4 ms·

Lots of comments either defending this ("it's taking a chance on being the first to build AGI with a proven team") or saying "it's a crazy valuation for a 3 mon

Lots of comments either defending this ("it's taking a chance on being the first to build AGI with a proven team") or saying "it's a crazy valuation for a 3 month old startup". But both of these "sides" feel like they miss the mark to me. On one hand, I think it's great that investors are willing to throw big chunks of money at hard (or at least expensive) problems. I'm pretty sure all the investors putting money in will do just fine even if their investment goes to zero, so this feels exactly what VC funding should be doing, rather than some other common "how can we get people more digitally addicted to sell ads?" play. On the other hand, I'm kind of baffled that we're still talking about "AGI" in the context of LLMs. While I find LLMs to be amazing, and an incredibly useful tool (if used with a good understanding of their flaws), the more I use them, the more that it becomes clear to me that they're not going to get us anywhere close to "general intelligence". That is, the more I have to work around hallucinations, the more that it becomes clear that LLMs really are just "fancy autocomplete", even if it's really really fancy autocomplete. I see lots of errors that make sense if you understand an LLM is just a statistical model of word/token frequency, but you would expect to never see these kinds of errors in a system that had a true understanding of underlying concepts. And while I'm not in the field so I may have no right to comment, there are leaders in the field, like LeCun, who have expressed basically the same idea. So my question is, has Sutskever et al provided any acknowledgement of how they intend to "cross the chasm" from where we are now with LLMs to a model of understanding, or has it been mainly "look what we did before, you should take a chance on us to make discontinuous breakthroughs in the future"?

thefounder 2y ago
I think the plan is to raise a lot of cash and then more and then maybe something comes up that brings us closer to AGI(i.e something better than LLM). The investors know that AGI is not really the goal but they can’t miss the next trillion dollar company.
petulla 2y ago
Ilya has discussed this question: https://www.youtube.com/watch?v=YEUclZdj_Sc https://www.youtube.com/watch?v=YEUclZdj_Sc
- hn_throwaway_99 2y ago
  Thank you very much for posting! This is exactly what I was looking for. On one hand, I understand what he's saying, and that's why I have been frustrated in the past when I've heard people say "it's just fancy autocomplete" without emphasizing the awesome capabilities that can give you. While I haven't seen this video by Sutskever before, I have seen a very similar argument by Hinton: in order to get really good at next token prediction, the model needs to "discover" the underlying rules that make that prediction possible. All that said, I find his argument wholly unconvincing (and again, I may be waaaaay stupider than Sutskever, but there are other people much smarter than I who agree). And the reason for this is because every now and then I'll see a particular type of hallucination where it's pretty obvious that the LLM is confusing similar token strings even when their underlying meaning is very different. That is, the underlying "pattern matching" of LLMs becomes apparent in these situations. As I said originally, I'm really glad VCs are pouring money into this, but I'd easily make a bet that in 5 years that LLMs will be nowhere near human-level intelligence on some tasks, especially where novel discovery is required.
  - JamesSwift 2y ago
    Watching that video actually makes me completely unconvinced that SSI will succeed if they are hinging it on LLM... He puts a lot of emphasis on the fact that 'to generate the next token you must understand how', when thats precisely the parlor trick that is making people lose their minds (myself included) with how effective current LLMs are. The fact that it can simulate some low-fidelity reality with _no higher-level understanding of the world_, using purely linguistic/statistical analysis, is mind-blowing. To say "all you have to do is then extrapolate" is the ultimate "draw the rest of the owl" argument.
  - otabdeveloper4 2y ago
    > but I'd easily make a bet that in 5 years that LLMs will be nowhere near human-level intelligence on some tasks I wouldn't. There are some extraordinarily stupid humans out there. Worse, making humans dumber is a proven and well-known technology.
  - pajeets 2y ago
maximinus_thrax 2y ago
> On the other hand, I'm kind of baffled that we're still talking about "AGI" in the context of LLMs. I'm not. Lots of people and companies have been sinking money into these ventures and they need to keep the hype alive by framing this as being some sort of race to AGI. I am aware that the older I get the more cynical I become, but I bucket all discussions about AGI (including the very popular 'open letters' about AI safety and Skynet) in the context of LLMs into the 'snake oil' bucket.
nilkn 2y ago
The argument about AGI from LLMs is not based on the current state of LLMs, but on the rate of progress over the last 5+ years or so. It wasn't very long ago that almost nobody outside of a few niche circles seriously thought LLMs could do what they do right now. That said, my personal hypothesis is that AGI will emerge from video generation models rather than text generation models. A model that takes an arbitrary real-time video input feed and must predict the next, say, 60 seconds of video would have to have a deep understanding of the universe, humanity, language, culture, physics, humor, laughter, problem solving, etc. This pushes the fidelity of both input and output far beyond anything that can be expressed in text, but also creates extraordinarily high computational barriers.
- hn_throwaway_99 2y ago
  > The argument about AGI from LLMs is not based on the current state of LLMs, but on the rate of progress over the last 5+ years or so. And what I'm saying is that I find that argument to be incredibly weak. I've seen it time and time again, and honestly at this point just feels like a "humans should be a hundred feet tall based on on their rate of change in their early years" argument. While I've also been amazed at the past progress in LLMs, I don't see any reason to expect that rate will continue in the future. What I do see the more and more I use the SOTA models is fundamental limitations in what LLMs are capable of.
  - ldjkfkdsjnv 2y ago
    10 years of progress is a flash in the pan of human progress. The first deep learning models that worked appeared in 2012. That was like yesterday. You are completely underestimating the rate of change we are witnessing. Compute scaling is not at all similar to biological scaling.
  - nilkn 2y ago
    Expecting the rate of progress to drop off so abruptly after realistically just a few years of serious work on the problem seems like the more unreasonable and grander prediction to me than expecting it to continue at its current pace for even just 5 more years.
wubrr 2y ago
> the more that it becomes clear that LLMs really are just "fancy autocomplete", even if it's really really fancy autocomplete I also don't really see AGI emerging from LLMs any time soon, but it could be argued that human intelligence is also just 'fancy autocomplete'.
- hn_throwaway_99 2y ago
  > but it could be argued that human intelligence is also just 'fancy autocomplete'. But that's my point - in some ways it's obvious that humans are not just doing "fancy autocomplete" because humans generally don't make the types of hallucination errors that LLMs make. That is, the hallucination errors do make sense if you think of how an LLM is just a statistical relationship between tokens. One thing to emphasize, I'm not saying the "understanding" that humans seem to possess isn't just some lower level statistical process - I'm not "invoking a soul". But I am saying it appears to be fundamentally different, and in many cases more useful, than what an LLM can do.
  - wubrr 2y ago
    > because humans generally don't make the types of hallucination errors that LLMs make. They do though - I've noticed myself and others saying things in conversation that sound kind of right, and are based on correct things they've learned previously, but because memory of those things is only partial and mixed with other related information things are often said that are quite incorrect or combine two topics in a way that doesn't make sense.
- slashdave 2y ago
  > but it could be argued that human intelligence is also just 'fancy autocomplete'. Well, no. Humans do not think sequentially. But even if we were to put that aside, any "autocomplete" we perform is based on a world model, and not tokens in a string.
deleted 2y ago
[deleted]
tim333 2y ago
>"We’ve identified a new mountain to climb that’s a bit different from what I was working on previously. We’re not trying to go down the same path faster. If you do something different, then it becomes possible for you to do something special." Doesn't really imply let's just do more LLMs.
sirspacey 2y ago
Two interesting points to consider 1. If it’s really amazing autocomplete, is there a distinction between AGI? Being able to generalize, plan, execute, evaluate and learn from the results could all be seen as a search graph building on inference from known or imagined data points. So far LLMs are being used on all of those and we haven’t even tested the next level of compute power being built to enable its evolution. 2. Fancy autocomplete is a bit broad for the comprehensive use cases CUDA is already supporting that go way beyond textual prediction. If all information of every type can be “autocompleted” that’s a pretty incredible leap for robotics. * edited to compensate for iPhone autocomplete, the irony.

