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In the biggest news of all, Rivian and Volkswagen announced a $5 billion joint venture that will co-develop core parts of the hardware and software platform to be used in cars from both automakers.
We love that because it aligns so beautifully with our mission: the ability to help accelerate putting highly compelling electric vehicles into the market, which will ultimately drive more demand.
A core objective of how we’ve structured the joint venture is that we don’t lose the velocity and the speed and the decisiveness and lack of bureaucracy that exists within our software function today.
Beyond just simplification of how we manage running over-the-air updates across so many different instances, it also gets us a lot of supply chain leverage in a way that we, Rivian, haven’t had in the past.
In fact, you can imagine the day of the announcement, I had a handful of phone calls from CEOs of big semiconductor suppliers, and they’re like, “Hey, we can work harder on pricing.” So, that was awesome.
So, taking away all those mechanical design studio packaging constraints that we had before, and then solving the biggest challenge, which was network architecture by this being that as a project, it’s just a very different type of relationship.
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Researchers at the University of Hull recently unveiled a novel method for detecting AI-generated deepfake images by analyzing reflections in human eyes.
Adejumoke Owolabi, an MSc student at the University of Hull, headed the research under the guidance of Dr. Kevin Pimbblet, professor of astrophysics.
In some ways, the astronomy angle isn’t always necessary for this kind of deepfake detection because a quick glance at a pair of eyes in a photo can reveal reflection inconsistencies, which is something artists who paint portraits have to keep in mind.
They used the Gini coefficient, typically employed to measure light distribution in galaxy images, to assess the uniformity of reflections across eye pixels.
The approach also risks producing false positives, as even authentic photos can sometimes exhibit inconsistent eye reflections due to varied lighting conditions or post-processing techniques.
But analyzing eye reflections may still be a useful tool in a larger deepfake detection toolset that also considers other factors such as hair texture, anatomy, skin details, and background consistency.
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