To efficiently navigate their surrounding environments and complete missions, unmanned aerial systems (UASs) should be able to detect multiple objects in their surroundings and track their movements over time. So far, however, enabling multi-object tracking in unmanned aerial vehicles has proved to be fairly challenging.
To efficiently navigate their surrounding environments and complete missions, unmanned aerial systems (UASs) should be able to detect multiple objects in their surroundings and track their movements over time. So far, however, enabling multi-object tracking in unmanned aerial vehicles has proved to be fairly challenging.
Researchers at Lockheed Martin AI Center have recently developed a new deep learning technique that could allow UASs to track multiple objects in their surroundings. Their technique, presented in a paper pre-published on arXiv, could aid the development of better performing and more responsive autonomous flying systems.
“We present a robust object tracking architecture aimed to accommodate for the noise in real-time situations,” the researchers wrote in their paper. “We propose a kinematic prediction model, called deep extended Kalman filter (DeepEKF), in which a sequence-to-sequence architecture is used to predict entity trajectories in latent space.”
Founders tend to think responsible AI practices are challenging to implement and may slow the progress of their business. They often jump to mature examples like Salesforce’s Office of Ethical and Humane Use and think that the only way to avoid creating a harmful product is building a big team. The truth is much simpler.
I set out to learn how founders were thinking about responsible AI practices on the ground by speaking with a handful of successful early-stage founders and found many of them were implementing responsible AI practices.
Only they didn’t call it that. They just call it “good business.”
Facebook’s latest long-term research project, Ego4D, focuses on developing AI with an ‘egocentric,’ first-person perspective.
Even robots from down under are going to the moon.
Australia is kicking off its first-ever mission to the moon, investing $50 million to build an operational lunar rover as a part of NASA’s Artemis project, according to a recent post on the nation’s website.
While NASA will ultimately fly the rover to the moon, it could touch down as early as 2026.
In 2009—four years after it was published—I read Ray Kurzweil’s The Singularity Is Near. It is an optimistic view of the future—a future that depends on computational technology. A future of superintelligent machines. It is also a future where humans will transcend our present biological limits.
I had to read the book twice—once for the sense and once for the detail.
After that, just for my own interest, year-in, year-out, I started to track this future; that meant a weekly read through New Scientist, Wired, the excellent technology pieces in the New York Times and the Atlantic, as well as following the money via the Economist and Financial Times. I picked up any new science and tech books that came out, but it wasn’t enough for me. I felt I wasn’t seeing the bigger picture.
AI paints a Picasso nude, just like Pablo did.
“Lost” Picasso nude comes out of hiding, thanks to artificial intelligence and 3D printing.
As technology rapidly progresses, some proponents of artificial intelligence believe that it will help solve complex social challenges and offer immortality via virtual humans.
But AI’s critics are sounding the alarm, going so far as to call its development an “existential threat” to mankind. Is this the stuff of science fiction? Could the “Terminator” become reality, or will these fears prevent the next technological revolution?
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