151–160 of 307 posts
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…
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> Language and vision are just the beginning — the parts we were able to digitize first - not necessarily the most central to intelligence. I probably made a mistake when i asserted that -- should have thought it over. Vision is evolutionarily older and more “primitive”, while language is uniquely human [or maybe, more broadly, primate, cetacean, cephalopod, avian...] symbolic, and abstract — arguably a different ord…
People who lack one of those senses, or even two of them, tend to do just fine.
If all humans lacked vision, the human race would definitely not do just fine.
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I don't get why people are so invested in framing it this way. I'm sure there are ways to do the stated objective. John Carmack isn't even an AI guy why is he suddenly the standard.
Who is an "AI guy"? The field as we know it is fairly new. Sure, neural nets are old hat, but a lot has happened in the last few years. John Carmack founded Keen technology in 2022 and has been working seriously on AI since 2019. From his experience in the video game industry, he knows a thing or two about linear algebra and GPUs, that is the underlying maths and the underlying hardware. So, for all intent and purpos…
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.
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…
I don't get why people are so invested in framing it this way. I'm sure there are ways to do the stated objective. John Carmack isn't even an AI guy why is he suddenly the standard.
Not sure why justanotherjoe is a credible resource on who is and isn’t expert in some new dialectic and euphemism for machine state management. You’re that nobody to me :shrug:
Yann LeCun is an AI guy and has simplified it as “not much more than physical statistics.”
WWhole lot of AI is decades old info theory books applied to modern computer.
Either a mem value is or isn’t what’s expected. Either an entire matrix of values is or isn’t what’s expected. Store the results of some such rules. There’s your model.
The words are made up and arbitrary because human existence is arbitrary. You’re being sold on a bridge to nowhere.
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Just go and ask ChatGPT or Claude something that can't possibly be in its training set. Make something up. If it is only memorising answers then it will be impossible for it to get the correct result. A simple nonsense programming task would suffice. For example "write a Python function to erase every character from a string unless either of its adjacent characters are also adjacent to it in the alphabet. The string…
People who say that LLMs memorize stuff are just as clueless who assume that there's any reasoning happening. They generate statistically plausible answers (to simplify the answer) based on the training set and weights they have.
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It’s really easy: go to Claude and ask it a novel question. It will generally reason its way to a perfectly good answer even if there is no direct example of it in the training data.
How do you know it’s a novel question?
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They learn the value of specific actions in specific contexts based on the rewards they received during their play time. Specific actions and specific contexts are not transferable for various reasons. John quoted that varying frame rates and variable latency between action and effect really confuse the models.
Okay, so fuzz the frame rate and latency? That feels very easy to fix.
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How do you know it’s a novel question?
You have probably seen examples of LLMs doing the "mirror test", i.e. identifying themselves in screenshots and referring to the screenshot from the first person. That is a genuinely novel question as an "LLM mirror test" wasn't a concept that existed before about a year ago.
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I'm not sure why you think so. AI outperforms humans in many games already. Basically all the games we care to put money to train a model. AI has beat the best human players in Chess, Go, Mahjong, Texas hold'em, Dota, Starcraft, etc. It would be really, really surprising that some Atari game is the holy grail of human performance that AI cannot beat.
I recall this not being true at all for Dota and Starcraft. I recall AlphaStar performed much better than the top non-pro players, but it couldn't consistently beat the pro players with the budget that Google was willing to spend, and I believe the same was true of Dota II (and there they were even playing a limited form of the game, with fewer heroes and without the hero choice part, I believe).
In other words, the Starcraft AIs that win do so by microing every single unit in the entire game at the same time, which is pretty clever, but if you reduce them to interfacing with the game in the same way a human does, they start losing.
One of my pet peeves when we talk about the various chess engines is yes, given a board state they can output the next set of moves to beat any human, but can they teach someone else to play chess? I'm not trying to activate some kinda "gotcha" here, just getting at what does it actually mean to "know how to play chess". We'd expect any human that claimed to know how to play to be able to teach any other human pretty trivially.
If you work with model architecture and read papers, how could not know there are a flood of new ideas? Only few yield interesting results though. I kind of wonder if libraries like pytorch have hurt experimental development. So many basic concepts no one thinks about anymore because they just use the out of the box solutions. And maybe those solutions are great and those parts are "solved", but I am not sure. How ma…
But yes, there's a ton of interesting and useful stuff (beyond datasets and data related improvements) going on right now, and I'm not even talking about LLMs. I don't do anything related to LLM and even then I still see tons of new stuff popping up regularly.