To all Dreamers, it's time to take action! I am so inspired by the warm welcome this year! And really delighted to inspire you all to take your #ai action strategically and ethically with AI Tech UK! I am so excited to announce the launch of the first AI Accelerator programme backed by Leeds City Council, and funded by the UK Government through the UK Shared Prosperity Fund If you are planning for business growth, want to improve efficiencies, gain a competitive edge, and are interested in the role of data and technology, then this programme is for you! You do not need to be a "techie", you just need to be an enthusiast.

Generative AI is poised to be the free world's next great gift to authoritarians. The viral launch of ChatGPT -- a system with eerily human-like capabilities in composing essays, poetry and computer code -- has awakened the world's dictators to the transformative power of generative AI to create unique, compelling content at scale. But the fierce debate that has ensued among Western industry leaders on the risks of releasing advanced generative AI tools has largely missed where their effects are likely to be most pernicious: within autocracies. AI companies and the U.S. government alike must institute stricter norms for the development of tools like ChatGPT in full view of their game-changing potential for the world's authoritarians -- before it is too late. So far, concerns around generative AI and autocrats have mostly focused on how these systems can turbocharge Chinese and Russian propaganda efforts in the United States.

A text-to-image AI Dall-E2 generated image with a prompt: Edward Hopper-style image of a man and a woman sitting at a cafe table, both absorbed in their smartphones without speaking. Video artist Jason Allen's Midjourney generated piece called "Théâtre D'opéra Spatial" won first place at the Colorado State Fair.

What happened: U.S. and EU regulators have increasingly opened up to the possibility of regulating artificial intelligence (AI). AI poses an interesting challenge as regulators look to define what it is and the reasonable balance between regulation and the progression of the technology. On one hand, AI can drastically improve quality of life by running complex algorithms to help create cures for diseases or invent new technology. On the other hand, it can -- and is -- replacing jobs and careers around the globe. People like Tesla Inc. CEO Elon Musk have warned it will eventually replace every job, making the need to work obsolete.

Graph unlearning, which involves deleting graph elements such as nodes, node labels, and relationships from a trained graph neural network (GNN) model, is crucial for real-world applications where data elements may become irrelevant, inaccurate, or privacy-sensitive. However, existing methods for graph unlearning either deteriorate model weights shared across all nodes or fail to effectively delete edges due to their strong dependence on local graph neighborhoods. To address these limitations, we introduce GNNDelete, a novel model-agnostic layer-wise operator that optimizes two critical properties, namely, Deleted Edge Consistency and Neighborhood Influence, for graph unlearning. Deleted Edge Consistency ensures that the influence of deleted elements is removed from both model weights and neighboring representations, while Neighborhood Influence guarantees that the remaining model knowledge is preserved after deletion. GNNDelete updates representations to delete nodes and edges from the model while retaining the rest of the learned knowledge. We conduct experiments on seven real-world graphs, showing that GNNDelete outperforms existing approaches by up to 38.8% (AUC) on edge, node, and node feature deletion tasks, and 32.2% on distinguishing deleted edges from non-deleted ones. Additionally, GNNDelete is efficient, taking 12.3x less time and 9.3x less space than retraining GNN from scratch on WordNet18.

Efforts to promote equitable public policy with algorithms appear to be fundamentally constrained by the "impossibility of fairness" (an incompatibility between mathematical definitions of fairness). This technical limitation raises a central question about algorithmic fairness: How can computer scientists and policymakers support equitable policy reforms with algorithms? In this article, I argue that promoting justice with algorithms requires reforming the methodology of algorithmic fairness. First, I diagnose the problems of the current methodology for algorithmic fairness, which I call "formal algorithmic fairness." Because formal algorithmic fairness restricts analysis to isolated decision-making procedures, it leads to the impossibility of fairness and to models that exacerbate oppression despite appearing "fair." Second, I draw on theories of substantive equality from law and philosophy to propose an alternative methodology, which I call "substantive algorithmic fairness." Because substantive algorithmic fairness takes a more expansive scope of analysis, it enables an escape from the impossibility of fairness and provides a rigorous guide for alleviating injustice with algorithms. In sum, substantive algorithmic fairness presents a new direction for algorithmic fairness: away from formal mathematical models of "fair" decision-making and toward substantive evaluations of whether and how algorithms can promote justice in practice.

Traditionally, writing assistance systems have focused on short or even single-word suggestions. Recently, large language models like GPT-3 have made it possible to generate significantly longer natural-sounding suggestions, offering more advanced assistance opportunities. This study explores the trade-offs between sentence- vs. message-level suggestions for AI-mediated communication. We recruited 120 participants to act as staffers from legislators' offices who often need to respond to large volumes of constituent concerns. Participants were asked to reply to emails with different types of assistance. The results show that participants receiving message-level suggestions responded faster and were more satisfied with the experience, as they mainly edited the suggested drafts. In addition, the texts they wrote were evaluated as more helpful by others. In comparison, participants receiving sentence-level assistance retained a higher sense of agency, but took longer for the task as they needed to plan the flow of their responses and decide when to use suggestions. Our findings have implications for designing task-appropriate communication assistance systems.

As powerful tools for representation learning on graphs, graph neural networks (GNNs) have facilitated various applications from drug discovery to recommender systems. Nevertheless, the effectiveness of GNNs is immensely challenged by issues related to data quality, such as distribution shift, abnormal features and adversarial attacks. Recent efforts have been made on tackling these issues from a modeling perspective which requires additional cost of changing model architectures or re-training model parameters. In this work, we provide a data-centric view to tackle these issues and propose a graph transformation framework named GTrans which adapts and refines graph data at test time to achieve better performance. We provide theoretical analysis on the design of the framework and discuss why adapting graph data works better than adapting the model. Extensive experiments have demonstrated the effectiveness of GTrans on three distinct scenarios for eight benchmark datasets where suboptimal data is presented. Remarkably, GTrans performs the best in most cases with improvements up to 2.8%, 8.2% and 3.8% over the best baselines on three experimental settings. Code is released at https://github.com/ChandlerBang/GTrans.

The U.S. Air Force now has the capability to use facial recognition on drones that could target specific people. Special operations forces can use the drones to gather intelligence and to aid in other missions, according to a contract first spotted by New Scientist. It's part of a growing movement to develop automated weaponry that raises legal and ethical questions. The drone software maker, Seattle-based firm RealNetworks, claims the uncrewed craft will use artificial intelligence (AI) to fly itself and discriminate between friend and foe. The company has said that its software can also be used for rescue missions, perimeter protection, and domestic search operations.

Our mission is to ensure that artificial general intelligence--AI systems that are generally smarter than humans--benefits all of humanity. If AGI is successfully created, this technology could help us elevate humanity by increasing abundance, turbocharging the global economy, and aiding in the discovery of new scientific knowledge that changes the limits of possibility. AGI has the potential to give everyone incredible new capabilities; we can imagine a world where all of us have access to help with almost any cognitive task, providing a great force multiplier for human ingenuity and creativity. On the other hand, AGI would also come with serious risk of misuse, drastic accidents, and societal disruption. Because the upside of AGI is so great, we do not believe it is possible or desirable for society to stop its development forever; instead, society and the developers of AGI have to figure out how to get it right.[1]