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https://openai.com/index/introducing-openai-o1-preview/ https://openai.com/index/introducing-openai-o1-preview/ > ChatGPT Plus and Team users will be able to a

by notamy 2y ago

https://openai.com/index/introducing-openai-o1-preview/ https://openai.com/index/introducing-openai-o1-preview/ > ChatGPT Plus and Team users will be able to access o1 models in ChatGPT starting today. Both o1-preview and o1-mini can be selected manually in the model picker, and at launch, weekly rate limits will be 30 messages for o1-preview and 50 for o1-mini. We are working to increase those rates and enable ChatGPT to automatically choose the right model for a given prompt. Weekly? Holy crap, how expensive is it to run is this model?

HPMOR 2y ago
It's probably running several lines of COT. I imagine, each single message you send is probably at __least__ 10x to the actual model. So in reality it's like 300 messages, and honestly it's probably 100x, given how constrained they're being with usage.
theLiminator 2y ago
Anyone know when o1 access in ChatGPT will be open?
- tedsanders 2y ago
  Rolling out over the next few hours to Plus users.
narrator 2y ago
The human brain uses 20 watts, so yeah we figured out a way to run better than human brain computation by using many orders of magnitude more power. At some point we'll need to reject exponential power usage for more computation. This is one of those interesting civilizational level problems. There's still a lack of recognition that we aren't going to be able to compute all we want to, like we did in the pre-LLM days.
- seydor 2y ago
  we ll ask it to redesign itself for low power usage
- cma 2y ago
  For 20 watts of work on stuff like this for about 4 hours a day counting vacations and weekends and attention span. So 20 hours of rest, relaxation, distraction, household errands and stuff, so that maybe bumps it up to 120 watts per work hour. Then 22.5 years of training or so per worker, 45 year work period, 22.5 year retirement. So double it there to 240 watts. We can't run brains without bodies, so multiply that by 6 giving 1440 watts + the air conditioning, commuting to school and work, etc., maybe 2000 watts? We're getting close to parity if things keep getting more efficient as fast as they have been. But that's without accounting for the AI training, which can on the plus side be shared among multiple agents, but on the down side can't really do continuous learning very well without catastrophic forgetting.

by notamy 2y ago

HPMOR 2y ago

It's probably running several lines of COT. I imagine, each single message you send is probably at __least__ 10x to the actual model. So in reality it's like 300 messages, and honestly it's probably 100x, given how constrained they're being with usage.

theLiminator 2y ago

Anyone know when o1 access in ChatGPT will be open?

tedsanders 2y ago
Rolling out over the next few hours to Plus users.

narrator 2y ago

The human brain uses 20 watts, so yeah we figured out a way to run better than human brain computation by using many orders of magnitude more power. At some point we'll need to reject exponential power usage for more computation. This is one of those interesting civilizational level problems. There's still a lack of recognition that we aren't going to be able to compute all we want to, like we did in the pre-LLM days.

seydor 2y ago
we ll ask it to redesign itself for low power usage
cma 2y ago
For 20 watts of work on stuff like this for about 4 hours a day counting vacations and weekends and attention span. So 20 hours of rest, relaxation, distraction, household errands and stuff, so that maybe bumps it up to 120 watts per work hour. Then 22.5 years of training or so per worker, 45 year work period, 22.5 year retirement. So double it there to 240 watts. We can't run brains without bodies, so multiply that by 6 giving 1440 watts + the air conditioning, commuting to school and work, etc., maybe 2000 watts? We're getting close to parity if things keep getting more efficient as fast as they have been. But that's without accounting for the AI training, which can on the plus side be shared among multiple agents, but on the down side can't really do continuous learning very well without catastrophic forgetting.