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Claim: reinforcement learning AI beats humans in physical domain, playing the “labyrinth” game.

In six hours of training, it solves faster than any human.

Video is fun to watch.

We have created an AI robot named CyberRunner whose task is to learn how to play the popular and widely accessible labyrinth marble game. The labyrinth is a game of physical skill whose goal is to steer a marble from a given start point to the end point. In doing so, the player must prevent the ball from falling into any of the holes that are present on the labyrinth board.

CyberRunner applies recent advances in model-based reinforcement learning to the physical world and exploits its ability to make informed decisions about potentially successful behaviors by planning real-world decisions and actions into the future.

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In a new breakthrough, researchers have used a novel technique to confirm a previously undetected physics phenomenon that could be used to improve data storage in the next generation of computer devices.

Spintronic memories, utilized in advanced computers and satellites, leverage the magnetic states produced by the intrinsic angular momentum of electrons for data storage and retrieval. Depending on its physical motion, an electron’s spin produces a magnetic current. Known as the “spin Hall effect,” this has key applications for magnetic materials across many different fields, ranging from low-power electronics to fundamental quantum mechanics.

More recently, scientists have found that electrons are also capable of generating electricity through a second kind of movement: orbital angular momentum, similar to how Earth revolves around the sun. This is known as the “orbital Hall effect,” said Roland Kawakami, co-author of the study and a professor in physics at The Ohio State University.

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“The R in R30 stands for Revolution; we decided to offer something radically different, radically better,” say the creators of the boat.

The Blue Innovations Group, founded by the former Global Head of Manufacturing at Tesla, has officially launched the R30 electric boat.

John Vo, founder and CEO of the Blue Innovations Group, proudly introduced the R30, emphasizing its radical departure from conventional boat designs.

Vo highlighted the distinctiveness of the R30, stating, “The R30 was designed from scratch. We threw out the playbook most boatbuilders follow because if you squint your eyes at a boat show, most boats look the same. The R in R30 stands for Revolution; we decided to offer something radically different, radically better.”

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Butterfly, the inaugural eVTOL incorporating Optimum Speed Tilt Rotor and Individual Blade Control technologies, promises optimal efficiency and a refined ride.

In response to the dynamic demands of evolving personal mobility, the electric Vertical Takeoff and Landing (eVTOL) industry has flourished in recent years. Numerous aviation startups are actively competing to be the pioneers in introducing their respective models to the market.

Aiming to make a significant impact in the eVTOL sector, California-based aerospace company Overair has successfully built and assembled the first-ever eVTOL aircraft utilizing Optimum Speed Tilt Rotor (OSTR) and Individual Blade Control (IBC) Technology. Named Butterfly, the firm claims that it features a “completely novel approach to aviation that unlocks a new standard in quietness, performance, and reliability,” according to a statement by the company.

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The robot is blind and cannot see its environment but can continue to balance and walk, even if an object is hurled at it.

UC researchers Ilija Radosavovic and Bike Zhang wondered if “reinforcement learning,” a concept made popular by large language models (LLMs) last year, could also teach the robot how to adapt to changing needs. To test their theory, the duo started with one of the most basic functions humans can perform — walking.

Transformer model for learning

The researchers started in the simulation world, running billions of scenarios in Isaac Gym, a high-performance GPU-based physics simulation environment. The algorithm in the simulator rewarded actions that mimicked human-like walking while punishing the ones that didn’t. Once the simulation perfected the task, it was transferred to a real-world humanoid bot that did not require further fine-tuning.

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The deal comes a week after a jury found Google guilty of violating antitrust laws in a similar case brought by Epic Games, the maker of the popular Fortnite game.

As part of the settlement, Google will pay $630 million to refund US consumers overcharged for in-app purchases made through the Play Store between August 2016 and September 2023. The estimated 102 million eligible consumers will receive at least $2 each and may get more depending on their spending. They will be automatically notified about how to claim their money.

Google will also pay $70 million in penalties and other costs to the states involved in the lawsuit.

Besides the monetary compensation, Google has also agreed to make some concessions to app developers and consumers to foster more competition and choice in the Android app market.

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Google, the internet giant now a subsidiary of Alphabet, announced on Tuesday that it will limit the kinds of queries related to elections that its chatbot Bard and search generative experience can answer ahead of the 2024 U.S. Presidential election.

The company said that the new restrictions will be implemented by early 2024. The company recently made a landmark change to its location data privacy which has made it difficult for law enforcement agencies to access private location data of people near the crime scene and issue geofence warrants.

According to Reuters, the U.S. is not the only country that will witness crucial elections in 2024. India, the world’s largest democracy, and South Africa, among others, will also hold national elections in the same year.

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“I believe we have found one of the brain’s prototypes for building sequences” says Professor Edvard Moser.

Scientists at NTNU’s Kavli Institute for Systems Neuroscience in Norway have discovered a pattern of activity in the brain that serves as a template for building sequential experiences.

“I believe we have found one of the brain’s prototypes for building sequences,” says Professor Edvard Moser. He describes the activity pattern as “a fundamental algorithm that is intrinsic to the brain and independent of experience.”

The breakthrough discovery was published in Nature.

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