Placebo and "knowcebo" effects are a problem. But they can also help people feel better. This week I want to look at where we are with psychedelics, the mind-altering substances that have somehow made the leap from counterculture to major focus of clinical research. Compounds like psilocybin--which is found in magic mushrooms--are being explored for all sorts of health applications, including treatments for depression, PTSD, addiction, and even obesity. Over the last decade, we've seen scientific interest in these drugs explode. But most clinical trials of psychedelics have been small and plagued by challenges.

A trial involving thousands of flights between the US and Europe has found that planes produce fewer contrails if they follow flight paths recommended by an artificial intelligence to reduce their global warming impact. The streaks of condensation triggered by soot particles produced by aircraft engines are thought to cause more warming than the carbon dioxide that planes emit. Research has also shown that some ice-rich regions of the upper atmosphere are more likely to form contrails when a plane passes through them, and that AI can predict where these regions will be using detailed weather forecasts. We're finally solving the puzzle of how clouds will affect our climate There have been small-scale trials showing that planes rerouted through these regions will produce fewer contrails, but the practice has yet to be applied to commercial flights at scale. Now, Dinesh Sanekommu at Google and his colleagues have used an AI contrail-forecasting tool to give routing advice in a randomised control trial of more than 2400 real American Airlines flights.

A trial involving thousands of flights between the US and Europe has found that planes produce fewer contrails if they follow flight paths recommended by an artificial intelligence to reduce their global warming impact. The streaks of condensation triggered by soot particles produced by aircraft engines are thought to cause more warming than the carbon dioxide that planes emit. Research has also shown that some ice-rich regions of the upper atmosphere are more likely to form contrails when a plane passes through them, and that AI can predict where these regions will be using detailed weather forecasts. We're finally solving the puzzle of how clouds will affect our climate There have been small-scale trials showing that planes rerouted through these regions will produce fewer contrails, but the practice has yet to be applied to commercial flights at scale. Now, Dinesh Sanekommu at Google and his colleagues have used an AI contrail-forecasting tool to give routing advice in a randomised control trial of more than 2400 real American Airlines flights.

A trial involving thousands of flights between the US and Europe has found that planes produce fewer contrails if they follow flight paths recommended by an artificial intelligence to reduce their global warming impact. The streaks of condensation triggered by soot particles produced by aircraft engines are thought to cause more warming than the carbon dioxide that planes emit. Research has also shown that some ice-rich regions of the upper atmosphere are more likely to form contrails when a plane passes through them, and that AI can predict where these regions will be using detailed weather forecasts. We're finally solving the puzzle of how clouds will affect our climate There have been small-scale trials showing that planes bypassing these regions will produce fewer contrails, but the practice has yet to be applied to commercial flights at scale. Now, Dinesh Sanekommu at Google and his colleagues have used an AI contrail-forecasting tool to give routing advice in a randomised control trial of more than 2400 real American Airlines flights.

As datasets grow richer, an important challenge is to leverage the full features in the data to maximize the number of useful discoveries while controlling for false positives. We address this problem in the context of multiple hypotheses testing, where for each hypothesis, we observe a p-value along with a set of features specific to that hypothesis. For example, in genetic association studies, each hypothesis tests the correlation between a variant and the trait. We have a rich set of features for each variant (e.g. its location, conservation, epigenetics etc.) which could inform how likely the variant is to have a true association. However popular testing approaches, such as Benjamini-Hochberg's procedure (BH) and independent hypothesis weighting (IHW), either ignore these features or assume that the features are categorical. We propose a new algorithm, NeuralFDR, which automatically learns a discovery threshold as a function of all the hypothesis features. We parametrize the discovery threshold as a neural network, which enables flexible handling of multi-dimensional discrete and continuous features as well as efficient end-to-end optimization. We prove that NeuralFDR has strong false discovery rate (FDR) guarantees, and show that it makes substantially more discoveries in synthetic and real datasets. Moreover, we demonstrate that the learned discovery threshold is directly interpretable.

Our study of 22 mitigation techniques and five baselines reveals up to 12.6% fairness variance across identical training runs with identical seeds.

From reducing dementia to speeding up recovery after surgery, music is more powerful than you knew. Listening to music can help your brain, research suggests. Breakthroughs, discoveries, and DIY tips sent six days a week. The oldest known musical instruments-- flutes carved from bones --are over 40,000 years old . And humans were likely making music before that, based on fossils showing our ancestors had the ability to sing over 530,000 years ago.

We develop a finite-sample, design-based theory for random forests in which each tree is a randomized conditional predictor acting on fixed covariates and the forest is their Monte Carlo average. An exact variance identity separates Monte Carlo error from a covariance floor that persists under infinite aggregation. The floor arises through two mechanisms: observation reuse, where the same training outcomes receive weight across multiple trees, and partition alignment, where independently generated trees discover similar conditional prediction rules. We prove the floor is strictly positive under minimal conditions and show that alignment persists even when sample splitting eliminates observation overlap entirely. We introduce procedure-aligned synthetic resampling (PASR) to estimate the covariance floor, decomposing the total prediction uncertainty of a deployed forest into interpretable components. For continuous outcomes, resulting prediction intervals achieve nominal coverage with a theoretically guaranteed conservative bias direction. For classification forests, the PASR estimator is asymptotically unbiased, providing the first pointwise confidence intervals for predicted conditional probabilities from a deployed forest. Nominal coverage is maintained across a range of design configurations for both outcome types, including high-dimensional settings. The underlying theory extends to any tree-based ensemble with an exchangeable tree-generating mechanism.

In healthcare, predictive models increasingly inform patient-level decisions, yet little attention is paid to the variability in individual risk estimates and its impact on treatment decisions. For overparameterized models, now standard in machine learning, a substantial source of variability often goes undetected. Even when the data and model architecture are held fixed, randomness introduced by optimization and initialization can lead to materially different risk estimates for the same patient. This problem is largely obscured by standard evaluation practices, which rely on aggregate performance metrics (e.g., log-loss, accuracy) that are agnostic to individual-level stability. As a result, models with indistinguishable aggregate performance can nonetheless exhibit substantial procedural arbitrariness, which can undermine clinical trust. We propose an evaluation framework that quantifies individual-level prediction instability by using two complementary diagnostics: empirical prediction interval width (ePIW), which captures variability in continuous risk estimates, and empirical decision flip rate (eDFR), which measures instability in threshold-based clinical decisions. We apply these diagnostics to simulated data and GUSTO-I clinical dataset. Across observed settings, we find that for flexible machine-learning models, randomness arising solely from optimization and initialization can induce individual-level variability comparable to that produced by resampling the entire training dataset. Neural networks exhibit substantially greater instability in individual risk predictions compared to logistic regression models. Risk estimate instability near clinically relevant decision thresholds can alter treatment recommendations. These findings that stability diagnostics should be incorporated into routine model validation for assessing clinical reliability.