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There are no new ideas in AI, only new datasets

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Re: There are no new ideas in AI, only new datasets

#221

Earlier quoted context omitted.

But the logic seems flawed. He has built an AI system that fails to do X. That does not mean there isn't an AI system that can do X. Especially considering that a lot is happening in AI, as you say. Anyway, Carmack knows a lot about optimizing computations on modern hardware. In practice, that happens to be also necessary for AI. However, it is not __sufficient__ for AI.

> That does not mean there isn't an AI system that can do X. You are holding the burden of proof here...

No, Carmack holds the burden of proof because he started the argument. His incapable program does not prove anything.

Maybe this is formulated a bit harshly, but let us respect the logic here.

Re: There are no new ideas in AI, only new datasets

#222
post #123

Earlier quoted context omitted.

LLMs don't "memorize" concepts like humans do. They generate output based on token patterns in their training data. So instead of having to be trained on every possible problem, they can still generate output that solves it by referencing the most probable combination of tokens for the specified input tokens. To humans this seems like they're truly solving novel problems, but it's merely a trick of statistics. These…

> To humans this seems like they're truly solving novel problems Because they are . This is some crazy semantic denial. I should stop engaging with this nonsense. We have AI that is kind of close to passing the Turing test and people still say it's not intelligent...

Depending on the interviewer, you could make a non-AI program pass the Turing test. It's quite a meaningless exercise.

Re: There are no new ideas in AI, only new datasets

#223

I'd say with confidence: we're living in the early days. AI has made jaw-dropping progress in two major domains: language and vision. With large language models (LLMs) like GPT-4 and Claude, and vision models like CLIP and DALL·E, we've seen machines that can generate poetry, write code, describe photos, and even hold eerily humanlike conversations. But as impressive as this is, it’s easy to lose sight of the bigger…

In a way until AI systems can feel the weight of a cup or flinch from heat, we're not close to modeling anything like embodied cognition

Re: There are no new ideas in AI, only new datasets

#224

What John Carmack is exploring is pretty revealing. Train models to play 2D video games to a superhuman level, then ask them to play a level they have not seen before or another 2D video game they have not seen before. The transfer function is negative. So, in my definition, no intelligence has been developed, only expertise in a narrow set of tasks. It’s apparently much easier to scare the masses with visions of ASI…

There's something fascinating about this, because the human ability to "transfer knowledge" (eg pick up some other never before seen video game and quickly understand it) isn't really that general. There's a very particular "overtone window" of the sort of degrees of difference where it is possible. If I were to hand you a version of a 2d platformer (lets say Mario) where the gimmick is that you're actually playing t…

One of my favourite examples of games that are hard to train an AI on is The Legend of Zelda for NES. Many other games of the NES era have (at least in the short term) a goal function which almost perfectly corresponds to some simple memory value such as score or x-position.

Not Zelda. That game is highly nonlinear and its measurable goals (triforce pieces) are long-term objectives that take a lot of gameplay to obtain. As far as I’m aware, no AI has been able to make even modest progress without any prior knowledge of the game itself.

Yet many humans can successfully play and complete the first dungeon without any outside help. While completing the full game is a challenge that takes dedication, many people achieved it long before having access to the internet and its spoiler resources.

So why is this? Why are humans so much better at Zelda than AIs? I believe that transfer knowledge has a lot to do with it. For starters, Link is approximately human (technically Hylian, but they are considered a race of humans, not a separate species) which means his method of sensing and interacting with his world will be instantly familiar to humans. He’s not at all like an earthworm or an insect in that regard.

Secondly, many of the objects Link interacts with are familiar to most modern humans today: swords, shields, keys, arrows, money, bombs, boomerangs, a ladder, a raft, a letter, a bottle of medicine, etc. Since these objects in-game have real world analogues, players will already understand their function without having to figure it out. Even the triforce itself functions similarly to a jigsaw puzzle, making it obvious what the player’s final objective should be. Furthermore, many players would be familiar with the tropes of heroic myths from many cultures which the Zelda plot closely adheres to (undertake a quest of personal growth, defeat the nemesis, rescue the princess).

