The emergence of LLM-based agents has garnered considerable attention, yet their trustworthiness remains an under-explored area. As agents can directly interact with the physical environment, their reliability and safety is critical. This paper presents an Agent-Constitution-based agent framework, TrustAgent, an initial investigation into improving the safety dimension of trustworthiness in LLM-based agents. This framework consists of threefold strategies: pre-planning strategy which injects safety knowledge to the model prior to plan generation, in-planning strategy which bolsters safety during plan generation, and post-planning strategy which ensures safety by post-planning inspection. Through experimental analysis, we demonstrate how these approaches can effectively elevate an LLM agent's safety by identifying and preventing potential dangers. Furthermore, we explore the intricate relationships between safety and helpfulness, and between the model's reasoning ability and its efficacy as a safe agent. This paper underscores the imperative of integrating safety awareness and trustworthiness into the design and deployment of LLM-based agents, not only to enhance their performance but also to ensure their responsible integration into human-centric environments. Data and code are available at https://github.com/agiresearch/TrustAgent.

Satellite data has the potential to inspire a seismic shift for machine learning -- one in which we rethink existing practices designed for traditional data modalities. As machine learning for satellite data (SatML) gains traction for its real-world impact, our field is at a crossroads. We can either continue applying ill-suited approaches, or we can initiate a new research agenda that centers around the unique characteristics and challenges of satellite data. This position paper argues that satellite data constitutes a distinct modality for machine learning research and that we must recognize it as such to advance the quality and impact of SatML research across theory, methods, and deployment. We outline critical discussion questions and actionable suggestions to transform SatML from merely an intriguing application area to a dedicated research discipline that helps move the needle on big challenges for machine learning and society.

We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware. To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources. To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81 % of the transparency criteria, far beyond the scores of even most open initiatives. This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.

Meta shares soared 12% in after-hours trading following a strong fourth-quarter earnings report released the day after CEO Mark Zuckerberg took a beating in a contentious congressional hearing. The company also announced it will pay a 50 cent-per-share dividend to investors for the first time, and has authorized a 50bn share buyback program. Overall, Meta reported fourth-quarter revenue of 40.1bn, beating the predicted 39.18bn and up 25% year-over-year. The report comes as Meta, like many of its big tech peers, is seeking to integrate artificial intelligence tools into its core products. In a statement accompanying the report, Zuckerberg said Meta has "made a lot of progress on our vision for advancing AI and the metaverse".

'Special Report' all-star panelists discuss the Biden admin's foreign policy and U.S. preparations for a response to the deadly Jordan drone attack. A United Kingdom citizen was sentenced to 18 months in prison after pleading guilty to conspiring to procure a high-powered microwave system and counter-drone system from the United States to Iran, the U.S. Attorney's Office announced Thursday. U.S. Attorney Matthew Graves said Saber Fakih, 48, conspired with Bader Fakih, 43, of Canada, Altaf Faquih, 72, of the United Arab Emirates, and Alireza Taghavi, 48, of Iran, to export and attempt to export an industrial microwave system (IMS) and counter-drone system to Iran. "The potential military uses of the IMS could include high-power microwave-based directed-energy weapon systems. The counter-drone system, which has both commercial and military uses, can be used to stop, identify, redirect, land or take total control of a target unmanned aerial vehicle," the attorney's office said.

Heritage Foundation tech policy director Kara Frederick joins'America's Newsroom' to discuss pornographic AI photos of Taylor Swift sparking conversations about deepfake regulation. Even before pornographic and violent deepfake images of Taylor Swift began widely circulating in the past few days, state lawmakers across the U.S. had been searching for ways to quash such nonconsensual images of both adults and children. But in this Taylor-centric era, the problem has been getting a lot more attention since she was targeted through deepfakes, the computer-generated images using artificial intelligence to seem real. Here are things to know about what states have done and what they are considering. HOUSE LAWMAKERS TO SHINE LIGHT ON HOW AI CAN MAKE CONGRESS'MORE EFFICIENT' Artificial intelligence hit the mainstream last year like never before, enabling people to create ever-more realistic deepfakes.

Elon Musk has announced Tesla will hold a vote on moving the company's legal base to Texas after the state of Delaware threw out his 56bn pay package at the electric vehicle maker. The world's richest person, whose No 1 status is endangered by the Delaware ruling, held a poll on X asking whether Tesla should change the company's state of incorporation from Delaware to Texas. With more than 1m votes cast, the poll recorded 87% in favour of moving. Responding on Thursday, Musk wrote on his X account: "The public vote is unequivocally in favour of Texas! Tesla will move immediately to hold a shareholder vote to transfer state of incorporation to Texas."

Global climate change is a huge challenge facing society today. The rapid growth of computing overall and of machine learning (ML) in particular rightfully raises concerns about their carbon footprints. As an early and enthusiastic adopter of ML, a manufacturer of millions of smartphones annually, and a significant cloud provider, Google is in a nearly unique position to compare the impact and efficiency of ML on the two ends of the information technology (IT) computing spectrum. Keep in mind this article is not a comparison of all computation done on phones and the cloud, but solely on the impact of ML on energy use and operational CO2e. We provide the data to support these insights. While primarily focused on operational CO2e generated from computer use, we also address the relative impact of embodied CO2e. Computers in datacenters draw electricity from the grid continuously. Because smartphones operate from a battery, they only draw electricity from the grid when connected to a charger. To account for smartphone ML energy accurately, we must include the energy overhead of their chargers. Wireless charging is increasingly popular due to its convenience and the reduction in smartphone wear and tear by avoiding the repeated insertion of a cable. For wired charging, energy is lost from the AC/DC power adapter in the charger and in the power management integrated circuit (PMIC) battery charger in the phone. Wireless charging loses additional energy through the inductive coils.

AI fairness should not be considered a panacea: It may have the potential to make society more fair than ever, but it needs critical thought and outside help to make it happen.

Artificial Intelligence (AI) is proliferating throughout society, but so too are calls for practicing Responsible AI.4 The ACM Code of Ethics and Professional Conduct states computing professionals should contribute to society and human well-being (General Ethical Principle 1.1), but it can be difficult for a computer scientist to judge the impacts of a particular application in all fields. AI is influencing a range of social domains from law and medicine to journalism, government, and education. Technologists do not just need to make the technology work and scale it up, they must make it work while also being responsible for a host of societal, ethical, legal, and other human-centered concerns in these domains.11 There is no shortcut to becoming an expert social scientist, ethicist, or legal scholar.