As autonomous systems rapidly become ubiquitous, there is a growing need for a legal and regulatory framework toaddress when and how such a system harms someone. There have been several attempts within the philosophy literature to define harm, but none of them has proven capable of dealing with with the many examples that have been presented, leading some to suggest that the notion of harm should be abandoned and ``replaced by more well-behaved notions''. As harm is generally something that is caused, most of these definitions have involved causality at some level. Yet surprisingly, none of them makes use of causal models and the definitions of actual causality that they can express. In this paper we formally define a qualitative notion of harm that uses causal models and is based on a well-known definition of actual causality (Halpern, 2016). The key novelty of our definition is that it is based on contrastive causation and uses a default utility to which the utility of actual outcomes is compared. We show that our definition is able to handle the examples from the literature, and illustrate its importance for reasoning about situations involving autonomous systems.

Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior: At each time, the algorithm observes an action chosen by a fallible agent, and decides whether to that agent's decision, with an alternative, or the expert's opinion. For instance, in clinical diagnosis, fully-autonomous machine behavior is often beyond ethical affordances, thus real-world decision support is often limited to monitoring and forecasting. Instead, such an intermediary would strike a prudent balance between the former (purely prescriptive) and latter (purely descriptive) approaches, while providing an efficient interface between human mistakes and expert feedback. In this work, we first formalize the sequential problem of ---that is, of simultaneously learning and evaluating mediator policies from scratch with: In each round, deferring to the oracle obviates the risk of error, but incurs an upfront penalty, and reveals the otherwise hidden expert action as a new training data point. Second, we motivate and propose a solution that seeks to trade off (immediate) loss terms against (future) improvements in generalization error; in doing so, we identify why conventional bandit algorithms may fail. Finally, through experiments and sensitivities on a variety of datasets, we illustrate consistent gains over applicable benchmarks on performance measures with respect to the mediator policy, the learned model, and the decision-making system as a whole.

A large amount of recent research has the far-reaching goal of finding training methods for deep neural networks that can serve as alternatives to backpropagation~(BP). A prominent example is predictive coding (PC), which is a neuroscience-inspired method that performs inference on hierarchical Gaussian generative models. These methods, however, fail to keep up with modern neural networks, as they are unable to replicate the dynamics of complex layers and activation functions. In this work, we solve this problem by generalizing PC to arbitrary probability distributions, enabling the training of architectures, such as transformers, that are hard to approximate with only Gaussian assumptions. We perform three experimental analyses. First, we study the gap between our method and the standard formulation of PC on multiple toy examples. Second, we test the reconstruction quality on variational autoencoders, where our method reaches the same reconstruction quality as BP. Third, we show that our method allows us to train transformer networks and achieve performance comparable with BP on conditional language models. More broadly, this method allows neuroscience-inspired learning to be applied to multiple domains, since the internal distributions can be flexibly adapted to the data, tasks, and architectures used.

Switch 2 games are on sale through Jan. 5 Carreyrou is perhaps for exposing the Theranos fraudulent blood test scandal. According to, the lawsuit was filed alongside five other writers who all claim big tech companies have been violating their intellectual property rights in the name of building large language models. This comes after a banner year for IP lawsuits against AI companies brought by rights holders. Just about every type of entity that deals in protected content has gone to court against AI companies this year, from movie studios like to papers like the . Some of these cases have led to settlements in the form of partnerships, such as the between Disney and OpenAI.

Pope Leo XIV made his first address to the College of Cardinals on May 10, 2025 in Vatican City, and touched upon the rise of artificial intelligence. Pope Leo XIV made his first address to the College of Cardinals on May 10, 2025 in Vatican City, and touched upon the rise of artificial intelligence. As technologists race to accelerate AI's progress with minimal guardrails, they are being met with increasing resistance from a powerful global contingent: Christian leaders and their congregations. Christians are not a monolith by any means. But this year, Christian leaders across sects--including Catholics, Evangelicals, and Baptists--sounded the alarm on AI's potential impact on family, human relationships, labor, and the church itself.

Users of AI image generators are offering each other instructions on how to use the tech to alter pictures of women into realistic, revealing deepfakes. Some users of popular chatbots are generating bikini deepfakes using photos of fully clothed women as their source material. Most of these fake images appear to be generated without the consent of the women in the photos. Some of these same users are also offering advice to others on how to use the generative AI tools to strip the clothes off of women in photos and make them appear to be wearing bikinis. Under a now-deleted Reddit post titled "gemini nsfw image generation is so easy," users traded tips for how to get Gemini, Google's generative AI model, to make pictures of women in revealing clothes.

Ting Cai, head of Rakuten Group's artificial intelligence team, has the task of creating AI systems that would augment the company's many businesses at a minimal cost. Rakuten Group is expanding its AI team under the stewardship of a Google veteran and building models with a focus on cost efficiency. Ting Cai, now three years into his tenure at the head of the e-commerce pioneer's artificial intelligence team, has the task of creating AI systems that would augment the company's many businesses and support the handling of commercial transactions at a minimal cost. He oversees a team that's grown to 1,000 this year and has a battery of "thousands" of Nvidia chips to work with. Tokyo-based Rakuten is wrestling with a struggling mobile business and constant competition in online shopping, both of which could get a significant boost from effective deployment of new AI tools.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Self-driving vehicles charge at the Waymo station in Santa Monica. This is read by an automated voice. Please report any issues or inconsistencies here . Waymo is taking the city of Santa Monica to court after the city ordered the company to cease charging its autonomous vehicles at two facilities overnight, claiming the lights and beeping at the lots were a nuisance to residents.

The latest Epstein Files release appears to contain hundreds of photographs along with court records and other materials. Over the weekend, the Justice Department released three new data sets comprising files related to Jeffrey Epstein . The DOJ had previously released nearly 4,000 documents prior to the Friday midnight deadline required by the Epstein Files Transparency Act . As with Friday's release, the new tranche appears to contain hundreds of photographs, along with various court records pertaining to Epstein. There are around 1,200 pages in all, including images WIRED is currently going through the materials and will update with more detail.

OpenAI's Child Exploitation Reports Increased Sharply This Year The company made 80 times as many reports to the National Center for Missing & Exploited Children during the first six months of 2025 as it did in the same period a year prior. OpenAI sent 80 times as many child exploitation incident reports to the National Center for Missing & Exploited Children during the first half of 2025 as it did during a similar time period in 2024, according to a recent update from the company. The NCMEC's CyberTipline is a Congressionally authorized clearinghouse for reporting child sexual abuse material (CSAM) and other forms of child exploitation. Companies are required by law to report apparent child exploitation to the CyberTipline. When a company sends a report, NCMEC reviews it and then forwards it to the appropriate law enforcement agency for investigation.