A portable ultrasound system could make reliable breast imaging more accessible

Heads in the game

MIT Sports Lab, co-founded in 2015, has become a key technology partner for major sports organizations. The lab played a central role in validating FIFA's semi-automated offside technology (SAOT) used at the 2022 World Cup, processing over 108,900 skeletal data points per second to ensure accuracy. Beyond soccer, the lab developed an Expected Action Value (EAV) metric for the NBA to quantify player decision-making quality, helped Adidas optimize 3D-printed midsole designs using biomechanical models, and conducted a COVID-19 stadium attendance analysis for the NFL. The lab bridges academic research and industry needs, connecting MIT students and faculty with professional sports organizations.

Berlin’s Almetra raises €16.3M to turn factory video into live data

Berlin startup Almetra (formerly Deltia) has raised €16.3M in Series A funding to expand its AI-powered factory floor analytics platform. The company mounts cameras above assembly lines at manufacturers like Bosch, Siemens Energy, and ABB, converting video footage into live production data — cycle times, output rates, equipment utilisation — without requiring IT system integration. Customers report productivity gains of 15–19%. The round was led by blisce/, with participation from Merantix Capital and others. Almetra has been accepted into Google DeepMind's Robotics Accelerator and an AWS/Nvidia/MassRobotics Physical AI Fellowship, positioning it as a potential data source for industrial robotics. The company plans to use the funding to expand into the US and build out robotics applications.

Someone built a functional Odradek from Death Stranding that actually tracks Bluetooth devices

A maker built a functional replica of the Odradek scanner from Death Stranding using a Raspberry Pi 5, AI HAT+2, and a XIAO ESP32-C6 in the head unit. Instead of tracking ghostly BTs from the game, it scans for real Bluetooth signals — specifically targeting AI glasses like Meta Ray-Bans — and physically points at people wearing them. The head unit controls an actuator, motor, and lights, while the Pi handles local AI processing via a camera.

Flock surveillance cameras are spreading across the US

Flock Safety vận hành hơn 100.000 camera nhận diện biển số tự động trên khắp nước Mỹ, sử dụng Android biến thể và AI để tìm kiếm bằng ngôn ngữ tự nhiên. Hệ thống chia sẻ dữ liệu toàn quốc cho phép cảnh sát truy cập dữ liệu từ các bang khác, trong khi lỗ hổng bảo mật nghiêm trọng (như 70 camera không mật khẩu) và lạm dụng theo dõi cá nhân đã bị phát hiện. Dù thiếu bằng chứng giảm tội phạm, mạng lưới vẫn mở rộng bất chấp phản đối.

Những lỗ hổng bảo mật và sử dụng sai mục đích của hệ thống giám sát plate reader Flock Safety cho thấy cần cảnh giác về sự phát triển nhanh chóng của công nghệ giám sát đại trà và cách bảo vệ quyền riêng tư cũng như an ninh dữ liệu trong thời đại số.

Into the Omniverse: Three Workflows for Improving Vision AI Agent Accuracy With Synthetic Data and Fine-Tuning

Vision AI agents are increasingly used to extract operational intelligence from video data in factories, cities, and warehouses. Three common blockers — data gaps causing accuracy plateaus, lack of fine-tuning expertise, and complex agent assembly — can be addressed using NVIDIA Metropolis blueprints, NVIDIA Omniverse for OpenUSD-based synthetic data generation, and NVIDIA TAO for model fine-tuning. Three real-world workflows illustrate the approach: Roboflow and Corning used synthetic defect images to achieve 95% average precision from just eight real training samples; Linker Vision cut smart city development effort by 85% and incident response times by 80% using the VSS blueprint; and DeepHow's SOP Verification agent at Foxconn improved first-pass yield by 3% and achieved 99% task-level accuracy on assembly line operations.

Why Different Wearables Report Different Heart Rates

Consumer wearables report different heart rates for the same person due to several compounding factors. All wrist and finger wearables use photoplethysmography (PPG), but sensor placement matters — finger-based devices like Oura Ring sit closer to surface arteries and move less during sleep, giving them an accuracy edge over wrist devices. Sampling rate also plays a major role: WHOOP samples 26 times per second continuously, while Apple Watch and Garmin use periodic or adaptive sampling. Beyond hardware, proprietary algorithms translate raw light signals into heart rate values, and software updates alone can shift reported numbers without any hardware change. Additional variables include skin tone (melanin absorbs more light, reducing signal quality), tattoos over sensor areas, and device fit on the wrist. During exercise, motion artifacts further diverge readings between brands. The practical takeaway is that cross-device comparisons are unreliable; what matters is consistent trends on a single device over time.

Schneier on Security

AI is transforming video surveillance by enabling natural language queries over massive video streams. Unlike older tools limited to preset searches, new AI systems let intelligence officers search for complex behavioral patterns — such as a person changing clothes multiple times or a vehicle repeatedly passing the same spot. This shift from object-based to behavior-based surveillance represents a qualitative leap in mass monitoring capabilities, with real-world deployments reported in Israel, Iran, and Russia.