361–370 of 725 posts
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…
AI is the subtrate the future runs on. And so I think the idea is more to understand tomorrow ... from first principles. In the late 80's, as a teenager, I learned x86 assembly and C because that was the only way to squeeze out enough juice from my shitty CGA (and later VGA) card to programm the games/graphics that interested me. I haven't written assembly in years. But whatever I did in my career: it helped me and g…
Current AI can automate significant amounts of grunt work in programming and math. It's good at running web searches and writing summaries. There are a few other niches where it is currently successful. But other than that, many corporate AI projects are spectacular failures.
So just given what we have in hand, assuming no further breakthroughs, then we're maybe looking at AI being somewhat bigger than the Internet. Which would make it a revolutionary technology, sure.
But to get from "a revolutionary technology" to "the substrate the future runs on", then you need to assume more breakthroughs: long-context operation over weeks or months, displacing human workers 100% instead of 75%, and the ability to directly economically compete with actual humans. And people are investing literal trillions of dollars to make that future come true, without really thinking through what truly competitive-with-human AI would actually mean. We might be looking at massive job loss, centralization of power, fully automated "companies" with no humans dominating markets, and other dystopian scenarios.
And in those worlds, it's unclear that being good at CUDA and matrix math will be all that helpful, careerwise. The AIs are already pretty good at that stuff. Data scientists get paid OK when they actually get hired, but it's not everything college students were promised in the 2010s, either.
We can't yet build a fully-general competitor for the human mind. But we're getting closer. And if we ever do build one, the consequences will be really weird in any number of ways. So I worry about visions of the future that assume AI keeps improving significantly, but that also assume it still somehow remains a "normal" technology that doesn't, for example, render most humans fundamentally uncompetitive.
Earlier quoted context omitted.
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...
> 2% of the UK population has a PhD Citation needed. I'm guessing you're not counting children.
Good times.
I wonder if you can dig that out of the historical reddit database. I'd like to see that again. I love how everything is recorded now.
Earlier quoted context omitted.
It's like looking at the early internet and then suggesting teenagers should write browsers as their projects instead of webpages.
I mean... that's actually amazing advice. Not because they would grow up to create browser startups. But because they would grow up to create web startups that succeed because of very fundamental of how the web is rendered. Which is paulg's point really
Jeff Bezos did similar, but he did his own fulfillment and hired out the coding.
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…
A lot of startup companies are not training frontier models but help solve and optimize pain points of LLMs: cyber security, token usage, harnesses etc. These jobs don't require a PHD in machine learning but it does help if you understand LLMs at a deeper level.
While knowledge is always great, I would encourage people not to seek advice from successful people like this (survivorship bias). Moreover I am not sure it is even good advice? Would you advise a 17 y.o. to learn how transistors work or how to code (i.e. is LLM training the right level in the stack)? LLM training, a discipline where relevant work is already out of reach for 99.999% of budgets really as essential as…
> I would encourage people not to seek advice from successful people like this (survivorship bias). Personally I don't see the problem, as long as you're aware there is survivorship bias involved here. What's the alternative really, seek advice from unsuccessful people? That seems worse :) Personally I do both, read about what worked for people, also read about what didn't work for people, then ignore both and do wha…
Learning from their mistakes (where it did go wrong) can be as valuable as.
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…
In that regard, it's not too different from mobile telephony. Mobile phones drove the electronics industry 20 years ago, but there is limited demand for people who really know how to build a phone (ie. build the hardware and write all the signal processing from scratch), as there aren't that many companies that do phones at the lowest level. A few of the engineers got rich (eg. Viterbi), but most 'just' made a good l…