The release of DeepSeek R1 stunned Wall Street and Silicon Valley this month, spooking investors and impressing tech leaders. But amid all the talk, many overlooked a critical detail about the way the new Chinese AI model functions--a nuance that has researchers worried about humanity's ability to control sophisticated new artificial intelligence systems. It's all down to an innovation in how DeepSeek R1 was trained--one that led to surprising behaviors in an early version of the model, which researchers described in the technical documentation accompanying its release. During testing, researchers noticed that the model would spontaneously switch between English and Chinese while it was solving problems. When they forced it to stick to one language, thus making it easier for users to follow along, they found that the system's ability to solve the same problems would diminish.

The fast-rising Chinese AI lab DeepSeek is sparking national security concerns in the U.S., over fears that its AI models could be used by the Chinese government to spy on American civilians, learn proprietary secrets, and wage influence campaigns. In her first press briefing, White House Press Secretary Karoline Leavitt said that the National Security Council was "looking into" the potential security implications of DeepSeek. This comes amid news that the U.S. Navy has banned use of DeepSeek among its ranks due to "potential security and ethical concerns." DeepSeek, which currently tops the Apple App Store in the U.S., marks a major inflection point in the AI arms race between the U.S. and China. For the last couple years, many leading technologists and political leaders have argued that whichever country developed AI the fastest will have a huge economic and military advantage over its rivals. DeepSeek shows that China's AI has developed much faster than many had believed, despite efforts from American policymakers to slow its progress.

In a section on "labour market risks", the report warns that the impact on jobs will "likely be profound", particularly if AI agents – tools that can carry out tasks without human intervention – become highly capable. "General-purpose AI, especially if it continues to advance rapidly, has the potential to automate a very wide range of tasks, which could have a significant effect on the labour market. This means that many people could lose their current jobs," says the report. The report adds that many economists believe job losses could be offset by the creation of new jobs or demand from sectors not touched by automation. According to the International Monetary Fund, about 60% of jobs in advanced economies such as the US and UK are exposed to AI and half of these jobs may be negatively affected.

President Donald Trump has revealed the mysterious drones over New Jersey were'not the enemy' and had been authorized to conduct'research'. In the first press briefing of Trump's second administration, White House Press Secretary Karoline Leavitt said the Federal Aviation Administration (FAA) had been authorized to fly the drones for'research and various other reasons'. Leavitt said many of the drones were also'hobbyists, recreational and private individuals that enjoy flying drones' and claims that'in time, it got worse due to curiosity.' She added information had come'directly from the president of the United States that was just shared with me in the Oval Office'. But the White House's vague explanation has raised even more questions, especially after the FAA - which investigated the sightings after receiving reports from'concerned citizens' - failed to previously mention the alleged research.

This paper argues that market governance mechanisms should be considered a key approach in the governance of artificial intelligence (AI), alongside traditional regulatory frameworks. While current governance approaches have predominantly focused on regulation, we contend that market-based mechanisms offer effective incentives for responsible AI development. We examine four emerging vectors of market governance: insurance, auditing, procurement, and due diligence, demonstrating how these mechanisms can affirm the relationship between AI risk and financial risk while addressing capital allocation inefficiencies. While we do not claim that market forces alone can adequately protect societal interests, we maintain that standardised AI disclosures and market mechanisms can create powerful incentives for safe and responsible AI development. This paper urges regulators, economists, and machine learning researchers to investigate and implement market-based approaches to AI governance.

I am honoured to present the International AI Safety Report. It is the work of 96 international AI experts who collaborated in an unprecedented effort to establish an internationally shared scientific understanding of risks from advanced AI and methods for managing them. We embarked on this journey just over a year ago, shortly after the countries present at the Bletchley Park AI Safety Summit agreed to support the creation of this report. Since then, we published an Interim Report in May 2024, which was presented at the AI Seoul Summit. We are now pleased to publish the present, full report ahead of the AI Action Summit in Paris in February 2025. Since the Bletchley Summit, the capabilities of general-purpose AI, the type of AI this report focuses on, have increased further. For example, new models have shown markedly better performance at tests of Professor Yoshua Bengio programming and scientific reasoning.

