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The Machine Learning Job Market

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Re: The Machine Learning Job Market

#271
post #47

Earlier quoted context omitted.

These outliers are rare but they do exist. I knew someone at Google who was hired as L3 straight out of college (as all non-PhDs are) and got promoted once a year to L6 (Staff) so 3 years. He got promoted to L7 2 years after that. It's a rare combination of talent and the right circumstances but it does happen.

I tried to hint at this by using quotes, I don't doubt that L6 is possible. But, please elucidate, are there L6s at Google making "low 7 figures"? From levels.fyi, there are no such reports. The average is about half and matches what I know from other companies. Those that are approaching 7 figures have at least a decade of experience. Anyway, the pay he describes is much closer to L8.

Are those SWE paybands or a Research Scientist pay band?

That might matter.

Re: The Machine Learning Job Market

#272
post #22

Interesting that Tesla gets its own row in the pro/con table while the faangs get lumped together.

Why is this interesting? Tesla is... not a FAANG company? Not only is there no 'T' in FAANG, but the industry/product is completely different. Or maybe the author just wanted to make a joke about coffee. Who knows.

Not an internet based company might be also factor

Re: The Machine Learning Job Market

#273

I don't want to derail the conversation, but OPs career path really stood out to me. He graduated in 2016, worked at Google in Bay Area, and now is joining a startup at a VP level. I graduated in 2008, obtained a PhD in 2014 in a no name EU university, worked in odd companies for a while and joined FAANG 4 years ago as a mid level developer, where I am still ATM. Looking at this disparity I wonder what could be possi…

How about Anthony Levandowski?

Re: The Machine Learning Job Market

#274

Earlier quoted context omitted.

1960s were 60 years ago, we're talking about AI, a fast paced field in the last 15 years.

Definitely! There was a time when compilers were part of AI research. Now they are just another tool. Same with DL, they are amazing tool. We need them and they provide value if used correctly. I just didn't want to call it as "intelligent" and use this as basis for defining "intelligence." We can call them something else. It's learning to do a specialized job as intended and in "intelligent" manner. But it is not in…

> I just didn't want to call it as "intelligent" and use this as basis for defining "intelligence." We can call them something else.

TL;DR We're also mostly brute forcing our way to discoveries. We're not that smart.

People too are relying on cultural handouts, maybe most of our intelligence is also "something else". Before electricity was discovered we had superstitious ideas about electrical phenomena. Before germ theory was discovered we were getting sick and dying like animals, helpless. Not so smart, even though it was a life and death situation for us.

It's easy to be "intelligent" when you're given the solutions beforehand by culture. ML learns from the same culture, like 99.99% of us who can't discover new things even to save our lives. And many of our discoveries are a gradual work of trial and error, we don't go directly to the target but stumble/brute force our way to it.

There was a news story recently title "Elegant Six-Page Proof Reveals the Emergence of Random Structure". The funny part is how the authors stumbled onto the amazing solution after many many unsuccessful trials by all the math community. Not a great sign of intelligence when you have to rely on chance so much and so many fail before one succeeds.

This tells me we're also mostly doing "something else". Intelligence means solving novel problems with few attempts, not spamming our attempts to death until something comes out. ML research looks more like spamming than intelligence too.

You know what else looks like spamming? Evolution. It's a blind search process brute forcing the problem of self replication for billions of years. It created us and everything else in one run but it's not very intelligent, it just spams a lot.

Re: The Machine Learning Job Market

#275

Earlier quoted context omitted.

Eric Jang is top ML talent, these numbers are accurate. I work in ML and have followed his work for years

He claims to be solving general intelligence in 20 years. Your advocacy is not enough to convince me.

He says AGI could happen in 20 years, not that he will single handedly manifest it into existence. That seems like a reasonable timeline given the field's current pace and may even be conservative.

Re: The Machine Learning Job Market

#276

Earlier quoted context omitted.

Pretty much everything in the high end large language model area is off limits to people without access to a supercomputer (we're talking hundreds of A100s or several $100k in cloud computing equivalent). Open Source efforts like BigScience may open up downstream tasks for normal people, but the forefront of this research is no longer accessible to individuals.

You might be surprised to hear that the KenLM language models that are used for speech recognition are actually trained on-disk using CPU. With a €149 monthly bare metal server, I could train my own LM on OSCAR DE and EN. Where I do agree with you is that transformer-style text generation models in the billion parameter range are off-limits for hobbyists. But that's only a tiny part of the useful applications of AI.…

KenLM ist not a neural network and instead a purely statistical n-gram model. So it's no surprise that it would be faster on a CPU in many cases. However, as soon as you have to deal with noisy data, KenLM gets blown out of the water by DL architectures like LSTM and, more recently, Transformers. There's a reason why purely statistical models have seen very little progress in the last 10 years (KenLM was published 11 years ago) and that reason is that this "noise" is basically just a consequence of the central limit theorem applied to data with a huge amount of nuance - much more than any human coded feature vector could ever account for.
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