If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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.
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.
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.
It looks like a futuristic racing craft from a video game popular more than 25 years ago. But this insect-like flying car should soon be a reality. French company Maca says it plans to test its eco-friendly £665,000 hydrogen-powered'carcopter' on racetracks this year. It will have a top speed of 155mph, meaning a pilot onboard the 23ft craft can give Formula One stars a run for their money. But unlike the gas-guzzling machines driven by Sir Lewis Hamilton and co, this vehicle does not create any CO2 emissions and is fully recyclable.
The Formula 1 (F1) live steaming service, F1 TV, has live automated closed captions in three different languages: English, Spanish, and French. For the 2021 season, FORMULA 1 has achieved another technological breakthrough, building a fully automated workflow to create closed captions in three languages and broadcasting to 85 territories using Amazon Transcribe. Amazon Transcribe is an automatic speech recognition (ASR) service that allows you to generate audio transcription. In this post, we share how Formula 1 joined forces with the AWS Professional Services team to make it happen. We discuss how they used Amazon Transcribe and its custom vocabulary feature as well as custom-built postprocessing logic to improve their live transcription accuracy in three languages.