GM's robotaxi unit Cruise has agreed to pay a 500,000 for submitting a false accident report as part of a deferred prosecution agreement. The US Justice Department (DoJ) said that Cruise failed to disclose vital details about a serious October 2023 accident in which one of its vehicles struck a pedestrian and dragged her 20 feet after she was hit by another vehicle. "Federal laws and regulations are in place to protect public safety on our roads. Companies with self-driving cars that seek to share our roads and crosswalks must be fully truthful in their reports to their regulators," said Martha Boersch, Chief of the Office of the U.S. Attorney's Criminal Division. Uber has yet to comment on the matter.

A laser beam downs drones by heating up and "frying" their electronics – each invisible, soundless "shot" is more precise and less costly than an air defence missile. Hanwha Aerospace, a South Korean defence company, has fire-tested and is about to start mass-producing the world's first-ever optical fibre laser weapon. And Hanwha is ready to supply it to Ukraine if Seoul lifts a ban on the export of lethal weapons to Kyiv "in light of North Korean military activities", South Korean President Yoon Suk Yeol said in late October. His statement followed North Korea's deployment of about 10,000 soldiers to western Russia, becoming the first foreign power to step into the Russia-Ukrainian war. However, the US president-elect may stand in the way.

Technological progress can excite us, politics can infuriate us, and wars can mobilize us. But faced with the risk of human extinction that the rise of artificial intelligence is causing, we have remained surprisingly passive. In part, perhaps this was because there did not seem to be a solution. This is an idea I would like to challenge. Since the release of ChatGPT two years ago, hundreds of billions of dollars have poured into AI.

North Korean leader Kim Jong Un has called for accelerating the mass production of attack drones, according to state media, as international concerns mount over the country's deepening military cooperation with Russia. The official Korean Central News Agency (KCNA) reported on Friday that Kim supervised the latest tests of "various types of suicide attack drones" produced by Pyongyang's Unmanned Aerial Technology Complex. The unmanned drones can hit land and sea targets, effectively acting as guided missiles. Kim "underscored the need to build a serial production system as early as possible and go into full-scale mass production", noting how drones are becoming crucial in modern warfare as he oversaw the tests on Thursday, KCNA said. North Korea first unveiled its suicide drones in August and military experts said the capability could be attributed to the country's growing alliance with Russia, with both sides signing a mutual defence pact.

Artificial Intelligence is rapidly embedding itself within militaries, economies, and societies, reshaping their very foundations. Given the depth and breadth of its consequences, it has never been more pressing to understand how to ensure that AI systems are safe, ethical, and have a positive societal impact. This book aims to provide a comprehensive approach to understanding AI risk. Our primary goals include consolidating fragmented knowledge on AI risk, increasing the precision of core ideas, and reducing barriers to entry by making content simpler and more comprehensible. The book has been designed to be accessible to readers from diverse backgrounds. You do not need to have studied AI, philosophy, or other such topics. The content is skimmable and somewhat modular, so that you can choose which chapters to read. We introduce mathematical formulas in a few places to specify claims more precisely, but readers should be able to understand the main points without these.

A yet unmet challenge in algorithmic fairness is the problem of intersectionality, that is, achieving fairness across the intersection of multiple groups -- and verifying that such fairness has been attained. Because intersectional groups tend to be small, verifying whether a model is fair raises statistical as well as moral-methodological challenges. This paper (1) elucidates the problem of intersectionality in algorithmic fairness, (2) develops desiderata to clarify the challenges underlying the problem and guide the search for potential solutions, (3) illustrates the desiderata and potential solutions by sketching a proposal using simple hypothesis testing, and (4) evaluates, partly empirically, this proposal against the proposed desiderata.

Motivated by the transformative capabilities of large language models (LLMs) across various natural language tasks, there has been a growing demand to deploy these models effectively across diverse real-world applications and platforms. However, the challenge of efficiently deploying LLMs has become increasingly pronounced due to the varying application-specific performance requirements and the rapid evolution of computational platforms, which feature diverse resource constraints and deployment flows. These varying requirements necessitate LLMs that can adapt their structures (depth and width) for optimal efficiency across different platforms and application specifications. To address this critical gap, we propose AmoebaLLM, a novel framework designed to enable the instant derivation of LLM subnets of arbitrary shapes, which achieve the accuracy-efficiency frontier and can be extracted immediately after a one-time fine-tuning. In this way, AmoebaLLM significantly facilitates rapid deployment tailored to various platforms and applications. Specifically, AmoebaLLM integrates three innovative components: (1) a knowledge-preserving subnet selection strategy that features a dynamic-programming approach for depth shrinking and an importance-driven method for width shrinking; (2) a shape-aware mixture of LoRAs to mitigate gradient conflicts among subnets during fine-tuning; and (3) an in-place distillation scheme with loss-magnitude balancing as the fine-tuning objective. Extensive experiments validate that AmoebaLLM not only sets new standards in LLM adaptability but also successfully delivers subnets that achieve state-of-the-art trade-offs between accuracy and efficiency.

LLM alignment ensures that large language models behave safely and effectively by aligning their outputs with human values, goals, and intentions. Aligning LLMs employ huge amounts of data, computation, and time. Moreover, curating data with human feedback is expensive and takes time. Recent research depicts the benefit of data engineering in the fine-tuning and pre-training paradigms to bring down such costs. However, alignment differs from the afore-mentioned paradigms and it is unclear if data efficient alignment is feasible. In this work, we first aim to understand how the performance of LLM alignment scales with data. We find out that LLM alignment performance follows an exponential plateau pattern which tapers off post a rapid initial increase. Based on this, we identify data subsampling as a viable method to reduce resources required for alignment. Further, we propose an information theory-based methodology for efficient alignment by identifying a small high quality subset thereby reducing the computation and time required by alignment. We evaluate the proposed methodology over multiple datasets and compare the results. We find that the model aligned using our proposed methodology outperforms other sampling methods and performs comparable to the model aligned with the full dataset while using less than 10% data, leading to greater than 90% savings in costs, resources, and faster LLM alignment.

If an AI system makes decisions over time, how should we evaluate how aligned it is with a group of stakeholders (who may have conflicting values and preferences)? In this position paper, we advocate for consideration of temporal aspects including stakeholders' changing levels of satisfaction and their possibly temporally extended preferences. We suggest how a recent approach to evaluating fairness over time could be applied to a new form of pluralistic alignment: temporal pluralism, where the AI system reflects different stakeholders' values at different times.

The demands for accurate and representative generative AI systems means there is an increased demand on participatory evaluation structures. While these participatory structures are paramount to to ensure non-dominant values, knowledge and material culture are also reflected in AI models and the media they generate, we argue that dominant structures of community participation in AI development and evaluation are not explicit enough about the benefits and harms that members of socially marginalized groups may experience as a result of their participation. Without explicit interrogation of these benefits by AI developers, as a community we may remain blind to the immensity of systemic change that is needed as well. To support this provocation, we present a speculative case study, developed from our own collective experiences as AI researchers. We use this speculative context to itemize the barriers that need to be overcome in order for the proposed benefits to marginalized communities to be realized, and harms mitigated.