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Shadow Robot’s partnership with Google DeepMind exemplifies how collaboration drives innovation in robotics and AI. This article explores how their expertise in dexterous robotic hands, particularly the DEX-EE, is advancing machine learning research.
New research could improve the safety of drone shows, warehouse robots, and self-driving cars.
By automatically generating code that leverages two types of data redundancy, the system saves bandwidth, memory, and computation.
Building on the success of conventional wearables, smart rings have been enthusiastically embraced by early adopters with a finger on the pulse of cutting-edge yet fashionable health and fitness wireless products.
3 minutes read
Associate Professor Luca Carlone is working to give robots a more human-like awareness of their environment.
NPUs are integrated units that excel in real-time AI tasks on edge devices like smartphones and IoT systems with low power consumption. TPUs are standalone processors designed for large-scale AI workloads in data centers, delivering exceptional performance in deep learning tasks.
Texas A&M University is creating advanced mapping technologies to further the evolution of autonomous vehicles.
3 minutes read
Learn how agnostic systems like Awentia's No-Data Vision Foundation Model addresses key barriers to AI adoption such as data dependency, cost, and complexity across industries like agriculture, robotics and manufacturing.
EPFL researchers have developed 4M, a next-generation, open-sourced framework for training versatile and scalable multimodal foundation models that go beyond language.
4 minutes read
Using this model, researchers may be able to identify antibody drugs that can target a variety of infectious diseases.
GPUs excel in parallel processing for graphics and AI training with scalability, while NPUs focus on low-latency AI inference on edge devices, enhancing privacy by processing data locally. Together, they complement each other in addressing different stages of AI workloads efficiently.
A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.
The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore requests.
Researchers at Stanford Engineering have developed an AI-trained model to accurately recreate the hand movements of elite-level pianists and the physical stresses they endure while playing.
4 minutes read
According to , the global AI robotics market will reach $17 billion in 2024 and will keep growing by 24% annually. Today, robots deliver packages, conduct geological surveys, and entertain visitors of parks and exhibitions – however, that’s not the limit of their abilities.
5 minutes read
Wevolver 2025
