Knowledge editing has emerged as an efficient technology for updating the knowledge of large language models (LLMs), attracting increasing attention in recent years. However, there is a lack of effective measures to prevent the malicious misuse of this technology, which could lead to harmful edits in LLMs. These malicious modifications could cause LLMs to generate toxic content, misleading users into inappropriate actions. In front of this risk, we introduce a new task, Knowledge Editing Type Identification (KETI), aimed at identifying different types of edits in LLMs, thereby providing timely alerts to users when encountering illicit edits. As part of this task, we propose KETIBench, which includes five types of harmful edits covering most popular toxic types, as well as one benign factual edit. We develop four classical classification models and three BERT-based models as baseline identifiers for both open-source and closedsource LLMs. Our experimental results, across 42 trials involving two models and three knowledge editing methods, demonstrate that all seven baseline identifiers achieve decent identification performance, highlighting the feasibility of identifying malicious edits in LLMs. Additional analyses reveal that the performance of the identifiers is independent of the reliability of the knowledge editing methods and exhibits cross-domain generalization, enabling the identification of edits from unknown sources. All data and code are available in https://github.com/xpq-tech/KETI. Warning: This paper contains examples of toxic text. Knowledge editing is an emerging technology designed to efficiently rectify errors or outdated knowledge in large language models (LLMs) (Yao et al., 2023). In recent years, it has garnered increasing attention (Wang et al., 2023).

Recent advances in large language models (LLMs) have significantly impacted the domain of multi-hop question answering (MHQA), where systems are required to aggregate information and infer answers from disparate pieces of text. However, the autoregressive nature of LLMs inherently poses a challenge as errors may accumulate if mistakes are made in the intermediate reasoning steps. This paper introduces Monte-Carlo tree search for Zero-shot multi-hop Question Answering (MZQA), a framework based on Monte-Carlo tree search (MCTS) to identify optimal reasoning paths in MHQA tasks, mitigating the error propagation from sequential reasoning processes. Unlike previous works, we propose a zero-shot prompting method, which relies solely on instructions without the support of hand-crafted few-shot examples that typically require domain expertise. We also introduce a behavioral cloning approach (MZQA-BC) trained on self-generated MCTS inference trajectories, achieving an over 10-fold increase in reasoning speed with bare compromise in performance. The efficacy of our method is validated on standard benchmarks such as HotpotQA, 2WikiMultihopQA, and MuSiQue, demonstrating that it outperforms existing frameworks.

On Monday, the National Highway Traffic Safety Administration (NHTSA) fined Cruise, GM's self-driving vehicle division, 1.5 million. The penalty was imposed for omitting key details from an October 2023 accident in which one of the company's autonomous vehicles struck and dragged a San Francisco pedestrian. Cruise is being fined for initially submitting several incomplete reports. The NHTSA's reports require pre-crash, crash and post-crash details, which the company gave to the agency without a critical detail: that the pedestrian was dragged by the vehicle for 20 feet at around 7 MPH, causing severe injuries. Eventually, the company released a 100-page report from a law firm detailing its failures surrounding the accident.

Co-hosts on'The Big Weekend Show' discuss Gov. Gavin Newsom's latest attempts to continue to expand benefits for illegal migrants in the state of California. California Gov. Gavin Newsom tossed out a slew of bills over the weekend, bucking several of his Democratic Party's more progressive initiatives on things like standards for transgender care, regulating gas stoves and providing additional benefits for noncitizens. Newsom, who has had to review more than 1,000 bills over the last few months ahead of Monday's legislative deadline, vetoed AB 2442, AB 2513 and SB 227. AB 2442, which would have expedited medical licenses for out-of-state applicants seeking to perform transgender surgical procedures, was declined by Newsom on Friday. NEWSOM'S DEEPFAKE ELECTION LAWS ARE ALREADY BEING CHALLENGED IN FEDERAL COURT Gov. Gavin Newsom addressed the press over a new state budget.

As first reported by Reuters, ByteDance, the Chinese parent company of TikTok, is planning to train and develop an AI model using chips from fellow Chinese company Huawei. Three anonymous sources approached Reuters with this information; a fourth source couldn't confirm that ByteDance was using Huawei chips but did say that a new AI model was in development. Previously, ByteDance's AI projects used NVIDIA's H20 AI chips, which were designed for the Chinese market and avoided the trade restrictions the US government placed in 2022. Chinese customers were only allowed to purchase select models of AI chips, which was an attempt to slow down Chinese technological advancement. ByteDance has ordered 100,000 Ascend 910B chips from Huawei this year but only received 30,000 of them.

