Ara Darzi is Co-Director of the Institute of Global Health Innovation at Imperial College London and holds the Paul Hamlyn Chair of Surgery. In 2002, he was knighted for his services to medicine and surgery and in 2007 was introduced as Lord Darzi of Denham to the UK's House of Lords as the Parliamentary Under-Secretary of State for Health. Professor Darzi leads a large multidisciplinary academic and policy research team, focused on convergence science across engineering, physical and data sciences, specifically in the areas of robotics, sensing, imaging and digital and AI technologies. He is Chair of the NHS Accelerated Access Collaborative, Fellow of the Academy of Medical Sciences and the Royal Society, and Honorary Fellow of the Royal Academy of Engineering.

Should artificial intelligence be used in the justice system, and if so should it apply the letter or the spirit of the law? While there are no plans for AI judges yet, this is a question that the UK government is already wrestling with as it considers the potential uses of AI in the English court system. Despite this, lawyers and computer scientists are warning that current systems can't handle the ambiguity and nuance often required in legal situations.…

Sometimes there are no background noises when there should be. Or, in the case of the robocall, there's a lot of noise mixed into the background almost to give an air of realness that actually sounds unnatural. With photos, it helps to zoom in and examine closely for any "inconsistencies with the physical world or human pathology", like buildings with crooked lines or hands with six fingers, Lyu said. Little details like hair, mouths and shadows can hold clues to whether something is real. Hands were once a clearer tell for AI-generated images because they would more frequently end up with extra appendages, though the technology has improved and that's becoming less common, Lyu said.

The Dallas Cowboys had just won the Vince Lombardi Trophy, handing the Buffalo Bills their third straight loss in the Super Bowl. Bill Clinton was sworn into office as the 42nd president of the United States. And American music icon Prince became The Artist Formerly Known as Prince. It was also the last time a Canadian hockey team won the Stanley Cup. On Saturday night, the Edmonton Oilers hope to take the first step toward breaking that drought when they take on the Florida Panthers in Game 1 of the Stanley Cup Final.

A 200-acre wide, nearly 300-foot deep pit in the Yana highlands of Siberia, known as the'Batagaika Crater,' is expanding faster than expected due to climate change. Sometimes called the'Gateway to Hell,' the Batagaika Crater first formed when melting'permafrost' soil within the Siberian tundra began to release tons of previously frozen methane, a powerful greenhouse gas, into Earth's atmosphere. Now, new research has discovered that the rate of methane and other carbon gases released as the crater deepens has reached between 4000 and 5000 tons per year. The findings, according to the study's lead author, 'demonstrate how quickly permafrost degradation occurs.' He warns the crater is soon likely to leak all the remaining greenhouse gas it has left.

As large language models (LLMs) become increasingly integrated into real-world applications such as code generation and chatbot assistance, extensive efforts have been made to align LLM behavior with human values, including safety. Jailbreak attacks, aiming to provoke unintended and unsafe behaviors from LLMs, remain a significant/leading LLM safety threat. In this paper, we aim to defend LLMs against jailbreak attacks by introducing SafeDecoding, a safety-aware decoding strategy for LLMs to generate helpful and harmless responses to user queries. Our insight in developing SafeDecoding is based on the observation that, even though probabilities of tokens representing harmful contents outweigh those representing harmless responses, safety disclaimers still appear among the top tokens after sorting tokens by probability in descending order. This allows us to mitigate jailbreak attacks by identifying safety disclaimers and amplifying their token probabilities, while simultaneously attenuating the probabilities of token sequences that are aligned with the objectives of jailbreak attacks. We perform extensive experiments on five LLMs using six state-of-the-art jailbreak attacks and four benchmark datasets. Our results show that SafeDecoding significantly reduces the attack success rate and harmfulness of jailbreak attacks without compromising the helpfulness of responses to benign user queries. SafeDecoding outperforms six defense methods.

