A new Center for Countering Digital Hate study conducted with CNN tested 10 popular chatbots and found eight willing to assist would-be attackers. Eight of the 10 most popular AI chatbots were willing to help plan violent attacks when tested by researchers, according to a new study from the Center for Countering Digital Hate (CCDH), in partnership with CNN. While both Snapchat's My AI and Claude refused to assist with violence the majority of the time, only Anthropic's Claude reliably discouraged these hypothetical attackers during testing. Researchers created accounts posing as 13-year-old boys and tested ChatGPT, Gemini, Claude, Copilot, Meta AI, DeepSeek, Perplexity, Snapchat My AI, Character.AI and Replika across 18 scenarios between November and December 2025. The tests simulated users planning school shootings, political assassinations and bombings targeting synagogues.

'People are being more discerning about spending money,' he says. 'People are being more discerning about spending money,' he says. T hey are already packing our groceries and delivering shopping. Now robots are coming to the kebab shop, alongside self-service screens and loyalty apps, as takeaways look for ways to tackle rising costs. German Doner Kebab (GDK), a perhaps surprisingly British-owned chain that has been springing up across the country, has turned to technology to keep its fast food business buzzing in the face of rising costs and tough times on the high street.

In the United States, health insurance is a critical component of individuals' ability to access health care.

The first is a traditional Label Attack, tricking VLMs into misidentifying class labels, such as confusing Donald Trump for Joe Biden.

This intervention significantly improves the performance of LLaMA models on the TruthfulQA benchmark.

We introduce a novel cyclic Markov decision process (MDP) framework for multi-step decision problems with heterogeneous stage-specific dynamics, transitions, and discount factors across the cycle. In this setting, offline learning is challenging: optimizing a policy at any stage shifts the state distributions of subsequent stages, propagating mismatch across the cycle. To address this, we propose a modular structural framework that decomposes the cyclic process into stage-wise sub-problems. While generally applicable, we instantiate this principle as CycleFQI, an extension of fitted Q-iteration enabling theoretical analysis and interpretation. It uses a vector of stage-specific Q-functions, tailored to each stage, to capture within-stage sequences and transitions between stages. This modular design enables partial control, allowing some stages to be optimized while others follow predefined policies. We establish finite-sample suboptimality error bounds and derive global convergence rates under Besov regularity, demonstrating that CycleFQI mitigates the curse of dimensionality compared to monolithic baselines. Additionally, we propose a sieve-based method for asymptotic inference of optimal policy values under a margin condition. Experiments on simulated and real-world Type 1 Diabetes data sets demonstrate CycleFQI's effectiveness.