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He claims that language models will soon run out of data to train on, and therefore stop getting better. Language models have already essentially run out of dat

He claims that language models will soon run out of data to train on, and therefore stop getting better. Language models have already essentially run out of data to train on, yet they continue to get actionably better. Data quality is still a huge space for continued improvement. Also around 46:20: > the fundamental thing of knowing [the user] requires knowledge, which requires intellect, which GPT cannot have - it's mathematics. I disagree with basically all of this except that knowing requires knowledge, but that's basically a tautology. Firstly, knowledge and reason do not depend on each other. Wikipedia is a highly knowledgeable system that demonstrates little to no reason (maybe the search can be considered a demonstration of reason). A calculator is a highly reasonable system with extremely primitive knowledge. Intelligent systems have knowledge and reason. Anyone who has played around with an LLM would agree that they are able to demonstrate some level of both knowledge and reason. Also the implication that biological brains simply exist outside the domain of mathematics is... interesting. Then they go on to talking about how you can't make small tweaks with generative video AI, like telling the actor to walk a little bit faster or slower. To that I want to highlight that ComfyUI has support for video nodes[0]. If you're not familiar with ComfyUI, just check out some tutorials. Generative AI art is a really cool skill that people downplay for usually wrong reasons. > It can't generate new things It's hard to take the host seriously when they say things like this... > The technology is fundamentally unpredictable It simply isn't. When you find a seed that is generally close to what you want, you fix the seed and tweak (because you can) from there. [0] https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite

blactuary 2y ago
"Anyone who has played around with an LLM would agree that they are able to demonstrate some level of both knowledge and reason" Completely disagree, having used them quite a bit. They are able to regurgitate knowledge, but have no ability to reason
- Zambyte 2y ago
  What does it mean to be able to reason to you?
  - blactuary 2y ago
    To think, to understand context, and to intelligently explain something. To understand WHY you are doing what you are doing. That is not what an LLM is doing.
    - gaganyaan 2y ago
      Humans can't explain why we do things, so I guess we're not intelligent either?
      - blactuary 2y ago
        Yes we most certainly can
        gaganyaan 2y ago
        We can invent post hoc rationalizations, just like LLMs can
    - Zambyte 2y ago
      > To think You can think without reasoning. Recalling knowledge does not require reason. > to understand context Can you break this down? What does it mean to understand context? > to intelligently explain something LLMs can do this. > To understand WHY you are doing what you are doing. You can ask an LLM why they do things they have and they may reply, even with an answer that is likely correct. This again depends on how you break down "understand".
    - rickydroll 2y ago
      Humans do lots of things about reasoning. How many times have you heard: "We've always done it this way." "It's tradition." The daughter asks, "Mom, "why do you cut the end off the roast before cooking it?" "I learned that from your grandmother," replied the mom. The daughter turned to her grandmother and asked, "Why did you do it?" Grandma rolled her eyes, sighed, and said, "My oven was too short for the roast."
- GaggiX 2y ago
  I've used them extensively and they are very able to solve the problems that sometimes appear in my code, Claude 3.5 Sonnet in particular is able to not get stuck in an infinite loop causing the same error, and able to rewrite the entire code if it is necessary, it is surprisingly intelligent.
- wongarsu 2y ago
  To me the ability to reason is the biggest difference you can currently observe between 7B and 70B models. People love to test this with "brain teasers". You could argue that LLMs can pattern match "what's heavier? 1kg of feathers or 2kg of steel", but there are enough examples of at the time novel puzzles that I feel confident to say that good LLMs can absolutely reason (at small scales, but it's getting better)
somenameforme 2y ago
I think the easiest way to get the gist of what he (and I) are arguing is a thought experiment. Take an LLM back to the dawn of mankind, and train it on all of the collective knowledge of man. This would actually be impossible, because language hadn't yet been invented, so let's just imagine it could somehow read the collective mind of humanity. So cutting edge technology is poke 'em with the pointy side, science is nonexistent, and knowledge isn't that far behind. Now run this system through our LLM for as long as you want with as much power as you want. Where is it going to take you? Nowhere really, it's still just going to be constrained to its pool of data, recombining things in primitive ways and be essentially permanently stuck in the present, until somebody gives it some new data to train on it and mix and match. Yet somehow humanity, starting from that exact same basis, would soon (relatively speaking) put a man on the Moon, unlock the secrets of the atom, discover quantum mechanics, invent/discover mathematics, and much more. This is what is fundamentally meant by LLMs cannot create "new" knowledge. They absolutely can mix and match their pool of training statements in ways that can generate new statements. But if it's not an extremely simple 'remix' and we're in a domain where there are right and wrong answers, there's good chance it's just a nonsensical hallucination.
- Zambyte 2y ago
  You are comparing a species going from developing language to building spaceships on the order of hundreds of thousands of years, to a technology that was invented 7 years ago and only started to really be taken seriously 4 years ago. Working memory systems required for learning and iteratively developing ideas are in their infancy (on the scale of recent developments), but the recall technology (vector databases a la RAG) is quite well proven. I see no reason that a language model couldn't do iterative science in the same way humans have been with the resources available to it (APIs) using the composition of current technologies.
  - somenameforme 2y ago
    The current technology for LLMs is still a rather complex guess the next word algorithm. But this leaves the software with no real room to ever move beyond its training, regardless of how much time or training you give it. You could give them literally infinite processing power and they would not suddenly start developing new meaningful knowledge - it would still be little more than simple recombinations of its training dataset, until somebody gave it something new to train upon.
deleted 2y ago
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blactuary 2y ago

