251–260 of 283 posts
But selling shovels that are useful in many small markets can still be a viable play, and that’s how I understand YC’s position here.
Anything that requires deep “understanding” or novel invention is not a job for a statistical word regurgitator. I’ve yet to see a single example, in any field, of an LLM actually inventing something truly novel (as judged by the experts in that space). Where LLMs shine is in producing boilerplate -- though that is super useful. So far I have yet to see anything resembling an original “thought” from an LLM (and I use…
Earlier quoted context omitted.
First, VCs don't get paid when "dogshit startups" get acquired, they get paid when they have true outlier successes. It's the only way to reliably make money in the VC business. Second, want to give any examples of "shitty, hype-based compan[ies]" (I assume you mean companies with no real revenue traction) getting bought out for "a few billion". Third, investment banks facilitate sales of assets, they don't buy them…
> First, VCs don't get paid when "dogshit startups" get acquired https://www.reuters.com/article/business/peloton-raises-12-b...
Anything that requires deep “understanding” or novel invention is not a job for a statistical word regurgitator. I’ve yet to see a single example, in any field, of an LLM actually inventing something truly novel (as judged by the experts in that space). Where LLMs shine is in producing boilerplate -- though that is super useful. So far I have yet to see anything resembling an original “thought” from an LLM (and I use…
Experiment: you think LLMs can innovate on chip design? Ask it to do something much simpler: invent a new better sorting algorithm. We use names such as Timsort or Djikstra for a specific reason: because it requires rare human ingenuity to invent such things. If an LLM can’t invent a new sorting algorithm that is meaningfully better in some way than existing known algorithms, then good luck on something much harder l…
But I certainly agree in general. It’s been years and there are still no independent novel discoveries afaik.
LLMs are wrong for most things imo. LLMs are great conversational assistants, but there is very little linguistic rigor to them, if any. They have almost no generalization ability, and anecdotally they fall for the same syntactic pitfalls they've fallen for since BERT. Models have gotten so good at predicting this n-dimensional "function" that sounds like human speech, we're getting distracted from seeing their actua…
The 2 first paragraphs are in contradiction with my results with working with LLMs. There is definitely some form of reasoning that has emerged. Some people will still find it not convincing enough to be called reasoning, but that just a quantitative limitation at the moment. With respect to AGI in its broadest sense: indeed it is not in reach. I think that is for the better!
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That’s what your VC investment would be buying; the model of “pay experts to create a private training set for fine tuning” is an obvious new business model that is probably under-appreciated. If that’s the biggest gap, then YC is correct that it’s a good area for a startup to tackle.
It would be hard to find any experts that could be paid "to create a private training set for fine tuning". The reason is that those experts do not own the code that they have written. The code is owned by big companies like NVIDIA, AMD, Intel, Samsung and so on. It is unlikely that these companies would be willing to provide the code for training, except for some custom LLM to be used internally by them, in which ca…
Not my field of expertise but there seem to be experts founding startups etc in the ASIC space, and Bitcoin miners were designed and built without any of the big companies participating. So I’m not following why we need Intel to be involved.
An obvious way to set up the flywheel here is to hire experts to do professional services or consulting on customer-submitted designs while you build up your corpus. While I said “fine-tuning”, there is probably a lot of agent scaffolding to be built too, which disproportionately helps bigger companies with more work throughput. (You can also acquire a company with the expertise and tooling, as Apple did with PA Semi in ~2008, though obviously $100m order of magnitude is out of reach for a startup. https://www.forbes.com/2008/04/23/apple-buys-pasemi-tech-ebi...)
Anything that requires deep “understanding” or novel invention is not a job for a statistical word regurgitator. I’ve yet to see a single example, in any field, of an LLM actually inventing something truly novel (as judged by the experts in that space). Where LLMs shine is in producing boilerplate -- though that is super useful. So far I have yet to see anything resembling an original “thought” from an LLM (and I use…
Experiment: you think LLMs can innovate on chip design? Ask it to do something much simpler: invent a new better sorting algorithm. We use names such as Timsort or Djikstra for a specific reason: because it requires rare human ingenuity to invent such things. If an LLM can’t invent a new sorting algorithm that is meaningfully better in some way than existing known algorithms, then good luck on something much harder l…
Earlier quoted context omitted.
I dropped EE entirely and switched from Computer Engineering to Computer Science because of my entry level EE course professor. I know I'm not the only person pushed away from EE due to Neil Cotter. Boggles my mind why he's still allowed to be the gateway to that discipline for so many people.
Most entry level engineering classes (first 3/4 semesters) in most of Europe (all kinds) are designed to gate keep. I graduated in chemistry, and Chemistry 1 in engineering had tests much more difficult than any other Chemistry 1 in any other faculty. After noticing that the same pattern applied to Physics 1 or Calculus I started realizing it was an engineering thing, which was later confirmed to me by an associate p…
And this at a top ten school for CS.
There are healthy ways to exploit an urge to procrastinate but this is just feeding the monster, and I hope the prof was ashamed of himself.
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I dropped EE entirely and switched from Computer Engineering to Computer Science because of my entry level EE course professor. I know I'm not the only person pushed away from EE due to Neil Cotter. Boggles my mind why he's still allowed to be the gateway to that discipline for so many people.
While I didn’t switch majors, I had a similar experience with my intro EE class. My theory was that it was intentionally a weeder class to push students towards the other engineering concentrations. Intro EE is kinda brutal in that there’s a lot of theory to cover, and you need to build the intuition on how it applies to real world circuit design on the fly. I had a bit of an epiphany when I was in a set theory/numbe…
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I still have nightmares about the entry level EE class I was required to take for a CS degree. RC circuits man.
“Oh shit I better remember all that matrix algebra I forgot already!” …Then takes a class on anything with 3d graphics… “oh shit matrix algebra again!” …then takes a class on machine learning “urg more matrix math!”