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### You probably know someone who just got into Formula One

De Rochefort is one of those F1 initiates eager to integrate her latest fascination into her lifelong gaming hobby. That being said, what she likes most about the sport -- the characters, the grudges, the meta-narratives surrounding every hairpin turn -- is not easily replicated in a racing series. She's more excited about the forthcoming "F1 Manager 2022" from Frontier Games, a spiritual sequel to 2000′s "F1 Manager" and the first officially licensed F1 management simulation to come out in over 20 years. It's a game that allows players to fine-tune their rosters of drivers, scientists and engineers between each season -- perhaps poaching a pitman from a cross-country automotive adversary. You know, the sort of chicanery that's ripe for a "Drive To Survive" arc.

### Metric-Distortion Bounds under Limited Information

In this work, we study the metric distortion problem in voting theory under a limited amount of ordinal information. Our primary contribution is threefold. First, we consider mechanisms that perform a sequence of pairwise comparisons between candidates. We show that a popular deterministic mechanism employed in many knockout phases yields distortion O(log m) while eliciting only m − 1 out of the Θ(m2 ) possible pairwise comparisons, where m represents the number of candidates. Our analysis for this mechanism leverages a powerful technical lemma developed by Kempe (AAAI ‘20). We also provide a matching lower bound on its distortion. In contrast, we prove that any mechanism which performs fewer than m−1 pairwise comparisons is destined to have unbounded distortion. Moreover, we study the power of deterministic mechanisms under incomplete rankings. Most notably, when agents provide their k-top preferences we show an upper bound of 6m/k + 1 on the distortion, for any k ∈ {1, 2, . . . , m}. Thus, we substantially improve over the previous bound of 12m/k established by Kempe (AAAI ‘20), and we come closer to matching the best-known lower bound. Finally, we are concerned with the sample complexity required to ensure near-optimal distortion with high probability. Our main contribution is to show that a random sample of Θ(m/ϵ2 ) voters suffices to guarantee distortion 3 + ϵ with high probability, for any sufficiently small ϵ > 0. This result is based on analyzing the sensitivity of the deterministic mechanism introduced by Gkatzelis, Halpern, and Shah (FOCS ‘20). Importantly, all of our sample-complexity bounds are distribution-independent. From an experimental standpoint, we present several empirical findings on real-life voting applications, comparing the scoring systems employed in practice with a mechanism explicitly minimizing (metric) distortion. Interestingly, for our case studies, we find that the winner in the actual competition is typically the candidate who minimizes the distortion.

### Artificial Intelligence: How Formula One's McLaren team is using AI to fuel performance

'These bits of data shape a driver and can make him either a very good driver, a good driver, or a bad driver--it's just how you use it.'

### How Formula One's McLaren team is using A.I. to fuel performance

Twin Omicron subvariants have taken over the U.S., but they're not quite identical. One is'the worst version of the virus we've seen'

### Red Bull Racing suspends Juri Vips for use of racial slur on Twitch

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.

### Oracle LiveLabs: Learn Analytics and Machine Learning with Red Bull Racing workshop

Learn Analytics and Machine Learning with Red Bull Racing – Find the BEST race of all time!

### Round 6 F1 GFT AI Driver Rankings: Verstappen Wins in Spain, Now leads Leclerc

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.

### How Formula 1 teams are using tech to find an advantage in a lower budget cap season

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.

### Hugging Face CEO calls huge ML models Formula 1 of machine learning

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.

### Inside the £100,000 Axsim Formula Simulator that helps F1 drivers practise from home

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.