The term "AI" is inescapable right now, spreading its way across every facet of every market, whether it actually makes sense or not. And as it turns out, the US Federal Trade Commission (FTC) is as sick of all this AI-related branding as everyone else. A recent regulatory push dubbed "Operation AI Comply" now has the FTC cracking down on some of the more notable AI implementations, including at least three alleged scams. A press release from last week detailed five new cases that the FTC is taking on, specifically targeting firms that sprinkle AI-related claims into their businesses. It quotes chairperson Lina M. Khan, who's been particularly proactive since her appointment in 2021: "Using AI tools to trick, mislead, or defraud people is illegal. The FTC's enforcement actions make clear that there is no AI exemption from the laws on the books. By cracking down on unfair or deceptive practices in these markets, FTC is ensuring that honest businesses and innovators can get a fair shot and consumers are being protected."

California Governor Gavin Newsom has vetoed what would have become one of the most comprehensive policies governing the safety of artificial intelligence in the U.S. The bill would've been among the first to hold AI developers accountable for any severe harm caused by their technologies. It drew fierce criticism from some prominent Democrats and major tech firms, including ChatGPT creator OpenAI and venture capital firm Andreessen Horowitz, who warned it could stall innovation in the state. Newsom described the legislation as "well-intentioned" but said in a statement that it would've applied "stringent standards to even the most basic functions." Regulation should be based on "empirical evidence and science," he said, pointing to his own executive order on AI and other bills he's signed that regulate the technology around known risks such as deepfakes. The debate around California's SB 1047 bill highlights the challenge that lawmakers around the world are facing in controlling the risks of AI while also supporting the emerging technology.

Backstage at one of the largest artificial intelligence conferences in the world, Gov. Gavin Newsom listened to two leaders in the field debate opposite views of a high-profile bill on his desk to protect Californians from the technology. "Honestly, I take advantage of opportunities like this," Newsom said recounting the exchange later during an interview at the Salesforce conference in San Francisco in mid-September. "I just watched them, and I was like, 'Here we go. Should I sign it, or should I not?' Then'absolutely,' 'absolutely not' and back and forth." The scene offered a peek into Newsom's deliberations on regulating the tech industry, including an explosion of AI companies, and the forces seeking to influence him during bill-signing season at the state Capitol.

California Gov. Gavin Newsom on Sunday vetoed a hotly contested artificial intelligence safety bill after the tech industry raised objections, saying it could drive AI companies from the state and hinder innovation. Newsom said the bill "does not take into account whether an AI system is deployed in high-risk environments, involves critical decision-making or the use of sensitive data" and would apply "stringent standards to even the most basic functions -- so long as a large system deploys it." Newsom said he had asked leading experts on generative AI to help California "develop workable guardrails" that focus "on developing an empirical, science-based trajectory analysis." He also ordered state agencies to expand their assessment of the risks from potential catastrophic events tied to AI use.

Current efforts to detect nuclear detonations and correctly categorize explosion sources with ground- and space-collected discriminants presents challenges that remain unaddressed by the Event Categorization Matrix (ECM) model. Smaller events (lower yield explosions) often include only sparse observations among few modalities and can therefore lack a complete set of discriminants. The covariance structures can also vary significantly between such observations of event (source-type) categories. Both obstacles are problematic for ``classic'' ECM. Our work addresses this gap and presents a Bayesian update to the previous ECM model, termed B-ECM, which can be trained on partial observations and does not rely on a pooled covariance structure. We further augment ECM with Bayesian Decision Theory so that false negative or false positive rates of an event categorization can be reduced in an intuitive manner. To demonstrate improved categorization rates with B-ECM, we compare an array of B-ECM and classic ECM models with multiple performance metrics that leverage Monte Carlo experiments. We use both synthetic and real data. Our B-ECM models show consistent gains in overall accuracy and a lower false negative rates relative to the classic ECM model. We propose future avenues to improve B-ECM that expand its decision-making and predictive capability.