Just to be clear, my intended belief there was a 5-15% of meat industry taking dominance for 2-3 decades after the introduction of an alternative that is very close or beats meat entirely. Though I do think it is plausible the alternatives come soon, AI technology is advancing rapidly; and I don’t believe many people’s models are properly factoring in AI technology of our current level being applied to more areas, much less what we’ll have in the future after significant advancements. Of course, EA tends to be a lot better at that than other charities.
As an example: Theorem proving is bottlenecked by the annoying but solvable triplet of: data, money to train larger models, and companies focusing on it. Scaling the current methods would hit noticeable limits due to planning/search being hard, but would allow a lot of automation towards proving software correct. AlphaProof itself is then a step above the methods that came before it. This could provide a good amount of value in terms of ensuring important software is correct but is generally ignored or assumed to need massive breakthroughs.
I find it plausible that more systems in the vein of AlphaFold (protein prediction, most centrally relevant to meat) can be extended to other areas of chemistry with a significant amount of time & effort to collect data and design. There’s big data collection problems here, we have a lot of data about food but it is more locked away inside companies and not as carefully researched to a low level as proteins. I know the theorem proving better than I do the AlphaFold area, but that gets across my general view of “many mental models assume too much like we are in 2018 but with single isolated notable advancements like AlphaFold/AlphaProof/ChatGPT rather than a field with much to explore via permutations of those core ideas”.
Interesting. It feel like this still requires a lot of effort to make it usable in the context of alternative proteins (plus marketing, developing incentives, fighting the opposition, etc.), but if it works, that could indeed be a good news.
Just to be clear, my intended belief there was a 5-15% of meat industry taking dominance for 2-3 decades after the introduction of an alternative that is very close or beats meat entirely.
Though I do think it is plausible the alternatives come soon, AI technology is advancing rapidly; and I don’t believe many people’s models are properly factoring in AI technology of our current level being applied to more areas, much less what we’ll have in the future after significant advancements.
Of course, EA tends to be a lot better at that than other charities.
As an example: Theorem proving is bottlenecked by the annoying but solvable triplet of: data, money to train larger models, and companies focusing on it. Scaling the current methods would hit noticeable limits due to planning/search being hard, but would allow a lot of automation towards proving software correct. AlphaProof itself is then a step above the methods that came before it. This could provide a good amount of value in terms of ensuring important software is correct but is generally ignored or assumed to need massive breakthroughs.
I find it plausible that more systems in the vein of AlphaFold (protein prediction, most centrally relevant to meat) can be extended to other areas of chemistry with a significant amount of time & effort to collect data and design. There’s big data collection problems here, we have a lot of data about food but it is more locked away inside companies and not as carefully researched to a low level as proteins.
I know the theorem proving better than I do the AlphaFold area, but that gets across my general view of “many mental models assume too much like we are in 2018 but with single isolated notable advancements like AlphaFold/AlphaProof/ChatGPT rather than a field with much to explore via permutations of those core ideas”.
Interesting. It feel like this still requires a lot of effort to make it usable in the context of alternative proteins (plus marketing, developing incentives, fighting the opposition, etc.), but if it works, that could indeed be a good news.