Musk’s latest compensation windfall, which must be certified by Tesla’s board, comes days after he offered to buy Twitter for $43 billion, with analysts suggesting he could sell Tesla shares to help finance the deal.
Musk already is the world’s richest person, according to Forbes.
Tesla reported quarterly revenue of $18.76 billion and so-called adjusted earnings before interest, taxes, depreciation and amortization (EBITDA) of $5.02 billion. Combined with the previous three quarters’ results, that surpasses milestones that trigger the vesting of the ninth through 11th of 12 tranches of options granted to Musk in his 2018 pay package.
Labeling data can be a chore. It’s the main source of sustenance for computer-vision models; without it, they’d have a lot of difficulty identifying objects, people, and other important image characteristics. Yet producing just an hour of tagged and labeled data can take a whopping 800 hours of human time. Our high-fidelity understanding of the world develops as machines can better perceive and interact with our surroundings. But they need more help.
Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Microsoft, and Cornell University have attempted to solve this problem plaguing vision models by creating “STEGO,” an algorithm that can jointly discover and segment objects without any human labels at all, down to the pixel.
STEGO learns something called “semantic segmentation”—fancy speak for the process of assigning a label to every pixel in an image. Semantic segmentation is an important skill for today’s computer-vision systems because images can be cluttered with objects. Even more challenging is that these objects don’t always fit into literal boxes; algorithms tend to work better for discrete “things” like people and cars as opposed to “stuff” like vegetation, sky, and mashed potatoes. A previous system might simply perceive a nuanced scene of a dog playing in the park as just a dog, but by assigning every pixel of the image a label, STEGO can break the image into its main ingredients: a dog, sky, grass, and its owner.
Imagine growing crops with 95% less water, or producing meat through methods that free up 80% of the world’s agricultural land. And how about eliminating the CO2 of global supply chains by simply moving production facilities closer to customers and cutting the parts used in the final product a hundredfold? What might sound like crazy ideas are solutions available today through green technologies.
Green tech describes the technology and science-based solutions that mitigate the negative human impact on the environment in a broad range of fields from agriculture to construction. Sixteen per cent of global emissions are caused by transportation, 19% by agriculture, 27% by energy production, 31% by construction and production, with the remaining 7% caused by heating. Green technologies can be applied in all of these CO2-emitting sectors, thus offering broad solutions for sustainable growth.
10 people will take the better part of a year to port a new technology library. Now we can do it with a couple of GPUs running for a few days.
Nvidia has been quick to hop on the artificial intelligence bus一with many of its consumer facing technologies, such as Deep Learning Super Sampling (DLSS) and AI-accelerated denoising exemplifying that. However, it has also found many uses for AI in its silicon development process and, as Nvidia’s chief scientist Bill Dally said in a GTC conference, even designing new hardware.
Dally outlines a few use cases for AI in its own development process of the latest and greatest graphic cards (among other things), as noted by HPC Wire.
Countries, especially potential exporters, should improve hydrogen statistics to justify and promote investments in hydrogen. The measures should result from broad cooperation, also intended to standardize and homogenize measurements, said Columbia University‘s Anne-Sophie Corbeau. “Countries could start working together to determine how best to collect hydrogen data, both on the demand and production sides, and include existing consumption as well as potential future consumption in new sectors. Statistics on the demand side need to anticipate new uses in buildings, industry, transport, and power, as well as account for hydrogen’s potential use to produce other energy products such as ammonia and methanol,” the French scholar wrote on Monday.
Vancouver-based First Hydrogen has identified four industrial sites in the United Kingdom and is advancing discussions with landowners to secure land rights to develop green hydrogen production projects. It said it would be working with engineering consultants Ove Arup & Partners Limited (ARUP) for engineering studies and designs. “The sites are all in prime industrial areas spread strategically across the North and South of the United Kingdom and will each accommodate both a large refueling station — for light, medium and heavy commercial vehicles with on-site hydrogen production, to serve the urban areas of Greater Liverpool, Greater Manchester, the London area, and the Thames Estuary — and a larger hydrogen production site of between 20 and 40 MW, for a total for the four sites of between 80 MW and 160 MW,” First Hydrogen wrote on Monday. The Canadian company wants to use the production facilities to serve customers of its automotive division. “First Hydrogen’s green hydrogen van is to begin demonstrator testing in June with final delivery for road use in September 2022.”
