This is marketing material from Google and people are accepting the premises uncritically.
Accelerating scientific breakthroughs with an AI co-scientist
61–70 of 202 posts
Re: Accelerating scientific breakthroughs with an AI co-scientist
#62Earlier quoted context omitted.
It sounds like you're suggesting that we need machines that mass produce things like automated pipetting machines and the robots that glue those sorts of machines together.
They already exist, and we use them. They are not cheap though!
Any idea why they're they so expensive?
Re: Accelerating scientific breakthroughs with an AI co-scientist
#63This is marketing material from Google and people are accepting the premises uncritically.
Almost this entire thread is criticism
Re: Accelerating scientific breakthroughs with an AI co-scientist
#64I'm not sure if people here even read the entirety of the article. From the article: > We applied the AI co-scientist to assist with the prediction of drug repurposing opportunities and, with our partners, validated predictions through computational biology, expert clinician feedback, and in vitro experiments. > Notably, the AI co-scientist proposed novel repurposing candidates for acute myeloid leukemia (AML). Subse…
I read the cf-PICI paper (abstract) and the hypothesis from the AI co-scientist. While the mechanism from the actual paper is pretty cool (if I'm understanding it correctly), I'm not particularly impressed with the hypothesis from the co-scientist.
It's quite a natural next step to take to consider the tails and binding partners to them, so much so that it's probably what I would have done and I have a background of about 20 minutes in this particular area. If the co-scientist had hypothesised the novel mechanism to start with, then I would be impressed at the intelligence of it. I would bet that there were enough hints towards these next steps in the discussion sections of the referenced papers anyway.
What's a bit suspicious is in the Supplementary Information, around where the hypothesis is laid out, it says "In addition, our own preliminary data indicate that cf-PICI capsids can indeed interact with tails from multiple phage types, providing further impetus for this research direction." (Page 35). A bit weird that it uses "our own preliminary data".
Re: Accelerating scientific breakthroughs with an AI co-scientist
#65I'm not sure if people here even read the entirety of the article. From the article: > We applied the AI co-scientist to assist with the prediction of drug repurposing opportunities and, with our partners, validated predictions through computational biology, expert clinician feedback, and in vitro experiments. > Notably, the AI co-scientist proposed novel repurposing candidates for acute myeloid leukemia (AML). Subse…
This is one thing I've been wondering about AI: will its broad training enable it to uncover previously covered connections between areas the way multi-disciplinary people tend to, or will it still miss them because it's still limited to its training corpus and can't really infer. If it ends up being more the case that AI can help us discover new stuff, that's very optimistic.
In some sense, AI should be the most capable at doing this within math. Literally the entire domain in its entirety can be tokenized. There are no experiments required to verify anything, just theorem-lemma-proof ad nauseam.
Doing this like in this test, it's very tricky to rule out the hypothesis that the AI is just combining statements from the Discussion / Future Outlook sections of some previous work in the field.
Re: Accelerating scientific breakthroughs with an AI co-scientist
#66I'm not sure if people here even read the entirety of the article. From the article: > We applied the AI co-scientist to assist with the prediction of drug repurposing opportunities and, with our partners, validated predictions through computational biology, expert clinician feedback, and in vitro experiments. > Notably, the AI co-scientist proposed novel repurposing candidates for acute myeloid leukemia (AML). Subse…
I read the cf-PICI paper (abstract) and the hypothesis from the AI co-scientist. While the mechanism from the actual paper is pretty cool (if I'm understanding it correctly), I'm not particularly impressed with the hypothesis from the co-scientist. It's quite a natural next step to take to consider the tails and binding partners to them, so much so that it's probably what I would have done and I have a background of…
> A bit weird that it uses "our own preliminary data"
I think potential of LLM based analysis is sky high given the amount of concurrent research happening and high context load required to understand the papers. However there is a lot of pressure to show how amazing AI is and we should be vigilant. So, my first thought was - could it be that training data / context / RAG having access to a file it should not have contaminated the result? This is indirect evidence that maybe something was leaked.
Re: Accelerating scientific breakthroughs with an AI co-scientist
#67Earlier quoted context omitted.
They already exist, and we use them. They are not cheap though!
Any idea why they're they so expensive?
There is a big range in both automation capabilities and prices.
We have a couple automation systems that are semi-custom - the robot can handle operation of highly specific, non-standard instruments that 99.9% of labs aren't running. Systems have to handle very accurate pipetting of small volumes (microliters), moving plates to different stations, heating, shaking, tracking barcodes, dispensing and racking fresh pipette tips, etc. Different protocols/experiments and workflows can require vastly different setups.
See something like:
[1] https://www.hamiltoncompany.com/automated-liquid-handling/pl...
[2] https://www.revvity.com/product/fontus-lh-standard-8-96-ruo-...
Re: Accelerating scientific breakthroughs with an AI co-scientist
#68It seems in general we’re heading toward’s Minsky’s society of minds concept. I know OpenAI is wanting to collapse all their models into a single omni model that can do it all, but I wonder if under the hood it’d just be about routing. It’d make sense to me for agents to specialize in certain tool calls, ways of thinking, etc that as a conceptual framework/scaffolding provides a useful direction.
I wonder if OpenAI might be routing already based on speed of some "O1" responses I receive. It does make sense.
Also, for some more complex questions I’ve noticed that it doesn’t expose its reasoning. Specifically, yesterday I asked it to perform a search algorithm provided a picture of a grid, and it reasoned for 1-2 minutes but didn’t show any of it (neither in real time nor afterwords), whereas for simpler questions I’ve asked it the reasoning is provided as well. Not sure what this means, but it suggests some type of different treatment based on complexity.
Re: Accelerating scientific breakthroughs with an AI co-scientist
#69I recently ran across this toaster-in-dishwasher article [1] again and was disappointed that the LLMs I have access to could replicate the "hairdryer-in-aquarium" breakthrough (or the toaster-in-dishwasher scenario, although I haven't explored it as much), which has made me a bit skeptical of the ability of LLMs to do novel research. Maybe the new OpenAI research AI is smart enough to figure it out?
[1] https://jdstillwater.blogspot.com/2012/05/i-put-toaster-in-d...
Re: Accelerating scientific breakthroughs with an AI co-scientist
#70Earlier quoted context omitted.
We don't need an army of high school sophomores, unless they are in the lab pipetting. The expensive part of drug discovery is not the ideation phase, it is the time and labor spent running experiments and synthesizing analogues.
It sounds like you're suggesting that we need machines that mass produce things like automated pipetting machines and the robots that glue those sorts of machines together.
Replacing a skilled technician is remarkably challenging. Often times, when you automate this, you just end up wasting a ton of resources rather than accelerating discovery. Often, simply integrating devices from several vendors (or even one vendor) takes months.