Safety cases - clear, assessable arguments for the safety of a system in a given context - are a widely-used technique across various industries for showing a decision-maker (e.g. boards, customers, third parties) that a system is safe. In this paper, we cover how and why frontier AI developers might also want to use safety cases. We then argue that writing and reviewing safety cases would substantially assist in the fulfilment of many of the Frontier AI Safety Commitments. Finally, we outline open research questions on the methodology, implementation, and technical details of safety cases.

Safety has become the central value around which dominant AI governance efforts are being shaped. Recently, this culminated in the publication of the International AI Safety Report, written by 96 experts of which 30 nominated by the Organisation for Economic Co-operation and Development (OECD), the European Union (EU), and the United Nations (UN). The report focuses on the safety risks of general-purpose AI and available technical mitigation approaches. In this response, informed by a system safety perspective, I refl ect on the key conclusions of the report, identifying fundamental issues in the currently dominant technical framing of AI safety and how this frustrates meaningful discourse and policy efforts to address safety comprehensively. The system safety discipline has dealt with the safety risks of software-based systems for many decades, and understands safety risks in AI systems as sociotechnical and requiring consideration of technical and non-technical factors and their interactions. The International AI Safety report does identify the need for system safety approaches. Lessons, concepts and methods from system safety indeed provide an important blueprint for overcoming current shortcomings in technical approaches by integrating rather than adding on non-technical factors and interventions. I conclude with why building a system safety discipline can help us overcome limitations in the European AI Act, as well as how the discipline can help shape sustainable investments into Public Interest AI.

With the rise of large multi-modal AI models, fuelled by recent interest in large language models (LLMs), the notion of artificial general intelligence (AGI) went from being restricted to a fringe community, to dominate mainstream large AI development programs. In contrast, in this paper, we make a \emph{case for specialisation}, by reviewing the pitfalls of generality and stressing the industrial value of specialised systems. Our contribution is threefold. First, we review the most widely accepted arguments \emph{against} specialisation, and discuss how their relevance in the context of human labour is actually an argument \emph{for} specialisation in the case of non human agents, be they algorithms or human organisations. Second, we propose four arguments \emph{in favor of} specialisation, ranging from machine learning robustness, to computer security, social sciences and cultural evolution. Third, we finally make a case for \emph{specification}, discuss how the machine learning approach to AI has so far failed to catch up with good practices from safety-engineering and formal verification of software, and discuss how some emerging good practices in machine learning help reduce this gap. In particular, we justify the need for \emph{specified governance} for hard-to-specify systems.

The UK AI Safety Institute (UK AISI) and its parallel organisation in the United States (US AISI) take up a unique position in the recently established International Network of AISIs. Both are in jurisdictions with frontier AI companies and are assuming leading roles in the international conversation on AI Safety. This paper argues that it is in the interest of both institutions to share specific categories of information with the International Network of AISIs, deliberately abstain from sharing others and carefully evaluate sharing some categories on a case by case basis, according to domestic priorities. The paper further proposes a provisional framework with which policymakers and researchers can distinguish between these three cases, taking into account the potential benefits and risks of sharing specific categories of information, ranging from pre-deployment evaluation results to evaluation standards. In an effort to further improve the research on AI policy relevant information sharing decisions, the paper emphasises the importance of continuously monitoring fluctuating factors influencing sharing decisions and a more in-depth analysis of specific policy relevant information categories and additional factors to consider in future research.

Biologically inspired neural networks offer alternative avenues to model data distributions. FlyVec is a recent example that draws inspiration from the fruit fly's olfactory circuit to tackle the task of learning word embeddings. Surprisingly, this model performs competitively even against deep learning approaches specifically designed to encode text, and it does so with the highest degree of computational efficiency. We pose the question of whether this performance can be improved further. For this, we introduce Comply. By incorporating positional information through complex weights, we enable a single-layer neural network to learn sequence representations. Our experiments show that Comply not only supersedes FlyVec but also performs on par with significantly larger state-of-the-art models. We achieve this without additional parameters. Comply yields sparse contextual representations of sentences that can be interpreted explicitly from the neuron weights.

