Plus: The NSA tests Anthropic's Mythos Preview to find vulnerabilities, a Finnish teen is charged over the Scattered Spider hacking spree, and more. A gunman attempted to enter the White House Correspondents' Dinner in Washington, DC, last weekend, while President Donald Trump, Vice President JD Vance, and other administration officials were in attendance. Media reports and Trump himself quickly identified the suspected shooter as 31-year-old engineer and computer scientist Cole Tomas Allen. The California resident was arrested at the scene on Saturday and appeared Monday in the US District Court for the District of Columbia to face three federal charges: attempting to assassinate the president, transportation of a firearm in interstate commerce, and discharge of a firearm during a crime of violence. The authentication standards body known as the FIDO Alliance announced working groups this week along with Google and Mastercard to develop technical guardrails for validating and protecting transactions initiated by an AI agent .

Musk v. Altman week 1: Elon Musk says he was duped, warns AI could kill us all, and admits that xAI distills OpenAI's models Musk kept his cool, and OpenAI's lawyer bulldozed him with piercing questions about his motivations for suing the company. In the first week of the landmark trial between Elon Musk and OpenAI, Musk took the stand in a crisp black suit and tie and argued that OpenAI CEO Sam Altman and president Greg Brockman had deceived him into bankrolling the company. Along the way, he warned that AI could destroy us all and sat through revelations that he had poached OpenAI employees for his own companies. He even confessed, to some audible gasps in the courtroom, that his own AI company, xAI, which makes the chatbot Grok, uses OpenAI's models to train its own. The federal courthouse in Oakland, California, was packed with armies of lawyers carrying boxes of exhibits, journalists typing away at their laptops, and a handful of concerned OpenAI employees. Outside, protesters lined the streets, carrying signs urging people to quit ChatGPT, boycott Tesla, or both.

We propose Confusions over Time (CoT), a novel generative framework which facilitates a multi-granular analysis of the decision making process. The CoT not only models the confusions or error properties of individual decision makers and their evolution over time, but also allows us to obtain diagnostic insights into the collective decision making process in an interpretable manner.

Different from general assembly datasets, we treat assemblable features, such as holes, stud and USB female, as objects, to enable finer-grained assembly knowledge understanding.

High-dimensional mediation analysis is often associated with a multiple testing problem for detecting significant mediators. Assessing the uncertainty of this detecting process via false discovery rate (FDR) has garnered great interest. To control the FDR in multiple testing, two essential steps are involved: ranking and selection. Existing approaches either construct p-values without calibration or disregard the joint information across tests, leading to conservation in FDR control or non-optimal ranking rules for multiple hypotheses. In this paper, we develop an adaptive mediation detection procedure (referred to as "AMDP") to identify relevant mediators while asymptotically controlling the FDR in high-dimensional mediation analysis. AMDP produces the optimal rule for ranking hypotheses and proposes a data-driven strategy to determine the threshold for mediator selection. This novel method captures information from the proportions of composite null hypotheses and the distribution of p-values, which turns the high dimensionality into an advantage instead of a limitation. The numerical studies on synthetic and real data sets illustrate the performances of AMDP compared with existing approaches.

Language models can generate harmful and biased outputs and exhibit undesirable behavior according to a given cultural context. We propose a Process for Adapting Language Models to Society (PALMS) with ValuesTargeted Datasets, an iterative process to significantly change model behavior by crafting and fine-tuning on a dataset that reflects a predetermined set of target values. We evaluate our process using three metrics: quantitative metrics with human evaluations that score output adherence to a target value, toxicity scoring on outputs; and qualitative metrics analyzing the most common word associated with a given social category. Through each iteration, we add additional training dataset examples based on observed shortcomings from evaluations. PALMS performs significantly better on all metrics compared to baseline and control models for a broad range of GPT-3 language model sizes without compromising capability integrity. We find that the effectiveness of PALMS increases with model size. We show that significantly adjusting language model behavior is feasible with a small, hand-curated dataset.

Messages presented at trial reveal how Zilis, the mother of four of Musk's children, acted as an intermediary between him and OpenAI. As the first week of trial in comes to a close, one person has emerged as a critical behind-the-scenes manager of communications and egos in OpenAI's early years: Shivon Zilis. A longtime employee of Musk and the mother to four of his children, Zilis first joined OpenAI as an advisor in 2016. She later served as a director of its nonprofit board from 2020 until 2023 and has also worked as an executive at Musk's other companies, Neuralink and Tesla. When asked about the nature of his relationship with Zilis in court, Musk offered several answers.