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This automated CPR system could save countless lives.
Category: robotics/AI – Page 1,935
AI gatekeepers are taking baby steps toward raising ethical standards
For years, Brent Hecht, an associate professor at Northwestern University who studies AI ethics, felt like a voice crying in the wilderness. When he entered the field in 2008, “I recall just agonizing about how to get people to understand and be interested and get a sense of how powerful some of the risks [of AI research] could be,” he says.
To be sure, Hecht wasn’t—and isn’t—the only academic studying the societal impacts of AI. But the group is small. “In terms of responsible AI, it is a sideshow for most institutions,” Hecht says. But in the past few years, that has begun to change. The urgency of AI’s ethical reckoning has only increased since Minneapolis police killed George Floyd, shining a light on AI’s role in discriminatory police surveillance.
This year, for the first time, major AI conferences—the gatekeepers for publishing research—are forcing computer scientists to think about those consequences.
Future shocks: 17 technology predictions for 2025
1. AI-optimized manufacturing
Paper and pencil tracking, luck, significant global travel and opaque supply chains are part of today’s status quo, resulting in large amounts of wasted energy, materials and time. Accelerated in part by the long-term shutdown of international and regional travel by COVID-19, companies that design and build products will rapidly adopt cloud-based technologies to aggregate, intelligently transform, and contextually present product and process data from manufacturing lines throughout their supply chains. By 2025, this ubiquitous stream of data and the intelligent algorithms crunching it will enable manufacturing lines to continuously optimize towards higher levels of output and product quality – reducing overall waste in manufacturing by up to 50%. As a result, we will enjoy higher quality products, produced faster, at lower cost to our pocketbooks and the environment.
Anna-Katrina Shedletsky, CEO and Founder of Instrumental.
Smart farms of the future: Making bioenergy crops more environmentally friendly
Farmers have enough worries—between bad weather, rising costs, and shifting market demands—without having to stress about the carbon footprint of their operations. But now a new set of projects by scientists at Lawrence Berkeley National Laboratory (Berkeley Lab), including scientists at the Joint BioEnergy Institute (JBEI), could make agriculture both more sustainable and more profitable.
The three projects, funded by the U.S. Department of Energy (DOE), leverage Berkeley Lab’s strengths in artificial intelligence, sensors, and ecological biology. They aim to quantify and reduce the carbon intensity of agriculture, including the farming of biofuel feedstocks such as corn, soy, and sorghum, while also increasing yield.
Crop-based biofuels have the potential to supply up to about 5% of U.S. energy demand, according to the DOE. Two of the new projects are part of the SMARTFARM program of DOE’s Advanced Research Projects Agency-Energy (ARPA-E). This initiative aspires to make the biofuel supply chain carbon negative—meaning it removes or sequesters more carbon than it emits—which would greatly improve biofuel’s benefits to the broader economy and environment. Scientists also hope that the increased productivity will have the effect of lowering costs and increasing farmers’ income.
Harmonic Bionics Demonstrates Robotic Rehabilitation Exoskeleton
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):
Let us know if you have suggestions for next week, and enjoy today’s videos.
Welcome! You are invited to join a webinar: Live Webinar: MIT Professional Education’s Machine Learning: From Data to Decisions. After registering, you will receive a confirmation email about joining the webinar
Join us for an online webinar on Tuesday, June 30th at 9:30 a.m. ET with MIT faculty member and expert in machine learning, Professor Devavrat Shah.
This webinar is a way to understand the topics covered in the ‘Machine Learning: From Data to Decisions’ online course, ask questions, and get a preview of the content.
This is a 60-minute webinar with Prof. Devavrat Shah to learn more about the upcoming Machine Learning: From Data to Decisions (Online) Program, followed by a Q&A session.
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