In a groundbreaking achievement, scientists have launched the world’s first wooden satellite into space to explore the material’s suitability for orbit. Named LignoSat, derived from the Latin for “wood,” the satellite was sent into space on a SpaceX mission to the International Space Station (ISS) on Monday. It will later be deployed into orbit, where researchers will observe how the wood withstands the extreme conditions of space over a period of six months.

The initiative, a collaboration between Kyoto University and Sumitomo Forestry, began in 2020. In 2022, they conducted exposure tests aboard the ISS for over 240 days, ultimately selecting Hoonoki, a type of Magnolia wood known for its strength, stability, and workability. This wood is traditionally used in Japan for crafting sword sheaths due to its shatter-resistant properties, according to Reuters.

Since space is devoid of water and oxygen, the wood is shielded from fire and decay, Kyoto University scientists explain. Additionally, they will evaluate the wood’s potential to protect semiconductors from space radiation, Reuters reports.

“If our wooden satellite succeeds, we plan to propose it to Elon Musk’s SpaceX,” said Takao Doi, an astronaut and professor at Kyoto University. The team envisions that wooden satellites could reduce space pollution, as they would not emit aluminum oxide when burning up upon re-entry, unlike traditional metal satellites. Over the next 50 years, Doi’s team envisions growing wood for timber-based habitats on the Moon and Mars. “With timber, a material we can cultivate ourselves, we could construct homes and sustain life in space indefinitely,” Doi told Reuters.

Geoffrey Hinton, professor emeritus at the University of Toronto, and John Hopfield, professor at Princeton University, were honored with the Nobel Prize in Physics for their pioneering contributions that laid the “foundation of today’s powerful machine learning.”

The Royal Swedish Academy of Sciences highlighted their work from the 1980s, which led to the development of artificial neural networks—computer systems inspired by the brain’s structure. These neural networks, which enable AI to “learn by example,” have been instrumental in advances like language processing and image recognition.

Hinton, often called the “Godfather of AI,” expressed his surprise at the award, stating, “I had no expectations of this. I am extremely surprised and I’m honoured.” His key contribution, the development of the Boltzmann machine, a generative model, played a significant role in modern AI.

Despite his monumental achievements, Hinton has raised concerns about the potential misuse of AI. In a 2023 New York Times interview, he expressed regret over his life’s work, noting, “It is hard to see how you can prevent the bad actors from using it for bad things.” He left his position at Google in 2023 to more openly discuss the dangers AI might pose.

The Nobel committee also acknowledged the work of John Hopfield, whose Hopfield network provided early insights into how artificial neural networks can replicate brain patterns. Both scientists’ discoveries have been crucial in shaping today’s AI technologies.

Hinton used tools from statistical physics, the science of systems built from many similar components. The machine is trained by feeding it examples that are very likely to arise when the machine is run. The Boltzmann machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning.

Hinton’s contributions build on the work of fellow Nobel laureate John Hopfield, who developed the Hopfield network, an artificial neural network designed to recreate patterns and store memory. This type of network, introduced in the 1980s, models how neurons in the brain interact, using a system that can “remember” and retrieve stored information. Hopfield’s work provided early insight into how artificial neural networks could replicate brain-like processes, paving the way for the more advanced machine learning and neural network models that Hinton and others would later develop.

The Hopfield network utilises physics that describes a material’s characteristics due to its atomic spin – a property that makes each atom a tiny magnet. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by finding values for the connections between the nodes so that the saved images have low energy. When the Hopfield network is fed a distorted or incomplete image, it methodically works through the nodes and updates their values so the network’s energy falls. The network thus works stepwise to find the saved image that is most like the imperfect one it was fed with.

Hinton continues to express his concerns about the future of AI, reiterating these in a recent call with reporters. He noted, “We have no experience of what it’s like to have things smarter than us. And it’s going to be wonderful in many respects.” However, he also cautioned about the potential dangers, emphasizing the need to remain vigilant about “a number of possible bad consequences, particularly the threat of these things getting out of control.” Hinton’s remarks reflect his growing unease about the rapid development of AI technologies and their potential misuse.

Scientists suggest that a day on Earth might extend to 25 hours in the future due to the Moon’s gradual drift away from our planet.

Research from the University of Wisconsin-Madison indicates that the Moon is receding from Earth at a rate of approximately 3.8 centimeters per year. This phenomenon could result in Earth days lasting 25 hours in 200 million years. Historically, a day on Earth lasted just over 18 hours around 1.4 billion years ago.

Stephen Meyers, a professor in the geoscience department at the University of Wisconsin-Madison, points to the gravitational interactions between Earth and the Moon as a primary cause. “As the Moon moves away, the Earth is like a spinning figure skater who slows down as they stretch their arms out,” explained Meyers.

Meyers and his team are using ‘astrochronology’ to study ancient geological processes. “We want to be able to study rocks that are billions of years old in a way that is comparable to how we study modern geologic processes,” he said.

The concept of the Moon’s recession is not new, but the Wisconsin research delves deeper into its historical and geological context. By examining ancient geological formations and sediment layers, researchers have tracked the Earth-Moon system over billions of years. They found that while the Moon’s current recession rate is relatively stable, it has fluctuated due to various factors such as Earth’s rotational speed and continental drift.