Machine learning tool pinpoints optimal locations for tree planting, offering a powerful tool for climate mitigation

Afforestation—establishing forests on previously non-forested land, or where forests have not existed for a long time—is one ...
Harvard Business ReviewOpinion

Decision-Making by Consensus Doesn’t Work in the AI Era

While most leaders probably agree that their organizations will need to adapt to AI, too few are willing to admit that this ...
Nature

AI models ‘subliminally’ transmit biases when training other systems

Using artificial-intelligence to teach other models can be cheaper and faster than building them from scratch, but this ...
Frontiers

Beyond the Algorithm: Scalable, Explainable, and Adaptive AI for High-Stakes Clinical Decision Support

Artificial Intelligence (AI) has shown strong potential in supporting clinical decision-making through Clinical Decision Support Systems (CDSSs). However, ...
Frontiers

Computational Frameworks for Decision-Making: From Bayesian Inference to Reinforcement Learning Models

The ability to make adaptive decisions in uncertain environments is a fundamental characteristic of biological intelligence. Historically, computational ...
Nature

Learning algorithms articles from across Nature Portfolio

This study investigated the computational mechanisms linking momentary craving and decision-making in people with moderate to high addiction risk levels for alcohol or cannabis use, uncovering ...
CU Boulder News & Events

CSCA 5632: Unsupervised Algorithms in Machine Learning

Start working toward program admission and requirements right away. Work you complete in the non-credit experience will transfer to the for-credit experience when you ...
The New York Times

Meta and YouTube Found Negligent in Landmark Social Media Addiction Case

A jury found the companies harmed a young user with design features that were addictive and led to her mental health distress. By Cecilia Kang Ryan Mac and Eli Tan Cecilia Kang reported from ...
eLife

New idtracker.ai rethinks multi-animal tracking as a representation learning problem to increase accuracy and reduce tracking time

Identification of each animal in a collective becomes possible even when individuals are never all visible simultaneously, enabling faster and more accurate analysis of collective behavior.