Lifeboat Foundation

Meta is promising to roll out auto-labeling for AI-generated images — as soon as it figures out how, that is.

Nick Clegg, Meta’s president of global affairs, said in a policy update that the company is currently working with “industry partners” to formulate criteria that will help identify AI content. Once those criteria are determined, Meta will begin automatically labeling posts featuring any AI-generated images, video, or audio “in the coming months.”

“This approach represents the cutting edge of what’s technically possible right now. But it’s not yet possible to identify all AI-generated content, and there are ways that people can strip out invisible markers,” Clegg wrote. “So we’re pursuing a range of options. We’re working hard to develop classifiers that can help us to automatically detect AI-generated content, even if the content lacks invisible markers.”

Aside from things like special tread design and the use of foam, EV tires and regular tires are nearly identical.

Don’t fret, 911 lovers: this is probably good news.

With a 400-mile range and solar capabilities, Aptera’s solar EVs are setting the stage for sustainability and accessibility.

Discover how Aptera Motors achieved $33 million through its community-led Accelerator Program to fuel production for its solar electric vehicle.

Ziyan’s Shadow S3 sets new standards with a sub-7kg take-off weight, exceptional wind resistance, and an impressive 100-minute endurance.

Researchers have not only built an AI child, but are now training AI using headcam footage from a human baby as well.

In a press release, New York University announced that its data science researchers had strapped a camera to the head of a real, live, human toddler for 18 months to see how much an AI model could learn from it.

Most large language models (LLMs), like OpenAI’s GPT-4 and its competitors, are trained on “astronomical amounts of language input” that are many times larger than what infants receive when learning to speak a language during the first years of their lives.

Californian students from the Art Center College had to imagine four concepts of cars of the future for Lincoln, by 2040. One of them, a four-seater sedan, was entitled to the realization of its 1:1 scale model, presented during Monterey Car Week, completed this weekend. Connected, autonomous, shared (“shared”), and electric, as suggested by the acronym “CASE” used by Ford’s luxury brand, which is found in this sedan called “Anniversary”

The Liberty Lifter will have the cargo volume of a C-17 Globemaster III transport aircraft, yet carry up to 100 tons of cargo.

Aurora Flight Sciences, reveals the latest updates with its Liberty Lifter seaplane aimed to transform seaborne heavy-lift transportation.

Facial recognition is a technology that can identify or verify a person’s identity based on their face. It can be used for various purposes, such as unlocking smartphones, verifying identities at airports, or finding missing persons. However, facial recognition also seriously threatens personal privacy, as it can be used to track, monitor, or profile people without their consent or knowledge. For example, some governments or companies may use facial recognition to spy on citizens, customers, or competitors or to collect and sell their data.

How can we prevent facial recognition from invading our privacy?

To protect our facial privacy, some researchers have proposed different methods to prevent facial recognition from working. These methods, collectively called anti-facial recognition (AFR), aim to hide, distort, or replace the faces in images or videos. For instance, some AFR methods use masks, makeup, glasses, or hats to cover or alter facial features. Other AFR methods use software to blur, pixelate, or swap the faces in digital media.