141–150 of 307 posts
What about actively obtained data - models seeking data, rather than being fed. Human babies put things in their mouths, they try to stand and fall over. They “do stuff” to learn what works. Right now we’re just telling models what works. What about simulation: models can make 3D objects so why not give them a physics simulator? We have amazing high fidelity (and low cost!) game engines that would be a great building…
Earlier quoted context omitted.
In other words, they learn the game, not how to play games .
Well yeah... If you only ever played one game in your life you would probably be pretty shit at other games too. This does not seem very revealing to me.
Of course, this because I have spent a lot of time TRAINING to play chess and basically none training to play go.
I am good on guitar because I started training young but can't play the flute or piano to save my life.
Most complicated skills have basically no transfer or carry over other than knowing how to train on a new skill.
What about actively obtained data - models seeking data, rather than being fed. Human babies put things in their mouths, they try to stand and fall over. They “do stuff” to learn what works. Right now we’re just telling models what works. What about simulation: models can make 3D objects so why not give them a physics simulator? We have amazing high fidelity (and low cost!) game engines that would be a great building…
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…
Organic adaption and persistence of memory I would say are the two major advancements that need to happen. Human neural networks are dynamic, they change and rearrange, grow and sever. An LLM is fixed and relies on context, if you give it the right answer it won't "learn" that is the correct answer unless it is fed back into the system and trained over months. What if it's only the right answer for a limited period o…
What about actively obtained data - models seeking data, rather than being fed. Human babies put things in their mouths, they try to stand and fall over. They “do stuff” to learn what works. Right now we’re just telling models what works. What about simulation: models can make 3D objects so why not give them a physics simulator? We have amazing high fidelity (and low cost!) game engines that would be a great building…
Simulation and embodied AI (putting the AI in a robotic arm or a car so it can try stuff and gather information about the results) are very actively being explored.
We let models write code and run it. Which gives them a high chance of getting arithmetic right.
Solving the “crossing the river” problem by letting the model create and run a simulation would give a pretty high chance of getting it right.
What about actively obtained data - models seeking data, rather than being fed. Human babies put things in their mouths, they try to stand and fall over. They “do stuff” to learn what works. Right now we’re just telling models what works. What about simulation: models can make 3D objects so why not give them a physics simulator? We have amazing high fidelity (and low cost!) game engines that would be a great building…
That would be reinforcement learning. The juice is quite hard to squeeze.
Each Cursor rule is a byproduct of tons of work and probably contains lots that can be unpacked. Any research on that?
Earlier quoted context omitted.
Is any model currently known to succeed in the scenario that Carmack’s inappropriate model failed?
No monolithic models but us ng hybrid approaches we've been able to beet humans for some time now.
Earlier quoted context omitted.
I think the problem is we train models to pattern match, not to learn or reason about world models
Where do you draw the line between pattern matching and reasoning about world models? A lot of intelligence is just pattern matching and being quick about it.
Current AI only does one of those (pattern matching, not evolution), and the prospects of simulating evolution is kind of bleak, given I don’t think we can simulate a full living cell yet from scratch? Building a world model requires life (or something that has undergone a similar evolutionary survivorship path), not something that mimics life.
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.
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 purposes, he is an "AI guy" now.