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A new VLF antenna developed by SLAC researchers is compact (4-inches tall) and weighs only a few pounds. The device emits very low frequency (VLF) radiation with wavelengths of tens to hundreds of miles and could enable mobile communication where conventional radios don't work such as through ground and water and thousands of miles beyond the horizon through air.

Researchers from Stanford and Scandia National Labs previously developed a device that acts as an artificial synapse, imitating the brain's efficiency and ability to learn. The team's prototype array of nine of these devices out performed expectations in processing speed, energy efficiency, reproducibility and durability. The hope is that this will be a step toward supporting artificially intelligent learning on small devices.

The Green New Deal, the sweeping plan to overhaul transportation, energy and other sectors, labels climate change as a "direct threat to the national security of the United States" failed a recent U.S. Senate vote. Here Stanford experts discuss the strengths and weaknesses of the Green New Deal and the urgency and importance of decarbonization.

A team of Stanford engineers have devised a way to ensure uninterrupted wireless connectivity as 5G begins rolling out next year. 5G radios will be delivering data at 100 Mbps to as many as a million devices per square kilometer on vehicles traveling up to 330 mph. The team has developed AI algorithms based on reinforcement learning and this system is able to adapt even after the self-driving radio is deployed.

One of the assignments for students in CS 181: Computers, Ethics and Public Policy involved regulating campus vehicles in a future where autonomous vehicles are prevalent and considering speed limits, pedestrians and how to handle accidents. This and other assignments helped them explore technology's impact on people and society from the perspective of a policymaker, computer programmer or philosopher.

To help autonomous vehicles drive safely at the limits of handling for situations such as obstacle avoidance or encountering a patch of ice, Stanford researchers used data from two test tracks: one from a low-friction environment with ice and snow and the other from Thunderhill Raceway in Willows, CA for high-friction data to develop a neural-network based system. In testing, the AI based system outperformed a more traditional physics-based system.