TL;DR: The 2021 All-in-One Data Scientist Mega Bundle is on sale for £28.17 as of June 10, saving you 99% on list price. What do the Champions League, Netflix, and self-driving cars all have in common? How else would you receive recommendations on what to binge next? Or know that Lionel Messi is breaking records practically every time he steps on the pitch? Basically, our world runs on data.
A leading Chinese Internet company rolled out a fleet of fully driverless robot taxis on Sunday. Ten Apollo Go Robotaxis started picking up passengers in west Beijing, charging approximately $4.60 per ride. Developed by Baidu, they're the first paid autonomous taxis in China that don't have a safety driver behind the steering wheel. The vehicles are programmed with eight destinations in Shougang Park, home of the upcoming 2022 Winter Olympics. However, there is a remote operator on hand that can assume control of the vehicle in case of emergency.
In many robotic tasks, such as drone racing, the goal is to travel through a set of waypoints as fast as possible. A key challenge for this task is planning the minimum-time trajectory, which is typically solved by assuming perfect knowledge of the waypoints to pass in advance. The resulting solutions are either highly specialized for a single-track layout, or suboptimal due to simplifying assumptions about the platform dynamics. In this work, a new approach to minimum-time trajectory generation for quadrotors is presented. Leveraging deep reinforcement learning and relative gate observations, this approach can adaptively compute near-time-optimal trajectories for random track layouts. Our method exhibits a significant computational advantage over approaches based on trajectory optimization for non-trivial track configurations. The proposed approach is evaluated on a set of race tracks in simulation and the real world, achieving speeds of up to 17 m/s with a physical quadrotor.
Zhang, Daniel, Mishra, Saurabh, Brynjolfsson, Erik, Etchemendy, John, Ganguli, Deep, Grosz, Barbara, Lyons, Terah, Manyika, James, Niebles, Juan Carlos, Sellitto, Michael, Shoham, Yoav, Clark, Jack, Perrault, Raymond
Welcome to the fourth edition of the AI Index Report. This year we significantly expanded the amount of data available in the report, worked with a broader set of external organizations to calibrate our data, and deepened our connections with the Stanford Institute for Human-Centered Artificial Intelligence (HAI). The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. The report aims to be the most credible and authoritative source for data and insights about AI in the world.
The widespread development of driverless vehicles has led to the formation of autonomous racing competitions, where the high speeds and fierce rivalry in motorsport provide a testbed to accelerate technology development. A particular challenge for an autonomous vehicle is that of identifying a target trajectory - or in the case of a racing car, the ideal racing line. Many existing approaches to identifying the racing line are either not the time-optimal solutions, or have solution times which are computationally expensive, thus rendering them unsuitable for real-time application using on-board processing hardware. This paper describes a machine learning approach to generating an accurate prediction of the racing line in real-time on desktop processing hardware. The proposed algorithm is a dense feed-forward neural network, trained using a dataset comprising racing lines for a large number of circuits calculated via a traditional optimal control lap time simulation. The network is capable of predicting the racing line with a mean absolute error of +/-0.27m, meaning that the accuracy outperforms a human driver, and is comparable to other parts of the autonomous vehicle control system. The system generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal racing line. Results suggest that a data-driven approach may therefore be favourable for real-time generation of near-optimal racing lines than traditional computational methods.
This article is part of KrASIA's partnership with Web Summit. The last 12 months have seen decisive change in the way we spend our free time. Mobility solutions are becoming increasingly popular, with driverless vehicles popping up across the world, while our urban spaces are evolving into smart city projects. Web Summit's lifestyle content covers it all. What CNN calls "Europe's largest tech event" gathers experts from the industries that play vital roles in our lifestyles.
Roborace team SIT Acronis Autonomous suffered a "computer says no" moment on Thursday when its race car drove straight into a wall, mere seconds after it had started driving. If you're familiar with the Little Britain T.V. show, you'll understand the meaning of "computer says no." And it couldn't be more true for this moment. Luckily no one was hurt. But, you live and you learn, and this is one of the ways people working in robotics learn how to improve their systems.
Robots still have some trouble handling the basics when put to the test, apparently. Roborace team SIT Acronis Autonomous suffered an embarrassment in round one of the Season Beta 1.1 race after its self-driving car abruptly drove directly into a wall. It's not certain what led to the mishap, but track conditions clearly weren't at fault -- the car had been rounding a gentle curve and wasn't racing against others at the same time. It wasn't the only car to suffer a problem, either. Autonomous Racing Graz's vehicle had positioning issues that got it "lost" on the track and cut its race short.
Humans are good at figuring out the intricate physics of object-ice interactions that affect how giant stones slide across a frozen surface. Machines, however, can freeze up in the real world. Curly, a new curling-playing robot, has a better handle on those complexities, thanks to an artificially intelligent brain that can quickly assess and map the icy environment, the state of play and optimal strategies for winning, according to a paper published Wednesday in the journal Science Robotics by a team of roboticists at Korea University in Seoul. The white, turtle-shaped robot, recently beat out elite curling South Korean players in a series of four matches, losing only once, according to the study. Curly's triumph is the latest example of machines besting humans at their own games--but it marks an important step forward: Other big wins for the robots have been in digital environments, where the physics of the real world didn't get in the way.
One factor that could prevent a similar outcome in the upcoming race is the ability to test-run cars on a virtual racetrack. The simulation software company Ansys Inc. has already developed a model of the Indianapolis Motor Speedway on which teams will test their algorithms as part of a series of qualifying rounds. "We can create, with physics, multiple real-life scenarios that are reflective of the real world," Ansys President Ajei Gopal told The Wall Street Journal. "We can use that to train the AI, so it starts to come up to speed." Still, the race could reveal that self-driving cars aren't quite ready to race at speeds of over 110 mph.