“Here we present Copilot for Real-world Experimental Scientists (CRESt), a platform that integrates large multimodal models (LMMs, incorporating chemical compositions, text embeddings, and microstructural images) with Knowledge-Assisted Bayesian Optimization (KABO) and robotic automation. [...] CRESt explored over 900 catalyst chemistries and 3500 electrochemical tests within 3 months, identifying a state-of-the-art catalyst in the octonary chemical space (Pd–Pt–Cu–Au–Ir–Ce–Nb–Cr) which exhibits a 9.3-fold improvement in cost-specific performance.”
“We leveraged frontier genome language models, Evo 1 and Evo 2, to generate whole-genome sequences with realistic genetic architectures and desirable host tropism [...] Experimental testing of AI-generated genomes yielded 16 viable phages with substantial evolutionary novelty. [...] This work provides a blueprint for the design of diverse synthetic bacteriophages and, more broadly, lays a foundation for the generative design of useful living systems at the genome scale.”
FWIW, my understanding is that Evo 2 is not a generic language model that is able to produce innovations, it’s a transformer model trained on a mountain of genetic data which gave it the ability to produce new functional genomes. The distinction is important, see a very similar case of GPT-4b.
A couple more (recent) results that may be relevant pieces of evidence for this update:
A multimodal robotic platform for multi-element electrocatalyst discovery
“Here we present Copilot for Real-world Experimental Scientists (CRESt), a platform that integrates large multimodal models (LMMs, incorporating chemical compositions, text embeddings, and microstructural images) with Knowledge-Assisted Bayesian Optimization (KABO) and robotic automation. [...] CRESt explored over 900 catalyst chemistries and 3500 electrochemical tests within 3 months, identifying a state-of-the-art catalyst in the octonary chemical space (Pd–Pt–Cu–Au–Ir–Ce–Nb–Cr) which exhibits a 9.3-fold improvement in cost-specific performance.”
Generative design of novel bacteriophages with genome language models
“We leveraged frontier genome language models, Evo 1 and Evo 2, to generate whole-genome sequences with realistic genetic architectures and desirable host tropism [...] Experimental testing of AI-generated genomes yielded 16 viable phages with substantial evolutionary novelty. [...] This work provides a blueprint for the design of diverse synthetic bacteriophages and, more broadly, lays a foundation for the generative design of useful living systems at the genome scale.”
I don’t feel equipped to assess this.
FWIW, my understanding is that Evo 2 is not a generic language model that is able to produce innovations, it’s a transformer model trained on a mountain of genetic data which gave it the ability to produce new functional genomes. The distinction is important, see a very similar case of GPT-4b.
This may help with the second one:
https://www.lesswrong.com/posts/k5JEA4yFyDzgffqaL/guess-i-was-wrong-about-aixbio-risks
How about this one?
https://scottaaronson.blog/?p=9183
That appears to be the same one I linked.
Though possibly you grabbed the link in a superior way (not to comments).