Artificial intelligence (AI) systems hold great promise for advancing various scientific disciplines, and are increasingly used in real-world applications. Despite their remarkable progress, further capabilities are expected in order to achieve more general types of intelligence. A critical distinction in this context is between factual knowledge, which can be evaluated against true or false answers (e.g., "what is the capital of England?"), and probabilistic knowledge, reflecting probabilistic properties of the real world (e.g., "what is the sex of a computer science graduate in the US?"). In this paper, our goal is to build a benchmark for understanding the capabilities of LLMs in terms of knowledge of probability distributions describing the real world. Given that LLMs are trained on vast amounts of text, it may be plausible that they internalize aspects of these distributions. Indeed, LLMs are touted as powerful universal approximators of real-world distributions. At the same time, classical results in statistics, known as curse of dimensionality, highlight fundamental challenges in learning distributions in high dimensions, challenging the notion of universal distributional learning. In this work, we develop the first benchmark to directly test this hypothesis, evaluating whether LLMs have access to empirical distributions describing real-world populations across domains such as economics, health, education, and social behavior. Our results demonstrate that LLMs perform poorly overall, and do not seem to internalize real-world statistics naturally. When interpreted in the context of Pearl's Causal Hierarchy (PCH), our benchmark demonstrates that language models do not contain knowledge on observational distributions (Layer 1 of PCH), and thus the Causal Hierarchy Theorem implies that interventional (Layer 2) and counterfactual (Layer 3) knowledge of these models is also limited.

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The rise of Large Language Models (LLMs) as evaluators offers a scalable alternative to human annotation, yet existing Supervised Fine-Tuning (SFT) for judges approaches often fall short in domains requiring complex reasoning. In this work, we investigate whether LLM judges truly benefit from enhanced reasoning capabilities. Through a detailed analysis of reasoning requirements across evaluation tasks, we reveal a negative correlation between SFT performance gains and the proportion of reasoning-demanding samples - highlighting the limitations of SFT in such scenarios. To address this, we introduce JudgeLRM, a family of judgment-oriented LLMs trained using reinforcement learning (RL) with judge-wise, outcome-driven rewards. JudgeLRM models consistently outperform both SFT-tuned and state-of-the-art reasoning models. Notably, JudgeLRM-3B surpasses GPT-4, and JudgeLRM-7B outperforms DeepSeek-R1 by 2.79% in F1 score, particularly excelling in judge tasks requiring deep reasoning.

There are mornings when you can tell you're not feeling well just by looking in the mirror -- but a new AI'mirror' debuting this week at the Consumer Electronics Show (CES) promises to tell you a whole lot more. The product (effectively a 21.5-inch vertical tablet with a mounted camera) tracks over 100 health parameters by scanning blood flow under the surface of your face. The device promises to detect signs of everything from high blood pressure, to fever symptoms, to depression or mental health risk, to 10-year stroke risk, to'facial skin age.' But it can also warn you if it thinks you are about to die. One telemedicine expert praised the device as'ideal for clinics, elder care homes.'

Fox News contributor Dr. Marc Siegel says the pill stays in the stomach for four to five days and utilizes AI. A vibrating pill has shown promise in early studies as a possible obesity treatment. Developed by engineers at MIT (Massachusetts Institute of Technology), the capsule is intended to be swallowed prior to eating in order to "trick" the brain into thinking the body is full -- thereby reducing the amount of calories consumed. The vibrations activate the "stretch receptors" that detect satiety after eating, sending a signal to the brain that the stomach is full even if it's not. In a study published in the journal Science Advances on Dec. 22, researchers revealed that animals who swallowed the pill 20 minutes before eating consumed 40% less food than those who weren't given the pill, according to a press release from MIT.

Using observational data to learn causal relationships is essential when randomized experiments are not possible, such as in healthcare. Discovering causal relationships in time-series health data is even more challenging when relationships change over the course of a disease, such as medications that are most effective early on or for individuals with severe disease. Stage variables such as weeks of pregnancy, disease stages, or biomarkers like HbA1c, can influence what causal relationships are true for a patient. However, causal inference within each stage is often not possible due to limited amounts of data, and combining all data risks incorrect or missed inferences. To address this, we propose Causal Discovery with Stage Variables (CDSV), which uses stage variables to reweight data from multiple time-series while accounting for different causal relationships in each stage. In simulated data, CDSV discovers more causes with fewer false discoveries compared to baselines, in eICU it has a lower FDR than baselines, and in MIMIC-III it discovers more clinically relevant causes of high blood pressure.

Detecting heart disease early is one of the key steps in treating the condition. But until now, doctors have had difficulty differentiating cardiovascular conditions through ultrasounds alone. In February 2022, scientists at the Smidt Heart Institute at Cedars-Sinai announced the creation of artificial intelligence that can not only detect heart disease early, but can tell the difference between conditions that look almost similar to the naked eye. More than half of U.S. adults suffer with some form of heart disease, according to an American Heart Association report in 2019. There are several causes of heart disease, including obesity, smoking, poor diet, lack of exercise, hypertension and genetics.

An artificial intelligence (AI) algorithm, derived from the features of individual heartbeats recorded on an ECG (electrocardiogram), can accurately predict diabetes and pre-diabetes, suggests preliminary research published in the online journal BMJ Innovations. If validated in larger studies, the approach could be used to screen for the disease in low resource settings, say the researchers. An estimated 463 million adults around the world had diabetes in 2019, and picking up the disease in its early stages is key to preventing subsequent serious health problems. But diagnosis relies heavily on the measurement of blood glucose. This is not only invasive but also challenging to roll out as a mass screening test in low resource settings, point out the researchers.

The eyes may offer a "window into the soul," as poets say, but they also have a lot to say about your health. Dry eyes can be a sign of rheumatoid arthritis. High levels of cholesterol can cause a white, gray or blue ring to form around the colored part of your eye, called the iris. A coppery gold ring circling the iris is a key sign of Wilson's disease, a rare genetic disorder that causes copper to build up in the brain, liver and other organs, slowing poisoning the body. And that's not all: Damage to blood vessels in the back of your eye, called the retina, can be early signs of nerve damage due to diabetes, high blood pressure, coronary artery disease, even cancer, as well as glaucoma and age-related macular degeneration.

You may be twice as likely to develop late-life memory and cognitive issues if you are overweight or have high blood pressure or high glucose levels in your 20s/30s. They found that high BMI and blood pressure in early adulthood can double the rate of cognitive decline -- while high blood glucose levels increased in five-fold. The researchers cautioned, however, that they only established an association between these health issues and late-life cognitive problems, not a causal link. 'These results are striking and suggest that early adulthood may be a critical time for the relationship between these health issues and late-life cognitive skills,' said paper author and neurologist Kristine Yaffe of the University of California, San Francisco. 'It's possible that treating or modifying these health issues in early adulthood could prevent or reduce problems with thinking skills in later life.'