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.

Warnings are being issued for the Indian Himalayan Region (IHR) due to a new study emphasizing the rising hazards and risks from climate change.

Researchers at the Indian Institute of Technology-Madras (IIT-M) have created a new climate risk index, revealing that all states within the IHR, including the eight northeastern states, are exposed to varying degrees of climate change danger.

The report, titled “Climate-change-induced risk mapping of the Indian Himalayan districts using the latest IPCC framework,” was shared with The Diplomat. It highlights that Arunachal Pradesh, Meghalaya, Nagaland, Manipur, Tripura, Jammu and Kashmir, and Uttarakhand are particularly vulnerable. The research was led by Assistant Professor Krishna Malakar and research scholar Aayush Shah from the Department of Humanities and Social Sciences at IIT-M.

The study indicates that, while districts in the Western Indian Himalayan Region (WIHR) are generally more at risk, the three most risk-prone districts are in the Eastern Indian Himalayan Region (EIHR). WIHR faces more hazards and higher exposure, though EIHR exhibits greater vulnerability.

Spanning 12 states and approximately 1,550 miles from west to east, the IHR covers 16.2 percent of India’s land area and is home to about 47 million people, or 3.88 percent of the country’s population. The region is crucial for environmental stability, offering dense forests, biodiversity conservation, vital water sources, and sustainable tourism.

This IIT-M report follows several studies in recent years on the impacts of climate change in the Himalayas. A study by the Forest Research Institute, conducted with other institutes four years ago, found the eastern region more risk-prone than the west. IIT-M’s findings suggest that both regions are vulnerable, with the western area more susceptible to climate change risks.

Based on the IPCC framework, the IIT-M report combines physical and socio-economic indicators to assess hazards in the Himalayan region, aiming to create a climate change risk index for IHR districts. It utilized data from India’s 2011 census and other government sources to develop a comprehensive and replicable index.

To calculate risk, the study considered hazard, vulnerability, and exposure. Eleven physical indicators, such as earthquakes, cold wave days, floods, snowfall, and hailstorms, represented hazards. Vulnerability included susceptibility to harm and adaptation capacity, while exposure measured significant climate fluctuations’ impact on a region.

Shimla in Himachal Pradesh was identified as the most hazard-prone district, followed by East Sikkim in the Northeast. The high-hazard category included 32 districts—23 in the west and nine in the east. Medium-hazard districts were evenly divided between the two zones with 22 districts, while the low-hazard category comprised 17 districts—15 in the east and two in the west. The lowest hazard category included 25 eastern zone districts.

The west is more hazard-prone, with 34 out of 47 districts in the highest and high-hazard categories, and only two in the low-hazard category. Conversely, the east had 25 districts in the lowest hazard category out of 62, with 51 districts in the lowest, low, and medium hazard categories.

The study found that 64 out of 109 IHR districts are highly vulnerable, with the highest vulnerability districts spread across nine states. The eastern zone is more vulnerable, with 43 out of 62 districts classified as highest and high-vulnerability, while the western zone is more risk-prone due to higher hazards and greater exposure.

Climate change’s adverse impacts are evident across the IHR, with erratic rainfall causing floods and droughts, particularly in the northeastern region. For example, Assam has recently faced severe floods, resulting in 109 deaths.

To combat climate change, the Indian government implemented the National Action Plan on Climate Change in 2010, including a focus on the IHR. However, this plan has not sufficiently mitigated climate change’s adverse effects in the region.

The IIT-M study underscores the need for effective communication with remote Himalayan areas to mitigate climate change impacts. This strategy would enable residents to plan and respond swiftly to minimize losses, particularly those in poorly constructed structures during disasters like the Kedarnath floods in Uttarakhand.

The report also calls for better integration of Himalayan communities into the mainstream, noting that many residents belong to rural tribal communities. Suggestions include increasing employment opportunities, improving infrastructure, healthcare, access to cleaner fuels, education, and diversifying income sources beyond agriculture.