"Anyone who has played around with an LLM would agree that they are able to demonstrate some level of both knowledge and reason" Completely disagree, having used them quite a bit. They are able to regurgitate knowledge, but have no ability to reason

Zambyte 2y ago
What does it mean to be able to reason to you?
- blactuary 2y ago
  To think, to understand context, and to intelligently explain something. To understand WHY you are doing what you are doing. That is not what an LLM is doing.
  - gaganyaan 2y ago
    Humans can't explain why we do things, so I guess we're not intelligent either?
    - blactuary 2y ago
      Yes we most certainly can
      - gaganyaan 2y ago
        We can invent post hoc rationalizations, just like LLMs can
  - Zambyte 2y ago
    > To think You can think without reasoning. Recalling knowledge does not require reason. > to understand context Can you break this down? What does it mean to understand context? > to intelligently explain something LLMs can do this. > To understand WHY you are doing what you are doing. You can ask an LLM why they do things they have and they may reply, even with an answer that is likely correct. This again depends on how you break down "understand".
  - rickydroll 2y ago
    Humans do lots of things about reasoning. How many times have you heard: "We've always done it this way." "It's tradition." The daughter asks, "Mom, "why do you cut the end off the roast before cooking it?" "I learned that from your grandmother," replied the mom. The daughter turned to her grandmother and asked, "Why did you do it?" Grandma rolled her eyes, sighed, and said, "My oven was too short for the roast."
GaggiX 2y ago
I've used them extensively and they are very able to solve the problems that sometimes appear in my code, Claude 3.5 Sonnet in particular is able to not get stuck in an infinite loop causing the same error, and able to rewrite the entire code if it is necessary, it is surprisingly intelligent.
wongarsu 2y ago
To me the ability to reason is the biggest difference you can currently observe between 7B and 70B models. People love to test this with "brain teasers". You could argue that LLMs can pattern match "what's heavier? 1kg of feathers or 2kg of steel", but there are enough examples of at the time novel puzzles that I feel confident to say that good LLMs can absolutely reason (at small scales, but it's getting better)

somenameforme 2y ago

I think the easiest way to get the gist of what he (and I) are arguing is a thought experiment. Take an LLM back to the dawn of mankind, and train it on all of the collective knowledge of man. This would actually be impossible, because language hadn't yet been invented, so let's just imagine it could somehow read the collective mind of humanity. So cutting edge technology is poke 'em with the pointy side, science is nonexistent, and knowledge isn't that far behind. Now run this system through our LLM for as long as you want with as much power as you want. Where is it going to take you? Nowhere really, it's still just going to be constrained to its pool of data, recombining things in primitive ways and be essentially permanently stuck in the present, until somebody gives it some new data to train on it and mix and match. Yet somehow humanity, starting from that exact same basis, would soon (relatively speaking) put a man on the Moon, unlock the secrets of the atom, discover quantum mechanics, invent/discover mathematics, and much more. This is what is fundamentally meant by LLMs cannot create "new" knowledge. They absolutely can mix and match their pool of training statements in ways that can generate new statements. But if it's not an extremely simple 'remix' and we're in a domain where there are right and wrong answers, there's good chance it's just a nonsensical hallucination.

Zambyte 2y ago
You are comparing a species going from developing language to building spaceships on the order of hundreds of thousands of years, to a technology that was invented 7 years ago and only started to really be taken seriously 4 years ago. Working memory systems required for learning and iteratively developing ideas are in their infancy (on the scale of recent developments), but the recall technology (vector databases a la RAG) is quite well proven. I see no reason that a language model couldn't do iterative science in the same way humans have been with the resources available to it (APIs) using the composition of current technologies.
- somenameforme 2y ago
  The current technology for LLMs is still a rather complex guess the next word algorithm. But this leaves the software with no real room to ever move beyond its training, regardless of how much time or training you give it. You could give them literally infinite processing power and they would not suddenly start developing new meaningful knowledge - it would still be little more than simple recombinations of its training dataset, until somebody gave it something new to train upon.

deleted 2y ago

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