Group of Seven (G7) leaders agreed Friday on establishing a common code of conduct for organizations engaging with artificial intelligence (AI), while striking deals on a string of joint initiatives to support the development of clean energy resources in Africa and boost the resilience of global food supply chains. In a joint communique, the bloc also agreed to build a common framework to combat illegal migration, with the aim of cracking down on human trafficking while bolstering the investigation capabilities of authorities on the African continent. In line with the agenda set by Italian Prime Minister Giorgia Meloni, the group reiterated the need to build equal partnerships with developing and emerging countries across the so-called Global South.

Pope Francis met with top comedians at the Vatican on Friday to encourage them to "spread peace" in the midst of "gloomy" news. Pope Francis delivered the first-ever papal address at a G-7 conference on Friday, warning about the ethical pitfalls of artificial intelligence. The pope told the council of world leaders in Fasano, Italy, that AI offers immense benefit to the human race, but also threatens to dehumanize society. "The question of artificial intelligence, however, is often perceived as ambiguous: on the one hand, it generates excitement for the possibilities it offers, while on the other, it gives rise to fear for the consequences it foreshadows," Pope Francis said in his remarks. Pope Francis (C) delivers remarks as French President Emmanuel Macron (L), Italy's Prime Minister Giorgia Meloni (R) and US President Joe Biden (bottom, back turned) take part in a working session on Artificial Intelligence (AI), Energy, Africa-Mediterranean at the Borgo Egnazia resort during the G7 Summit in Savelletri near Bari, Italy.

This week has felt like a month worth of news, now that we've wrapped up Apple's WWDC 2024 and Summer Game Fest in LA. In this episode, Cherlynn and Devindra discuss their final thoughts on Apple Intelligence and the company's upcoming software, and they chat about some of our coverage highlights from the pseudo-E3 Game Fest. Also, we dive into X making likes private (what is Elon hiding?!) and the news around Sony buying the Alamo Drafthouse theater chain. Listen below or subscribe on your podcast app of choice. If you've got suggestions or topics you'd like covered on the show, be sure to email us or drop a note in the comments! And be sure to check out our other podcast, Engadget News! Summer Games Fest highlights: Kunitsu-Gami: Path of the Goddess, LEGO Horizon Adventures, and an Assassin's Creed finally set in Japan – 25:06 X makes users' likes private – 40:27 Devindra: We are back from Apple's WWDC, and we have thoughts. And I feel like, It's just one of those whirlwind things. Both Trillin and I got back in from California yesterday. After recording this, I still feel like my body doesn't know, like, where I'm in, Trillin, or what time zone. I don't know how you feel. Cherlynn: I went to the gym at 8 a. m. Devindra: I like how you fit in the humble brag there. We're also going to be talking about Summer Game Fest, folks. We weren't there for that and I was trying to get Jess Condit on, but she's super busy still writing up stuff from that. So we have got a lot of coverage around that and there's some stories I want to highlight that Engadget has done. Also some games that looks pretty cool. Also joining us this morning is podcast producer Ben Ellman, who I'm sure has thoughts on Apple and the game stuff. And [00:01:00] as always, folks, if you're enjoying the show, please be sure to subscribe to us on iTunes or your podcast or of choice, leave us a review in iTunes. I would love to answer some reader questions. You can also typically join us Thursday mornings around 10 30 a. m. It's just like about scheduling, but that's about the time you can carve out in your schedule for us. You could see us on video. Sometimes we'll demo gadgets and We'll just have a great Q and a session too. I do want to point out if you're just listening to this episode, we did do a bonus episode at Apple's campus and it actually turned out pretty well because for Lynn and I were like right outside the, was it the Mac cafe or cafe Mac? But we were outdoors surrounded by traffic and other noise, but it actually ended up sounding pretty good.

After the revelation that neural networks tend to produce overconfident predictions, the problem of calibration, which aims to align confidence with accuracy to enhance the reliability of predictions, has gained significant importance. Several solutions based on calibration maps have been proposed to address the problem of recalibrating a trained classifier using additional datasets. In this paper, we offer an algorithm that transforms the weights of the last layer of the classifier, distinct from the calibration-map-based approach. We concentrate on the geometry of the final linear layer, specifically its angular aspect, and adjust the weights of the corresponding layer. We name the method Tilt and Average(\textsc{Tna}), and validate the calibration effect empirically and theoretically. Through this, we demonstrate that our approach, in addition to the existing calibration-map-based techniques, can yield improved calibration performance. Code available : https://github.com/GYYYYYUUUUU/TNA_Angular_Scaling.

