Two parallel experiments in protein self-assembly produced strikingly different results, demonstrating that protein designers ...

Epshtein argues that a deeper understanding of generalization and principled use of prior knowledge are essential to building ...

Explainability tools are commonly used in AI development to provide visibility into how models interpret data. In healthcare machine learning systems, explainability techniques may highlight factors ...

If there’s a legal reckoning to come over the use of intellectual property in training AI, there are also several methods of ...

Correlation can look convincing in dashboards. Without causal analysis, organizations risk optimizing for the wrong signals.

Integrating AI into chip workflows is pushing companies to overhaul their data management strategies, shifting from passive storage to active, structured, and machine-readable systems. As training and ...

Humanoid robots are rapidly improving in motion fluidity, making them more human-like and suitable for complex tasks.

Traditional hydraulics are falling away, as electric lift systems and quasi-direct drive (QDD) motors enable smarter, more ...

A new international review suggests that while artificial intelligence has made major strides in measuring engagement and behavioral patterns in online education, the integration of emotional and ...

Google Research has proposed a training method that teaches large language models to approximate Bayesian reasoning by learning from the predictions of an optimal Bayesian system. The approach focuses ...

Abstract: Efficient prediction of tropospheric ozone $\left({O}_{3}\right)$ is crucial to monitor its harmful effects on the ecosystem and thereby take necessary actions. Owing to the complex ...

Cambridge, MA — In high-stakes settings like medical diagnostics, users often want to know what led a computer vision model to make a certain prediction, so they can determine whether to trust its ...