March 2026

Contrastive Learning Boosts Deterministic and Generative Models for Weather Data

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Weather data, comprising multiple variables, poses significant challenges due to its high dimensionality and multimodal nature. Creating low-dimensional embeddings requires compressing this data into a compact, shared latent space. This compression is required to improve the efficiency and performance of downstream tasks, such as forecasting or extreme-weather detection. Self-supervised learning, particularly contrastive learning, offers a way to generate low-dimensional, robust embeddings from unlabelled data, enabling downstream tasks when labelled data is scarce. Despite initial exploration of contrastive learning […]

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Meta, YouTube must pay $3M to woman who got hooked on apps as a child

Meta, YouTube must pay $3M to woman who got hooked on apps as a child

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On Wednesday, a Los Angeles jury ordered Meta and YouTube to pay $3 million in damages to a young woman who successfully argued that the companies’ social media apps were designed to addict children. Meta will pay the majority of the fine, 70 percent, while YouTube-owner Google is on the hook for 30 percent, the jury decided. During the six-week trial, the jury heard that Meta and Google designed apps with features like auto-play, infinite scroll, and algorithmic […]

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Decentralized Task Scheduling in Distributed Systems: A Deep Reinforcement Learning Approach

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Efficient task scheduling in large-scale distributed systems presents significant challenges due to dynamic workloads, heterogeneous resources, and competing quality-of-service requirements. Traditional centralized approaches face scalability limitations and single points of failure, while classical heuristics lack adaptability to changing conditions. This paper proposes a decentralized multi-agent deep reinforcement learning (DRL-MADRL) framework for task scheduling in heterogeneous distributed systems. We formulate the problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and develop a lightweight actor-critic architecture implemented using […]

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Nintendo is raising prices of Switch 2 game cartridges starting in May

Nintendo is raising prices of Switch 2 game cartridges starting in May

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The downloadable versions of Nintendo’s first-party Switch games have always cost the same amount to buy, despite the costs of manufacturing and shipping physical releases. This was still true when the Switch 2 launched last year, despite persistent rumors and misinformation to the contrary. But that’s finally, definitively changing later this year. Nintendo announced today that beginning in May and for new game releases going forward, the physical releases of new Switch 2-exclusive first-party games will cost more […]

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Unlocking video insights at scale with Amazon Bedrock multimodal models

Unlocking video insights at scale with Amazon Bedrock multimodal models

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Video content is now everywhere, from security surveillance and media production to social platforms and enterprise communications. However, extracting meaningful insights from large volumes of video remains a major challenge. Organizations need solutions that can understand not only what appears in a video, but also the context, narrative, and underlying meaning of the content. In this post, we explore how the multimodal foundation models (FMs) of Amazon Bedrock enable scalable video understanding through three distinct architectural approaches. Each […]

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Deploy voice agents with Pipecat and Amazon Bedrock AgentCore Runtime – Part 1

Deploy voice agents with Pipecat and Amazon Bedrock AgentCore Runtime – Part 1

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This post is a collaboration between AWS and Pipecat. Deploying intelligent voice agents that maintain natural, human-like conversations requires streaming to users where they are, across web, mobile, and phone channels, even under heavy traffic and unreliable network conditions. Even small delays can break the conversational flow, causing users to perceive the agent as unresponsive or unreliable. For use cases such as customer support, virtual assistants and outbound campaigns, a natural flow is critical for user experience. In […]

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Supreme Court rejects Sony's attempt to kick music pirates off the Internet

Supreme Court rejects Sony’s attempt to kick music pirates off the Internet

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The Supreme Court today decided that Internet service providers cannot be held liable for their customers’ copyright infringement unless they take specific steps that cause users to violate copyrights. The court ruled unanimously in favor of Internet provider Cox Communications, though two justices did not agree with the majority’s reasoning. The ruling effectively means that ISPs do not have to conduct mass terminations of Internet users accused of illegally downloading or uploading pirated files. If the court had […]

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Spectral methods: crucial for machine learning, natural for quantum computers?

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This article presents an argument for why quantum computers could unlock new methods for machine learning. We argue that spectral methods, in particular those that learn, regularise, or otherwise manipulate the Fourier spectrum of a machine learning model, are often natural for quantum computers. For example, if a generative machine learning model is represented by a quantum state, the Quantum Fourier Transform allows us to manipulate the Fourier spectrum of the state using the entire toolbox of quantum […]

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Google's TurboQuant AI-compression algorithm can reduce LLM memory usage by 6x

Google’s TurboQuant AI-compression algorithm can reduce LLM memory usage by 6x

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Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without getting fleeced. Google Research recently revealed TurboQuant, a compression algorithm that reduces the memory footprint of large language models (LLMs) while also boosting speed and maintaining accuracy. TurboQuant is aimed at reducing the size of the key-value cache, which Google likens […]

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DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving

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We introduce DreamerAD, the first latent world model framework that enables efficient reinforcement learning for autonomous driving by compressing diffusion sampling from 100 steps to 1 – achieving 80x speedup while maintaining visual interpretability. Training RL policies on real-world driving data incurs prohibitive costs and safety risks. While existing pixel-level diffusion world models enable safe imagination-based training, they suffer from multi-step diffusion inference latency (2s/frame) that prevents high-frequency RL interaction. Our approach leverages denoised latent features from video […]

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