New York state has required companies to disclose if "technological innovation or automation" was the cause of job loss for nearly a year. Over 160 companies in New York state have filed notices of mass layoffs since last March. None--in a group that includes Amazon, Goldman Sachs, and other employers that are adopting AI tools --attributed their workforce cuts in those filings to "technological innovation or automation." That option was added 11 months ago to a required question on paperwork that businesses with 50 or more employees must file with the state to notify of sizable job losses. New York's Department of Labor told WIRED that, as of the end of January, no employer had marked tech as the reason for their workforce reduction.

If you were watching the downhill skiing or luge at Milan-Cortina 2026 over the weekend, you will have noticed the dramatic new camera angles being provided at these Games. Drones have been used in Olympic coverage since 2014, but they have been much more prevalent at these Winter Games. Carrying cameras, the drones have been flown close behind athletes as they ski or slide, capturing dramatic footage which has never been seen at a Games before. But they have proved divisive for audiences, with social media split between admiring the footage or being put off by the noise. The whirring of the drone blades is audible in the live coverage.

We examine an adaptive learning framework for nonatomic congestion games where the players' cost functions may be subject to exogenous fluctuations (e.g., due to disturbances in the network, variations in the traffic going through a link, etc.).

Actor -Critic Fictitious Playin Simultaneous Move Multistage Games.

Meanwhile, a Neural V ariational Inference (NVI) approach is proposed to learn our model with graph neural networks to encode the document graphs. Besides, we theoretically demonstrate that Latent Dirichlet Allocation (LDA) can be derived from GNTM as a special case with similar objective functions.

Low-dimensional embeddings, from classical spectral embeddings to modern neural-net-inspired methods, are a cornerstone in the modeling and analysis of complex networks. Recent work by Seshadhri et al. (PNAS 2020) suggests that such embeddings cannot capture local structure arising in complex networks.