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.