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#ai4science

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MottG

(copied from post on X by Jure Leskovec)

Announcing Biomni — the first general-purpose biomedical AI agent. Biomni is a free web platform where biomedical scientists can immediately delegate their tasks to Biomni.

Biomni is an open-source initiative: we invite the community to build on it and advance biomedical research at scale.
- Try it now: biomni.stanford.edu
- Paper: biomni.stanford.edu/paper.pdf
- Code: github.com/snap-stanford/biomn

#research #medicine #AI4Science
#AItools #bioinformatics

Screenshot of summary details of capabilities of Biomni environment.
Screenshot of part of Jure Leskovic's post on X describing the capabilities of Biomni.
Public
Dr. Chris Rackauckas :julia:

New blog post: How chaotic is chaos? How some AI for Science / SciML papers are overstating accuracy claims.

stochasticlifestyle.com/how-ch

Stochastic Lifestyle · How chaotic is chaos? How some AI for Science / SciML papers are overstating accuracy claims - Stochastic LifestyleJust how chaotic are chaotic systems? Many of you may have heard of “the butterfly effect” but don’t quite know the mathematics behind such systems. What I want to demonstrate is the “sensitive dependence to initial conditions” property of chaotic systems and just how sensitive these systems are. The reason this has come up is that I have seen some AI papers claiming to be able to predict the timeseries of a chaotic system (many more can be found online too, just highlighting a few random ones). What I want to bring to the forefront is an examination of what is really being claimed: just how hard is it to actually forecast a chaotic system? And if they aren’t doing that, what have they done instead? Quick Understanding of Chaos: Sensitive Dependence and the Shadowing Lemma First of ... READ MORE
#julialang#sciml#chaos
Public
Thomas Ekman Jorgensen

"The unreasonable effectiveness of in the natural sciences"

Good read about - where does it work, and where to be careful.

doi.org/10.48550/arXiv.2405.18

arXiv.orgIs machine learning good or bad for the natural sciences?Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology - in which only the data exist - and a strong epistemology - in which a model is considered good if it performs well on held-out training data. These philosophies are in strong conflict with both standard practices and key philosophies in the natural sciences. Here, we identify some locations for ML in the natural sciences at which the ontology and epistemology are valuable. For example, when an expressive machine learning model is used in a causal inference to represent the effects of confounders, such as foregrounds, backgrounds, or instrument calibration parameters, the model capacity and loose philosophy of ML can make the results more trustworthy. We also show that there are contexts in which the introduction of ML introduces strong, unwanted statistical biases. For one, when ML models are used to emulate physical (or first-principles) simulations, they introduce strong confirmation biases. For another, when expressive regressions are used to label datasets, those labels cannot be used in downstream joint or ensemble analyses without taking on uncontrolled biases. The question in the title is being asked of all of the natural sciences; that is, we are calling on the scientific communities to take a step back and consider the role and value of ML in their fields; the (partial) answers we give here come from the particular perspective of physics.
Public
Thomas Ekman Jorgensen

The Commission has published guidelines for using in . Mostly non-surprising content. It is noticeable, however, that there is a strong recommendation to "implement locally hosted or cloud-based generative tools that [research performing organisations] govern themselves", instead of using providers.

research-and-innovation.ec.eur

Research and innovationGuidelines on the responsible use of generative AI in research developed by the European Research Area ForumResearch and innovation news alert: The Commission, together with the European Research Area countries and stakeholders, has put forward a set of guidelines to support the European research community in their responsible use of generative artificial intelligence (AI).
Public
Thomas Ekman Jorgensen

“We need to facilitate a discussion about what the real issues are [concerning and ]. What do we know, what do we need to know? It’s been a year of experimentation, really. We can begin to share the outcomes of that experimentation, maybe share a bit about the methods of that experimentation,”

Good, longish read in University World News about where we are on

universityworldnews.com/post.p

University World NewsGenerative AI action hints at core future roles in universitiesWith the arrival of generative AI such as ChatGPT, science fiction took a big step towards fact. Last year, universities explored the implications of ...
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