It is an unlikely alliance: the billionaire media mogul Rupert Murdoch and a panoply of leading artists including the Radiohead singer, Thom Yorke, the actors Kevin Bacon and Julianne Moore, and the author Kazuo Ishiguro. This week, they began two very public fights with artificial intelligence companies, accusing them of using their intellectual property without permission to build the increasingly powerful and lucrative new technology. More than 13,000 creative professionals from the worlds of literature, music, film, theatre and television released a statement warning that AI firms training programs such as ChatGPT on their works without a licence posed a "major, unjust threat" to their livelihoods. By the end of the week that number had almost doubled to 25,000. It came a day after Murdoch, owner of the publishing group News Corp, whose newspapers include the Wall Street Journal, the Sun, the Times and the Australian, launched a legal action against the AI-powered search engine Perplexity, accusing it of "illegally copying" some of his US titles' journalism.

When Melissa Kono, the town clerk in Burnside, Wisconsin, began training election workers in 2015, their questions were relatively mundane. They asked about election rules, voter eligibility, and other basic procedures. The job was gratifying and enjoyable; they helped their neighbors while sipping coffee. But over the past few years, everything has changed. Kono now finds herself fielding questions about what to do when approached by suspicious voters who ask provocative questions or gripe about fraud.

The 27th European Conference on Artificial Intelligence (ECAI-2024) took place from 19-24 October. Held in Santiago de Compostela, Spain, the event featured a full programme of technical papers, keynote and invited talks, workshops and tutorials, and panels. We took a look at what participants got up to over the second half of the event. AI Regulation: The European Scenario shed light on policy shifts, and The Economic Impact of AI discussed the challenges and opportunities ahead. Huge thanks to everyone who came to my #ecai2024 presentation and made it such a rewarding experience with your insightful questions and discussions!

Large Language Models have introduced novel opportunities for text comprehension and generation. Yet, they are vulnerable to adversarial perturbations and data poisoning attacks, particularly in tasks like text classification and translation. However, the adversarial robustness of abstractive text summarization models remains less explored. In this work, we unveil a novel approach by exploiting the inherent lead bias in summarization models, to perform adversarial perturbations. Furthermore, we introduce an innovative application of influence functions, to execute data poisoning, which compromises the model's integrity. This approach not only shows a skew in the models behavior to produce desired outcomes but also shows a new behavioral change, where models under attack tend to generate extractive summaries rather than abstractive summaries.

The proliferation of the Internet of Things (IoT) such as smartphones, wearables, drones, and smart speakers, as well as the gigantic amount of data they capture, have revolutionized the way we work, live, and interact with the world. Equipped with sensing, computing, networking, and communication capabilities, these devices are able to collect, analyze and transmit a wide range of data including images, videos, audio, texts, wireless signals, physiological signals from individuals and the physical world. In recent years, advancements in Artificial Intelligence (AI), particularly in deep learning (DL)/deep neural network (DNN), foundation models, and Generative AI, have propelled the integration of AI with IoT, making the concept of Artificial Intelligence of Things (AIoT) a reality. The synergy between IoT and modern AI enhances decision making, improves human-machine interactions, and facilitates more efficient operations, making AIoT one of the most exciting and promising areas that have the potential to fundamentally transform how people perceive and interact with the world. As illustrated in Figure 1, at its core, AIoT is grounded on three key components: sensing, computing, and networking & communication.

Retrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs) in many knowledge-based tasks. However, existing RAG methods struggle with knowledge-intensive reasoning tasks, because useful information required to these tasks are badly scattered. This characteristic makes it difficult for existing RAG methods to accurately identify key information and perform global reasoning with such noisy augmentation. In this paper, motivated by the cognitive theories that humans convert raw information into various structured knowledge when tackling knowledge-intensive reasoning, we proposes a new framework, StructRAG, which can identify the optimal structure type for the task at hand, reconstruct original documents into this structured format, and infer answers based on the resulting structure. Extensive experiments across various knowledge-intensive tasks show that StructRAG achieves state-of-the-art performance, particularly excelling in challenging scenarios, demonstrating its potential as an effective solution for enhancing LLMs in complex real-world applications.

Financial intelligence generation from vast data sources has typically relied on traditional methods of knowledge-graph construction or database engineering. Recently, fine-tuned financial domain-specific Large Language Models (LLMs), have emerged. While these advancements are promising, limitations such as high inference costs, hallucinations, and the complexity of concurrently analyzing high-dimensional financial data, emerge. This motivates our invention FISHNET (Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert swarming, and Task planning), an agentic architecture that accomplishes highly complex analytical tasks for more than 98,000 regulatory filings that vary immensely in terms of semantics, data hierarchy, or format. FISHNET shows remarkable performance for financial insight generation (61.8% success rate over 5.0% Routing, 45.6% RAG R-Precision). We conduct rigorous ablations to empirically prove the success of FISHNET, each agent's importance, and the optimized performance of assembling all agents. Our modular architecture can be leveraged for a myriad of use-cases, enabling scalability, flexibility, and data integrity that are critical for financial tasks.

Balancing helpfulness and safety (harmlessness) is a critical challenge in aligning large language models (LLMs). Current approaches often decouple these two objectives, training separate preference models for helpfulness and safety, while framing safety as a constraint within a constrained Markov Decision Process (CMDP) framework. However, these methods can lead to ``safety interference'', where average-based safety constraints compromise the safety of some prompts in favor of others. To address this issue, we propose \textbf{Rectified Policy Optimization (RePO)}, which replaces the average safety constraint with stricter (per prompt) safety constraints. At the core of RePO is a policy update mechanism driven by rectified policy gradients, which penalizes the strict safety violation of every prompt, thereby enhancing safety across nearly all prompts. Our experiments on Alpaca-7B demonstrate that RePO improves the safety alignment and reduces the safety interference compared to baseline methods. Code is available at https://github.com/pxyWaterMoon/RePO.

The UK's competition regulator is probing Alphabet's investment in AI startup Anthropic. After opening public comments this summer, the Competition and Market Authority (CMA) said on Thursday it has "sufficient information" to begin an initial investigation into whether Alphabet's reported 2.3 billion investment in the Claude AI chatbot maker harms competition in UK markets. The CMA breaks its merger probes into two stages: a preliminary scan to determine whether there's enough evidence to dig deeper and an optional second phase where the government gathers as much evidence as possible. After the second stage, it ultimately decides on a regulatory outcome. The probe will formally kick off on Friday.

Mental models are an important concept in UX and product design, but they need to be more readily embraced by the AI community. At one level, mental models often don't appear because they are routine patterns of our assumptions about an AI system. This is something we discussed at length in the process of putting together the latest volume of the Thoughtworks Technology Radar, a biannual report based on our experiences working with clients all over the world. For instance, we called out complacency with AI generated code and replacing pair programming with generative AI as two practices we believe practitioners must avoid as the popularity of AI coding assistants continues to grow. Both emerge from poor mental models that fail to acknowledge how this technology actually works and its limitations.