3 ms·

In many cases, isn't the time-consuming part of experiments irreducible? E.g. breeding plants? Or to take another example, Make Solar Energy Economical How do

In many cases, isn't the time-consuming part of experiments irreducible? E.g. breeding plants? Or to take another example, Make Solar Energy Economical How does Discovery Loop make this go faster in a way that a different group of scientists, also using frontier models, will proceed? I'm sure Discovery Loop has considered this and has good answers to this question. I'd be interested in hearing more about this.

theptip 2mo ago
It’s true in the limit, but I think we are nowhere near that limit. A friend recently started a bio startup and automated parts of mouse experiments enabling higher throughput on in vivo experimentation. This is not commonplace, and there is a lot of room for further automation here. As anyone who works with agents daily can attest, 1) you can use agents to help with hypothesis refinement, bridging into areas adjacent to your expertise, etc. 2) once you have a rigorous /goal definition you can parallelize and let the agent crank. It seems pretty obvious to me that with the right actuators and sensors you can apply this to real physical research loops too. (To be clear, this is not easy; a lot of bench work is Métis and needs experts in the loop at every stage.) To your point, you can’t make plants grow faster but you can increase research throughput by enabling a researcher to have 10x or 100x as many experiments going at once.
summerlight 2mo ago
This is a valid take but at this moment, AI is not trying to solve this fundamental dynamic. But it can still accelerate the process by aggressive exploration of the solution space which cannot be done even with an army of human researchers. Many ideas can be relatively quickly verified (and discarded if needed) by proper simulation even before real world experimentation, but we don't have enough capacity to process all potential ideas. If you can build a good model for simulation and establish a robust methodologies, we can use some ideas which never had a chance before.

theptip 2mo ago

It’s true in the limit, but I think we are nowhere near that limit. A friend recently started a bio startup and automated parts of mouse experiments enabling higher throughput on in vivo experimentation. This is not commonplace, and there is a lot of room for further automation here. As anyone who works with agents daily can attest, 1) you can use agents to help with hypothesis refinement, bridging into areas adjacent to your expertise, etc. 2) once you have a rigorous /goal definition you can parallelize and let the agent crank. It seems pretty obvious to me that with the right actuators and sensors you can apply this to real physical research loops too. (To be clear, this is not easy; a lot of bench work is Métis and needs experts in the loop at every stage.) To your point, you can’t make plants grow faster but you can increase research throughput by enabling a researcher to have 10x or 100x as many experiments going at once.

summerlight 2mo ago

This is a valid take but at this moment, AI is not trying to solve this fundamental dynamic. But it can still accelerate the process by aggressive exploration of the solution space which cannot be done even with an army of human researchers. Many ideas can be relatively quickly verified (and discarded if needed) by proper simulation even before real world experimentation, but we don't have enough capacity to process all potential ideas. If you can build a good model for simulation and establish a robust methodologies, we can use some ideas which never had a chance before.