thefounder 2y ago

I think the plan is to raise a lot of cash and then more and then maybe something comes up that brings us closer to AGI(i.e something better than LLM). The investors know that AGI is not really the goal but they can’t miss the next trillion dollar company.

petulla 2y ago

Ilya has discussed this question: https://www.youtube.com/watch?v=YEUclZdj_Sc https://www.youtube.com/watch?v=YEUclZdj_Sc

hn_throwaway_99 2y ago
Thank you very much for posting! This is exactly what I was looking for. On one hand, I understand what he's saying, and that's why I have been frustrated in the past when I've heard people say "it's just fancy autocomplete" without emphasizing the awesome capabilities that can give you. While I haven't seen this video by Sutskever before, I have seen a very similar argument by Hinton: in order to get really good at next token prediction, the model needs to "discover" the underlying rules that make that prediction possible. All that said, I find his argument wholly unconvincing (and again, I may be waaaaay stupider than Sutskever, but there are other people much smarter than I who agree). And the reason for this is because every now and then I'll see a particular type of hallucination where it's pretty obvious that the LLM is confusing similar token strings even when their underlying meaning is very different. That is, the underlying "pattern matching" of LLMs becomes apparent in these situations. As I said originally, I'm really glad VCs are pouring money into this, but I'd easily make a bet that in 5 years that LLMs will be nowhere near human-level intelligence on some tasks, especially where novel discovery is required.
- JamesSwift 2y ago
  Watching that video actually makes me completely unconvinced that SSI will succeed if they are hinging it on LLM... He puts a lot of emphasis on the fact that 'to generate the next token you must understand how', when thats precisely the parlor trick that is making people lose their minds (myself included) with how effective current LLMs are. The fact that it can simulate some low-fidelity reality with _no higher-level understanding of the world_, using purely linguistic/statistical analysis, is mind-blowing. To say "all you have to do is then extrapolate" is the ultimate "draw the rest of the owl" argument.
- otabdeveloper4 2y ago
  > but I'd easily make a bet that in 5 years that LLMs will be nowhere near human-level intelligence on some tasks I wouldn't. There are some extraordinarily stupid humans out there. Worse, making humans dumber is a proven and well-known technology.
- pajeets 2y ago

