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A nice Tesla Video. Hope it’s not censored.

#TeslaFans #teslanews #teslamotorfans #gigaberlin.
Please Subcribe my channel: http://bit.ly/3clkTm6
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Tesla Giga Shanghai Production Speed 38 Seconds, Changes Everything!
Huge thank to:
CCTV https://www.youtube.com/c/cctv.
wu wa https://www.youtube.com/c/%E7%83%8F%E7%93%A6
Jason Yang https://www.youtube.com/c/JasonYang.
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Tesla’s Giga Shanghai is a trump card for the EV revolution of Tesla.
Recently, Elon Musk revealed how fast Giga Shanghai could produce a car in just 38 seconds.
So how did that change everything?
The first video includes a ten-minute and five-minute segment of the Model Y leaving the workshop.
And during the 10-minute segment, 16 new cars were completed, which would be 38 seconds per car on average.
7 cars were completed in the following 5-minute segment, translating to an average of 44 seconds per car.

We will not count it to double-check, but the peak rate may be amazingly high. Of course, not necessarily constant and simultaneous for both models.
Based on production and statistics, we can completely believe in the super-fast production speed of Giga Shanghai.

This is the carmaker’s first Gigafactory outside the United States, with an industrial chain localization rate of more than 95 percent and 99.99 percent of the employees being Chinese.
Especially, Giga Shanghai’s production and sales volume reached a new record.
This $2 billion US factory produced nearly 300,000 cars in the first half of this year.
Tesla delivered a record high of 77,938 vehicles in China in June, up 177 percent yearly.
It’s a big step for Tesla though it has faced unprecedented delays in vehicle production due to pandemic-related supply chain disruptions. Giga Shanghai was shut down for over three weeks in April, and no vehicles were exported to other territories.

Tesla says its Giga Shanghai is now the most productive EV factory in the world. It saw a projected annual capacity increase from just 450,000 vehicles to a whopping 750,000 from Q1 to Q2 2022.

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Daniel Lidar, the Viterbi Professor of Engineering at USC and Director of the USC Center for Quantum Information Science & Technology, and first author Dr. Bibek Pokharel, a Research Scientist at IBM Quantum, achieved this quantum speedup advantage in the context of a “bitstring guessing game.”

By effectively mitigating the errors often encountered at this level, they have successfully managed bitstrings of up to 26 bits long, significantly larger than previously possible. (For context, a bit refers to a binary number that can either be a zero or a one).

Quantum computers promise to solve certain problems with an advantage that increases as the problems increase in complexity. However, they are also highly prone to errors, or noise. The challenge, says Lidar, is “to obtain an advantage in the real world where today’s quantum computers are still ‘noisy.’”.

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Join top executives in San Francisco on July 11–12, to hear how leaders are integrating and optimizing AI investments for success. Learn More

The skies above where I reside near New York City were noticeably apocalyptic last week. But to some in Silicon Valley, the fact that we wimpy East Coasters were dealing with a sepia hue and a scent profile that mixed cigar bar, campfire and old-school happy hour was nothing to worry about. After all, it is AI, not climate change, that appears to be top of mind to this cohort, who believe future superintelligence is either going to kill us all, save us all, or almost kill us all if we don’t save ourselves first.

Whether they predict the “existential risks” of runaway AGI that could lead to human “extinction” or foretell an AI-powered utopia, this group seems to have equally strong, fixed opinions (for now, anyway — perhaps they are “loosely held”) that easily tip into biblical prophet territory.

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Finally, stories born from paranoia teach you to see A.I. as the ultimate surveillance tool, watching your every eye moment and jiggle of your mouse. But what if it’s used instead to catch you doing things well, and to foster trust between managers and employees?

With the ability to compile reports of your accomplishments—or even assess their quality—A.I. can help managers better appreciate the output of their employees, rather than relying on quantified inputs, like time spent at your desk. It can watch out for deadlines and critical paths, automatically steering you toward the work that’s most urgent. And if you do fall behind on deadlines, A.I. can let your manager know: They don’t have to poke their nose in all the time just to catch the one time you fell behind. With A.I. helping everyone focus their attention to match intentions as they do their work, managers can instead spend their time investing in ways to support their team and grow individuals.

The way we work right now will soon look vestigial, a kind of social scaffolding in our journey to build something better. We know that A.I. will transform the future of work. Will the future edifices of our labor be austere, brutalist towers that callously process resources? Or will they be beautiful, intricate monuments to growth and thriving?

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