There is a decent (<50%, >20%) chance that frontier foundation models are less oligopoly like than it seems. The reason is that there are so many levers to pull, so much low hanging fruit.
For example:
* Read the Bloomberg GPT paper - they create their own tokenizer. For specialized domains (finance, law, medicine, etc) the vocabulary is very different and there is likely a lot to do here, where individual tokens really need to map to specific concepts and having a concept capture in several tokens makes it too hard to learn on limited domain data.
* Data - so many ways to do different data - more/less, cleaner, "better" on some dimension.
* Read the recent papers on different decoding strategies - there seems to be a lot to do here.
* Model architecture (SSM etc). If you speak to people who aren't even researchers, they have 10 ideas around architecture and some of them are decent sounding ideas - lots of low hanging fruit.
* System architecture - ie likely to see more and more "models" served via API which are actually systems of several model calls, and there is a lot to do here.
* Hardware, lower precision etc likely to make training much cheaper
It's reasonably likely (again, guessing < 50% > 20%) that this large set of levers to pull become ways to see constant leap-frogging for years and years. Or, at least they become choices/trade-offs rather than strictly "better".
I agree this is a potential outcome. One big question is generalizability versus niche models. For example, is the best legal model a frontier model + a giant context window + RAG? Or is it a niche model trained or fine tuned for law?
Right now at least people seem to decouple some measures of how smart the model is from knowledge base, and at least for now the really big models seem smartest. So part of the question is well is how insightful / synthesis centric the model needs to be versus effectively doing regressions....
I haven't listen to your great podcasts so hard to say what is not covered.
Architectures matter a lot less than data. "Knowledge" and "reasoning" in LLMs is a manifestation of instruct-style data. It won't matter how much cheaper training gets if there is limited instruct data for use cases.
How do you make 100k context window data for example? Still need thousands of people. Same with so-called niches.
Maybe it turns out to be a complex coordination problem to share the data. That's bad for equity investors and giant companies. Anyway, all of this would cost less than the moon landing so it's practicable, you don't need cheaper, you just need risk-taking.
The obviousness of the path from here to there means it's not about innovation. It's all about strategy.
If Google could marshal $10b for Stadia it could spend $10b on generating 100k context window instruct style data and have the best model. It could also synthesize videos from Unity/Unreal for Sora-style generation. It would just be very hard in an org with 100,000+ people to spend $10b on 10 developers and 10,000 writers compared to 400 developers and 3,600 product managers and other egos. At the end of the day you are revisiting the weaknesses that brought Google and other big companies to this mess in the first place.
Anyway I personally think the biggest weakness with ChatGPT and the chat-style UX is that it feels like work. Netflix, TikTok, etc. don't feel like work. Nobody at Google (or OpenAI for that matter) knows how to make stuff that doesn't feel like work. And you can't measure "fun." So the biggest thing to figure out is how much technical stuff matters in a world where people can be poached here and there and walk out with the whole architecture in their heads, versus the non-technical stuff that takes decades of hard-worn personal experiences and strongly held opinions like answers to questions "How do you make AI fun?"
Arguably one of the earliest consumer use cases that found footing was AI girlfriend/boyfriend. Large amounts of revenue spread across many small players are generated here but it's glossed over due to the category.
Given that how widespread romance scam schemes already are (the "market" is at least $0.5 billion/year), I would expect any reasonably functioning AI girlfriend/boyfriend model to be massively (ab)used also against unwilling/unwitting "partners".
I think one related area we'll start seeing more of in the future is "resurrected" companions. You have a terminally ill family member, so you train a model on a bunch of video recordings of them, then you can talk to "them" after they've shuffled off this mortal coil.
Do you think there is the possibility of consumer or end-user apps collecting enough specialized data to move downwards on your graph to infra and foundational models?
danielmarkbruce|2 years ago
For example: * Read the Bloomberg GPT paper - they create their own tokenizer. For specialized domains (finance, law, medicine, etc) the vocabulary is very different and there is likely a lot to do here, where individual tokens really need to map to specific concepts and having a concept capture in several tokens makes it too hard to learn on limited domain data. * Data - so many ways to do different data - more/less, cleaner, "better" on some dimension. * Read the recent papers on different decoding strategies - there seems to be a lot to do here. * Model architecture (SSM etc). If you speak to people who aren't even researchers, they have 10 ideas around architecture and some of them are decent sounding ideas - lots of low hanging fruit. * System architecture - ie likely to see more and more "models" served via API which are actually systems of several model calls, and there is a lot to do here. * Hardware, lower precision etc likely to make training much cheaper
It's reasonably likely (again, guessing < 50% > 20%) that this large set of levers to pull become ways to see constant leap-frogging for years and years. Or, at least they become choices/trade-offs rather than strictly "better".
eladgil|2 years ago
Right now at least people seem to decouple some measures of how smart the model is from knowledge base, and at least for now the really big models seem smartest. So part of the question is well is how insightful / synthesis centric the model needs to be versus effectively doing regressions....
doctorpangloss|2 years ago
Architectures matter a lot less than data. "Knowledge" and "reasoning" in LLMs is a manifestation of instruct-style data. It won't matter how much cheaper training gets if there is limited instruct data for use cases.
How do you make 100k context window data for example? Still need thousands of people. Same with so-called niches.
Maybe it turns out to be a complex coordination problem to share the data. That's bad for equity investors and giant companies. Anyway, all of this would cost less than the moon landing so it's practicable, you don't need cheaper, you just need risk-taking.
The obviousness of the path from here to there means it's not about innovation. It's all about strategy.
If Google could marshal $10b for Stadia it could spend $10b on generating 100k context window instruct style data and have the best model. It could also synthesize videos from Unity/Unreal for Sora-style generation. It would just be very hard in an org with 100,000+ people to spend $10b on 10 developers and 10,000 writers compared to 400 developers and 3,600 product managers and other egos. At the end of the day you are revisiting the weaknesses that brought Google and other big companies to this mess in the first place.
Anyway I personally think the biggest weakness with ChatGPT and the chat-style UX is that it feels like work. Netflix, TikTok, etc. don't feel like work. Nobody at Google (or OpenAI for that matter) knows how to make stuff that doesn't feel like work. And you can't measure "fun." So the biggest thing to figure out is how much technical stuff matters in a world where people can be poached here and there and walk out with the whole architecture in their heads, versus the non-technical stuff that takes decades of hard-worn personal experiences and strongly held opinions like answers to questions "How do you make AI fun?"
mywittyname|2 years ago
Bring back text-based adventure games.
jchonphoenix|2 years ago
rockostrich|2 years ago
PeterisP|2 years ago
notpachet|2 years ago
texas2toss|2 years ago
eladgil|2 years ago
For LLMs, Character and ChatGPT are arguably two vertically integrated consumer apps (with some B2B applications for ChatGPT as well)
3abiton|2 years ago