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If you don't know Gary Marcus: He keeps repeating this same argument since many years. He has always downplayed the capabilities of LLMs since the beginning. He

If you don't know Gary Marcus: He keeps repeating this same argument since many years. He has always downplayed the capabilities of LLMs since the beginning. He never really added much useful (constructive) content to the discussion or to the research, though (except that he is advocating symbolic AI methods). So he is not really taken seriously by the community, but rather annoying on this. Although, of course he is not alone with his argument. E.g. even Yann LeCun is repeating a similar argument on LLMs, and many other serious researches as well, that we probably need a bit more than just LLMs + our current training method + scaling up. E.g. some model extension which handles long-term memory better (lots of work on this already), or other model architectures (e.g. Yann LeCun proposed JEPA), or online learning (unclear how this should work with LLMs), or also different training criteria, etc. In any case, multi-modality (not just text) is important. Maybe embodiment (robots, interaction with the real world) as well. (Edit Wow, the votes on this comment go up and down. I guess it's a controversial topic.)

nabla9 3y ago
I lost all respect to Gary Marcus when he in 2018 claimed that autonomous driving is decades away. Oh wait. I lost all respect to those hype-men who claimed it's just around the corner. Before transformers, people claimed that you just need bigger neural network, not something new. It did not work. Then came transformers, the new thing can again scale up better than previous era. Transformers have obvious limit: the computational complexity of self-attention module scales quadratically with the sequence length. We are trying to get partial fix with sparcity and linear tranformers and other techniques, but it gets harder at every turn. We make progress, but "just scaling" is not working.
- Anotheroneagain 3y ago
  There are robotaxis being tested in the west coast US and Wuhan, China.
- prasoonds 3y ago
  I’m not sure if I’m parsing your sentence right but surely you can’t be implying that autonomous driving isn’t possible or that it is some undermined time away? You can come down to SF and take a waymo today. It works great.
  - mjburgess 3y ago
    No one doubted you could take precise area scans of a city and create motorised robots on fixed routes.
    - School-Cotton 3y ago
      Actually, a lot of people doubted that you could ever do what Waymo is doing today in SF (and Phoenix, and probably other places).
      - nabla9 3y ago
        Not anyone important.
  - nabla9 3y ago
    The context was SAE level 5 (Full Driving Automation) Level 5 vehicles do not require human attention―the “dynamic driving task” is eliminated. Level 5 cars won’t even have steering wheels or acceleration/braking pedals. They will be free from geofencing, able to go anywhere and do anything that an experienced human driver can do.
chilmers 3y ago
Right, but is any serious researcher insisting that just scaling will be enough to achieve AGI? Because while I'm a layperson on this stuff, I don't get that impression, meaning this criticism from Marcus is mostly attacking a strawman. And he's not the only one peddling it. The understanding I have is that scalability of LLMs with data took even their developers by surprise. They kept at it because empirically, so far, it's worked. But nobody assumes it'll keep working indefinitely or that it'll lead to AGI alone. If they did, OpenAI, Google, etc. would have fired most of their researchers and would simply focus everything they have on scaling.
- thiago_fm 3y ago
  There are plenty of leaders in SV, one example being Sam Altman that is preaching that hardware is key. Just take a look at nVidia's $2t market cap. When we all know it isn't, and this can have the side effect of creating a bubble. I would be concerned with this as it can have terrible effects to AI research long-term.
- albertzeyer 3y ago
  When you look at the history of scaling up Transformers, e.g. GPT2, most people in the community were in fact quite surprised that just scaling up those models worked so well. And every time you read a lot of opinions that this is the end and scaling them up further will not give (much) improvements. This criticism is lower now but still there. Scaling self-attention has also worked much further than it was initially believed it could work. Initially it was always believed to be the main bottleneck, but this turned out wrong. The feed-forward layers are much more the bottleneck when looking at where most of the compute is spent. It's still a bit unclear how much further we can take the scaling. We are mostly at the limit of what is financial feasible today but as long as some form of generalized Moore's law continues (e.g. number of transistors per dollar), it becomes financial feasible to scale further. At some point we also hit a limit of available data. But maybe self-play (or variants) might solve this. I guess most researchers agree that just scaling up further is maybe not optimal (w.r.t. reaching AGI), or also will not be enough, but it's a somewhat open question.
- xiphias2 3y ago
HeatrayEnjoyer 3y ago
