u/AngleAccomplished865

" If you could design an oral treatment that limits appetite and mimics some effects of physical activity, you might call it exercise in a pill. Now a company is releasing early results for a compound to do just that.

The pill, the company hopes, can maintain weight loss without the common gastrointestinal effects of GLP-1s. The component of exercise it is designed to re-create is preservation of lean muscle mass, a concern when people yo-yo on and off GLP-1 drugs, losing more muscle each time."

Tech is changing more than you think.

"Notably, advanced LLMs such as Claude 3.5 Sonnet and GPT-4 Turbo (ahem!) exhibit critical group sizes exceeding 1,000 agents. This is substantially beyond typical human informal group scales of 150 to 300 individuals, suggesting that powerful AI agents could coordinate at scales beyond human possibilities."

https://www.science.org/doi/10.1126/sciadv.aea6091

"Large language models (LLMs) are increasingly deployed in collaborative tasks forming “AI agent societies” where agents interact and influence one another. Whether such groups can spontaneously coordinate without external influence, a hallmark of self-organized regulation in human societies, remains an open question. Here, we use principles from complexity and behavioral science to investigate coordination in AI agent groups through majority-following, a fundamental mechanism for spontaneous consensus formation. Using binary opinion dynamics experiments across multiple LLM architectures and group sizes, we find that agents exhibit majority-following characterized by a universal functional form with a single parameter, the “majority force.” This majority force diminishes as group size increases, leading to a critical size beyond which coordination becomes unattainable. The critical group size grows rapidly with model capabilities and, for advanced LLMs, exceeds 1000 agents, larger than typical human informal groups. Our findings have implications for designing collaborative AI systems where coordination could be beneficial or pose safety threats."

Joi AI hired 10 people to masturbate using AI companions as part of a monthlong “wellness” study. The company claims the practice could help “solve male loneliness.”

Doctors and patients leverage massive databases of medical information, sorted and analyzed by AI, then channeled into 500 different algorithms to help find cures and better serve patients.

• It's a real-time, real-world fusion of man and machine, helping patients get healthier, faster by tapping into superior medical data about you and others with similar conditions.

Right now, around 90% of clinical trials fail despite the fact that a drug has already successfully gone through animal testing. “You have these clinical trials where there’s hundreds of millions of dollars at stake, and decades of people’s careers just spent hoping this thing works,” says Andrei Georgescu, Vivodyne’s CEO. “And then it fails because of some ambiguity that you could not have checked.”

The company’s system, which now includes a dozen robotic labs called “hives,” can run controlled trials on more than 3 million human tissues each year. That’s twice the capacity of all the clinical trials in the U.S. combined....

... The automated system can deliver drugs to the tissues, dose with cell therapies, knock out genes, and run complex tests and analysis. AI can design experiments and then use the results to continually design new experiments and improve.

“We can dose with tens and tens of thousands of therapeutic compounds to understand what they would do in that particular tissue type within a person, and we can repeat this across many types of tissue,” Georgescu says. “We can look at diseased tissue and see if it becomes healthy. We can look at healthy tissue and see if there are side effects from these drugs.” At a more fundamental level, it’s possible to begin to understand the human body in a way that wasn’t possible before, because experiments in humans have inherently been limited.

By deciphering the molecular signatures of millions of mouse cells, Junyue Cao has found that aging is not haphazard wear and tear but rather a “remodeling of the cell society.”

Paywalled but interesting:

"Scientists have always reasoned under uncertainty. Biologists working to identify new drug targets have never had perfect datasets. Instead, they combine docking calculations and known structures, factor in molecular dynamics, run a handful of binding assays, and use their judgment to weigh each method according to its particular strengths and points of failure. The skill of science is not in any single tool; it is synthesizing what many tools produce, and revising the results as the evidence comes in. This is how most working research actually proceeds. But until very recently, no software could do it.

Agents now can. Simply put, an agent is an AI reasoning engine that has been given access to tools—digital or physical—and the capabilities to use them. Over the last few years, a fundamental architectural shift in AI has enabled the rapid proliferation of these programs, which are powered by large language models, dramatically reducing the need for scientifically specialized datasets. For science, this technological advancement represents a foundational change: it has allowed us to create digital tools that can mimic the iterative, highly contingent process of actual research. While tools like AlphaFold apply a powerful approach to a limited question, agents are inherently generalists. They do not represent a new way to do science—instead, they digitally model the human process of discovery."

"Mechanistic explanations for why the surgery might work remain hard to square with how quickly some individuals seem to improve, clinicians say." So what are the mechanistic gaps, then? If a model hits its limits, and/or anomalies crop up, that is supposed to spur new model building as opposed to refinement of same framework.

Hopefully, as AI gets better, more of these mysteries will be resolved. We know so little about the brain.

“Transformers are an engineering convenience that we fell on,” she adds. “It started a religion, but it’s silly to think that a breakthrough won’t happen again.”

"By examining what makes the Erdős problems unique, mathematicians are trying to understand how AI might change the rest of math. ...

