With the development of artificial intelligence techniques, transportation system optimization is evolving from traditional methods relying on expert experience to simulation and learning-based decision optimization methods. Learning-based optimization methods require extensive interaction with highly realistic microscopic traffic simulators for optimization. However, existing microscopic traffic simulators are computationally inefficient in large-scale scenarios and therefore significantly reduce the efficiency of the data sampling process of optimization algorithms. In addition, the optimization scenarios supported by existing simulators are limited, mainly focusing on the traffic signal control. To address these challenges and limitations, we propose the first open-source GPU-accelerated large-scale microscopic simulator for transportation system simulation. The simulator is able to iterate at 84.09Hz, which achieves 88.92 times computational acceleration in the large-scale scenario with more than a million vehicles compared to the best baseline. Based on the simulator, we implement a set of microscopic and macroscopic controllable objects and metrics to support most typical transportation system optimization scenarios. These controllable objects and metrics are all provided by Python API for ease of use. We choose five important and representative transportation system optimization scenarios and benchmark classical rule-based algorithms, reinforcement learning, and black-box optimization in four cities.

Hallucination in Natural Language Generation (NLG) is like the elephant in the room, obvious but often overlooked until recent achievements significantly improved the fluency and grammaticality of generated text. As the capabilities of text generation models have improved, researchers have begun to pay more attention to the phenomenon of hallucination. Despite significant progress in this field in recent years, the evaluation system for hallucination is complex and diverse, lacking clear organization. We are the first to comprehensively survey how various evaluation methods have evolved with the development of text generation models from three dimensions, including hallucinated fact granularity, evaluator design principles, and assessment facets. This survey aims to help researchers identify current limitations in hallucination evaluation and highlight future research directions.

This paper critically discusses how generative artificial intelligence (GenAI) might impose Western ideologies on non-Western societies, perpetuating digital neocolonialism in education through its inherent biases. It further suggests strategies for local and global stakeholders to mitigate these effects. Our discussions demonstrated that GenAI can foster cultural imperialism by generating content that primarily incorporates cultural references and examples relevant to Western students, thereby alienating students from non-Western backgrounds. Also, the predominant use of Western languages by GenAI can marginalize non-dominant languages, making educational content less accessible to speakers of indigenous languages and potentially impacting their ability to learn in their first language. Additionally, GenAI often generates content and curricula that reflect the perspectives of technologically dominant countries, overshadowing marginalized indigenous knowledge and practices. Moreover, the cost of access to GenAI intensifies educational inequality and the control of GenAI data could lead to commercial exploitation without benefiting local students and their communities. We propose human-centric reforms to prioritize cultural diversity and equity in GenAI development; a liberatory design to empower educators and students to identify and dismantle the oppressive structures within GenAI applications; foresight by design to create an adjustable GenAI system to meet future educational needs; and finally, effective prompting skills to reduce the retrieval of neocolonial outputs.

We present NewsQs (news-cues), a dataset that provides question-answer pairs for multiple news documents. To create NewsQs, we augment a traditional multi-document summarization dataset with questions automatically generated by a T5-Large model fine-tuned on FAQ-style news articles from the News On the Web corpus. We show that fine-tuning a model with control codes produces questions that are judged acceptable more often than the same model without them as measured through human evaluation. We use a QNLI model with high correlation with human annotations to filter our data. We release our final dataset of high-quality questions, answers, and document clusters as a resource for future work in query-based multi-document summarization.

Hyperbolic models can reproduce the heavy-tailed degree distribution, high clustering, and hierarchical structure of empirical networks. Current algorithms for finding the hyperbolic coordinates of networks, however, do not quantify uncertainty in the inferred coordinates. We present BIGUE, a Markov chain Monte Carlo (MCMC) algorithm that samples the posterior distribution of a Bayesian hyperbolic random graph model. We show that combining random walk and random cluster transformations significantly improves mixing compared to the commonly used and state-of-the-art dynamic Hamiltonian Monte Carlo algorithm. Using this algorithm, we also provide evidence that the posterior distribution cannot be approximated by a multivariate normal distribution, thereby justifying the use of MCMC to quantify the uncertainty of the inferred parameters.

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.