LeCun has startling bad takes for a major player in the space to the point that they can only be bad faith and driven by adverse motivations.
- HarHarVeryFunny 3y ago
  This may be part of the reason. https://www.linkedin.com/posts/yann-lecun_what-meta-learned-from-galactica-the-doomed-activity-7130214818862567424-tCWL https://www.linkedin.com/posts/yann-lecun_what-meta-learned-... One might also imagine that as one of the "godfathers of AI" he feels a bit sidelined by the success of LLMs (especially given above), and wants to project an image of visionary ahead of the pack. I actually agree with him that if the goal is AGI and full animal intelligence then LLMs are not really the right path (although a very useful validation of the power of prediction). We really need much greater agency (even if only in a virtual world), online learning, innate drives, prediction applied to sensory inputs and motor outputs, etc. Still, V-JEPA is nothing more than a pre-trained transformer applied to vision (predicting latent visual representations rather than text tokens), so it is just a validation of the power of transformers, rather than being any kind of architectural advance.
YeGoblynQueenne 3y ago
Your first paragraph is an ad-hominem that you should be ashamed of. "Not really taken seriously by the community"? Which community is that? Last time I saw Marcus he was a co-organizer of a panel on Dug Lenat and Knowledge Representation in AAAI 2024 [1]. Does that sound like the AI community is not taking him seriously? Is anyone asking you to orgranise panels in AAAI? The question is rhetorical: I can see you're a PhD student. My advice is to learn to have some respect for the person of others, as you will want them to have respect of your person when, one day, you find yourself saying something that "the community" disagrees with. And if you're not planning to, one day, find yourself in that situation, consider the possibility that you're in the wrong job. And what is the above article saying that you think should not be taken seriously? Is it not a widely recognised fact that neural nets performance only improves with more data, more compute and more parameters? A five year old could tell you that. Is it controversial that this is a limitation? _____________________ [1] https://aaai.org/aaai-conference/aaai-24-panels/ https://aaai.org/aaai-conference/aaai-24-panels/
- albertzeyer 3y ago
  I did not intend to attack Gary or so in any way. But I realize that my statement is probably too strong. Of course it's not the whole AI community. My intention with my post was just to give some perspective, some background for people who have not heard about Gary Marcus before. Maybe I'm also in a bubble, but I was speaking mostly about the people I frequently read from, i.e. lots of people from Google Brain, DeepMind, other people who frequently publish on NeurIPS, ICLR, ICML, etc. Among those people, Gary is usually not taken seriously. At least that was my impression. But let's not make this so much about Gary: Most of these people disagree with the opinion that Gary shares, i.e. they don't really see such a big need for symbolic AI, or they see much more potential in pure neural approaches (after all, the human brain is fully neural).
  - YeGoblynQueenne 3y ago
    >> Of course it's not the whole AI community. My intention with my post was just to give some perspective, some background for people who have not heard about Gary Marcus before. Yes, I get it. And the perspective you wanted to give was to not take Marcus seriously because the people you follow on social media say he's not to be taken seriously. That's nothing but a form of collective online bullying that attacks the person and not the opinion, and like I say in my other comment above, shameful. Consider for a moment the impression that you make when you say that some people you know, when they're not publishing on NeurIPS, are on social media dogpiling on someone who criticises their work. That's not researchers any more, but common social media trolls. >> But let's not make this so much about Gary: Most of these people disagree with the opinion that Gary shares, i.e. they don't really see such a big need for symbolic AI, or they see much more potential in pure neural approaches (after all, the human brain is fully neural). To my experience, the majority of neural net researchers don't know anything concrete about symbolic AI, just what they have heard second-hand, usually on social media again, usually by people who disagree with Marcus, who's the most famous proponent of neuro-symbolic AI (NeSy). So whatever opinion they have on NeSy is not an informed opinion. There's plenty of literature on NeSy which is a bona fide field of research with a conference etc. This year Leslie Valiant was the keynote speaker and Yan LeCun the honoured guest: https://sites.google.com/view/nesy2023 https://sites.google.com/view/nesy2023 You really don't have to listen to what Marcus says to form an opinion on NeSy. Btw, I am not with them and I think they're going the wrong way, but at least I know what they're doing. That is much less than can be said about most neural net researchers, who rarely know anything outside their own work besides whatever preprint is trending on X. That's to the detriment of nobody but themselves.

