Learn how to use AI at work with Hubspot's FREE AI for GTM bundle: https://clickhubspot.com/u2oBig tech AI is really quite problematic and a lie. ✉️ NEWSLETT...
Edit: I know of Petals, I even discussed with some people working on it, and I learned about federated AI or federated learning back then, since at least 2019 (proof) so this isn’t new to me.
Again, I’m not arguing that open source automatically solves problems, just that since AI is obviously going to continue being developed, it’s better if it’s done in the open.
Well that’s one position, another is to say AI, being developed currently, is :
not working due to hallucinations
wasteful in terms of resources
creates problematic behaviors in terms of privacy
creates more inequality
and other problems and is thus in most cases (say outside of e.g numerical optimization as already done at e.g DoE, so in the “traditional” sense of AI, not the LLM craze) better be entirely ignored.
Edit : what I mean is that the argument of inevitability itself is dangerous, often abused.
It’s pretty clear that hallucinations are an issue only for specific use cases. This problem certainly doesn’t make ML useless. For example, I find it’s far faster to use a code oriented model to get an idea of how to solve a problem than going to stack overflow. The output of the model doesn’t need to be perfect, it just needs to get me moving in the right direction.
Furthermore, there is nothing to suggest that the problem of hallucinations is fundamental and can’t be addressed going forward. I’ve linked an example of a research team doing precisely that above.
wasteful in terms of resources
Sure, but so are plenty of other things. And as I’ve illustrated above, there are already drastic improvements happening in this area.
creates problematic behaviors in terms of privacy
Not really a unique problem either.
creates more inequality
Don’t see how that’s the case. In fact, I’d argue the opposite to be true, especially if the technology is open and available to everyone.
and other problems and is thus in most cases (say outside of e.g numerical optimization as already done at e.g DoE, so in the “traditional” sense of AI, not the LLM craze) better be entirely ignored.
There is a lot of hype around this tech, and some of it will die down eventually. However, it would be a mistake to throw the baby out with the bath water.
what I mean is that the argument of inevitability itself is dangerous, often abused.
The argument of inevitability stems from the fact that people have already found many commercial uses for this tech, and there is a ton of money being poured into it. This is unlikely to stop regardless of what your personal opinion on the tech is.
FWIW I do have my own page on FLOSS AI, cf https://fabien.benetou.fr/Content/SelfHostingArtificialIntelligence so I do believe it’s at least interesting, even important, to understand what it is.
Still, AFAIK both the electricity https://www.bloomberg.com/graphics/2024-ai-data-centers-power-grids/ and even the potential for correction https://arxiv.org/abs/2404.04125 from intrinsic properties of the dataset and learning but also as its marketed https://link.springer.com/article/10.1007/s10676-024-09775-5 today make me reiterate, AI FLOSS doesn’t not automatically solve all problems of closed source or proprietary AI.
Edit: I know of Petals, I even discussed with some people working on it, and I learned about federated AI or federated learning back then, since at least 2019 (proof) so this isn’t new to me.
Again, I’m not arguing that open source automatically solves problems, just that since AI is obviously going to continue being developed, it’s better if it’s done in the open.
Well that’s one position, another is to say AI, being developed currently, is :
and other problems and is thus in most cases (say outside of e.g numerical optimization as already done at e.g DoE, so in the “traditional” sense of AI, not the LLM craze) better be entirely ignored.
Edit : what I mean is that the argument of inevitability itself is dangerous, often abused.
It’s pretty clear that hallucinations are an issue only for specific use cases. This problem certainly doesn’t make ML useless. For example, I find it’s far faster to use a code oriented model to get an idea of how to solve a problem than going to stack overflow. The output of the model doesn’t need to be perfect, it just needs to get me moving in the right direction.
Furthermore, there is nothing to suggest that the problem of hallucinations is fundamental and can’t be addressed going forward. I’ve linked an example of a research team doing precisely that above.
Sure, but so are plenty of other things. And as I’ve illustrated above, there are already drastic improvements happening in this area.
Not really a unique problem either.
Don’t see how that’s the case. In fact, I’d argue the opposite to be true, especially if the technology is open and available to everyone.
There is a lot of hype around this tech, and some of it will die down eventually. However, it would be a mistake to throw the baby out with the bath water.
The argument of inevitability stems from the fact that people have already found many commercial uses for this tech, and there is a ton of money being poured into it. This is unlikely to stop regardless of what your personal opinion on the tech is.