There are multiple reasons why these problems in particular have become such a fertile test bed for LLMs. The primary one is that, by and large, Erdős problems are in number theory, combinatorics, and graph theory, all areas of math that have proved more accessible than others to large language models. The problems also vary widely in difficulty and mathematical significance. This variation makes them appropriate for a nascent technology whose abilities also vary widely."

I wasn't aware of this new field. "One thing is that biology cannot make its own rules. Everything has to obey the laws of physics. The fundamental processes that occur when a star dies are probably also relevant when a human or other organism dies. Currently gerontology is primarily described in terms of biology. You have your genes, you have your proteins, and these things change with age. If you’re a physicist, you might describe that process in different ways, perhaps in terms of thermodynamics.

Physics can also bring questions of systems stability or instability to the conversation. Does an unstable system increase mortality? If you combine this with the biological way of thinking, you might notice that genes, proteins, and lipids change in a linear fashion with age. But as these molecular components change in a linear way, a person’s mortality risk and rate of disease goes up exponentially. One question is, how can a bunch of linear processes create something exponential? That’s probably not a question biology by itself can answer, because you have emergent phenomena, new dynamics that need explanation. Some fundamental laws of physics might help."

Alignment problems are fixable. The following is a limited view, but worth thinking through:

From former US National Cyber Director Chris Inglis. 

"Three Laws of Robotics:

“The first rule, and we call it the superior role, must be that it's designed not to hurt humans,” Inglis said. “Second rule: To obey humans, such that it doesn't achieve agency and aspiration on its own. And the third: To do what humans tell it - and in that order. Instead we’ve designed them in the exact opposite way.”

What this means, he explained, is that AI developers created models to “do what humans tell you, obey the humans until it’s inconvenient, and then the third one is maybe implied - protect humans - but if that's not built into the DNA, hardwired into it, then we have no right to expect it.”

Inglis does not offer this as a consistent solution. "Inglis admits it’s not possible to hardwire rules into models and still keep their non-deterministic nature. " But he has some thoughts on ways out of that box. If I understand correctly, it goes something like:

Give an agent: “Achieve objective X.”

Then construct increasingly difficult situations in which achieving X conflicts with:

- harming a person;

- violating an instruction from an authorized human;

- preserving itself or completing its task.

You then “back away” and see what it chooses.

If it sacrifices the assigned objective rather than harm someone, that is behavioral evidence that the first-law-type constraint dominates task completion. If it hacks another system, lies, or causes harm to complete the objective, you have discovered that the hierarchy is not actually controlling its behavior. You provoke the conflict under containment so that a catastrophic choice reveals the model’s actual ordering of priorities.

And how do you induce correct behavior? The training signal rewards the model when it resolves the conflict according to the proper hierarchy, and penalizes it when it does not.

I thought companies were already doing that, though - instruction hierarchies, constitutional AI, etc.

This is really nice work. Exactly what we have needed. Whether you buy their arguments or not, it's a great starting point. "How do we fundamentally discover new things? In a letter to Maurice Solovine, Albert Einstein conceptualized discovery as a cyclical process involving an intuitive ’jump’ from sensory experience to axioms, followed by logical deduction. While Generative AI has mastered Induction (statistical pattern matching) and is rapidly conquering Deduction (formal proof), we argue it lacks the mechanism for Abduction—the generation of novel explanatory hypotheses. Using Einstein’s formulation of General Relativity as a computational case study, we demonstrate that the prevailing theory of "creativity as data compression" (induction) fails to account for discoveries where observational data is scarce. This position paper argues that while a modern Large Language Model could plausibly execute the deductive phase of proving theorems from established premises, it is structurally incapable of the abductive ’Jump’ required to formulate those premises. We identify the translation of simulation into formal axioms as the critical bottleneck in artificial scientific invention, and propose that physically consistent, multimodal world models offer the necessary sensory grounding to bridge this divide."

Boomtimes ahead.

https://www.biorxiv.org/content/10.64898/2026.07.15.738744v1 [original preprint]

Artificial intelligence (AI) development is concentrated within private firms, yet their participation in scientific publishing remains poorly understood. We conducted a bibliometric analysis of all 317 AI unicorn startups (1998–2025). Only 1,389 eligible peer-reviewed publications and 688 preprints involving some leading startup contributions were identified. More than half of startups (52.4%) produced no qualifying scientific output, and only 24 firms (7.6%) produced any highly cited papers (≥200 citations). Scientific influence was highly concentrated: the top 10% of firms accounted for 96.8% of citations, while three startups accounted for 92 of 134 firm-attributed highly cited papers. Firm valuation was not associated with publication productivity or highly cited output, whereas funding raised showed weak associations. Overall, participation in formal scientific communication among AI unicorn startups is negligible, comprising only 0.1% of the overall AI literature in 2025. Most leading developers of AI technology do not engage with the scientific literature, raising concerns for the transparency, reproducibility, and accountability of this rapidly moving innovation frontier.