Posted by bilsbie 20 hours ago
The original idea of connectionism is that neural networks can represent any function, which is the fundamental mathematical fact. So we should be optimistic, neural nets will be able to do anything. Which neural nets? So far people have stumbled on a few productive architectures, but it appears to be more alchemy than science. There is no reason why we should think there won't be both new ideas and new data. Biology did it, humans will do it too.
> we’re engaged in a decentralized globalized exercise of Science, where findings are shared openly
Maybe the findings are shared, if they make the Company look good. But the methods are not anymore
It can probably remember more facts about a topic than a PhD in that topic, but the PhD will be better at thinking about that topic.
"Thinking" is too broad a term to apply usefully but I would say its pretty clear we are not close to AGI.
Why should the model need to memorize facts we already have written down somewhere?
So can a notebook.
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.
For example, FPGAs use a lot of area and power routing signals across the chip. Those long lines have a large capacitance, and thus cause a large amount of dynamic power loss. So does moving parameters around to/from RAM instead of just loading up a vast array of LUTs with the values once.
> 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 not. I couldnt see any clear mapping so i asked gpt to do it. Big mess i had to untangle manually anyway it woulda been faster to look them up by hand (theres 214))
> The big kicker was like, it gave me 213. And i was like, "why is one missing?" Then i put it back in and said count how many numbers are here and it said 214, and there just werent. Like come on you SHOULD be able to count.
If you can make the language models actually interface with what we've been able to do with computers for decades, i imagine many paths open up.
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.
Because new methods unlock access to new datasets.
Edit: Oh I see this was a rhetorical question answered in the next paragraph. D'oh
"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.
If the technology is useful, the Slope of Enlightenment, followed by the Plateau of Productivity.
shortly thereafter the entire ecosystem will collapse