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> As someone working in science, this belief confuses me. How (by what means) do you think Fable 5.1 will be able to make further progress in scientific domains

> As someone working in science, this belief confuses me. How (by what means) do you think Fable 5.1 will be able to make further progress in scientific domains? The same way it did in the previous versions: brute force. I don't believe that LLMs have any particular intelligence we don't, but there's an endless list of problems we either don't have bodies to throw at, or the bodies we can throw at it, don't have such a huge large context to crunch problems. What LLMs will always intrinsically fail at is showing us genuine new intuitions. The technology is about predicting the next plausible token/sentence. They will not revolutionize human knowledge, but they can definitely widen it a lot.

gr_norm 1mo ago
> They will not revolutionize human knowledge, but they can definitely widen it a lot. I am generally quite enthusiastic about all this, but my biggest fear is that we will not recognize the extreme need for more scientists at a time when there is so much more science to be done. The rate of scientific understanding must keep pace with the amount of science being output, both for verification and further discovery. It's a pipelining issue, and I predict a stall in the bits that require the (currently rare) people who know what they're doing.
adastra22 1mo ago
We are not limited by intelligence, or bodies.
- epolanski 1mo ago
  Why would you think we aren't? There's an endless number of scientific problems out there in any field, and nobody able to dedicate themselves to it. I've been in research (you con check my name on Google Scholar for my released papers), there was always an endless number of experiments or paths more I could've taken than the time and resources to do so.
  - adastra22 1mo ago
    In many fields the limitation is not thinking. In my field (particularly obscure UHV surface science) we are limited by experimental results, and that experimental data is limited by the number of operable machines in particular configurations. These are multi-million dollar specialized machines that are artisanally made. There's a small, single-digit number produced each year, and each one is hand-calibrated to its task. Due to computational limitations, this is not work that can be effectively simulated on a classical computer. Actual experimentation is required. I fail to see what impact improved AI would have on this problem. Perhaps better selection of experimental problems for our limited capacity to run experiments, but that's assuming there is any slack left to take up. In reality we already have more brainpower than needed applied to this problem.

gr_norm 1mo ago

> They will not revolutionize human knowledge, but they can definitely widen it a lot. I am generally quite enthusiastic about all this, but my biggest fear is that we will not recognize the extreme need for more scientists at a time when there is so much more science to be done. The rate of scientific understanding must keep pace with the amount of science being output, both for verification and further discovery. It's a pipelining issue, and I predict a stall in the bits that require the (currently rare) people who know what they're doing.

adastra22 1mo ago

We are not limited by intelligence, or bodies.

epolanski 1mo ago
Why would you think we aren't? There's an endless number of scientific problems out there in any field, and nobody able to dedicate themselves to it. I've been in research (you con check my name on Google Scholar for my released papers), there was always an endless number of experiments or paths more I could've taken than the time and resources to do so.
- adastra22 1mo ago
  In many fields the limitation is not thinking. In my field (particularly obscure UHV surface science) we are limited by experimental results, and that experimental data is limited by the number of operable machines in particular configurations. These are multi-million dollar specialized machines that are artisanally made. There's a small, single-digit number produced each year, and each one is hand-calibrated to its task. Due to computational limitations, this is not work that can be effectively simulated on a classical computer. Actual experimentation is required. I fail to see what impact improved AI would have on this problem. Perhaps better selection of experimental problems for our limited capacity to run experiments, but that's assuming there is any slack left to take up. In reality we already have more brainpower than needed applied to this problem.