methods are discovered by professors of structural engineering and not physicists(and much of it is empirical).

You're confusing the occupation with the role. Just because your job title is professor of structural engineering it doesn't mean that you are not studying "matter, its motion and behavior through space and time, and the related entities of energy and force."

https://en.wikipedia.org/wiki/Physics

Re: My story as a self-taught AI researcher

#114

Post by tasogare »

mendeza wrote: post #27
woah wrote: post #3

Earlier quoted context omitted.

On the other hand, the lack of data for independent researchers may encourage the development of low data techniques which is much more exciting in the long term since humans are able to learn with much less data than required by most machine learning techniques

I think an exciting area that can innovate the lack of data is domain randomization, and synthetic data generation. Slides from Josh Tobin is a great introduction: http://josh-tobin.com/assets/pdf/randomization_and_the_reali... http://josh-tobin.com/assets/pdf/BeyondDomainRandomization_T... And a really cool project implementing synthetic generation of text in images: https://github.com/ankush-me/SynthText

How it that useful for subsequent learning? The output is random words that doesn't even forms phrases or sentences and has no relation with the image.

Re: My story as a self-taught AI researcher

#115

Post by tprice7 »

codebolt wrote: post #104
tprice7 wrote: post #82

Earlier quoted context omitted.

This is logically independent from any claim about the value of formal education. I speak from experience that an undergraduate degree is not necessary in order to gain a firm grasp of undergraduate level math. Happy to elaborate if that is desired.

I'm sure it's possible to learn on your own, but I think most people would benefit from taking a few years of their lives to dedicate to learning surrounded by a community of teachers and like-minded classmates. Learning on your own requires a lot of discipline and dealing with solitude.

I found it to take much less discipline actually. I find an unstructured and curiosity-driven approach to learning math to be much more enjoyable and effective than the typical school approach. You are right about the solitude issue, although I’m unsure about whether this approach to learning is an intrinsically lonely pursuit or if there’s a possible society where it’s not.

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.

Re: My story as a self-taught AI researcher

#116

Post by eanzenberg »

AlanSE wrote: post #24
cygaril wrote: post #15

Earlier quoted context omitted.

Or our entire evolutionary history of data.

...which fits into a size of less than 700Mb compressed. Some of the most exciting stories I've read recently for machine learning are cases where learning is re-used between different problems. Strip off a few layers, do minimal re-training and it learns a new problem, quickly. In the next decade, I can easily see some unanticipated techniques blowing the lid off this field.

I’m not sure our genetics encodes all the physics of being a person. A human brain is so complex we’re not even close to simulating it on silicon

Re: My story as a self-taught AI researcher

#117

Post by curiousgal »

LemonAndroid wrote: post #36

I don't see how this is self-taught, as the person got picked up for an internship and could learn from experts first-handly. FAKE.

He also studied at 42, which is most likely why he got picked for the internship to begin with. I don't get this self-congratulating BS, guy says he toured the world (good luck doing that with a shitty passport) and was named king of a village in Ghana (right..). I guess people who get lucky have to always go to great lengths to justify and spin that.

They're free to do so of course, but they should not give advice based on it.

Regardless of that, I suppose the bar for being a "researcher" has been stooped so low. According to this guy publishing an ML paper is equivalent to writing a blog post or making a video about "AI".

Re: My story as a self-taught AI researcher

#118

Post by floodyberry- »

octokatt wrote: post #51

Was anyone else really put off by the congratulatory tone of the article, and the #Quirks list on the resume?: https://github.com/emilwallner/Emil-Wallner-LinkedIn-Resume#...

Not enough, since his entire gimmick appears to be 'selling himself' and it's gotten him gainful employment at somewhere that you think would know better

Re: My story as a self-taught AI researcher

#119

Post by solveit »

JDiculous wrote: post #106
solveit wrote: post #101

Earlier quoted context omitted.

> Absolutely nothing you learn in a college education you can't learn yourself for free on the internet. This is categorically false. Face-to-face time with an expert is incredibly valuable and incredibly expensive outside of an academic setting. In fairness, you have to show some initiative in college to get quality face-to-face time with a professor, but it still takes a lot less motivation than self-studying a com…

> This is categorically false. Face-to-face time... Just because talking to an expert is valuable doesn't mean you can't learn it for free on the internet. Also most undergrad curriculums are teaching old stuff - not exactly cutting edge knowledge requiring face-to-face one-on-one time with an expert in your field. It's not as if the alternative to 4 years of undergrad and $100-250k in tuition + living costs is just…

I maintain that there still exist things that you can only learn via osmosis. Sometimes, books and MOOCs just will not do.

Also,

> It's not as if the alternative to 4 years of undergrad and $100-250k in tuition + living costs is just teaching yourself the same arbitrary curriculum alone in your room for 4 years getting a degree in some random field learning things you never actually use in the real world. One could instead intern or work, and not only potentially learn significantly more relevant and lucrative real-world skills for free, but actually get paid to do it.

I mean, sure you can not go to school and do different things, and it might even be a good idea, but that's a far cry from the original claim, which was

> Absolutely nothing you learn in a college education you can't learn yourself for free on the internet.

Re: My story as a self-taught AI researcher

#120

Post by z3t4 »

narenst wrote: post #2

This is a really good time to be a Independent Scientist (aka Gentleman scientist) in this field because how nascent deep learning and similar techniques are. It requires a lot of trial and error and time/cost investment to bring the AI techniques to the masses. The FAANGs are trying to hire all the top talent (including Emil who wrote the post) but I believe these independent researchers will be the one finding new…

> colorizing b&w photos

You will have unlimited training data. But its very difficult task even for humans. Its like trying to reverse a hash. Also a lot of information is lost when you store a color digitally.

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