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We got a short trailer with no real gameplay at the latest Nintendo Direct. Nintendo just officially announced a remake of, ending months of rumors and speculation . The company dropped a short trailer at today's Nintendo Direct livestream and this looks like a top-to-bottom remake with a massive graphical facelift. The game launches later this year for Switch 2, with more details to come. There's still a lot we don't know, including price and if there will be any new content.

PCWorld reports that Google's Manifest V3 update will permanently disable popular ad blockers like uBlock Origin in Chrome by late June. This transition from Manifest V2 aims to enhance Chrome's security and speed, but inadvertently limits ad blocker functionality as a side effect. Chrome 150 or 151 will likely remove all workarounds, forcing users to seek alternative browsers or accept reduced ad-blocking capabilities. Google has been working for some time on a way to block old browser extensions in Google Chrome. This goes hand in hand with the switch from Manifest V2 to Manifest V3, a newer and presumably more secure architecture for the popular browser. As early as March 2025, this rendered some extensions--including popular ad blockers such as uBlock Origin--suddenly unusable, even though it was still possible to access them with a workaround.

Accurately modeling the target-drug complex at atom level presents a significant challenge in the computer-aided drug design. Traditional methods that rely solely on rigid transformations often fail to capture the adaptive interactions between targets and drugs, particularly during substantial conformational changes in targets upon ligand binding, which becomes especially critical when learning target-drug interactions in drug design. Accurately modeling these changes is crucial for understanding target-drug interactions and improving drug efficacy. To address these challenges, we introduce DynaPhArM, an SE(3)-Equivariant Transformer model specifically designed to capture adaptive alterations occurring within target-drug interactions. DynaPhArM utilizes the cooperative scalar-vector representation, drug-specific embeddings, and a diffusion process to effectively model the evolving dynamics of interactions between targets and drugs. Furthermore, we integrate physical information and energetic principles that maintain essential geometric constraints, such as bond lengths, bond angles, van der Waals forces (vdW), within a multi-task learning (MTL) framework to enhance accuracy. Experimental results demonstrate that DynaPhArM achieves state-of-the-art performance with an overall root mean square deviation (RMSD) of 2.01 Å and a sc-RMSD of 0.29 Å while exhibiting higher success rates compared to existing methodologies. Additionally, DynaPhArM shows promise in enhancing drug specificity, thereby simulating how targets adapt to various drugs through precise modeling of atomic-level interactions and conformational flexibility.

Face recognition is essential for identity authentication, but the rich visual clues in facial images pose significant privacy risks, highlighting the critical importance of privacy-preserving solutions. For instance, numerous studies have shown that generative models are capable of effectively performing reconstruction attacks that result in the restoration of original visual clues. To mitigate this threat, we introduce FracFace, a fractal-based privacy-preserving face recognition framework. This approach effectively weakens the visual clues that can be exploited by reconstruction attacks by disrupting the spatial structure in frequency domain features, while retaining the vital visual clues required for identity recognition. To achieve this, we craft a Frequency Channels Refining module that reduces sparsity in the frequency domain. It suppresses visual clues that could be exploited by reconstruction attacks, while preserving features indispensable for recognition, thus making these attacks more challenging. More significantly, we design a Frequency Fractal Mapping module that obfuscates deep representations by remapping refined frequency channels into a fractal-based privacy structure. By leveraging the self-similarity of fractals, this module preserves identity relevant features while enhancing defense capabilities, thereby improving the overall robustness of the protection scheme. Experiments conducted on multiple public face recognition benchmarks demonstrate that the proposed FracFace significantly reduces the visual recoverability of facial features, while maintaining high recognition accuracy, as well as the superiorities over state-of-the-art privacy protection approaches.

You can read the roadmap in full here . Lucy Smith is Senior Managing Editor for Robohub and AIhub. Lucy Smith is Senior Managing Editor for Robohub and AIhub.

Maria Koskinopoulou is an Assistant Professor in Robotics and Computer Vision at Heriot-Watt University. Her research interests include robotic manipulation, perception, robot vision, medical robotics, human-robot interaction, and machine learning. She is involved in major UKRI and EU-funded research projects advancing robotic manipulation, surgical and underwater robotics, autonomous assembly, and waste sorting. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines.

And we answer some listener questions. Siri AI was the star of the show, of course, and we chat about how it differs from all of the other AI tools out there. Also, we dive into Apple's progress around child safety, as well as speed improvements coming to all of its platforms.

Biomolecular structure determination is essential to a mechanistic understanding of diseases and the development of novel therapeutics. Machine-learning-based structure prediction methods have made significant advancements by computationally predicting protein and bioassembly structures from sequences and molecular topology alone. Despite substantial progress in the field, challenges remain to deliver structure prediction models to real-world drug discovery. Here, we present NeuralPLexer3 -- a physics-inspired flow-based generative model that achieves state-of-the-art prediction accuracy on key biomolecular interaction types and improves training and sampling efficiency compared to its predecessors and alternative methodologies.

Each year the AAAI recognizes a group of individuals who have made significant, sustained contributions to the field of artificial intelligence by appointing them as Fellows. We're talking to some of the 2026 AAAI Fellows to find out more about their work. In this interview, we chat to Sanmay Das, who was elected as a Fellow . Could you start with a quick introduction, where you work, and your general area of research? Broadly speaking, I work in multiagent systems. I've done a lot of work at the intersection of AI and economics, and over the last decade or so I've thought a lot about projects in the AI for social impact and social good space. In particular, my interest has been in the allocation of scarce societal resources, thinking about how AI can be integrated, and what it tells us about systems where we don't necessarily want full free market resource allocation.