The constant engagement with extensive written materials is fundamental and immensely time-consuming [104]. Legal professionals often spend hours, if not days, combing through documents to find precedents or relevant cases that could be pivotal to their current cases. This laborious process is a significant part of the workload of legal professionals like lawyers and judges, taking up lots of time that could be invested otherwise. Automatic summarization tools could help to condense lengthy legal documents into concise summaries, helping to save both time and costs. Moreover, integrating advanced Natural Language Processing (NLP) techniques into legal research holds significant promise for democratizing access to legal information. Figure 1 shows the general pipeline for legal summarization. Compared to other domains, legal texts present unique challenges that distinguish them from other document types. Legal documents tend to be longer and more detailed than those from other domains.

Multimodal Machine Learning systems, particularly those aligning text and image data like CLIP/BLIP models, have become increasingly prevalent, yet remain susceptible to adversarial attacks. While substantial research has addressed adversarial robustness in unimodal contexts, defense strategies for multimodal systems are underexplored. This work investigates the topological signatures that arise between image and text embeddings and shows how adversarial attacks disrupt their alignment, introducing distinctive signatures. We specifically leverage persistent homology and introduce two novel Topological-Contrastive losses based on Total Persistence and Multi-scale kernel methods to analyze the topological signatures introduced by adversarial perturbations. We observe a pattern of monotonic changes in the proposed topological losses emerging in a wide range of attacks on image-text alignments, as more adversarial samples are introduced in the data. By designing an algorithm to back-propagate these signatures to input samples, we are able to integrate these signatures into Maximum Mean Discrepancy tests, creating a novel class of tests that leverage topological signatures for better adversarial detection.

In complex manipulation tasks, e.g., manipulation by pivoting, the motion of the object being manipulated has to satisfy path constraints that can change during the motion. Therefore, a single grasp may not be sufficient for the entire path, and the object may need to be regrasped. Additionally, geometric data for objects from a sensor are usually available in the form of point clouds. The problem of computing grasps and regrasps from point-cloud representation of objects for complex manipulation tasks is a key problem in endowing robots with manipulation capabilities beyond pick-and-place. In this paper, we formalize the problem of grasping/regrasping for complex manipulation tasks with objects represented by (partial) point clouds and present an algorithm to solve it. We represent a complex manipulation task as a sequence of constant screw motions. Using a manipulation plan skeleton as a sequence of constant screw motions, we use a grasp metric to find graspable regions on the object for every constant screw segment. The overlap of the graspable regions for contiguous screws are then used to determine when and how many times the object needs to be regrasped. We present experimental results on point cloud data collected from RGB-D sensors to illustrate our approach.

The rapid evolution of cyber threats has outpaced traditional detection methodologies, necessitating innovative approaches capable of addressing the adaptive and complex behaviors of modern adversaries. A novel framework was introduced, leveraging Temporal-Correlation Graphs to model the intricate relationships and temporal patterns inherent in malicious operations. The approach dynamically captured behavioral anomalies, offering a robust mechanism for distinguishing between benign and malicious activities in real-time scenarios. Extensive experiments demonstrated the framework's effectiveness across diverse ransomware families, with consistently high precision, recall, and overall detection accuracy. Comparative evaluations highlighted its better performance over traditional signature-based and heuristic methods, particularly in handling polymorphic and previously unseen ransomware variants. The architecture was designed with scalability and modularity in mind, ensuring compatibility with enterprise-scale environments while maintaining resource efficiency. Analysis of encryption speeds, anomaly patterns, and temporal correlations provided deeper insights into the operational strategies of ransomware, validating the framework's adaptability to evolving threats. The research contributes to advancing cybersecurity technologies by integrating dynamic graph analytics and machine learning for future innovations in threat detection. Results from this study underline the potential for transforming the way organizations detect and mitigate complex cyberattacks.