221–230 of 725 posts
I'm sure they'll get right on powering up their computer from the hamster wheel, Paul.
My heart goes to all the kids out there that didn't get the fair shake let alone fair access to tech that gets these condescending "learn to code/learn to LLM" bootstrappy talks from rich pricks that don't know what life really can be like for a lot of American kids out there.
As a platform engineer being based mainly out of Australia/Hong Kong, opportunities seem to be getting less unless targeting high frequency trading or banking.
It seems like building a startup with the help of some AI tools might be the best bet.
Earlier quoted context omitted.
Depends if you’re 17 with rich parents or not.
This only changes whether you are naive enough to believe “honest” business means anything in today’s age. If anything, I worry being honest is holding back smart people who try to compete in a rigged game.
It would be a good idea for young people to deeply know how these programs work. Not so that they can spend their career building them, but so that they can approach the next class of problems we'll all start trying to solve, with intuition all the way down to the weights and underlying mathematics. And also, to develop a healthy intuition of when "Just LLM it" will not be the right choice.
"Build an OS" wasn't a common university project because we were all expected to go out and work on Windows, but because understanding the bare-metal firmware for a computer helps you deeply understand how to intuit building for a whole class of problems.
Earlier quoted context omitted.
Yes. It's like looking at the (Apollo) moon rocket launch and then suggesting teenagers should learn to build rockets in their garages for the coming space age. It is viable as a toy project, but there are vanishingly few career opportunities.
It's like looking at the early internet and then suggesting teenagers should write browsers as their projects instead of webpages.
I think a problem a lot of people are grappling with here is that due to LLMs and AI generally, it’s basically impossible to predict what the future will look like or what jobs will still be around. I’d probably say something like: do something you enjoy and seems like it might be useful, but accept that the pace of change may mean that whatever you study ends up being irrelevant. Whatever solution there ends up bein…
Earlier quoted context omitted.
+1, I have a friend, math PhD that's been working on ML research 5+ years in London yet he has not been able to find any position. The only jobs that he found he was highly over qualified or paid very little. In any case, it doesn't look like there's this crazy rush to hire all ML talent, even the one that understand the math and technology deeply.
I mean it makes sense right? Anthropic for instance has like, a couple hundred staff in London with plans to expand to somewhere just shy of a thousand. There are far far more ML/Maths/CS/Stats PhD's than there are openings. Especially in London there is no shortage of suitable candidates given Cambridge/Oxford/Imperial/UCL are surrounding it. 2% of the UK population has a PhD alone...
Citation needed. I'm guessing you're not counting children.
Earlier quoted context omitted.
I suspect most who worked at Google did not work on Google infrastructure before getting there.
No, but the skills Google needed (back then) were just normal programming and sysadmin skills scaled up. I had eight interviews that covered Linux sysadmin, programming, debugging, networking, maths and more. If Google hadn't wanted me there'd have been plenty of other companies who needed those skills. If you look at model training jobs a lot of the work at this point is creating RL gyms (normal programming work), b…
I'm not sure why it's like this. If you look at the real world, you have stuff like ggml, which is about as hardcore as it gets in the LLM space, and it was made buy just a guy. Same for this like ComfyUI
If you get enough academics in a place, they tend to close rank, and not let anyone in without the same credentials. Data science used to be like this, they were constantly on about how you need a Math Phd to even apply, yet when I met these guys IRL, most of them were just running Python math libraries.
These previous examples show that if you understand at least a part of the problem space, you can 100% contribute without academic credentials.
There's this dilemma where in theory there's a ton of demand for engineers that can do real LLM machine-learning, but in practice there are very few available positions and entrepreneurship opportunities. The reality is that an incredibly small minority of companies in the world do any real training or optimisation. It's unnecessary and inefficient for most purposes unless you are fully dedicated to being an LLM comp…
I can see the point. It's unlikely that a 2.4T LLM will be integrated into, say, a pesticide drone. You'll still need some kind of LLMs to achieve maneuvers that "normal" programmings can't achieve.
But what if everything basically turn into that? Essentially, instead of build me a web app to solve X and do Y, build me an LLM to serve X and do Y. (unless the current LLMs are able to do it end-to-end but then they can hardly write coherent software/personal opinion).
There's this dilemma where in theory there's a ton of demand for engineers that can do real LLM machine-learning, but in practice there are very few available positions and entrepreneurship opportunities. The reality is that an incredibly small minority of companies in the world do any real training or optimisation. It's unnecessary and inefficient for most purposes unless you are fully dedicated to being an LLM comp…