maximinus_thrax 2y ago

> On the other hand, I'm kind of baffled that we're still talking about "AGI" in the context of LLMs. I'm not. Lots of people and companies have been sinking money into these ventures and they need to keep the hype alive by framing this as being some sort of race to AGI. I am aware that the older I get the more cynical I become, but I bucket all discussions about AGI (including the very popular 'open letters' about AI safety and Skynet) in the context of LLMs into the 'snake oil' bucket.

nilkn 2y ago

The argument about AGI from LLMs is not based on the current state of LLMs, but on the rate of progress over the last 5+ years or so. It wasn't very long ago that almost nobody outside of a few niche circles seriously thought LLMs could do what they do right now. That said, my personal hypothesis is that AGI will emerge from video generation models rather than text generation models. A model that takes an arbitrary real-time video input feed and must predict the next, say, 60 seconds of video would have to have a deep understanding of the universe, humanity, language, culture, physics, humor, laughter, problem solving, etc. This pushes the fidelity of both input and output far beyond anything that can be expressed in text, but also creates extraordinarily high computational barriers.

hn_throwaway_99 2y ago
> The argument about AGI from LLMs is not based on the current state of LLMs, but on the rate of progress over the last 5+ years or so. And what I'm saying is that I find that argument to be incredibly weak. I've seen it time and time again, and honestly at this point just feels like a "humans should be a hundred feet tall based on on their rate of change in their early years" argument. While I've also been amazed at the past progress in LLMs, I don't see any reason to expect that rate will continue in the future. What I do see the more and more I use the SOTA models is fundamental limitations in what LLMs are capable of.
- ldjkfkdsjnv 2y ago
  10 years of progress is a flash in the pan of human progress. The first deep learning models that worked appeared in 2012. That was like yesterday. You are completely underestimating the rate of change we are witnessing. Compute scaling is not at all similar to biological scaling.
- nilkn 2y ago
  Expecting the rate of progress to drop off so abruptly after realistically just a few years of serious work on the problem seems like the more unreasonable and grander prediction to me than expecting it to continue at its current pace for even just 5 more years.

wubrr 2y ago

> the more that it becomes clear that LLMs really are just "fancy autocomplete", even if it's really really fancy autocomplete I also don't really see AGI emerging from LLMs any time soon, but it could be argued that human intelligence is also just 'fancy autocomplete'.

hn_throwaway_99 2y ago
> but it could be argued that human intelligence is also just 'fancy autocomplete'. But that's my point - in some ways it's obvious that humans are not just doing "fancy autocomplete" because humans generally don't make the types of hallucination errors that LLMs make. That is, the hallucination errors do make sense if you think of how an LLM is just a statistical relationship between tokens. One thing to emphasize, I'm not saying the "understanding" that humans seem to possess isn't just some lower level statistical process - I'm not "invoking a soul". But I am saying it appears to be fundamentally different, and in many cases more useful, than what an LLM can do.
- wubrr 2y ago
  > because humans generally don't make the types of hallucination errors that LLMs make. They do though - I've noticed myself and others saying things in conversation that sound kind of right, and are based on correct things they've learned previously, but because memory of those things is only partial and mixed with other related information things are often said that are quite incorrect or combine two topics in a way that doesn't make sense.
slashdave 2y ago
> but it could be argued that human intelligence is also just 'fancy autocomplete'. Well, no. Humans do not think sequentially. But even if we were to put that aside, any "autocomplete" we perform is based on a world model, and not tokens in a string.

deleted 2y ago

[deleted]

tim333 2y ago

>"We’ve identified a new mountain to climb that’s a bit different from what I was working on previously. We’re not trying to go down the same path faster. If you do something different, then it becomes possible for you to do something special." Doesn't really imply let's just do more LLMs.

sirspacey 2y ago

Two interesting points to consider 1. If it’s really amazing autocomplete, is there a distinction between AGI? Being able to generalize, plan, execute, evaluate and learn from the results could all be seen as a search graph building on inference from known or imagined data points. So far LLMs are being used on all of those and we haven’t even tested the next level of compute power being built to enable its evolution. 2. Fancy autocomplete is a bit broad for the comprehensive use cases CUDA is already supporting that go way beyond textual prediction. If all information of every type can be “autocompleted” that’s a pretty incredible leap for robotics. * edited to compensate for iPhone autocomplete, the irony.