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AI helps cyclists work out how much to eat during the Tour de France

New Scientist

Elite cyclists are using artificial intelligence to get precise estimates of how many calories they will need for each stage of endurance races such as the Tour de France. Riders burn around 6000 calories a day during the Tour and food is so important to success that most teams employ numerous chefs and nutritionists.

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  Industry: Leisure & Entertainment > Sports > Cycling (0.82)

Tour of Real-World Machine Learning Problems

#artificialintelligence

Real-world examples make the abstract description of machine learning become concrete. In this post you will go on a tour of real world machine learning problems. You will see how machine learning can actually be used in fields like education, science, technology and medicine. Each machine learning problem listed also includes a link to the publicly available dataset. This means that if a particular concrete machine learning problem interest you, you can download the dataset and start practicing immediately.


Tour de France: Rembrandt, Vermeer, van Gogh, artificial intelligence inspire Jumbo-Visma kit

#artificialintelligence

Get access to everything we publish when you join VeloNews or Outside . Team Jumbo-Visma and its race clothing supplier AGU have created a limited-edition kit that will be worn during both the Tour de France and Tour de France Femmes. The new design was required because the team's typical yellow and black design is considered too similar to the maillot jaune worn by the race leader of the Tour de France. Last year the team created a bespoke kit for the Tour de France and the squad has followed suit for 2022. The Dutch team collaborated with AGU with the squad stating that its inspiration stemmed from Dutch artists Rembrandt Harmenszoon van Rijn, Johannes Vermeer, and Vincent van Gogh.


Last-Minute Travel Application

AI Magazine

It is impossible for a travel agent to keep track of all the offered tour packages. Traditional database-driven applications, as used by most of the tour operators, are not sufficient enough to implement a sales process with consultation on the World Wide Web. The last-minute travel application presented here uses case-based reasoning to bridge this gap and simulate the sales assistance of a human travel agent. A case retrieval net, as an internal data structure, proved to be efficient in handling the large amount of data. A usual tour package contains the flight to the destination and back, transfers from the airport to the hotel and back, board, and lodging.