Earlier quoted context omitted.
How do you know you're not on a logistic curve? Don't you think costs and the availability of training data might impose some constraints?
The entire universe is training data.
ResearchAgent: Iterative Research Idea Generation Using LLMs
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Re: ResearchAgent: Iterative Research Idea Generation Using LLMs
#62It would be fun to pair this with an automated lab that could run experiments and feed the results into generating the next set of ideas.
Re: ResearchAgent: Iterative Research Idea Generation Using LLMs
#63Re: ResearchAgent: Iterative Research Idea Generation Using LLMs
#64It would be fun to pair this with an automated lab that could run experiments and feed the results into generating the next set of ideas.
They have an automated robotics powered research lab
Re: ResearchAgent: Iterative Research Idea Generation Using LLMs
#65Earlier quoted context omitted.
rsfern is asking exactly that
No, I'm asking for an example of an idea that an LLM might produce that is too dangerous to implement but nobody at the hackathon has the relevant experience to notice. You can shut down any endeavour by imagining boogeymen that aren't actually real.
I might be biased in terms of the safety profile, my background is materials and chemistry, and there are loads of ways you can get into trouble if you don’t really have experience in the materials and synthesis routes you’re working with
One example I’ve heard of from my field (alloy design) is an ML model that suggested an composition high in Magnesium - perfectly reasonable if you’re interested in lightweight strong alloys, but the synthesis method was arc melting, which is a high risk for starting a metal fire if you aren’t careful because Mg has a low vapor pressure
If you’re doing organic chemistry it’s maybe even worse because there can be all kinds of side products or runaway exothermic reactions, and if you’re doing novel chemistry it might take deep experience in the field to know of those things are likely
All these concerns are manageable, but I think an ethics review panel would want to at least see that there is a reasonable safety review process in place before letting students try out random experiments in topic areas in which the models likely haven’t been fine tuned with safety in mind.
Re: ResearchAgent: Iterative Research Idea Generation Using LLMs
#66Earlier quoted context omitted.
This approach is already useful in functional genomics. A common type of question requires analysis of hundreds of potentially functional sequence variants. Hybrid LLM+ approaches are beginning to improve efficiency of ranking candidates and even proposing tests and soon I hope—higher order non-linear interactions among DNA variants.
I am interested in this. Can you point to a reference about the application of LLMs to sequence secreening? Thanks.
HyenaDNA is one to look at wrt DNA.
And here are some other interesting links from Erik Garrison—a leader in pangenomics.
https://hazyresearch.stanford.edu/blog/2023-06-29-hyena-dna
https://github.com/instadeepai/nucleotide-transformer
https://dl.acm.org/doi/pdf/10.1145/3535508.3545512
https://github.com/dnbaker/bioseq
https://huggingface.co/AIRI-Institute/gena-lm-bert-base
https://discuss.huggingface.co/t/dna-long-sequence-tokenizat...