Deriving nutritional information from online food posts is challenging, particularly when users do not explicitly log the macro-nutrients of a shared meal. In this work, we present an efficient and straightforward approach to approximating macro-nutrients based solely on the titles of food posts. Our method combines a public food database from the U.S. Department of Agriculture with advanced text embedding techniques. We evaluate the approach on a labeled food dataset, demonstrating its effectiveness, and apply it to over 500,000 real-world posts from Reddit's popular /r/food subreddit to uncover trends in food-sharing behavior based on the estimated macro-nutrient content. Altogether, this work lays a foundation for researchers and practitioners aiming to estimate caloric and nutritional content using only text data.

Physics-guided machine learning (PGML) has become a prevalent approach in studying scientific systems due to its ability to integrate scientific theories for enhancing machine learning (ML) models. However, most PGML approaches are tailored to isolated and relatively simple tasks, which limits their applicability to complex systems involving multiple interacting processes and numerous influencing features. In this paper, we propose a P hysics-G uided Foundation Model (PGFM) that combines pre-trained ML models and physics-based models and leverages their complementary strengths to improve the modeling of multiple coupled processes. To effectively conduct pre-training, we construct a simulated environmental system that encompasses a wide range of influencing features and various simulated variables generated by physics-based models. The model is pre-trained in this system to adaptively select important feature interactions guided by multi-task objectives. We then fine-tune the model for each specific task using true observations, while maintaining consistency with established physical theories, such as the principles of mass and energy conservation. We demonstrate the effectiveness of this methodology in modeling water temperature and dissolved oxygen dynamics in real-world lakes. The proposed PGFM is also broadly applicable to a range of scientific fields where physics-based models are being used.

Distributed learning of probabilistic models from multiple data repositories with minimum communication is increasingly important. We study a simple communication-efficient learning framework that first calculates the local maximum likelihood estimates (MLE) based on the data subsets, and then combines the local MLEs to achieve the best possible approximation to the global MLE given the whole dataset. We study this framework's statistical properties, showing that the efficiency loss compared to the global setting relates to how much the underlying distribution families deviate from full exponential families, drawing connection to the theory of information loss by Fisher, Rao and Efron. We show that the "full-exponential-family-ness" represents the lower bound of the error rate of arbitrary combinations of local MLEs, and is achieved by a KL-divergence-based combination method but not by a more common linear combination method. We also study the empirical properties of both methods, showing that the KL method significantly outperforms linear combination in practical settings with issues such as model misspecification, non-convexity, and heterogeneous data partitions.

'The Big Weekend Show' co-hosts discuss the impact of new artificial intelligence apps on national security and jobs. 'DEEPLY DISTURBING EVIDENCE': House lawmakers are introducing legislation Thursday to ban the Chinese AI DeepSeek from U.S. government devices, arguing that the software is "directly linked to the Chinese Communist Party" and poses a "five-alarm national security fire." AN AMERICAN TIKTOK: The CEO of AI startup Perplexity, Aravind Srinivas, confirmed his company's bid for TikTok U.S. and said the deal checks all the boxes for investors and President Donald Trump, including an ownership stake for the U.S. SEEING DOUBLE: Donny Osmond is bringing on a new co-star for his Las Vegas residency: himself. President Donald Trump speaks to reporters after signing a series of executive orders in the Oval Office of the White House on January 23, 2025, in Washington, D.C. 'AI POWERHOUSE': The White House is opening its artificial intelligence plan up to Americans to contribute policy ideas to the Trump administration to ensure that the United States is "the undeniable leader" in AI technology. PEDAL TO THE METAL: For a commander on the battlefield, a split second of decision advantage can determine the difference between victory and defeat.

Elon Musk's rapid attempt to defund and depopulate the federal government has thrown US politics into chaos while the billionaire's so-called "department of government efficiency" seizes control of operations at key agencies. Carrying out this hostile takeover are a team of staffers made up of wealthy executives, far-right ideologues and young engineers that have come to make up Doge. At government institutions such as the treasury department, General Services Administration and United States Agency for International Development, Musk's allies have gained access to computer systems, including the sensitive personal data and payment information of tens of millions of Americans. His team is working to shut down USAid, the world's largest single supplier of humanitarian aid, and members have been spotted at the Centers for Disease Control and Prevention, Department of Education and National Institutes of Health. To undertake this unprecedented and potentially illegal gutting of public funding, Musk has assembled loyalists who largely lack government experience and who range from tech elites to Maga diehards, according to a review of the people publicly associated with Doge.

