AI News Today

On a sunny Tuesday afternoon in May, San Francisco Bay is busy. Container ships the size of skyscrapers deliver their wares to the Port of Oakland, tankers bear fuel, and ferries carry tourists to their hikes and commuters to their jobs at AI startups. Looking down at this marine traffic from Angel Island, located near the entrance to the bay, a group of excited scientists point to some sparkles on the surface of the water: Three gray whales are coming up for breath.A collaboration of government

“Social engineering” sounds like something out of a conspiracy thriller, charged with totalitarian control and fringe paranoia. More mundanely, it’s come to be associated with phishing and other scams, in which fraudsters manipulate people into disclosing personal information. Yet the concept is older and more benign: it is the deliberate shaping of human behavior, often at scale. It predates silicon—and became pervasive, and ungoverned, especially once its practitioners learned to hide it. Auth

This webinar presents a workflow offering end-to-end solutions for designing, training, validating and verifying, compressing, and deploying AI-based virtual sensor models to embedded processors within a single environment.HighlightsIntegrate AI models into Simulink for system-level simulation, verification, and simulation-based testingApply formal verification techniques to assert neural network behaviorCompress the AI model for memory footprint reduction and execution speedupGenerate library-f

Bees and other pollinating insects play vital roles in food webs and crop pollination, yet monitoring them has proved difficult. That’s why researchers have developed a radar system that could lead to a cost-effective, noninvasive way to track pollinators.Traditionally, identifying pollinators has proven tricky and time-consuming, and typically requires capturing and killing insects to get a close look at them. To find a better way to monitor pollinators, scientists are developing vision systems

Streaming vision-language models (VLMs) continuously generate responses given an instruction prompt and an online stream of input frames. This is a core mechanism for real-time visual assistants. Existing VLM frameworks predominantly assess models in offline settings. In contrast, the performance of a streaming VLM depends on additional metrics beyond pure video understanding, including proactiveness, which reflects the timeliness of the model’s responses, and consistency, which captures the rob

MagenticLite is an agentic system for small models that works across the browser and local file system in a single workflow. It combines specialized models and orchestration to support efficient agentic performance on everyday tasks. The post MagenticLite, MagenticBrain, Fara1.5: An agentic experience optimized for small models appeared first on Microsoft Research.

New Zealand is a country famed for its dramatic landscapes, but its linguistic landscape is arguably just as interesting. Of its three official languages, only te reo Māori (the Māori language) could be described as indigenous. Though spoken fluently by just 4.3 percent of the population, national statistics show that about 30 percent of New Zealanders can speak more than a few words or phrases of the language.But ask ChatGPT to write te reo Māori and it will oblige, fluently answering your ques

When a group of academics started making open-source robotics hardware, a generation of roboticists got years of their lives back. Now, the bigger challenge is getting robots to think—and that’s starting to be open sourced too.The shift is still early, but companies including Hugging Face, Nvidia, and Alibaba have all made significant bets on open-source robotics in the last two years, releasing tools and models aimed at the higher-level work of getting robots to reason, decide, and act. The ope

Vega turns a full credential into a single proof, sharing only what is needed and nothing more, with performance that works in real apps. The post Vega: Zero-knowledge proofs for digital identity in the age of AI appeared first on Microsoft Research.

This sponsored article is brought to you by Wetour Robotics.A field technician on a wind turbine, harness clipped, both hands on a wrench, needs to send a command to the diagnostic device hanging at her belt. A logistics worker on a loading dock, gloves on, eyes on the pallet, needs to redirect a connected lift. A person using an assistive mobility device on a crowded street wants to nudge it forward without taking out a phone or speaking aloud. None of these moments call for a smarter robot. Th

General Science

Modern large language models (LLMs) extend context lengths to millions of tokens, enabling coherent, personalized responses grounded in long conversational history. However, the Key-Value (KV) cache grows linearly with the extended dialogue history, causing the model’s memory footprint to quickly exceed device limits. While recent KV cache compression methods attempt to reduce memory usage, most apply cache eviction after processing the entire context, incurring unbounded peak memory usage. Addi

