61–70 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…
Reinforcement learning from self-play/AlphaWhatever? Nah must just be datasets. :)
Big difference between a perfect information, completely specified zero sum game and the real world. As a simple analogy, read out the following sentence multiple times, stressing a different word each time. "I never said she stole my money" Note how the meaning changes and is often unique? That is a lens I to the frame problem and it's inverse, the specification problem. The above problem quickly becomes tower-compl…
and as far as synthetic vs real data, there's a lot of gaps in LLM knowledge; and vision models suffer from "limited tags", which used to have workarounds with textual embeddings and the like, but those went by the wayside as LoRA, controlnet, etc. appeared.
There's people who are fairly well known that LLMs have no idea about. There's things in books i own that the AI confidently tells me are either wrong or don't exist.
That one page about compressing 1 gig wikipedia as small as possible implicitly and explicitly states that AI is "basically compression" - and if the data isn't there, it's not in the compressed set (weights) either.
And i'll reply to another comment here, about "24/7 rolling/ for looped" AI - i thought of doing this when i first found out about LLMs, but context windows are the enemy, here. I have a couple of ideas about how to have a continuous AI, but i don't have the capital to test it out.
Here's an idea: make the AIs consistent at doing things computers are good at. Here's an anecdote from a friend who's living in Japan: > i used chatgpt for the first time today and have some lite rage if you wanna hear it. tldr it wasnt correct. i thought of one simple task that it should be good at and it couldnt do that. > (The kangxi radicals are neatly in order in unicode so you can just ++ thru em. The cjks are…
Many of us have solved this with internal tooling that has not yet been shared or released to the public.
There’s an infinite repertoire of such tasks that combine AI capabilities with traditional computer algorithms, and I don’t think we have a generic way of having AI autonomously outsource whatever parts require precision in a reliable way.
"There weren't really any advancements from around 2018. The majority of the 'advancements' were in the amount of parameters, training data, and its applications. What was the GPT-3 to ChatGPT transition? It involved fine-tuning, using specifically crafted training data. What changed from GPT-3 to GPT-4? It was the increase in the number of parameters, improved training data, and the addition of another modality. From GPT-4 to GPT-40? There was more optimization and the introduction of a new modality. The only thing left that could further improve models is to add one more modality, which could be video or other sensory inputs, along with some optimization and more parameters. We are approaching diminishing returns." [1]
10 months ago around o1 release:
"It's because there is nothing novel here from an architectural point of view. Again, the secret sauce is only in the training data. O1 seems like a variant of RLRF https://arxiv.org/abs/2403.14238
Soon you will see similar models from competitors." [2]
Winter is coming.
Earlier quoted context omitted.
They memorize the answers not the process to arrive at answers
This has been disproven so many times... They clearly do both. You can trivially prove this yourself.
Given the long list of dead philosophers of mind, if you have a trivial proof, would you mind providing a link?
Earlier quoted context omitted.
Can you please explain "the transfer function is negative"? I'm wondering whether one has tested with the same model but on two situations: 1) Bring it to superhuman level in game A and then present game B, which is similar to A, to it. 2) Present B to it without presenting A. If 1) is not significantly better than 2) then maybe it is not carrying much "knowledge", or maybe we simply did not program it correctly.
I think the problem is we train models to pattern match, not to learn or reason about world models
The ability to collect gene expression data at a tissue specific level has only been invented and automated in the last 4-5 years (see 10X Genomics Xenium, MERFISH). We've only recently figured out how to collect this data at the scale of millions of cells. A breakthrough on this front may be the next big area of advancement.
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 wrote about it around a year ago here: "There weren't really any advancements from around 2018. The majority of the 'advancements' were in the amount of parameters, training data, and its applications. What was the GPT-3 to ChatGPT transition? It involved fine-tuning, using specifically crafted training data. What changed from GPT-3 to GPT-4? It was the increase in the number of parameters, improved training data,…