Formula 1 broadcast footage from the Dutch Grand Prix qualifiers at Circuit Zandvoort this weekend that was captured by a gyro camera system mounted above the helmet of the weekend’s race winner, Lando Norris.

The camera, which F1 has been testing since 2022, was conceived with an eye particularly toward accentuating exaggerated angles of banked turns at Zandvoort, but despite using it there last year, F1 wasn’t able to broadcast it, according to AutoSport.

Dino Leone, Formula 1’s head of onboard camera operations, explained in a recent video that the camera allows for on-the-fly adjustments to increase or decrease the gyroscopic effect. Formula 1 used the camera in some 2023 races, such as in Brazil and Japan, and plans to use it more often this year.

If you’ve ever found yourself without a partner for a game of pingpong, you might be excited to hear that technology has come to the rescue. Imagine having a robot that can rally with you, challenge your skills and help you improve your game — all without needing a human opponent.

This is no longer a pipe dream. Thanks to advancements in robotics, it’s becoming a reality. 

Google’s DeepMind Robotics team has developed a table tennis robot that not only competes but also learns and adapts, making it an interesting player in the world of sports technology.

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DeepMind’s table tennis robot

Google’s DeepMind Robotics team recently made headlines with their development of a table tennis robot capable of playing at a “solidly amateur human level.” In their paper titled “Achieving Human Level Competitive Robot Table Tennis,” the researchers describe their robot’s performance against human opponents.

The robot managed to win all matches against beginner players and secured victory in 55% of games against intermediate players. However, it struggled against advanced players, losing every match. Overall, the robot won 45% of the 29 games it played.

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Table tennis robot playing a game of pingpong with a person. (DeepMind Robotics)

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How the table tennis robot works

The table tennis robot is built with a clever system that helps it decide which moves to make during a game. Think of it as having two levels of decision-making: the low-level controllers and the high-level controllers. The low-level controllers handle specific skills like forehand topspin or backhand targeting, similar to how a player might practice different shots. Meanwhile, the high-level controller acts like a coach, choosing which skill to use based on the situation in the game and what it knows about its opponent.

One of the coolest things about this robot is its ability to adapt in real time. It keeps track of how well each skill works and adjusts its strategy on the fly. As it plays, it learns more about its opponent’s strengths and weaknesses, allowing it to refine its approach and become a better competitor.

Before hitting the real table, the robot trains in a virtual world. This is where it practices and learns without any risk. Once it’s ready, it transitions smoothly to playing real matches. This process of moving from simulation to reality helps it improve its skills over time, making it more effective with each game.

The robot’s performance is constantly evaluated through matches against human players. Feedback from these games helps it get even better. This advanced system not only makes the robot a tough opponent but also a fun and engaging practice partner for players of all levels.

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Table tennis robot playing a game of pingpong with a person. (DeepMind Robotics)

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Challenges for the table tennis robot

Despite its successes, the robot faces significant challenges, particularly in reacting to fastballs. DeepMind attributes these difficulties to system latency, mandatory resets between shots and insufficient data.

To overcome these hurdles, the researchers are exploring advanced control algorithms and hardware optimizations, such as predictive models for ball trajectories and faster communication protocols between sensors and actuators.

Table tennis robot playing a game of ping-ong with a person. (DeepMind Robotics)

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Beyond table tennis

The implications of this research extend beyond the confines of table tennis. DeepMind highlights the potential of their robot’s policy architecture, simulation use, and real-time strategy adaptation to influence robotics in broader contexts. The ultimate goal is to achieve human-level performance in various real-world tasks, paving the way for robots capable of interacting safely and skillfully with humans.

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Table tennis robot playing a game of pingpong with a person. (DeepMind Robotics)

Kurt’s key takeaways

DeepMind’s table tennis robot demonstrates the potential for machines to engage in complex human activities. While there are still challenges to address, such as improving reaction times and handling diverse ball spins, the progress made so far is promising. As technology advances, we can expect robots to become even more adept at performing a wide range of tasks, making them valuable companions in both recreational and practical settings in our lives.

Would you feel comfortable playing a game of table tennis against a robot that learns and adapts to your moves, or does the idea of competing against AI in sports turn you off? Let us know by writing us at Cyberguy.com/Contact

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So, all those faked review quotes from the Megalopolis trailer that got pulled last week were apparently AI, after all. Not only that, but the person who was in charge of the materials for the trailer, Eddie Egan, has been removed from the movie’s marketing team.

That’s all according to Deadline, which reported on Friday that the AI-ness of it all was confirmed in an investigation after the fact. Neither Egan nor Megalopolis studio Lionsgate meant to fake the quotes, which had purported to be critical notes from contemporaneous reviews of Megalopolis director Francis Ford Coppola’s past films.

According to some of the trailer’s quotes, critics had called The Godfather a “sloppy, self-indulgent movie,” while Apocalypse Now was “an epic piece of trash.” But as Vulture noted when it first reported on the quotes’ spuriousness, the actual reviews the quotes were attributed to not only didn’t say those things, but often were actually written in praise of the movies.

It’s easy to see how this could have happened. AI’s casual, confident delivery of falsehoods is well documented at this point; even the companies that make AI products end up with factual errors in their own presentations.