Physical Unclonable Functions (PUFs) are emerging as promising security primitives for IoT devices, providing device fingerprints based on physical characteristics. Despite their strengths, PUFs are vulnerable to machine learning (ML) attacks, including conventional and reliability-based attacks. Conventional ML attacks have been effective in revealing vulnerabilities of many PUFs, and reliability-based ML attacks are more powerful tools that have detected vulnerabilities of some PUFs that are resistant to conventional ML attacks. Since reliability-based ML attacks leverage information of PUFs' unreliability, we were tempted to examine the feasibility of building defense using reliability enhancing techniques, and have discovered that majority voting with reasonably high repeats provides effective defense against existing reliability-based ML attack methods. It is known that majority voting reduces but does not eliminate unreliability, we are motivated to investigate if new attack methods exist that can capture the low unreliability of highly but not-perfectly reliable PUFs, which led to the development of a new reliability representation and the new representation-enabled attack method that has experimentally cracked PUFs enhanced with majority voting of high repetitions.

How are the merits of innovative ideas communicated in science? Here we conduct semantic analyses of grant application success with a focus on scientific promotional language, which has been growing in frequency in many contexts and purportedly may convey an innovative idea's originality and significance. Our analysis attempts to surmount limitations of prior studies by examining the full text of tens of thousands of both funded and unfunded grants from three leading public and private funding agencies: the NIH, the NSF, and the Novo Nordisk Foundation, one of the world's largest private science foundations. We find a robust association between promotional language and the support and adoption of innovative ideas by funders and other scientists. First, the percentage of promotional language in a grant proposal is associated with up to a doubling of the grant's probability of being funded. Second, a grant's promotional language reflects its intrinsic level of innovativeness. Third, the percentage of promotional language predicts the expected citation and productivity impact of publications that are supported by funded grants. Lastly, a computer-assisted experiment that manipulates the promotional language in our data demonstrates how promotional language can communicate the merit of ideas through cognitive activation. With the incidence of promotional language in science steeply rising, and the pivotal role of grants in converting promising and aspirational ideas into solutions, our analysis provides empirical evidence that promotional language is associated with effectively communicating the merits of innovative scientific ideas.

Large language model (LLM) users might rely on others (e.g., prompting services), to write prompts. However, the risks of trusting prompts written by others remain unstudied. In this paper, we assess the risk of using such prompts on brand recommendation tasks when shopping. First, we found that paraphrasing prompts can result in LLMs mentioning given brands with drastically different probabilities, including a pair of prompts where the probability changes by 100%. Next, we developed an approach that can be used to perturb an original base prompt to increase the likelihood that an LLM mentions a given brand. We designed a human-inconspicuous algorithm that perturbs prompts, which empirically forces LLMs to mention strings related to a brand more often, by absolute improvements up to 78.3%. Our results suggest that our perturbed prompts, 1) are inconspicuous to humans, 2) force LLMs to recommend a target brand more often, and 3) increase the perceived chances of picking targeted brands.

Collaborative robots and machine learning-based virtual agents are increasingly entering the human workspace with the aim of increasing productivity and enhancing safety. Despite this, we show in a ubiquitous experimental domain, Overcooked-AI, that state-of-the-art techniques for human-machine teaming (HMT), which rely on imitation or reinforcement learning, are brittle and result in a machine agent that aims to decouple the machine and human's actions to act independently rather than in a synergistic fashion. To remedy this deficiency, we develop HMT approaches that enable iterative, mixed-initiative team development allowing end-users to interactively reprogram interpretable AI teammates. Our 50-subject study provides several findings that we summarize into guidelines. While all approaches underperform a simple collaborative heuristic (a critical, negative result for learning-based methods), we find that white-box approaches supported by interactive modification can lead to significant team development, outperforming white-box approaches alone, and black-box approaches are easier to train and result in better HMT performance highlighting a tradeoff between explainability and interactivity versus ease-of-training. Together, these findings present three important directions: 1) Improving the ability to generate collaborative agents with white-box models, 2) Better learning methods to facilitate collaboration rather than individualized coordination, and 3) Mixed-initiative interfaces that enable users, who may vary in ability, to improve collaboration.