AI

Researchers from Amazon and the University of Michigan have developed HydroShear, a simulator that accurately models tactile forces, enabling robots to learn dexterous, contact-rich manipulation policies entirely in simulation with a 93 percent average success rate. HydroShear's key innovation is the addition of path-dependent force tracking to hydroelastic contact models, allowing for accurate tracking of forces accumulating over a soft sensor membrane during physical interactions. This approach enables the simulation of subtle tactile forces and shear feedback, bridging the tactile reality gap. The practical implication for engineers building AI systems is the potential to develop more advanced robotic manipulation capabilities.

The Naval Postgraduate School has commissioned an NVIDIA DGX GB300 system, one of the world's most powerful AI platforms, to provide students, researchers, and faculty with access to large-scale AI computing, including model training and inference capability. The system, anchored by the NVIDIA AI Technology Center on the Monterey campus, will support applications such as weather prediction, cybersecurity, and disaster resilience and response planning. With the DGX GB300, NPS students and faculty will be able to train foundation models in-house, run high-fidelity simulations at scale, and develop AI tools with immediate applicability. This will have a significant impact on the education and research capabilities of the institution, enabling the development of AI-based technologies for real-world applications.

The JARVIS Challenge, a four-week competition, tasked MIT undergraduates with designing, fabricating, assembling, and testing a small gas turbine aero engine using AI as their primary engineering partner, with the goal of building a "JARVIS-class" single-spool jet engine producing 50-100 pounds of thrust. The challenge showed that AI can substantially accelerate safety-critical hardware engineering, but engineering judgment remains crucial. The use of AI tools, including MIT Parley, a platform that aggregates frontier large language models, allowed students to explore new design and manufacturing possibilities. The practical implication for engineers building AI systems is that AI can be a powerful tool in engineering design, but human judgment and expertise are still essential for complex physical systems.