←back to thread

YC is wrong about LLMs for chip design

(www.zach.be)
317 points laserduck | 5 comments | 16 Nov 24 14:07 UTC | HN request time: 1.827s | source
Show context
EgoIncarnate ◴[16 Nov 24 16:57 UTC] No.42157406[source]▶
The article seems to be be based on the current limitations of LLMs. I don't think YC and other VCs are betting on what LLMs can do today, I think they are betting on what they might be able to do in the future.

As we've seen in the recent past, it's difficult to predict what the possibilities are for LLMS and what limitations will hold. Currently it seems pure scaling won't be enough, but I don't think we've reached the limits with synthetic data and reasoning.

replies(4): >>42157469 #>>42157563 #>>42157650 #>>42157754 #
kokanee ◴[16 Nov 24 17:24 UTC] No.42157563[source]▶
Tomorrow, LLMs will be able to perform slightly below-average versions of whatever humans are capable of doing tomorrow. Because they work by predicting what a human would produce based on training data.
replies(2): >>42157593 #>>42158067 #
1. herval ◴[16 Nov 24 17:29 UTC] No.42157593[source]▶
This severely discounts the fact that you’re comparing a model that _knows the average about everything_ to a single human’s capabilit. Also they can do it instantly, instead of having to coordinate many humans over long periods of time. You can’t straight up compare one LLM to one human
replies(1): >>42158301 #
2. namaria ◴[16 Nov 24 18:53 UTC] No.42158301[source]▶
"Knows the average relationship amongst all words in the training data" ftfy
replies(1): >>42159647 #
3. herval ◴[16 Nov 24 21:48 UTC] No.42159647[source]▶
it seems that's sufficient to do a lot of things better than the average human - including coding, writing, creating poetry, summarizing and explaining things...
replies(1): >>42159924 #
4. namaria ◴[16 Nov 24 22:25 UTC] No.42159924{3}[source]▶
A human specialized in any of those things vastly outperforms the average human let alone an LLM.
replies(1): >>42165781 #
5. herval ◴[17 Nov 24 18:15 UTC] No.42165781{4}[source]▶
You’re entirely missing the point