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Category: robotics/AI

https://www.youtube.com/watch?v=YXyzgTvZUFw

Blue Origin is set to launch William Shatner on their second crewed spaceflight of its New Shepard rocket. Takeoff is currently scheduled on Wednesday, October 13 at 9:00 am CDT / 14:00 UTC from Corn Ranch, Texas.

New Shepard is designed to take people and payloads to suborbital space and back. It is expected to start sending space tourists this year. Ticket reservations are still on hold.

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There is no doubt that artificial intelligence (AI) is on the cusp of achieving significant disruption across several sectors in the world — one can simply look to companies like American company Alfi (NASDAQ: ALF) which is attempting to revolutionize the ad-tech industry with privacy-conscious AI —. It is becoming a key driver of productivity and gross domestic product growth for many nations and is pushing the boundaries of technology as we know it.

According to a report, the United States leads the AI pack today, with China in a close 2nd and the European Union in 3rd. Out of 100 total available points in the report’s scoring methodology, the United States leads with 44.2 points, China with 32.3, and the European Union with 23.5.

Although it may seem like the U.S. has an unassailable lead, the fact is that China is rapidly catching up and stands today as a full-spectrum peer competitor of the U.S. in many applications of AI.

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China’s national share of smart-computing power is 52%, compared to 19% in the U.S.

Recently, the China Academy of Information and Communications Technology (CAICT) released a white paper on the country’s computing power. According to the paper, which was translated by ChinAI, the country’s computing power reached 135 exaFlops (EFlops), an increase of 48 EFlops from last year. One EFlop is equivalent to the computing power of roughly two million laptops.

So, what’s the point in all this computing speed? China is accelerating its computing power for a faster AI adoption. It is evident in the way it prioritizes its resources for next-generation computing. Beijing divides its AI needs into basic-, smart-, and super-computing. Between 2016 and 2,020 the country dropped its basic-computing share to 57% from 95% and increased smart-computing to 41% from 3%.

And according to the paper, China’s national share of smart-computing power is 52%, compared to 19% in the U.S. While the statistics need to be taken with a pinch of salt, it sure does reveal something about the direction in which China is moving.

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Thanks to artificial intelligence, drones can now fly autonomously at remarkably high speeds, while navigating unpredictable, complex obstacles using only their onboard sensing and computation.

This feat was achieved by getting the drone’s neural network to learn flying by watching a sort of “simulated expert” – an algorithm that flew a computer-generated drone through a simulated environment full of complex obstacles. Now, this “expert” could not be used outside of simulation, but its data was used to teach the neural network how to predict the best trajectory, based only on the data from the sensors.

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SparkBeyond, a company that helps analysts use AI to generate new answers to business problems without requiring any code, today has released its product SparkBeyond Discovery.

The company aims to automate the job of a data scientist. Typically, a data scientist looking to solve a problem may be able to generate and test 10 or more hypotheses a day. With SparkBeyond’s machine, millions of hypotheses can be generated per minute from the data it leverages from the open web and a client’s internal data, the company says. Additionally, SparkBeyond explains its findings in natural language, so a no-code analyst can easily understand it.

The product is the culmination of work that started in 2013 when the company had the idea to build a machine to access the web and GitHub to find code and other building blocks to formulate new ideas for finding solutions to problems. To use SparkBeyond Discovery, all a client company needs to do is specify its domain and what exactly it wants to optimize.

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There is quite a bit of handwringing going on that AI-based true self-driving cars are going to be a monopoly. This is based on the assumption that only a few purveyors will be able to attain true self-driving cars. A tiny set of automakers or self-driving tech firms will hold all the cards when it comes to self-driving cars.

We are not there yet, since the invention of self-driving cars is still being figured out.

But, if you are thinking ahead, those purveyors might be the only entities able to field self-driving cars and therefore be able to charge monopoly rent, as it were. This could include charging sky-high prices for the use of self-driving cars.

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I had both Park Place and Boardwalk in my gleeful hands.

Yes, in the venerated game of Monopoly, you can be darned happy when you manage to acquire various keystone properties on the gameboard. By establishing a monopoly in Monopoly, you can take decisive actions that will allow you to charge immense rents. When other players land on your monopoly, or perhaps monopolies if you’ve finagled a bunch of them, you know that those unlucky players will pay through the nose for the privilege of being on your monopolistic property.

That’s just how the game is played.

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AI.Reverie offered APIs and a platform that procedurally generated fully annotated synthetic videos and images for AI systems. Synthetic data, which is often used in tandem with real-world data to develop and test AI algorithms, has come into vogue as companies embrace digital transformation during the pandemic. In a recent survey of executives, 89% of respondents said synthetic data will be essential to staying competitive. And according to Gartner, by 2,030 synthetic data will overshadow real data in AI models.

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Facebook has quietly acquired AI.Reverie, a New York-based startup creating synthetic data to train machine learning models, VentureBeat has learned.

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