This presentation highlights recent efforts at the Johns Hopkins Applied Physics Laboratory to advance agentic AI for collaborative robotic teams. It begins by framing the core challenges of enabling autonomy, coordination, and adaptability across heterogeneous systems, then introduces a scalable architecture designed to support agentic behaviors in multi-robot environments. The talk concludes with key challenges encountered and practical lessons learned from ongoing research and development.Key

This sponsored article is brought to you by Melbourne Convention Bureau (MCB) supported by Business Events Australia.Melbourne’s reputation as a global events city, from the Australian Open tennis and Formula 1 Australian Grand Prix to hosting NFL regular season games, now intersects with a different form of scale: large-scale compute, data-intensive research, and advanced engineering. Long recognized for delivering complex international events, the city is applying the same organisational capab

AI-powered voice and audio tools are becoming increasingly embedded in daily life, from digital assistants to smart speakers and customer service bots. Advances in large audio-language models (LALMs), which can both analyze and generate audio, now make it possible to control devices using voice commands, transcribe meetings automatically, or identify a song playing in the background. These models are also increasingly equipped with the ability to communicate with external services and operate ot

Electronic rings wirelessly connected to an AI system are capable of translating multiple sign languages into text, a new study finds. “I believe this is an important step toward making sign language translation systems more practical, lightweight, and usable in real-world environments,” says Ki Jun Yu , an associate professor of electrical and electronic engineering at Yonsei University in Seoul, Korea. More than 300 different sign languages are used worldwide, and many research projects are de

Our recent paper, “LLMs Corrupt Your Documents When You Delegate”, has generated discussion about the reliability of AI systems in delegated workflows. We appreciate the interest in this work and want to clarify several important points about what the paper does—and does not—claim. The research aims to develop robust evaluation methods for long-horizon delegated and […] The post Further Notes on Our Recent Research on AI Delegation and Long-Horizon Reliability appeared first on Microsoft R

A new scaling law that relates particular architectural choices to loss helps identify models that improve throughput by up to 47% with no loss of accuracy.

A hydrated leaf is a healthy leaf. That’s true for the leaves of crop plants in a farmer’s field and for the leaves of trees in an area vulnerable to forest fires. But the traditional techniques to monitor leaf hydration require cutting them from their plants, which is time-consuming and cannot give live measurements. That’s why many researchers are building sensors that measure a plant’s health in real time. Now, researchers in Texas have developed a graphene “tattoo” that can be stuck directly

By focusing on specific failure points and suggesting targeted solutions, a new automated prompt-engineering framework improves prompt performance without compromising existing functionality.

This sponsored article is brought to you by Applied Materials . At pivotal moments in history, progress has required more than individual brilliance. The most consequential breakthroughs — such as those achieved under the Human Genome Project — required a new operating paradigm: Concentrate the world’s best talent around a single mission, establish a common platform, share critical infrastructure, and collapse feedback loops. When stakes are high and timelines are compressed, sequential and silo

mimalloc is an open-source, modern, scalable memory allocator that is a drop-in replacement for malloc and free. It is relatively small (~12K lines), with clear internal data structures, and is easy to build and integrate into other projects. It provides bounded worst-case allocation times (up to OS primitives), bounded space overhead, low internal fragmentation, and minimal contention by relying almost exclusively on atomic operations. The post mimalloc: A new, high-performance, scalable memory

Introducing GridSFM, a small foundation model that can predict AC optimal power flow in milliseconds, boosting efficiency and unlocking cost savings. Learn how GridSFM gives grid operators direct visibility into congestion, stability, and system health. The post GridSFM: A new, small foundation model for the electric grid appeared first on Microsoft Research.

