Priyanka Raina: How computer chips get speedier through specialization
Electrical engineer Priyanka Raina explains how we’re moving toward faster, more efficient computer chips for every task in this episode of The Future of Everything.
Electrical engineer Priyanka Raina explains how we’re moving toward faster, more efficient computer chips for every task in this episode of The Future of Everything.
The second in a planned series of studies, this AI100 Study Report, comes five years after the initial report. The study is a combination of two key features: it is written by a Study Panel of core multi-disciplinary researchers and it is a longitudinal study with planned reports published once every five years.
A research study by the Stanford Graduate School Business on the motorcycle industry found that the longer a company has served a particular market, the more challenging change will be. While specialization can lead to popularity and profits, long lasting specialization and changing consumer tastes, can be fatal especially with competitors ready to address these new trends.
An international team of experts propose that autonomous systems of all kinds from self-driving cars, care-bots and other robots should learn from the aviation industry by implementing a black box to analyze accidents when they occur and create an audit trail.
Stanford University’s Precourt Institute for Energy and Woods Institute for the Environment will fund three new research projects to make and use plastics more sustainably. One project lead by William Tarpeh, assistant professor, chemical engineering, reimagines manufacturing for plastic car components.
A discovery of a relatively simple pretreatment of catalysts based on palladium could lead to new types of catalytic flares and cleaner-burning car engines that would keep tons of the heat-trapping gas out of the skies.
Scholars propose a way to help mobile robots choose when to communicate with the cloud without latency or lost data issues. Marco Pavone and postdoctoral scholar Sandeep Chinchali used deep reinforcement learning algorithms to smartly and sparingly tap into the cloud to improve perception while reducing demands on data channels.
Stanford engineers have overcome a key obstacle that has limited the widespread adoption of phase-change memory which is thousands of times faster than conventional hard drives. “The big appeal of phase-change memory is speed, but energy-efficiency in electronics also matters,” Eric Pop, professor of electrical engineering said.
The Sustainable Procurement Barometer, a survey of more than 350 companies, found that most firms remained committed to integrating sustainability into their supply chains even with the challenges of the pandemic.