I assume that you’d have some sort of massive workload that you span over multiple plans. You just have software to switch you from one plan to the next once you saturate the plan.
Probably not all that hard to write some kind of software that tries to make massive use of LLMs. Like, oh, I don’t know. Getting all abstract here, any problem in computer science where you have a problem that you don’t know how to solve directly, but you can easily check whether an answer is correct. Then you just keep trying to solve it, and repeatedly check whether the generated answer is correct or not.
Another possibility is that you have a problem where you can quickly check the quality of a given solution (either via human assistance or software, even though you don’t know how to solve the problem yourself), and want to generate a number of solutions and pick the best.
I’ve certainly seen that with image-generating diffusion models, rather than LLMs — stuff like “batch-generate me N images using this prompt, and I’ll pick the best”. It’s an algorithmically-simple, brute-force way of improving quality, by just throwing more compute time at the problem. The human “quality evaluation” is cheap to do compared to the human time required to generate an image. Burns a lot of compute time, but the alternative to improve quality is improving the model, and if we don’t know how to do that yet…shrugs
Not even that. A business can “implement” AI agent on their website by forwarding client’s inputs to someone else’s API, adding a prompt pointing back at them.
I assume that you’d have some sort of massive workload that you span over multiple plans. You just have software to switch you from one plan to the next once you saturate the plan.
Probably not all that hard to write some kind of software that tries to make massive use of LLMs. Like, oh, I don’t know. Getting all abstract here, any problem in computer science where you have a problem that you don’t know how to solve directly, but you can easily check whether an answer is correct. Then you just keep trying to solve it, and repeatedly check whether the generated answer is correct or not.
Another possibility is that you have a problem where you can quickly check the quality of a given solution (either via human assistance or software, even though you don’t know how to solve the problem yourself), and want to generate a number of solutions and pick the best.
I’ve certainly seen that with image-generating diffusion models, rather than LLMs — stuff like “batch-generate me N images using this prompt, and I’ll pick the best”. It’s an algorithmically-simple, brute-force way of improving quality, by just throwing more compute time at the problem. The human “quality evaluation” is cheap to do compared to the human time required to generate an image. Burns a lot of compute time, but the alternative to improve quality is improving the model, and if we don’t know how to do that yet…shrugs
Not even that. A business can “implement” AI agent on their website by forwarding client’s inputs to someone else’s API, adding a prompt pointing back at them.