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I am starting to wonder if the AI market will diverge into two separate implementations where one is the open source (open models?) that people can use and depl

I am starting to wonder if the AI market will diverge into two separate implementations where one is the open source (open models?) that people can use and deploy will be done for people that are more technical and most people will use the easiest solutions. I have used some of the AI techniques in macOS and while they are helpful I probably won't use the ones that call out to ChatGPT. But, I have found that using o1 preview it helps me with my side projects. On the other hand I have started using llama 3.3 and it is approaching usefulness and maybe an open model with CoT and reinforcement learning or even a new version of Qwen 2.5 I could see replacing.

ethbr1 2y ago
The only future where this happens is if little, non- infrastructure / specialized hardware models maintain functional parity with "throw more hardware at the problem" big models. That seems... unlikely. And then we'll see the same evolution that built Google. Someone with the best algorithm leverages that into a revenue stream, then uses that revenue to buy hardware, then monetizes its userbase via advertisement, then uses that additional revenue to buy market share. I'm sure it'll make someone a ton of money, but it's not a future I'd like to live in (again). Diversity is strength.
dingnuts 2y ago
> On the other hand I have started using llama 3.3 and it is approaching usefulnes cute phrasing! so what you're saying is that you're using it, but it's not useful. You can imagine it being useful, and it produces output similar to useful output, but it isn't useful. That perfectly matches my experience with language models, going back to when I was running GPT2 locally. Has there been any evidence that the improvement is not asymptotic? For ten years language models have been approaching usefulness. When do we get to usefulness?
- Philpax 2y ago
  That's not what they said, and you're being purposely obtuse. They were saying that Llama 3.3, an open model, approaches usefulness, in comparison to o1, a closed model, which is already useful to them. As for usefulness in general, it's already here. It's not perfect, it can make mistakes, but saying they're not useful at all - especially for software engineers - is foolish. See https://news.ycombinator.com/item?id=42602940 https://news.ycombinator.com/item?id=42602940.
- zitterbewegung 2y ago
  I think LLAMA 4 will get to usefulness ( I think I should say I have evaluated it). Long term training seems to be the best solution to solve the issues I have been facing and also more polish using CoT with reinforcement learning. I don't want to be paying for o1 since I have a homelab that could easily use up to 70B for LLAMA 3.3 or more if I can figure out how to run larger versions of LLAMA but llama can parallelize it to use all of my GPUs and inference would be faster so I would settle for that.

ethbr1 2y ago

The only future where this happens is if little, non- infrastructure / specialized hardware models maintain functional parity with "throw more hardware at the problem" big models. That seems... unlikely. And then we'll see the same evolution that built Google. Someone with the best algorithm leverages that into a revenue stream, then uses that revenue to buy hardware, then monetizes its userbase via advertisement, then uses that additional revenue to buy market share. I'm sure it'll make someone a ton of money, but it's not a future I'd like to live in (again). Diversity is strength.

dingnuts 2y ago

> On the other hand I have started using llama 3.3 and it is approaching usefulnes cute phrasing! so what you're saying is that you're using it, but it's not useful. You can imagine it being useful, and it produces output similar to useful output, but it isn't useful. That perfectly matches my experience with language models, going back to when I was running GPT2 locally. Has there been any evidence that the improvement is not asymptotic? For ten years language models have been approaching usefulness. When do we get to usefulness?

Philpax 2y ago
That's not what they said, and you're being purposely obtuse. They were saying that Llama 3.3, an open model, approaches usefulness, in comparison to o1, a closed model, which is already useful to them. As for usefulness in general, it's already here. It's not perfect, it can make mistakes, but saying they're not useful at all - especially for software engineers - is foolish. See https://news.ycombinator.com/item?id=42602940 https://news.ycombinator.com/item?id=42602940.
zitterbewegung 2y ago
I think LLAMA 4 will get to usefulness ( I think I should say I have evaluated it). Long term training seems to be the best solution to solve the issues I have been facing and also more polish using CoT with reinforcement learning. I don't want to be paying for o1 since I have a homelab that could easily use up to 70B for LLAMA 3.3 or more if I can figure out how to run larger versions of LLAMA but llama can parallelize it to use all of my GPUs and inference would be faster so I would settle for that.