nabla9 3y ago

I lost all respect to Gary Marcus when he in 2018 claimed that autonomous driving is decades away. Oh wait. I lost all respect to those hype-men who claimed it's just around the corner. Before transformers, people claimed that you just need bigger neural network, not something new. It did not work. Then came transformers, the new thing can again scale up better than previous era. Transformers have obvious limit: the computational complexity of self-attention module scales quadratically with the sequence length. We are trying to get partial fix with sparcity and linear tranformers and other techniques, but it gets harder at every turn. We make progress, but "just scaling" is not working.

Anotheroneagain 3y ago
There are robotaxis being tested in the west coast US and Wuhan, China.
prasoonds 3y ago
I’m not sure if I’m parsing your sentence right but surely you can’t be implying that autonomous driving isn’t possible or that it is some undermined time away? You can come down to SF and take a waymo today. It works great.
- mjburgess 3y ago
  No one doubted you could take precise area scans of a city and create motorised robots on fixed routes.
  - School-Cotton 3y ago
    Actually, a lot of people doubted that you could ever do what Waymo is doing today in SF (and Phoenix, and probably other places).
    - nabla9 3y ago
      Not anyone important.
- nabla9 3y ago
  The context was SAE level 5 (Full Driving Automation) Level 5 vehicles do not require human attention―the “dynamic driving task” is eliminated. Level 5 cars won’t even have steering wheels or acceleration/braking pedals. They will be free from geofencing, able to go anywhere and do anything that an experienced human driver can do.

chilmers 3y ago

Right, but is any serious researcher insisting that just scaling will be enough to achieve AGI? Because while I'm a layperson on this stuff, I don't get that impression, meaning this criticism from Marcus is mostly attacking a strawman. And he's not the only one peddling it. The understanding I have is that scalability of LLMs with data took even their developers by surprise. They kept at it because empirically, so far, it's worked. But nobody assumes it'll keep working indefinitely or that it'll lead to AGI alone. If they did, OpenAI, Google, etc. would have fired most of their researchers and would simply focus everything they have on scaling.

thiago_fm 3y ago
There are plenty of leaders in SV, one example being Sam Altman that is preaching that hardware is key. Just take a look at nVidia's $2t market cap. When we all know it isn't, and this can have the side effect of creating a bubble. I would be concerned with this as it can have terrible effects to AI research long-term.
albertzeyer 3y ago
When you look at the history of scaling up Transformers, e.g. GPT2, most people in the community were in fact quite surprised that just scaling up those models worked so well. And every time you read a lot of opinions that this is the end and scaling them up further will not give (much) improvements. This criticism is lower now but still there. Scaling self-attention has also worked much further than it was initially believed it could work. Initially it was always believed to be the main bottleneck, but this turned out wrong. The feed-forward layers are much more the bottleneck when looking at where most of the compute is spent. It's still a bit unclear how much further we can take the scaling. We are mostly at the limit of what is financial feasible today but as long as some form of generalized Moore's law continues (e.g. number of transistors per dollar), it becomes financial feasible to scale further. At some point we also hit a limit of available data. But maybe self-play (or variants) might solve this. I guess most researchers agree that just scaling up further is maybe not optimal (w.r.t. reaching AGI), or also will not be enough, but it's a somewhat open question.
xiphias2 3y ago

HeatrayEnjoyer 3y ago

LeCun has startling bad takes for a major player in the space to the point that they can only be bad faith and driven by adverse motivations.

