Switch 2 games are on sale through Jan. 5 OpenAI is hiring a new Head of Preparedness to try to predict and mitigate AI's harms CEO Sam Altman posted about the role on X, saying the models'are starting to present some real challenges.' OpenAI is looking for a new Head of Preparedness who can help it anticipate the potential harms of its models and how they can be abused, in order to guide the company's safety strategy. It comes at the end of a year that's seen OpenAI hit with numerous accusations about ChatGPT's impacts on users' mental health, including a few wrongful death lawsuits . In a post on X about the position, OpenAI CEO Sam Altman acknowledged that the potential impact of models on mental health was something we saw a preview of in 2025, along with other real challenges that have arisen alongside models' capabilities. The Head of Preparedness is a critical role at an important time, he said.

What can be learned about causality and experimentation from passive data?

Due to its low storage cost and fast search speed, cross-modal retrieval based on hashing has attracted widespread attention and is widely used in real-world applications of social media search.

Text-to-image diffusion models have made significant progress in generating naturalistic images from textual inputs, and demonstrate the capacity to learn and represent complex visual-semantic relationships. While these diffusion models have achieved remarkable success, the underlying mechanisms driving their performance are not yet fully accounted for, with many unanswered questions surrounding what they learn, how they represent visual-semantic relationships, and why they sometimes fail to generalize.

Estimating the probabilities of connections between vertices in a random network using an observed adjacency matrix is an important task for network data analysis. Many existing estimation methods are based on certain assumptions on network structure, which limit their applicability in practice. Without making strong assumptions, we develop an iterative connecting probability estimation method based on neighborhood averaging. Starting at a random initial point or an existing estimate, our method iteratively updates the pairwise vertex distances, the sets of similar vertices, and connecting probabilities to improve the precision of the estimate. We propose a two-stage neighborhood selection procedure to achieve the trade-off between smoothness of the estimate and the ability to discover local structure. The tuning parameters can be selected by cross-validation. We establish desirable theoretical properties for our method, and further justify its superior performance by comparing with existing methods in simulation and real data analysis.

Vision-language models, such as CLIP, have shown impressive generalization capacities when using appropriate text descriptions.

The critical challenge when using the Shapley value is its well-known computational intractability.

Such models range broadly in both complexity and biological plausibility.