Recent advances in machine learning, particularly the emergence of foundation models, are leading to new opportunities to develop technology-based solutions to societal problems. However, the reasoning and inner workings of today's complex AI models are not transparent to the user, and there are no safety guarantees regarding their predictions. Consequently, to fulfill the promise of AI, we must address the following scientific challenge: how to develop AI-based systems that are not only accurate and performant but also safe and trustworthy? The criticality of safe operation is particularly evident for autonomous systems for control and robotics, and was the catalyst for the Safe Learning Enabled Systems (SLES) program at NSF. For the broader class of AI applications, such as users interacting with chatbots and clinicians receiving treatment recommendations, safety is, while no less important, less well-defined with context-dependent interpretations. This motivated the organization of a day-long workshop, held at University of Pennsylvania on February 26, 2025, to bring together investigators funded by the NSF SLES program with a broader pool of researchers studying AI safety. This report is the result of the discussions in the working groups that addressed different aspects of safety at the workshop. The report articulates a new research agenda focused on developing theory, methods, and tools that will provide the foundations of the next generation of AI-enabled systems.

Accurately decoding human motion intentions from surface electromyography (sEMG) is essential for myoelectric control and has wide applications in rehabilitation robotics and assistive technologies. However, existing sEMG-based motion estimation methods often rely on subject-specific musculoskeletal (MSK) models that are difficult to calibrate, or purely data-driven models that lack physiological consistency. This paper introduces a novel Physics-Embedded Neural Network (PENN) that combines interpretable MSK forward-dynamics with data-driven residual learning, thereby preserving physiological consistency while achieving accurate motion estimation. The PENN employs a recursive temporal structure to propagate historical estimates and a lightweight convolutional neural network for residual correction, leading to robust and temporally coherent estimations. A two-phase training strategy is designed for PENN. Experimental evaluations on six healthy subjects show that PENN outperforms state-of-the-art baseline methods in both root mean square error (RMSE) and $R^2$ metrics.

Claude Opus 4 has been built to execute complex tasks that involve completing thousands of steps over several hours. For example, it created a guide for the video game Pokémon Red while playing it for more than 24 hours straight. The company's previously most powerful model, Claude 3.7 Sonnet, was capable of playing for just 45 minutes, says Dianne Penn, product lead for research at Anthropic. Similarly, the company says that one of its customers, the Japanese technology company Rakuten, recently deployed Claude Opus 4 to code autonomously for close to seven hours on a complicated open-source project. Anthropic achieved these advances by improving the model's ability to create and maintain "memory files" to store key information. This enhanced ability to "remember" makes the model better at completing longer tasks.

Incorporating mathematical properties of a wireless policy to be learned into the design of deep neural networks (DNNs) is effective for enhancing learning efficiency. Multi-user precoding policy in multi-antenna system, which is the mapping from channel matrix to precoding matrix, possesses a permutation equivariance property, which has been harnessed to design the parameter sharing structure of the weight matrix of DNNs. In this paper, we study a stronger property than permutation equivariance, namely unitary equivariance, for precoder learning. We first show that a DNN with unitary equivariance designed by further introducing parameter sharing into a permutation equivariant DNN is unable to learn the optimal precoder. We proceed to develop a novel non-linear weighting process satisfying unitary equivariance and then construct a joint unitary and permutation equivariant DNN. Simulation results demonstrate that the proposed DNN not only outperforms existing learning methods in learning performance and generalizability but also reduces training complexity.

A unique online game of political skill is engaging players and users not just from across America but from all over the world -- who are learning about the American political system, including the Electoral College, especially as the 2024 presidential election season heats up. Mark J. Penn, chair and CEO of Stagwell Inc., is the creator of a virtual political game of strategy called TwoSeventy. "This is more or less the only serious political online game right now," Penn told Fox News Digital in an interview. "There are online games in which you can catch sharks, kill Mafiosi, shoot people -- but it's pretty rare for you to be able to play a sophisticated political game where you take on the characters in the campaigns and aim to become president," he said. "It's pretty rare for you to be able to play a sophisticated political game where you take on the characters in the campaigns and aim to become president," said Mark Penn, creator of the online game called TwoSeventy.

In The Legend of Zelda, Hyrule is a land constantly imperiled by maleficent lords of shadow, cataclysmic volcanic eruptions, and an intangible sense of paranormal gloom that sucks the will to live out of every man, Zora, and Goron. Its nations are stratified across the land, and all of them live under the muzzling bounds of an autocratic royal bloodline. In other words, the people of Zelda need a free press, and in the newest game of the franchise--called Tears of the Kingdom--Hylians have discovered that occasionally, the pen is mightier than the sword. Those who embark on the adventure will discover ancient vistas, glorious ruins, and, most surprisingly, a proud celebration of the power of journalism. At last, Link is asking the tough questions.

Graph neural network (GNN) is a promising approach to learning and predicting physical phenomena described in boundary value problems, such as partial differential equations (PDEs) with boundary conditions. However, existing models inadequately treat boundary conditions essential for the reliable prediction of such problems. In addition, because of the locally connected nature of GNNs, it is difficult to accurately predict the state after a long time, where interaction between vertices tends to be global. We present our approach termed physics-embedded neural networks that considers boundary conditions and predicts the state after a long time using an implicit method. It is built based on an E(n)-equivariant GNN, resulting in high generalization performance on various shapes. We demonstrate that our model learns flow phenomena in complex shapes and outperforms a well-optimized classical solver and a state-of-the-art machine learning model in speed-accuracy trade-off. Therefore, our model can be a useful standard for realizing reliable, fast, and accurate GNN-based PDE solvers.

The scowl on Cristiano Ronaldo's face made international headlines last month when the Portuguese superstar was pulled from a match between Manchester United and Newcastle with 18 minutes left to play. Few footballers agree with a manager's decision to substitute them in favour of a fresh replacement. During the upcoming football World Cup tournament in Qatar, players will have a more evidence-based way to argue for time on the pitch. Within minutes of the final whistle, tournament organizers will send each player a detailed breakdown of their performance. Strikers will be able to show how often they made a run and were ignored.

The Telstra Corporation Ltd (ASX: TLS) share price is currently up as its artificial intelligence play makes headlines. The telco giant is going to work with Woolworths Group Ltd (ASX: WOW) controlled Quantium to help accelerate its use of AI, according to reporting by the Australian Financial Review. The AFR reported that Telstra and Quantium form a joint venture that will start by developing products and services for Telstra's enterprise customers in industries like mining, agribusiness and logistics. Telstra can provide the data, whilst the analytics and AI capabilities will be provided by Quantium. The Telstra CEO, Andy Penn, believes that this partnership will mean that it will be able to attract some of the top data talent.