HarHarVeryFunny 3y ago
This may be part of the reason. https://www.linkedin.com/posts/yann-lecun_what-meta-learned-from-galactica-the-doomed-activity-7130214818862567424-tCWL https://www.linkedin.com/posts/yann-lecun_what-meta-learned-... One might also imagine that as one of the "godfathers of AI" he feels a bit sidelined by the success of LLMs (especially given above), and wants to project an image of visionary ahead of the pack. I actually agree with him that if the goal is AGI and full animal intelligence then LLMs are not really the right path (although a very useful validation of the power of prediction). We really need much greater agency (even if only in a virtual world), online learning, innate drives, prediction applied to sensory inputs and motor outputs, etc. Still, V-JEPA is nothing more than a pre-trained transformer applied to vision (predicting latent visual representations rather than text tokens), so it is just a validation of the power of transformers, rather than being any kind of architectural advance.

YeGoblynQueenne 3y ago

Your first paragraph is an ad-hominem that you should be ashamed of. "Not really taken seriously by the community"? Which community is that? Last time I saw Marcus he was a co-organizer of a panel on Dug Lenat and Knowledge Representation in AAAI 2024 [1]. Does that sound like the AI community is not taking him seriously? Is anyone asking you to orgranise panels in AAAI? The question is rhetorical: I can see you're a PhD student. My advice is to learn to have some respect for the person of others, as you will want them to have respect of your person when, one day, you find yourself saying something that "the community" disagrees with. And if you're not planning to, one day, find yourself in that situation, consider the possibility that you're in the wrong job. And what is the above article saying that you think should not be taken seriously? Is it not a widely recognised fact that neural nets performance only improves with more data, more compute and more parameters? A five year old could tell you that. Is it controversial that this is a limitation? _____________________ [1] https://aaai.org/aaai-conference/aaai-24-panels/ https://aaai.org/aaai-conference/aaai-24-panels/

albertzeyer 3y ago
I did not intend to attack Gary or so in any way. But I realize that my statement is probably too strong. Of course it's not the whole AI community. My intention with my post was just to give some perspective, some background for people who have not heard about Gary Marcus before. Maybe I'm also in a bubble, but I was speaking mostly about the people I frequently read from, i.e. lots of people from Google Brain, DeepMind, other people who frequently publish on NeurIPS, ICLR, ICML, etc. Among those people, Gary is usually not taken seriously. At least that was my impression. But let's not make this so much about Gary: Most of these people disagree with the opinion that Gary shares, i.e. they don't really see such a big need for symbolic AI, or they see much more potential in pure neural approaches (after all, the human brain is fully neural).
- YeGoblynQueenne 3y ago
  >> Of course it's not the whole AI community. My intention with my post was just to give some perspective, some background for people who have not heard about Gary Marcus before. Yes, I get it. And the perspective you wanted to give was to not take Marcus seriously because the people you follow on social media say he's not to be taken seriously. That's nothing but a form of collective online bullying that attacks the person and not the opinion, and like I say in my other comment above, shameful. Consider for a moment the impression that you make when you say that some people you know, when they're not publishing on NeurIPS, are on social media dogpiling on someone who criticises their work. That's not researchers any more, but common social media trolls. >> But let's not make this so much about Gary: Most of these people disagree with the opinion that Gary shares, i.e. they don't really see such a big need for symbolic AI, or they see much more potential in pure neural approaches (after all, the human brain is fully neural). To my experience, the majority of neural net researchers don't know anything concrete about symbolic AI, just what they have heard second-hand, usually on social media again, usually by people who disagree with Marcus, who's the most famous proponent of neuro-symbolic AI (NeSy). So whatever opinion they have on NeSy is not an informed opinion. There's plenty of literature on NeSy which is a bona fide field of research with a conference etc. This year Leslie Valiant was the keynote speaker and Yan LeCun the honoured guest: https://sites.google.com/view/nesy2023 https://sites.google.com/view/nesy2023 You really don't have to listen to what Marcus says to form an opinion on NeSy. Btw, I am not with them and I think they're going the wrong way, but at least I know what they're doing. That is much less than can be said about most neural net researchers, who rarely know anything outside their own work besides whatever preprint is trending on X. That's to the detriment of nobody but themselves.