- cross-posted to:
- technology@lemmy.zip
- technology@lemmy.ml
- technology@lemmy.ml
- cross-posted to:
- technology@lemmy.zip
- technology@lemmy.ml
- technology@lemmy.ml
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the electricity bill for each query – to power the servers and their chillers – would still make running these giant models very expensive.
This assumes there won’t be radical advances in cost-effective hardware to run the queries.
AI proponents work precisely towards such advances: hardware tailored to running the best performing models, at far lower costs than current GPUs and GPU derivates.
Something like “execute in RAM” neural network accelerators, could reduce query costs by several orders of magnitude.
The fraud of the cryptocurrency bubble was far more pervasive than the fraud in the dotcom bubble, so much so that without the fraud, there’s almost nothing left.
Ironically, what the crypto bubble left behind, was a surplus of GPUs, which got repurposed for AI… and just like crypto left GPUs to move onto purpose-built ASICs and other models like PoS instead of PoW, so does AI need to leave GPUs and move onto purpose-built hardware with better models (quantized NNs are a good example).
As a tangent, we could talk about how the gaming industry has enabled a GPU industry that in turn has enabled these off-shots.
It’s really hard to know how this will play out. The models only have to improve a bit at this point to be reliably better than humans, as which time it probably makes sense to replace humans. It seems they will probably still hallucinate but do it little enough that it’s still a net gain to use them. Compute power needed to run them will surely come down.
I’m as skeptical as the next guy, but I do think they will have uses, especially in examples like radiology which he he uses as a negative case. However I’m pretty sure it will eventually be able to do the initial screening to find the 95% of cases with nothing at a rate similar to existing medical diagnostic testing and then return the other 5% back to a human to review and decide further treatment. Based on my experience with speech language models, I’m pretty sure you’d be able to tweak the models to produce mostly false positives rather than false negatives and then run it through further layers of review afterwards.
A very dumb bubble that will pop and leave companies scrambling to be certified “AI Free” to gain customers and employees
The same way companies advertise they are certified to be “Privacy respecting”, right? right?
A problem of this bubble is that it is making AI synonymous with LLM - and when it goes down will burn other more sensibly forms of AI.
So far, the only thing AI has shown to be pretty good at is summerizing a large amount of data, and even then it cant be fully trusted to not make mistakes.
Hmm, I think summarization is a bad example. I’ve read quite some AI summaries that miss the point, sum up to a point where the simplification makes sth wrong or the AI added things or paraphrased and made things at least ambiguous. Even with the state of the art tech. Especially if the original texts were condensed or written by professionals. Like scientific papers or good news articles…
What I think works better are tasks like translating text. That works really well. Sometimes things like rewording text. Or the style-transfer the image generators can do. That’s impressive. Restoring old photos, coloring them or editing something in/out. I also like the creativity they provide me with. They can come up with ideas, flesh out my ideas.
I think AI is an useful tool for tasks like that. But not so much for summarization or handling factual information. I don’t see a reason why further research coudn’t improve on that… But at the current state it’s just the wrong choice of tools.
And sure, it doesn’t help that people hype AI and throw it at everything.