Noland Arbaugh's life changed in a fraction of a second in June 2016. He was a 22-year-old student, working at a kids' summer camp in upstate New York, when he went swimming in a lake. He can't tell me exactly what happened, but thinks one of his friends must have accidentally struck him very hard in the side of his head as they ran into the water and plunged beneath the surface. When he woke up face down in the water, unable to move or breathe, Noland immediately knew he was paralysed. He felt no fear at all, he says. "You never know what you're going to do in those high-stress situations. I found out that day that it's hard to shake me. I am very, very calm under pressure." Elon Musk would ultimately turn this quality to his advantage when, after nearly eight years of being quadriplegic, Noland agreed to allow the world's richest man to implant an electronic chip into his brain. In January 2024, Noland became the first human recipient of a brain-computer interface (BCI) developed by Musk's company, Neuralink. If it worked, it would allow him to control a computer using only the power of his mind. Only four months after he first heard about Neuralink, Noland was on an operating table, with a purpose-built robot poised to insert the N1 chip into his motor cortex. The stakes could not have been higher for him: he was risking infection, haemorrhage and brain damage. "My brain is the last part of myself that I really feel I have control over," he tells me from his wheelchair at his kitchen table in Yuma, Arizona.

Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC). By integrating context retrieval into content generation, RAG provides reliable and up-to-date external knowledge, reduces hallucinations, and ensures relevant context across a wide range of tasks. However, despite RAG's success and potential, recent studies have shown that the RAG paradigm also introduces new risks, including robustness issues, privacy concerns, adversarial attacks, and accountability issues. Addressing these risks is critical for future applications of RAG systems, as they directly impact their trustworthiness. Although various methods have been developed to improve the trustworthiness of RAG methods, there is a lack of a unified perspective and framework for research in this topic. Thus, in this paper, we aim to address this gap by providing a comprehensive roadmap for developing trustworthy RAG systems. We place our discussion around five key perspectives: reliability, privacy, safety, fairness, explainability, and accountability. For each perspective, we present a general framework and taxonomy, offering a structured approach to understanding the current challenges, evaluating existing solutions, and identifying promising future research directions. To encourage broader adoption and innovation, we also highlight the downstream applications where trustworthy RAG systems have a significant impact.

There is a general concern that present developments in artificial intelligence (AI) research will lead to sentient AI systems, and these may pose an existential threat to humanity. But why cannot sentient AI systems benefit humanity instead? This paper endeavours to put this question in a tractable manner. I ask whether a putative AI system will develop an altruistic or a malicious disposition towards our society, or what would be the nature of its agency? Given that AI systems are being developed into formidable problem solvers, we can reasonably expect these systems to preferentially take on conscious aspects of human problem solving. I identify the relevant phenomenal aspects of agency in human problem solving. The functional aspects of conscious agency can be monitored using tools provided by functionalist theories of consciousness. A recent expert report (Butlin et al. 2023) has identified functionalist indicators of agency based on these theories. I show how to use the Integrated Information Theory (IIT) of consciousness, to monitor the phenomenal nature of this agency. If we are able to monitor the agency of AI systems as they develop, then we can dissuade them from becoming a menace to society while encouraging them to be an aid.

Reasoning abilities of LLMs have been a key focus in recent years. One challenging reasoning domain with interesting nuances is legal reasoning, which requires careful application of rules, and precedents while balancing deductive and analogical reasoning, and conflicts between rules. Although there have been a few works on using LLMs for legal reasoning, their focus has been on overall accuracy. In this paper, we dig deeper to do a step-by-step analysis and figure out where they commit errors. We use the college-level Multiple Choice Question-Answering (MCQA) task from the \textit{Civil Procedure} dataset and propose a new error taxonomy derived from initial manual analysis of reasoning chains with respect to several LLMs, including two objective measures: soundness and correctness scores. We then develop an LLM-based automated evaluation framework to identify reasoning errors and evaluate the performance of LLMs. The computation of soundness and correctness on the dataset using the auto-evaluator framework reveals several interesting insights. Furthermore, we show that incorporating the error taxonomy as feedback in popular prompting techniques marginally increases LLM performance. Our work will also serve as an evaluation framework that can be used in detailed error analysis of reasoning chains for logic-intensive complex tasks.

Vision Large Language Models (VLLMs) integrate visual data processing, expanding their real-world applications, but also increasing the risk of generating unsafe responses. In response, leading companies have implemented Multi-Layered safety defenses, including alignment training, safety system prompts, and content moderation. However, their effectiveness against sophisticated adversarial attacks remains largely unexplored. In this paper, we propose MultiFaceted Attack, a novel attack framework designed to systematically bypass Multi-Layered Defenses in VLLMs. It comprises three complementary attack facets: Visual Attack that exploits the multimodal nature of VLLMs to inject toxic system prompts through images; Alignment Breaking Attack that manipulates the model's alignment mechanism to prioritize the generation of contrasting responses; and Adversarial Signature that deceives content moderators by strategically placing misleading information at the end of the response. Extensive evaluations on eight commercial VLLMs in a black-box setting demonstrate that MultiFaceted Attack achieves a 61.56% attack success rate, surpassing state-of-the-art methods by at least 42.18%.