Lifeboat Foundation

Researchers have taken 3D images by bouncing individual photons from a laser off a building 45 kilometres away, more than 4 times farther than ever before.

Drones of all sizes are being used by environmental advocates to monitor deforestation, by conservationists to track poachers, and by journalists and activists to document large protests. As a political sociologist who studies social movements and drones, I document a wide range of nonviolent and pro-social drone uses in my new book, “The Good Drone.” I show that these efforts have the potential to democratize surveillance.

But when the Department of Homeland Security redirects large, fixed-wing drones from the U.S.-Mexico border to monitor protests, and when towns experiment with using drones to test people for fevers, it’s time to think about how many eyes are in the sky and how to avoid unwanted aerial surveillance. One way that’s within reach of nearly everyone is learning how to simply disappear from view.

I am for ethical Ai. What about you?

Sophia interviews Stanford AI Researcher Tina White about how A.I. & Robotics can help stop the spread of Covid19 through contact tracing while preserving users’ privacy.

Continue reading “Sophia Life: Interview with AI Researcher Tina White” »

Anyone here comfortable with these levels of surveillance?

Such mercantile tactics are alleged, practices which have been able to undercut prices against those of the products of legitimately operating free market companies here in the US and elsewhere!

Continue reading “China Can Now Enforce Its ‘Brave New World’ Tyranny” »

China announced in 2017 its ambition to become the world leader in artificial intelligence (AI) by 2030. While the US still leads in absolute terms, China appears to be making more rapid progress than either the US or the EU, and central and local government spending on AI in China is estimated to be in the tens of billions of dollars.

The move has led — at least in the West — to warnings of a global AI arms race and concerns about the growing reach of China’s authoritarian surveillance state. But treating China as a “villain” in this way is both overly simplistic and potentially costly. While there are undoubtedly aspects of the Chinese government’s approach to AI that are highly concerning and rightly should be condemned, it’s important that this does not cloud all analysis of China’s AI innovation.

The world needs to engage seriously with China’s AI development and take a closer look at what’s really going on. The story is complex and it’s important to highlight where China is making promising advances in useful AI applications and to challenge common misconceptions, as well as to caution against problematic uses.

The best way to prevent this is by focusing on the basics. America needs a major all-of-society push to increase the number of U.S. students being trained in both the fundamentals of math and in the more advanced, rigorous, and creative mathematics. Leadership in implementing this effort will have to come from the U.S. government and leading technology companies, and through the funding of ambitious programs. A few ideas come to mind: talent-spotting schemes, the establishment of math centers, and a modern successor to the post-Sputnik National Defense Education Act, which would provide math scholarships to promising students along with guaranteed employment in either public or private enterprises.

Forget about “AI” itself: it’s all about the math, and America is failing to train enough citizens in the right kinds of mathematics to remain dominant.

By Michael Auslin

Continue reading “Why China’s Race For AI Dominance Depends On Math” »

China is even developing a satellite-based laser surveillance system aimed at detecting vessels submerged as deep as five hundred meters.

By Sebastien Roblin

Here’s What You Need To Remember: Time will tell which, if any, of these technologies can be developed into practical operational systems.

Over the past decade or so, researchers have been trying to develop techniques that could enable effective collaborative strategies among teams of robots. One of the tasks that teams of robots could complete better than individual robots is simultaneously searching for several targets or objects in their surrounding environment.

The ability of a team of robots to collectively seek and identify numerous targets at once could be useful for a wide range of applications. For instance, it could aid surveillance applications and help to better track individuals or vehicles.

Researchers at Tongji University and University of Stuttgart have recently devised a systematic framework for enabling more effective multiple target search in swarm robots. This framework, presented in a paper published in IEEE Access, is based on the use of a mechanical particle swarm optimization method and artificial potential fields.

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.