Sony shared a first glimpse of its plans to celebrate PlayStation's 30th anniversary, and it seems PS5 Pro is coming to the party. Its decorative logo includes an image of the rumored upgrade to the current-gen PS5 console. Zoom between the S of the PlayStation logo and the 3, to reveal a different rectangle to the PS5s that appears elsewhere. The main change appears to be a differently proportioned console -- if this is the Pro, it'll be shorter than the OG PS5 -- and have more stripes across the body, making it look a lot like the PS5 Pro rendering leaked in late August. Adobe's Photoshop can now generate AI images via prompts like Dall-E or Midjourney You can get these reports delivered daily direct to your inbox.

This article was featured in the One Story to Read Today newsletter. "About 14 billion years ago, matter, energy, time and space came into being." So begins Sapiens: A Brief History of Humankind (2011), by the Israeli historian Yuval Noah Harari, and so began one of the 21st century's most astonishing academic careers. Sapiens has sold more than 25 million copies in various languages. Since then, Harari has published several other books, which have also sold millions. He now employs some 15 people to organize his affairs and promote his ideas. Check out more from this issue and find your next story to read. Harari might be, after the Dalai Lama, the figure of global renown who is least online.

A musician in the US has been accused of using artificial intelligence (AI) tools and thousands of bots to fraudulently stream songs billions of times in order to claim millions of dollars of royalties. Michael Smith, of North Carolina, has been charged with three counts of wire fraud, wire fraud conspiracy and money laundering conspiracy charges. Prosecutors say it is the first criminal case of its kind they have handled. "Through his brazen fraud scheme, Smith stole millions in royalties that should have been paid to musicians, songwriters, and other rights holders whose songs were legitimately streamed," said US attorney Damian Williams. According to an unsealed indictment detailing the charges, the 52-year-old used hundreds of thousands of AI-generated songs to manipulate streams.

A few months ago, everyone was worried about how AI would impact the 2024 election. It seems like some of the angst has dissipated, but political deepfakes--including pornographic images and video--are still everywhere. Today on the show, WIRED reporters Vittoria Elliott and Will Knight talk about what has changed with AI and what we should worry about. Or you can write to us at politicslab@WIRED.com. Be sure to subscribe to the WIRED Politics Lab newsletter here.

Tesla has tweeted its roadmap for the remaining months of 2024 and early 2025, revealing that Full Self-Driving could be available in Europe and China in the first quarter of next year, if it gets the proper approval from each region's respective regulators. Company chief Elon Musk previously said that he expects to receive regulator clearance from the regions by the end of the year. The Wall Street Journal reported in April that authorities in China had already tentatively approved the launch of Tesla's Full Self-Driving software in their country. It's not quite clear where the company stands with European Union regulators at the moment. In a response to the original post, Musk added that he's hoping for FSD to be approved in Right-Hand Drive markets by the end of the first quarter or by early second quarter next year.

The administration of U.S. President Joe Biden plans to impose export controls on critical technologies including quantum computing and semiconductor goods, aligning the U.S. with allies working to thwart advancements in China and other adversarial nations. The rules target quantum computers and components, advanced chipmaking tools, a cutting-edge semiconductor technology called gate all-around, and various components and software related to metals and metal alloys. They cover all worldwide exports, but include exemptions for countries that implement similar measures. That group includes Japan and the Netherlands, among other allies, and the U.S. anticipates that more nations will follow, the Commerce Department said in a news release. Washington has been cracking down for years on China and other adversaries' ability to access cutting-edge technologies needed for artificial intelligence, over fears that advanced chips and components could lend Beijing a military edge.

Tokyo, Japan – Japanese scientists have developed a technique to attach self-healing, living skin to a robot face and make it "smile". The scientists, led by professor Shoji Takeuchi at the University of Tokyo's Biohybrid Systems Laboratory, connected cultured skin tissue in the likeness of a human face to an actuator – an external mechanical device – using "anchors" that mimic skin ligaments. In a video released by the team, the scientists can be seen manipulating the skin into a smile without causing the tissue to bunch, tear or get stuck in place. Previous efforts to attach tissue made from human cells to a solid surface would result in the skin being damaged when in motion. While Takeuchi's fleshy pink blob bears greater resemblance to a children's animated character than a human face, researchers hope the breakthrough will pave the way to realistic humanoids in the future.

Swarm robotics explores the coordination of multiple robots to achieve collective goals, with collective decision-making being a central focus. This process involves decentralized robots autonomously making local decisions and communicating them, which influences the overall emergent behavior. Testing such decentralized algorithms in real-world scenarios with hundreds or more robots is often impractical, underscoring the need for effective simulation tools. We propose SPACE (Swarm Planning and Control Evaluation), a Python-based simulator designed to support the research, evaluation, and comparison of decentralized Multi-Robot Task Allocation (MRTA) algorithms. SPACE streamlines core algorithmic development by allowing users to implement decision-making algorithms as Python plug-ins, easily construct agent behavior trees via an intuitive GUI, and leverage built-in support for inter-agent communication and local task awareness. To demonstrate its practical utility, we implement and evaluate CBBA and GRAPE within the simulator, comparing their performance across different metrics, particularly in scenarios with dynamically introduced tasks. This evaluation shows the usefulness of SPACE in conducting rigorous and standardized comparisons of MRTA algorithms, helping to support future research in the field.

Large Language Models (LLMs) have shown versatility in various Natural Language Processing (NLP) tasks, including their potential as effective question-answering systems. However, to provide precise and relevant information in response to specific customer queries in industry settings, LLMs require access to a comprehensive knowledge base to avoid hallucinations. Retrieval Augmented Generation (RAG) emerges as a promising technique to address this challenge. Yet, developing an accurate question-answering framework for real-world applications using RAG entails several challenges: 1) data availability issues, 2) evaluating the quality of generated content, and 3) the costly nature of human evaluation. In this paper, we introduce an end-to-end framework that employs LLMs with RAG capabilities for industry use cases. Given a customer query, the proposed system retrieves relevant knowledge documents and leverages them, along with previous chat history, to generate response suggestions for customer service agents in the contact centers of a major retail company. Through comprehensive automated and human evaluations, we show that this solution outperforms the current BERT-based algorithms in accuracy and relevance. Our findings suggest that RAG-based LLMs can be an excellent support to human customer service representatives by lightening their workload.

Leveraging the power of a graph neural network (GNN) with message passing, we present a Monte Carlo Tree Search (MCTS) method to solve stochastic orienteering problems with chance constraints. While adhering to an assigned travel budget the algorithm seeks to maximize collected reward while incurring stochastic travel costs. In this context, the acceptable probability of exceeding the assigned budget is expressed as a chance constraint. Our MCTS solution is an online and anytime algorithm alternating planning and execution that determines the next vertex to visit by continuously monitoring the remaining travel budget. The novelty of our work is that the rollout phase in the MCTS framework is implemented using a message passing GNN, predicting both the utility and failure probability of each available action. This allows to enormously expedite the search process. Our experimental evaluation shows that with the proposed method and architecture we manage to efficiently solve complex problem instances while incurring in moderate losses in terms of collected reward. Moreover, we demonstrate how the approach is capable of generalizing beyond the characteristics of the training dataset. The paper's website, open-source code, and supplementary documentation can be found at ucmercedrobotics.github.io/gnn-sop.