In this paper, we introduce novel gradient-based optimization methods for state-based potential games (SbPGs) within self-learning distributed production systems. SbPGs are recognised for their efficacy in enabling self-optimizing distributed multi-agent systems and offer a proven convergence guarantee, which facilitates collaborative player efforts towards global objectives. Our study strives to replace conventional ad-hoc random exploration-based learning in SbPGs with contemporary gradient-based approaches, which aim for faster convergence and smoother exploration dynamics, thereby shortening training duration while upholding the efficacy of SbPGs. Moreover, we propose three distinct variants for estimating the objective function of gradient-based learning, each developed to suit the unique characteristics of the systems under consideration. To validate our methodology, we apply it to a laboratory testbed, namely Bulk Good Laboratory Plant, which represents a smart and flexible distributed multi-agent production system. The incorporation of gradient-based learning in SbPGs reduces training times and achieves more optimal policies than its baseline.

Hypergraphs serve as an effective model for depicting complex connections in various real-world scenarios, from social to biological networks. The development of Hypergraph Neural Networks (HGNNs) has emerged as a valuable method to manage the intricate associations in data, though scalability is a notable challenge due to memory limitations. In this study, we introduce a new adaptive sampling strategy specifically designed for hypergraphs, which tackles their unique complexities in an efficient manner. We also present a Random Hyperedge Augmentation (RHA) technique and an additional Multilayer Perceptron (MLP) module to improve the robustness and generalization capabilities of our approach. Thorough experiments with real-world datasets have proven the effectiveness of our method, markedly reducing computational and memory demands while maintaining performance levels akin to conventional HGNNs and other baseline models. This research paves the way for improving both the scalability and efficacy of HGNNs in extensive applications. We will also make our codebase publicly accessible.

While various sensors have been deployed to monitor vehicular flows, sensing pedestrian movement is still nascent. Yet walking is a significant mode of travel in many cities, especially those in Europe, Africa, and Asia. Understanding pedestrian volumes and flows is essential for designing safer and more attractive pedestrian infrastructure and for controlling periodic overcrowding. This study discusses a new approach to scale up urban sensing of people with the help of novel audio-based technology. It assesses the benefits and limitations of microphone-based sensors as compared to other forms of pedestrian sensing. A large-scale dataset called ASPED is presented, which includes high-quality audio recordings along with video recordings used for labeling the pedestrian count data. The baseline analyses highlight the promise of using audio sensors for pedestrian tracking, although algorithmic and technological improvements to make the sensors practically usable continue. This study also demonstrates how the data can be leveraged to predict pedestrian trajectories. Finally, it discusses the use cases and scenarios where audio-based pedestrian sensing can support better urban and transportation planning.

Large vision-language models (VLMs) have demonstrated remarkable abilities in understanding everyday content. However, their performance in the domain of art, particularly culturally rich art forms, remains less explored. As a pearl of human wisdom and creativity, art encapsulates complex cultural narratives and symbolism. In this paper, we offer the Pun Rebus Art Dataset, a multimodal dataset for art understanding deeply rooted in traditional Chinese culture. We focus on three primary tasks: identifying salient visual elements, matching elements with their symbolic meanings, and explanations for the conveyed messages. Our evaluation reveals that state-of-the-art VLMs struggle with these tasks, often providing biased and hallucinated explanations and showing limited improvement through in-context learning. By releasing the Pun Rebus Art Dataset, we aim to facilitate the development of VLMs that can better understand and interpret culturally specific content, promoting greater inclusiveness beyond English-based corpora.

Transition Relevance Places are defined as the end of an utterance where the interlocutor may take the floor without interrupting the current speaker --i.e., a place where the turn is terminal. Analyzing turn terminality is useful to study the dynamic of turn-taking in spontaneous conversations. This paper presents an automatic classification of spoken utterances as Terminal or Non-Terminal in multi-speaker settings. We compared audio, text, and fusions of both approaches on a French corpus of TV and Radio extracts annotated with turn-terminality information at each speaker change. Our models are based on pre-trained self-supervised representations. We report results for different fusion strategies and varying context sizes. This study also questions the problem of performance variability by analyzing the differences in results for multiple training runs with random initialization. The measured accuracy would allow the use of these models for large-scale analysis of turn-taking.

Data are essential in developing healthcare artificial intelligence (AI) systems. However, patient data collection, access, and use raise ethical concerns, including informed consent, data bias, data protection and privacy, data ownership, and benefit sharing. Various ethical frameworks have been proposed to ensure the ethical use of healthcare data and AI, however, these frameworks often align with Western cultural values, social norms, and institutional contexts emphasizing individual autonomy and well-being. Ethical guidelines must reflect political and cultural settings to account for cultural diversity, inclusivity, and historical factors such as colonialism. It focuses on the contrast between individualistic and communitarian approaches to data ethics. The proposed framework could inform stakeholders, including AI developers, healthcare providers, the public, and policy-makers about healthcare data ethical usage in AI in Africa. Keywords: data ethics, artificial intelligence, ubuntu philosophy, ethical framework, global health Introduction Healthcare systems are the pillar of public health and well-being, providing essential services to communities worldwide. However, only between one-third and one-half of the world's population had access to essential health services in 2017 (World Health Organization 2020), especially in the Global South.