This article explores TPU vs GPU differences in architecture, performance, energy efficiency, cost, and practical implementation, helping engineers and designers choose the right accelerator for AI workloads today!
The landscape of IoT development is evolving rapidly, and staying ahead requires more than just technical know-how, it demands a strategic shift in how we approach building connected devices.
Discover how no-code/low-code platforms are transforming embedded development by enabling rapid prototyping and deployment with minimal coding. Explore tools like LabVIEW, Node-RED, and XOD that simplify IoT, automation, and industrial system design.
Teams that are looking at alternative programming languages have two options today: Ada and Rust. Both languages raise the bar in terms of safety and security
Join Prof. Fei Chen as he explores advanced bimanual manipulation and teleoperation techniques shaping the future of intelligent human-like robots in this expert-led robotics session.
The ETH spin-off Flink Robotics wants to revolutionize the handling of packages. Its founders Moritz Geilinger and Simon Huber have developed software that allows robots to work together and quickly take on new tasks.
In the current AI zeitgeist, sequence models have skyrocketed in popularity for their ability to analyze data and predict what to do next. For instance, you’ve likely used next-token prediction models like ChatGPT, which anticipate each word (token) in a sequence to form answers to users’ queries.
The widespread adoption of robotics is hindered by the complexity of system integration, lack of skilled labor, and diverse programming languages, but FuzzyRTOS addresses these challenges by providing a universal platform that simplifies robot programming.
Industrial process automation is changing manufacturing by using technology to reduce the need for human involvement. From robots assembling products to smart systems managing inventory, factories are being transformed. This article will explore the successful elements behind industrial automation.
In this episode, we explore how AI technology is revolutionizing the detection and monitoring of infrastructure defects and learn how these innovative solutions are enhancing safety, improving maintenance, and preventing failures in critical infrastructure systems
Edge AI is the process of running artificial intelligence (AI) and machine learning (ML) algorithms on computing devices at the periphery of a network, rather than on large cloud servers.
Similar to visual data, collecting and curating sound data that accurately reflects real-world scenarios is a major hurdle in training effective machine learning models.
‘Industry 4.0’—the next stage of the Industrial Revolution—leans heavily on the Industrial Internet of Things (IIoT) to support large scale automation of traditional manufacturing practices.
For robots, simulation is a great teacher for learning long-horizon (multi-step) tasks — especially compared to how long it takes to collect real-world training data.
To access information and services quickly and reliably, companies across all sectors are more invested in connectivity, which entails faster, more reliable networks and better encryption and security.
MIT CSAIL researchers developed an AI-driven approach to “low-discrepancy sampling,” a method that improves simulation accuracy by distributing data points more uniformly across space.