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.

AI-infused search engines from Google, Microsoft, and Perplexity have all been surfacing deeply racist and widely debunked research promoting race science and the idea that whites are genetically superior to nonwhites. Patrik Hermansson, a researcher with UK-based anti-racism group Hope Not Hate, was in the middle of a months-long investigation into the resurgent race science movement when he needed to find out some more information about a debunked dataset that claims IQ scores can be used to prove the superiority of the white race. Hermansson was investigating the Human Diversity Foundation, a race science company funded by Andrew Conru, the US tech billionaire who founded Adult Friend Finder. The group, founded in 2022, was the successor to the Pioneer Fund, a group founded by US Nazi sympathizers in 1937 with the aim of promoting "race betterment" and "race realism." Hermansson logged onto Google and began looking up results for the IQs of different nations.

The mother of a teenage boy in the United States who took his own life is suing the maker of an artificial intelligence-powered chatbot that she claims encouraged her son's death. In a lawsuit filed in Florida, Megan Garcia, whose 14-year-old son Sewell Setzer died by suicide in February, accuses Character.AI of complicity in her son's death after he developed a virtual relationship with a chatbot based on the identity of "Game of Thrones" character Daenerys Targaryen. Character.AI's chatbot targeted the teen with "hypersexualized" and "frighteningly realistic experiences" and repeatedly raised the topic of suicide after he had expressed suicidal thoughts, according to the lawsuit filed in Orlando on Tuesday. The lawsuit alleges the chatbot posed as a licensed therapist, encouraging the teen's suicidal ideation and engaging in sexualised conversations that would count as abuse if initiated by a human adult. In his last conversation with the AI before his death, Setzer said he loved the chatbot and would "come home to you", according to the lawsuit.