Lifeboat Foundation

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Working together as a consortium, FAIR or university partners captured these perspectives with the help of more than 800 skilled participants in the United States, Japan, Colombia, Singapore, India, and Canada. In December, the consortium will open source the data (including more than 1,400 hours of video) and annotations for novel benchmark tasks. Additional details about the datasets can be found in our technical paper. Next year, we plan to host a first public benchmark challenge and release baseline models for ego-exo understanding. Each university partner followed their own formal review processes to establish the standards for collection, management, informed consent, and a license agreement prescribing proper use. Each member also followed the Project Aria Community Research Guidelines. With this release, we aim to provide the tools the broader research community needs to explore ego-exo video, multimodal activity recognition, and beyond.

How Ego-Exo4D works.

Ego-Exo4D focuses on skilled human activities, such as playing sports, music, cooking, dancing, and bike repair. Advances in AI understanding of human skill in video could facilitate many applications. For example, in future augmented reality (AR) systems, a person wearing smart glasses could quickly pick up new skills with a virtual AI coach that guides them through a how-to video; in robot learning, a robot watching people in its environment could acquire new dexterous manipulation skills with less physical experience; in social networks, new communities could form based on how people share their expertise and complementary skills in video.

Tesla CEO Elon Musk has shared some new details about the automaker’s plans for its next-generation, $25,000 electric vehicle (EV), including that it will be built first in Austin, Texas.

On Tuesday, longtime auto industry veteran Sandy Munro shared a new interview with Musk, held following the Cybertruck delivery event. During the interview, the two talked mostly about the Cybertruck, though they also discussed Tesla’s next-generation vehicle for a few minutes.

While Musk said he couldn’t share any details about unit volume and dates for the next-gen EV, due to them being suggestive of Tesla’s financials, he did go on to share a few things. For one, Musk said that Tesla was “quite far advanced” in working to develop the low-cost, high-volume EV, adding that he reviews the production line plans for that on a weekly basis.

Elon Musk commented on the upcoming low-cost Tesla electric car, saying that it is “advanced” in its development. The CEO also commented on the “revolutionary” manufacturing advancements that Tesla is making to make the vehicle a reality.

While the cost of Tesla vehicles has come down recently, they are still not financially accessible to most people – like most new cars.

The launch of the Cybertruck, which is about 50% more expensive than when originally announced in 2019, is not helping Tesla’s vehicle price range in becoming more affordable.

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Continue reading “Billionaire Ron Baron talks Tesla: Cybertruck ‘Incredibly Cool’” »

It’s hard to believe that there’s a Tesla Model S out there with nearly 1.2 million miles on its odometer, but indeed there is. And here it is.

The car, a 2014 Model S P85, has racked up 1.18 million miles so far, which works out to approximately 131,000 miles per year. Could you imagine driving that much? And doing so in an electric car, which naysayers often state can’t go the distance. Well, we think this car proves that EVs can be driven a significant amount of miles per year and that the charging infrastructure, at least in some parts of the world, can support high-mileage drives.

Elon Musk hints at a low-cost electric vehicle in the works, made in high volume, with advanced production technology, and the progress in China is a significant factor in Tesla’s success.

Questions to inspire discussion.

Continue reading “Musk Comments on Next Gen Tesla, Cybertruck + China Sales Spring Ahead” »

On their way to building fully autonomous vehicles, self-driving car makers are facing a tall task: training their AIs to be able to respond reliably to any and all scenarios that a car, truck or bus might encounter as well as, or hopefully better, than a human would. Today, a startup with a platform to help with that challenge is announcing a sizeable round of funding to take those strategies up a gear.

Foretellix, which builds verification and validation solutions to test the full range of driver assistance and autonomous systems that are coming out on the market, has closed its Series C at $85 million. The round includes financial investors alongside strategic backers from the automotive and chip industries, a signal of who is already doing business with Foretellix, as well as the longer business trajectory for the startup.

The round is being led by Israeli VC 83North, with Singapore’s Temasek and carmaker Isuzu investing alongside Woven Capital (Toyota’s venture fund), Nvidia, Artofin and previous backers MoreTech, Nationwide, Volvo Group VC, Jump Capital, Next Gear Ventures and OurCrowd. Foretellix may ring a bell for readers: The first close of this round was in May of this year (at $43 million).

Languages are a key factor in human societies. They connect people, serve as a vehicle to pass on knowledge and ideas, but they also distinguish between different groups of people. Languages can therefore tell us a lot about the societies that use them. As languages are constantly changing, it is important to know the factors that play a role in this. Scientists can then reconstruct past processes on the basis of languages.

In a study published today (Dec. 5) in the online journal PNAS Nexus, Kiel linguist Dr. Søren Wichmann, together with colleagues from China, demonstrates that average ambient temperatures influence the loudness of certain speech sounds. “Generally speaking, languages in warmer regions are louder than those in colder regions,” says Dr. Wichmann.

The basic idea behind the study is that we are surrounded by air when we speak and listen. Spoken words are transmitted through the air as sound waves. The physical properties of air therefore influence how easy it is to produce and hear speech.

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Last week, a team of researchers from the University of California, Berkeley published a highly anticipated paper in the journal Nature describing an “autonomous laboratory” or “A-Lab” that aimed to use artificial intelligence (AI) and robotics to accelerate the discovery and synthesis of new materials.

Dubbed a “self-driving lab,” the A-Lab presented an ambitious vision of what an AI-powered system could achieve in scientific research when equipped with the latest techniques in computational modeling, machine learning (ML), automation and natural language processing.