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ChatGPT does have at least a year head start so this doesn't seem surprising. This proves that OpenAI doesn't really have any secret sauce that others can't rep

ChatGPT does have at least a year head start so this doesn't seem surprising. This proves that OpenAI doesn't really have any secret sauce that others can't reproduce. I suppose size will become the moat eventually but atm it looks like it could become anyone's game.

mikkom 3y ago
Anthropic is ex-openai so even if there is a secret sauce that openai uses, they might know it.
- worldsayshi 3y ago
  Yeah you might be right but Google and Mistral aren't that far behind either: https://twitter.com/NickADobos/status/1772764680639148285 https://twitter.com/NickADobos/status/1772764680639148285
CuriouslyC 3y ago
Size is absolutely not going to become the moat unless there's some hardware revolution that makes running big models very very cheap, but that requires a very large up-front capital cost to deploy. Big models are inefficient, and as smaller models improve there will be very few use cases where the big models are worth the compute.
- falcor84 3y ago
  I imagine that going forward, the typical approach would be a multi-level LLM, such that there's a relatively small and quick model in front of the user, which can in turn decide to consult an "expert" larger model as part of its "system 2".
  - CuriouslyC 3y ago
    Absolutely, that is 100% the way things are going to go. What's going to happen is that eventually there will be an online model directory that a local agent knows how to query to identify other models to call in order to build up an answer. Local agents will be empowered with online learning since it won't be possible to pre-train on the model catalog.
  - heyjamesknight 3y ago
    And then at the top of that stack, we’ll have a single, master model controlling everything. We could call it the Master Control Program.
- worldsayshi 3y ago
  > as smaller models improve there will be very few use cases where the big models are worth the compute I see very little evidence of this so far. The use cases I'm interested in just barely works on GPT-4 and lesser models give mostly garbage. I.e. function calling and inferring stuff like SQL queries. If there are smaller models that can do passable work on such use cases I'd be very interested to know.
  - CuriouslyC 3y ago
    Claude Haiku can do a LOT of the things you'd think you need GPT4 for. It's not as good at complex code and really tricky language use/abstractions, but it's very close for more superficial things, and you can call haiku like 60 times for each gpt4 call. I bet you could do multiple prompt variations with haiku and then do answer combining to compete with GPT4-T/Opus at a fraction of the price.
    - worldsayshi 3y ago
      Interesting! I just discovered that Anthropic indeed officially support commercial API access in (at least) some EU countries. They just don't support GUI access in all those countries: https://www.anthropic.com/supported-countries https://www.anthropic.com/supported-countries

mikkom 3y ago

Anthropic is ex-openai so even if there is a secret sauce that openai uses, they might know it.

worldsayshi 3y ago
Yeah you might be right but Google and Mistral aren't that far behind either: https://twitter.com/NickADobos/status/1772764680639148285 https://twitter.com/NickADobos/status/1772764680639148285

CuriouslyC 3y ago

Size is absolutely not going to become the moat unless there's some hardware revolution that makes running big models very very cheap, but that requires a very large up-front capital cost to deploy. Big models are inefficient, and as smaller models improve there will be very few use cases where the big models are worth the compute.

falcor84 3y ago
I imagine that going forward, the typical approach would be a multi-level LLM, such that there's a relatively small and quick model in front of the user, which can in turn decide to consult an "expert" larger model as part of its "system 2".
- CuriouslyC 3y ago
  Absolutely, that is 100% the way things are going to go. What's going to happen is that eventually there will be an online model directory that a local agent knows how to query to identify other models to call in order to build up an answer. Local agents will be empowered with online learning since it won't be possible to pre-train on the model catalog.
- heyjamesknight 3y ago
  And then at the top of that stack, we’ll have a single, master model controlling everything. We could call it the Master Control Program.
worldsayshi 3y ago
> as smaller models improve there will be very few use cases where the big models are worth the compute I see very little evidence of this so far. The use cases I'm interested in just barely works on GPT-4 and lesser models give mostly garbage. I.e. function calling and inferring stuff like SQL queries. If there are smaller models that can do passable work on such use cases I'd be very interested to know.
- CuriouslyC 3y ago
  Claude Haiku can do a LOT of the things you'd think you need GPT4 for. It's not as good at complex code and really tricky language use/abstractions, but it's very close for more superficial things, and you can call haiku like 60 times for each gpt4 call. I bet you could do multiple prompt variations with haiku and then do answer combining to compete with GPT4-T/Opus at a fraction of the price.
  - worldsayshi 3y ago
    Interesting! I just discovered that Anthropic indeed officially support commercial API access in (at least) some EU countries. They just don't support GUI access in all those countries: https://www.anthropic.com/supported-countries https://www.anthropic.com/supported-countries