One of the earliest stated goals for computing in medicine was to aid in clinical reasoning: the decision-making steps required to reach a diagnosis and form a treatment plan. And over the years, researchers have built many clinical decision support systems, which have typically been purpose-built, with painstakingly written rules about symptoms, test thresholds, and medication interactions. As artificial intelligence capabilities develop, clinical reasoning is a natural application. Now, a larg

When I sat down with bell hooks’ personal journals at an archive at Berea College in Kentucky, I expected an intimate peek into her private thoughts, her voice before the editing. What I got instead was frustration. Her handwriting was dense cursive, all loops that looked identical to my eye, and there were years of journals to go through. I found myself photographing pages and feeding them to ChatGPT just to read what she’d written. My tool of choice worked well, and it turns out I’m not the fi

This sponsored article is brought to you by Ampace . As AI workloads grow to gigascale levels, the global data center industry has hit a hidden physical wall. The real bottleneck is no longer just the thermal limit of the chip or the capacity of the cooling system — it is the dynamic resilience of the power chain. Modern AI computing clusters, driven by massive GPU clusters, generate high-frequency, abrupt, and synchronized spikey pulse loads. As rack densities soar beyond 100 kW, these fluctuat

MatterSim is expanding what AI can do for materials science—from faster large-scale simulations to MatterSim-MT, a new multi-task model for simulating properties beyond potential energy surfaces alone. The post Advancing AI for materials with MatterSim: experimental synthesis, faster simulation, and multi-task models appeared first on Microsoft Research.

Using SocialReasoning Bench, we observed a stable pattern across models—agents execute competently, but fail to consistently improve the user’s position, even with explicit instructions to optimize for user interest. The post SocialReasoning-Bench: Measuring whether AI agents act in users’ best interests appeared first on Microsoft Research.

Image captioning is one of the most fundamental tasks in computer vision. Owing to its open-ended nature, it has received significant attention in the era of multimodal large language models (MLLMs). In pursuit of ever more detailed and accurate captions, recent work has increasingly turned to reinforcement learning (RL). However, existing captioning-RL methods and evaluation metrics often emphasize a narrow notion of caption quality, inducing trade-offs across core dimensions of captioning. For

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At Apple, we believe privacy is a fundamental human right. As AI capabilities increase and become more integrated into people’s daily lives, advancing research in privacy-preserving techniques is increasingly important to ensure privacy is protected while users enjoy innovative AI experiences. Apple’s fundamental research has consistently pushed the state-of-the-art in this domain, and earlier this year, we hosted the Workshop on Privacy-Preserving Machine Learning & AI. This two-day event

Current critic-less RLHF methods aggregate multi-objective rewards via an arithmetic mean, leaving them vulnerable to constraint neglect: high-magnitude success in one objective can numerically offset critical failures in others (e.g., safety or formatting), masking low-performing “bottleneck” rewards vital for reliable multi-objective alignment. We propose Reward-Variance Policy Optimization (RVPO), a risk-sensitive framework that penalizes inter-reward variance during advantage aggregation, sh

We propose HeadsUp, a scalable feed-forward method for reconstructing high-quality 3D Gaussian heads from large-scale multi-camera setups. Our method employs an efficient encoder-decoder architecture that compresses input views into a compact latent representation. This latent representation is then decoded into a set of UV-parameterized 3D Gaussians anchored to a neutral head template. This UV representation decouples the number of 3D Gaussians from the number and resolution of input images, en

We introduce a framework for learning latent representations of 4D objects which are descriptive, faithfully capturing object geometry and appearance; compressive, aiding in downstream efficiency; and accessible, requiring minimal input, i.e., an unstructured dynamic point cloud, to construct. Specifically, Velox trains an encoder to compress spatiotemporal color point clouds into a set of dynamic shape tokens. These tokens are supervised using two complementary decoders: a 4D surface decoder, w

One of the major differentiators unlocked by learned codecs relative to their hard-coded traditional counterparts is their ability to be optimized directly to appeal to the human visual system. Despite this potential, a perceptual yet practical image codec is yet to be proposed. In this work, we aim to close this gap. We conduct a comprehensive study of the key modeling choices that govern the design of a practical learned image codec, jointly optimized for perceptual quality and runtime — inclu

Amazon engineers and scientists have created new tools to optimize delivery networks under uncertainty — and keep them adapting without missing a beat.

Agentic mechanism enables Amazon and vendors to optimize supply chain management without disclosing private information.

Amazon scientists and policy experts discuss how the company’s responsible-AI pipeline embeds safety and values throughout the AI development lifecycle.

Data Mining & Modeling

Data Mining & Modeling