All of this cultural knowledge is something we take for granted when we sit down to play Zelda for the first time. We’re able to transfer it to the game without any effort whatsoever, something I have yet to witness an AI achieve (train an AI on a general cultural corpus containing all of the background cultural information above and get it to transfer that knowledge into gameplay as effectively as an unspoiled Zelda beginner).

As for the Fourier transform, I don’t know. I do know that the Legend of Zelda has been successfully completed while playing entirely blindfolded. Of course, this wasn’t with Fourier transformed sound, though since the blindfolded run relies on sound cues I imagine a player could adjust to the Fourier transformed sound effects.

Re: There are no new ideas in AI, only new datasets

#225

Sometimes we get confused by the difference between technological and scientific progress. When science makes progress it unlocks new S-curves that progress at an incredible pace until you get into the diminishing returns region. People complain of slowing progress but it was always slow, you just didn’t notice that nothing new was happening during the exponential take off of the S-curve, just furious optimization.

The real trick is recognizing when you're in the thick of engineering polish versus standing on the edge of a new scientific leap

Re: There are no new ideas in AI, only new datasets

#226
post #212
post #190

Earlier quoted context omitted.

According to Carmack's recent talk [0], SOTA models that have been trained on game A don't perform better or train faster on game B. Even worse, training on game B negatively affects performance in game A when returning to it. [0] https://www.youtube.com/watch?v=3pdlTMdo7pY

You can see a similar effect with LLM finetunes. If you finetune a base model (or other instruct/finetune model) for a new task (e.g. better maths or programming language comprehension) it performs worse at other tasks like creative writing. To mitigate this you have to include the other categories in your finetune training dataset so it doesn't lose the existing knowledge. Otherwise, the backpropagation and training…

> In the game example having the weights optimized for game A doesn't help with game B. It would be interesting to see if training for both game A and B help it understand concepts in both.

Supposedly it does both A and B worse. That's their problem statement essentially. Current SOTA models don't behave like humans would. If you took a human that's really good at A and B, chances are they're gonna pick up C much quicker than a random person off the street that hasn't even seen Atari before. With SOTA models, the random "person" does better at C than the A/B master.

Re: There are no new ideas in AI, only new datasets

#227

What John Carmack is exploring is pretty revealing. Train models to play 2D video games to a superhuman level, then ask them to play a level they have not seen before or another 2D video game they have not seen before. The transfer function is negative. So, in my definition, no intelligence has been developed, only expertise in a narrow set of tasks. It’s apparently much easier to scare the masses with visions of ASI…

Generalization across tasks is clearly still elusive. The only reason we see such success with modern LLMs is because of the heroic amount of parameters used. When you are probing into a space of a billion samples, you will come back with something plausible every time.

The only thing I've seen approximating generalization has appeared in symbolic AI cases with genetic programming. It's arguably dumb luck of the mutation operator, but oftentimes a solution is found that does work for the general case - and it is possible to prove a general solution was found with a symbolic approach.

Re: There are no new ideas in AI, only new datasets

#228

I'd say with confidence: we're living in the early days. AI has made jaw-dropping progress in two major domains: language and vision. With large language models (LLMs) like GPT-4 and Claude, and vision models like CLIP and DALL·E, we've seen machines that can generate poetry, write code, describe photos, and even hold eerily humanlike conversations. But as impressive as this is, it’s easy to lose sight of the bigger…

> Language and vision are just the beginning — the parts we were able to digitize first - not necessarily the most central to intelligence. I respectfully disagree. Touch gives pretty cool skills, but language, video and audio are all that are needed for all online interactions . We use touch for typing and pointing, but that is only because we don't have a more efficient and effective interface. Now I'm not saying t…

Language is literally an abstraction of sensory inputs and cognitive processes. One can make similar arguments about image generation. These abstractions might characterize the higher cognitive abilities of humans, but it makes no sense to ignore "lower level" cognition. Embodiment is the foundation of our rich internal world models, in particular spacetime, causality, etc.

Current generative models merely mimic the output, with a fuzzy abstract linguistic mess in place of any physical/causal models. It's unsurprising that their capacity to "reason" is so brittle.

Re: There are no new ideas in AI, only new datasets

#230
At Axon, we see the same pattern: data quality and diversity make a bigger difference than architecture tweaks. Whether it's AI for logistics or enterprise automation, real progress comes when we unlock new, structured datasets, not when we chase “smarter” models on stale inputs.
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