Welcome to Fox News' Politics newsletter with the latest political news from Washington D.C. and updates from the 2024 campaign trail. Both Former President Trump and Hunter Biden have accused the Justice Department of bringing politically biased charges. Trump, ahead of campaign stops in the battleground states of Michigan and Wisconsin, claimed Biden has "orchestrated" every lawsuit and indictment against him with the help of the Justice Department. "Please remember, ALL of these Lawsuits, Charges, and Indictments that have been brought against me have been orchestrated and coordinated by Crooked Joe Biden, the White House, and the DOJ, as an ATTACK ON CROOKED'S POLITICAL OPPONENT, ME," Trump posted on his Truth Social account Tuesday morning. Similarly, Hunter Biden's attorney blasted the decision by a federal judge who refused to dismiss tax charges against the first son, saying they will continue to fight the "abnormal way" Special Counsel David Weiss has handled the case.

Amazon is removing Just Walk Out tech from all of its Fresh grocery stores in the US, as reported by The Information. The self-checkout system relies on a host of cameras, sensors and good old-fashioned human eyeballs to track what people leave the store with, charging the customers accordingly. The technology has been plagued by issues from the onset. Most notably, Just Walk Out merely presents the illusion of automation, with Amazon crowing about generative AI and the like. Here's where the smoke and mirrors come in.

Yelabuga is located in the Alabuga "special economic zone" - an area with a special legal system aimed at attracting foreign investment. Iranian Shahed drones - which are frequently used by Russia to attack Ukraine - are thought to be assembled in Yelabuga.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we find out about explainability and human-robot interaction, serverless computing for machine learning, and deep reinforcement learning for communication networks. We also chat to AAAI President Francesca Rossi, and congratulate the ACM/SIGAI Autonomous Agents Research Award winner Catholijn Jonker. "AI used to be a scientific and technical field, now it has become a socio-technical discipline." AIhub ambassador Andrea Rafai caught up with AAAI President Francesca Rossi to ask about her research, regulation of AI, and the UN sustainable development goals: Interview with Francesca Rossi – talking sustainable development goals, AI regulation, and AI ethics.

Instruction tuning is an important step in making language models useful for direct user interaction. However, many legal tasks remain out of reach for most open LLMs and there do not yet exist any large scale instruction datasets for the domain. This critically limits research in this application area. In this work, we curate LawInstruct, a large legal instruction dataset, covering 17 jurisdictions, 24 languages and a total of 12M examples. We present evidence that domain-specific pretraining and instruction tuning improve performance on LegalBench, including improving Flan-T5 XL by 8 points or 16\% over the baseline. However, the effect does not generalize across all tasks, training regimes, model sizes, and other factors. LawInstruct is a resource for accelerating the development of models with stronger information processing and decision making capabilities in the legal domain.

In various fields of knowledge creation, including science, new ideas often build on pre-existing information. In this work, we explore this concept within the context of language models. Specifically, we explore the potential of self-training models on their own outputs, akin to how humans learn and build on their previous thoughts and actions. While this approach is intuitively appealing, our research reveals its practical limitations. We find that extended self-training of the GPT-2 model leads to a significant degradation in performance, resulting in repetitive and collapsed token output.

Contemporary LLMs pretrained on code are capable of succeeding at a wide variety of programming tasks. However, their performance is very sensitive to syntactic features, such as the names of variables and types, the structure of code, and presence of type hints. We contribute an inference-time technique to make CodeLLMs more robust to syntactic distractors that are semantically irrelevant. Our methodology relies on activation steering, which involves editing internal model activations to steer the model towards the correct prediction. We contribute a novel way to construct steering vectors by taking inspiration from mutation testing, which constructs minimal semantics-breaking code edits. In contrast, we construct steering vectors from semantics-preserving code edits. We apply our approach to the task of type prediction for the gradually typed languages Python and TypeScript. This approach corrects up to 90% of type mispredictions. Finally, we show that steering vectors calculated from Python activations reliably correct type mispredictions in TypeScript, and vice versa. This result suggests that LLMs may be learning to transfer knowledge of types across programming languages.

This paper investigates the ethical implications of aligning Large Language Models (LLMs) with financial optimization, through the case study of "GreedLlama," a model fine-tuned to prioritize economically beneficial outcomes. By comparing GreedLlama's performance in moral reasoning tasks to a base Llama2 model, our results highlight a concerning trend: GreedLlama demonstrates a marked preference for profit over ethical considerations, making morally appropriate decisions at significantly lower rates than the base model in scenarios of both low and high moral ambiguity. In low ambiguity situations, GreedLlama's ethical decisions decreased to 54.4%, compared to the base model's 86.9%, while in high ambiguity contexts, the rate was 47.4% against the base model's 65.1%. These findings emphasize the risks of single-dimensional value alignment in LLMs, underscoring the need for integrating broader ethical values into AI development to ensure decisions are not solely driven by financial incentives. The study calls for a balanced approach to LLM deployment, advocating for the incorporation of ethical considerations in models intended for business applications, particularly in light of the absence of regulatory oversight.

Most previous research on moral frames has focused on social media short texts, little work has explored moral sentiment within news articles. In news articles, authors often express their opinions or political stance through moral judgment towards events, specifically whether the event is right or wrong according to social moral rules. This paper initiates a new task to understand moral opinions towards events in news articles. We have created a new dataset, EMONA, and annotated event-level moral opinions in news articles. This dataset consists of 400 news articles containing over 10k sentences and 45k events, among which 9,613 events received moral foundation labels. Extracting event morality is a challenging task, as moral judgment towards events can be very implicit. Baseline models were built for event moral identification and classification. In addition, we also conduct extrinsic evaluations to integrate event-level moral opinions into three downstream tasks. The statistical analysis and experiments show that moral opinions of events can serve as informative features for identifying ideological bias or subjective events.

On March 13, 2024, the European Parliament approved the final version of the European Artificial Intelligence Act (AI Act), and its publication in the Official Journal of the European Union is awaited. The AI Act is a long text comprising 180 recitals, XIII chapters with 113 articles, and XIII annexes. It is an essential legal framework for AI and the first comprehensive legislation on AI.