181–190 of 202 posts
Earlier quoted context omitted.
This line of reasoning implies "the stochastical parrot people are right, there is no intelligence in AI". Which is the opposite of what AI thought leaders are saying.
I think this may be the first time I've seen "thought leaders" used unironically. Is there any reason to believe they're right?
I don't think generating hypotheses is where AI is useful, I think it's more useful for doing back of napkin (or more serious) calculations, helping to find protocols, sourcing literature, etc. Grunt work basically. Generating hypotheses is the fun, exciting part that I doubt scientists want to outsource to AI.
> Generating hypotheses is the fun, exciting part that I doubt scientists want to outsource to AI
The latter doesn’t imply the former
I'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…
A lot of them have to do things on computers which has nothing to do with their expertise. Like coding a small tool for working their data, small tools crunching results, formatting text data, searching and finding the right materials.
A LLM which helps a scientist to code something in an hour instead of a week, makes this research A LOT faster.
And we know from another paper, that we have now so much data, you need to use systems to find the right information for you. The study estimated how much additionanl critical information a research paper missed.
“Drug repurposing for AML” lol As a person who is literally doing his PhD on AML by implementing molecular subtyping, and ex-vivo drug predictions. I find this super random. I would truly suggest our pipeline instead of random drug repurposing :) https://celvox.co/solutions/seAMLess edit: Btw we’re looking for ways to fund/commercialize our pipeline. You could contact us through the site if you’re interested!
Thank you for your work on this, truly.
Earlier quoted context omitted.
It's almost like scientists are doing something more than a random search over language.
This search is random in the same way that AlphaGo's move selection was random. In the Monte Carlo Tree Search part, the outcome distribution on leaves is informed by a neural network trained on data instead of a so-called playout. Sure, part of the algorithm does invoke a random() function, but by no means the result is akin to the flip of a coin. There is indeed randomness in the process, but making it sound like a…
On top of that you are looking at the results in cell-lines which might not reflect the true nature of what would happen in-vivo (a mouse model or a human).
So, there is domain specific knowledge, which one would like to take into account for decision-making. For me, I would trust a Molecular Tumor Board with hematologists, clinicians - and possibly computational biologists :) - over a language random tree search for treating my acute myeloid leukemia, but this is a personal choice.
I am generally down on AI these days but I still remember using Eliza for the first time. I think I could accept an AI prompting me instead of the other way around. Something to ask you a checklist of problems and how you will address them. I’d also love to have someone apply AI techniques to property based testing. The process of narrowing down from 2^32 inputs to six interesting ones works better if it’s faster.
AI prompting us sounds interesting
Earlier quoted context omitted.
But so are you and I. That's how you wrote this reply.
Preposterous- cavemen had no language but they could reason, think and learn. A child learns how to eat solid food and how to walk. That a square peg fits into a square hole. This has nothing to do with language. people who deaf and mute and cannot read can still reason and solve problems.
big if true
Earlier quoted context omitted.
Math seems to me like the hardest thing for LLMs to do. It requires going deep with high IQ symbol manipulation. The case for LLMs is currently where new discoveries can be made from interpolation or perhaps extrapolation between existing data points in a broad corpus which is challenging for humans to absorb.
This line of reasoning implies "the stochastical parrot people are right, there is no intelligence in AI". Which is the opposite of what AI thought leaders are saying.
Earlier quoted context omitted.
This line of reasoning implies "the stochastical parrot people are right, there is no intelligence in AI". Which is the opposite of what AI thought leaders are saying.
I reject the Stochastic Parrot theory. The claim is more about comparative advantage; AI systems already exist that are superhuman on breadth of knowledge at undergrad understanding depth. So new science should be discoverable in fields where human knowledge breadth is the limiting factor.
Two problems with this:
1. AI systems hallucinate stuff. If it comes up with some statement, how will you know that it did not just hallucinate it?
2. Human researchers don't work just on their own knowledge, they can use a wide range of search engines. Do we have any examples of AI systems like these that produce results that a third-year grad student couldn't do with Google Scholar and similar instructions? Tests like in TFA should always be compared to that as a baseline.
> new science should be discoverable in fields where human knowledge breadth is the limiting factor
What are these fields? Can you give one example? And what do you mean by "new science"?
The way I see it, at best the AI could come up with a hypothesis that human researchers could subsequently test. Again, you risk that the hypothesis is hallucination and you waste a lot of time and money. And again, researchers can google shit and put facts together from different fields than their own. Why would the AI be able to find stuff the researchers can't find?