Athletes are increasingly spending their free time on Twitch, streaming to thousands of fans as they play games. Among NBA players, De'Aaron Fox of the Scramento Kings and Josh Hart of the New Orleans Pelicans both have their own Twitch channels. Lando Norris, a Formula One driver for McLaren Racing, has more than 1.3 million followers on Twitch.
It looked like another battle was shaping up Sunday in Barcelona with Charles Leclerc on Pole and leading early with Max Verstappen P2 and chasing hard. Then on Lap 27 everything changed as Leclerc's Ferrari lost power and he was forced to retire with a DNF in 20th place. Verstappen went on to win with teammate Sergio Perez finishing P2 and earning the Fastest Lap point. The win vaults Verstappen to the F1 Drivers Points lead and to the top of our F1 GFT AI Driver Rankings for Round 6. How do the Go Full Throttle AI models work? Algorithms The Go Full Throttle AI Driver Rankings is a cloud based predictive analytics system that uses our proprietary algorithms utilizing artificial intelligence and machine learning technology to dynamically tune and improve accuracy over time.
April 29, 2022In 2015, McKinsey acquired QuantumBlack, a sophisticated analytics start-up of more than 30 data scientists, data engineers, and designers based in London. They had made their name in Formula 1 racing, applying data science to help teams gain every possible advantage in performance. Healthcare, transportation, energy, and other industry clients soon followed. Many times, acquisitions melt quietly into the parent company. This isn't the case for QuantumBlack; it has been an accelerating force for our work in analytics.
Campbell is a journalist for ZDNet, covering technology's impact across the gamut of government, law, and regulation. Charles Leclerc of Ferrari, Max Verstappen of Oracle Red Bull Racing, Sergio Perez of Oracle Red Bull Racing, and Lando Norris of McLaren wait on the grid prior to the F1 Grand Prix of Australia at Melbourne Grand Prix Circuit. In the latest Formula One (F1) season, racing teams have been slapped with a drop in the budget cap, from $145 million to $140 million per team. The dip in approved expenditure has meant individual teams must place more emphasis on cost efficiency and resource management than ever before. By that same token, the technology used by F1 racing teams also carries more importance than in previous seasons. Each team approaches this cost cap challenge differently, but here are some of the ways F1 racing teams are using technology to shave off dollars this season, from the factory all the way to the race track.
Clement Delangue, the co-founder and CEO of Hugging Face, has said huge ML models are to machine learning what formula 1 is to the car industry. He laid out his case in a series of tweets: First, like formula 1, it's obviously good PR and branding and very much driven by ego; Second, the resulting models are too costly, unusable and dangerous to use in real life just like you wouldn't drive a Formula 1 car to go to work; however, it's useful in the sense that by pushing everything to the extreme, you learn a ton! To me, huge ML models are to machine learning what formula 1 is to the car industry! Ironically, Delangue's bold statement was another PR stunt. He plugged the BigScience Research Workshop (a gathering of 1,000 researchers around the world.
Amazon Web Services (AWS) and Maple Leaf Sports & Entertainment (MLSE) announced a new deal that will see AWS provide technology services to notable Canadian sports franchises like the Toronto Maple Leafs, Toronto Raptors, Toronto Football Club (FC), and Toronto Argonauts. MLSE said it plans to use AWS' portfolio of cloud capabilities -- including machine learning, advanced analytics, compute, database, and storage services -- to support how its teams play; how players stay healthy; how fans connect with each other and experience games; and how sports franchises operate internally. MLSE added that it aims to offer its teams new AWS-powered insights to further improve the caliber of gameplay and develop new technology for sports fans. Humza Teherany, chief technology and digital officer at MLSE, said the company built its Digital Labs program to create solutions and products that drive the evolution of sports and elevate the fan experience. "We aim to offer new ways for fans to connect digitally with their favorite teams while also seeking to uncover digital sports performance opportunities in collaboration with our front offices. With AWS's advanced machine learning and analytics services, we can use data with our teams to help inform areas such as: team selection, training and strategy to deliver an even higher caliber of competition," Teherany said.
For Formula 1 fans it could be the ultimate way to see what it's really like to drive at the highest level. A UK company has built a simulator which uses fighter jet technology to help create the sensation of G-Force experienced by the likes of Lewis Hamilton, Max Verstappen and George Russell. The simulator is sure to give users a serious upper body workout, particularly so when it comes to the neck muscles, but any fans interested will have to splash out £100,000 ($135,450) to experience the full system for themselves. A stripped-down version, called the GFQ Simulator, is priced at £16,400 ($22,234). The F1-style bit of kit was made by Axsim, a sub-brand of a company called Cranfield Simulation, which is itself a subsidiary of UK-based Cranfield Aerospace Solutions.
Wischnewski, Alexander, Geisslinger, Maximilian, Betz, Johannes, Betz, Tobias, Fent, Felix, Heilmeier, Alexander, Hermansdorfer, Leonhard, Herrmann, Thomas, Huch, Sebastian, Karle, Phillip, Nobis, Felix, Ögretmen, Levent, Rowold, Matthias, Sauerbeck, Florian, Stahl, Tim, Trauth, Rainer, Lienkamp, Markus, Lohmann, Boris
Motorsport has always been an enabler for technological advancement, and the same applies to the autonomous driving industry. The team TUM Auton-omous Motorsports will participate in the Indy Autonomous Challenge in Octo-ber 2021 to benchmark its self-driving software-stack by racing one out of ten autonomous Dallara AV-21 racecars at the Indianapolis Motor Speedway. The first part of this paper explains the reasons for entering an autonomous vehicle race from an academic perspective: It allows focusing on several edge cases en-countered by autonomous vehicles, such as challenging evasion maneuvers and unstructured scenarios. At the same time, it is inherently safe due to the motor-sport related track safety precautions. It is therefore an ideal testing ground for the development of autonomous driving algorithms capable of mastering the most challenging and rare situations. In addition, we provide insight into our soft-ware development workflow and present our Hardware-in-the-Loop simulation setup. It is capable of running simulations of up to eight autonomous vehicles in real time. The second part of the paper gives a high-level overview of the soft-ware architecture and covers our development priorities in building a high-per-formance autonomous racing software: maximum sensor detection range, relia-ble handling of multi-vehicle situations, as well as reliable motion control under uncertainty.
This paper presents TableQuery, a novel tool for querying tabular data using deep learning models pre-trained to answer questions on free text. Existing deep learning methods for question answering on tabular data have various limitations, such as having to feed the entire table as input into a neural network model, making them unsuitable for most real-world applications. Since real-world data might contain millions of rows, it may not entirely fit into the memory. Moreover, data could be stored in live databases, which are updated in real-time, and it is impractical to serialize an entire database to a neural network-friendly format each time it is updated. In TableQuery, we use deep learning models pre-trained for question answering on free text to convert natural language queries to structured queries, which can be run against a database or a spreadsheet. This method eliminates the need for fitting the entire data into memory as well as serializing databases. Furthermore, deep learning models pre-trained for question answering on free text are readily available on platforms such as HuggingFace Model Hub (7). TableQuery does not require re-training; when a newly trained model for question answering with better performance is available, it can replace the existing model in TableQuery.