Fox News host Laura Ingraham gives her take on the spending freeze on USAID on'The Ingraham Angle.' Tesla and Space X CEO Elon Musk's DOGE efforts to slash government waste and streamline the federal bureaucracy include the hiring of several up-and-coming young software engineers tasked with "modernizing federal technology and software to maximize governmental efficiency and productivity." Six young men between the ages of 19 and 24 -- Akash Bobba, Edward Coristine, Luke Farritor, Gautier Cole Killian, Gavin Kliger and Ethan Shaotran -- have taken up various roles furthering the DOGE agenda, according to a report from Wired. Bobba was part of the highly regarded Management, Entrepreneurship, and Technology program at UC Berkeley and has held internships at the Bridgewater Associates hedge fund, Meta and Palantir. "Let me tell you something about Akash," Grata AI CEO Charis Zhang posted on X about Bobba in recent days. "During a project at Berkeley, I accidentally deleted our entire codebase 2 days before the deadline. Akash just stared at the screen, shrugged, and rewrote everything from scratch in one night -- better than before. We submitted early and got first in the class. I trust him with everything I own."

Shares of Google's parent company Alphabet fell more than 6% after the company reported a slight miss in expected revenue on Tuesday. The company reported 96.5bn, compared with analyst expectations of 96.67 bn. The company surpassed investors' expectations of 2.13 in earnings per share, however, with 2.15 in EPS. "Q4 was a strong quarter driven by our leadership in AI and momentum across the business," Alphabet chief executive Sundar Pichai wrote in a statement. "We are building, testing, and launching products and models faster than ever, and making significant progress in compute and driving efficiencies."

Google announced Tuesday that it is overhauling the principles governing how it uses artificial intelligence and other advanced technology. The company removed language promising not to pursue "technologies that cause or are likely to cause overall harm," "weapons or other technologies whose principal purpose or implementation is to cause or directly facilitate injury to people," "technologies that gather or use information for surveillance violating internationally accepted norms," and "technologies whose purpose contravenes widely accepted principles of international law and human rights." The changes were disclosed in a note appended to the top of a 2018 blog post unveiling the guidelines. "We've made updates to our AI Principles. Visit AI.Google for the latest," the note reads.

Since declaring his support for Donald Trump in July of last year and subsequently spending more than 250m on his re-election effort, Elon Musk has rapidly accumulated political influence and positioned himself at the heart of the new administration. Now as prominent as the president himself, Musk has begun to make use of that power, making decisions that could affect the health of millions of people, gaining access to highly sensitive personal data, and attacking anyone who opposes him. Musk, the world's richest man and an unelected official, has achieved an astonishing level of power over the federal government. Over the weekend, workers with Musk's "department of government efficiency" (Doge) clashed with civil servants over demands for unfettered access to the computer systems of major US government agencies in a breakneck series of confrontations. When the dust settled, several top officials who opposed the takeover had been pushed out, and Musk's allies had gained control. Musk, with the backing of Trump, is now working to shut down the US Agency for International Development (USAid) – the world's largest single supplier of humanitarian aid.

Over the weekend, the Federal Aviation Administration officially designated the airspace above the Caesars Superdome as a "No Drone Zone" during and ahead of the big game. Drone operators who do fly their devices into the restricted area, accidentally or otherwise, could have their drones confiscated or receive hefty fines up to 75,000. The decision comes just weeks after a hobbyist drone collided with a plane helping combat wildfires in California and amid an uptick in drone sightings around the country. Starting at 1:30 p.m. CST on game day (Sunday, February 9) the FAA will prohibit drones from flying within a 1.5 nautical miles radius and 2,000 feet in altitude of the Caesars Superdome. The restricted area space will expand to a 30 nautical-mile radius and 18,000 feet in altitude between 4:30 and 10:30 p.m CST that same day.