Scientists may have found evidence of a fifth ‘force of nature'

If you purchase an independently reviewed product or service through a link on our website, BGR may receive an affiliate commission.
Every action in our world is powered by a 'force of nature.' Currently, there are four main forces that scientists cling to; gravity, electromagnetism, weak interaction, and strong interaction. The latter two are technically considered nuclear forces. However, some scientists believe a fifth force of nature may exist, and a new paper claims to have found evidence of it.
A group of researchers from Switzerland, Australia, and Germany believe that this fifth force could be hiding deep within the hearts of atoms. While the Standard Model of physics has evolved over the years to help explain quantum and cosmic examples, there are still some massive gaps that leave scientists and physicists baffled.
Today's Top Deals
Best deals: Tech, laptops, TVs, and more sales
Best Ring Video Doorbell deals
Memorial Day security camera deals: Reolink's unbeatable sale has prices from $29.98
Dark matter is a big one, of course, and even gravity hasn't been fully solved, despite being one of the primary forces of nature. Introducing a fifth force of nature, as well as other fields and particles, could broaden our understanding of the universe in important ways. But finding the evidence to prove these forces actually exist is the difficult part.
That's why the researchers involved in this new study started small. Instead of trying to work at a cosmic scale, they started looking at things on an atomic level. They focused their attention on the nuclei of four different kinds of calcium. Typically, electrons are confined by the attraction between their own charge and the positively charged particles in the center of the atom.
But if you give them a little kick, they can actually transcend to a higher orbit. This phenomenon is known as atomic transition. The exact timing of the jump depends heavily on the construction of the nucleus, which means an element can have multiple atomic transitions depending on the number of neutrons found within it.
The researchers believe that a fifth force of nature could be the driving engine behind these small interactions. Their experiments found that there was a small amount of room between the atomic transitions — just enough room for a particle with a mass believed to be somewhere between 10 and 10 million electronvolts.
Determining whether or not that ambiguity is indeed another force of nature will require additional experimentation and improved calculations, though.
More Top Deals
Amazon gift card deals, offers & coupons 2025: Get $2,000+ free
See the

Try Our AI Features

Explore what Daily8 AI can do for you:

Learn with AIFact CheckingText to SpeechAI Summary

Related Articles

Yahoo

an hour ago

• Yahoo

Your AI use could have a hidden environmental cost

Sign up for CNN's Life, But Greener newsletter. Our limited newsletter series guides you on how to minimize your personal role in the climate crisis — and reduce your eco-anxiety. Whether it's answering work emails or drafting wedding vows, generative artificial intelligence tools have become a trusty copilot in many people's lives. But a growing body of research shows that for every problem AI solves, hidden environmental costs are racking up. Each word in an AI prompt is broken down into clusters of numbers called 'token IDs' and sent to massive data centers — some larger than football fields — powered by coal or natural gas plants. There, stacks of large computers generate responses through dozens of rapid calculations. The whole process can take up to 10 times more energy to complete than a regular Google search, according to a frequently cited estimation by the Electric Power Research Institute. So, for each prompt you give AI, what's the damage? To find out, researchers in Germany tested 14 large language model (LLM) AI systems by asking them both free-response and multiple-choice questions. Complex questions produced up to six times more carbon dioxide emissions than questions with concise answers. In addition, 'smarter' LLMs with more reasoning abilities produced up to 50 times more carbon emissions than simpler systems to answer the same question, the study reported. 'This shows us the tradeoff between energy consumption and the accuracy of model performance,' said Maximilian Dauner, a doctoral student at Hochschule München University of Applied Sciences and first author of the Frontiers in Communication study published Wednesday. Typically, these smarter, more energy intensive LLMs have tens of billions more parameters — the biases used for processing token IDs — than smaller, more concise models. 'You can think of it like a neural network in the brain. The more neuron connections, the more thinking you can do to answer a question,' Dauner said. Complex questions require more energy in part because of the lengthy explanations many AI models are trained to provide, Dauner said. If you ask an AI chatbot to solve an algebra question for you, it may take you through the steps it took to find the answer, he said. 'AI expends a lot of energy being polite, especially if the user is polite, saying 'please' and 'thank you,'' Dauner explained. 'But this just makes their responses even longer, expending more energy to generate each word.' For this reason, Dauner suggests users be more straightforward when communicating with AI models. Specify the length of the answer you want and limit it to one or two sentences, or say you don't need an explanation at all. Most important, Dauner's study highlights that not all AI models are created equally, said Sasha Luccioni, the climate lead at AI company Hugging Face, in an email. Users looking to reduce their carbon footprint can be more intentional about which model they chose for which task. 'Task-specific models are often much smaller and more efficient, and just as good at any context-specific task,' Luccioni explained. If you are a software engineer who solves complex coding problems every day, an AI model suited for coding may be necessary. But for the average high school student who wants help with homework, relying on powerful AI tools is like using a nuclear-powered digital calculator. Even within the same AI company, different model offerings can vary in their reasoning power, so research what capabilities best suit your needs, Dauner said. When possible, Luccioni recommends going back to basic sources — online encyclopedias and phone calculators — to accomplish simple tasks. Putting a number on the environmental impact of AI has proved challenging. The study noted that energy consumption can vary based on the user's proximity to local energy grids and the hardware used to run AI partly why the researchers chose to represent carbon emissions within a range, Dauner said. Furthermore, many AI companies don't share information about their energy consumption — or details like server size or optimization techniques that could help researchers estimate energy consumption, said Shaolei Ren, an associate professor of electrical and computer engineering at the University of California, Riverside who studies AI's water consumption. 'You can't really say AI consumes this much energy or water on average — that's just not meaningful. We need to look at each individual model and then (examine what it uses) for each task,' Ren said. One way AI companies could be more transparent is by disclosing the amount of carbon emissions associated with each prompt, Dauner suggested. 'Generally, if people were more informed about the average (environmental) cost of generating a response, people would maybe start thinking, 'Is it really necessary to turn myself into an action figure just because I'm bored?' Or 'do I have to tell ChatGPT jokes because I have nothing to do?'' Dauner said. Additionally, as more companies push to add generative AI tools to their systems, people may not have much choice how or when they use the technology, Luccioni said. 'We don't need generative AI in web search. Nobody asked for AI chatbots in (messaging apps) or on social media,' Luccioni said. 'This race to stuff them into every single existing technology is truly infuriating, since it comes with real consequences to our planet.' With less available information about AI's resource usage, consumers have less choice, Ren said, adding that regulatory pressures for more transparency are unlikely to the United States anytime soon. Instead, the best hope for more energy-efficient AI may lie in the cost efficacy of using less energy. 'Overall, I'm still positive about (the future). There are many software engineers working hard to improve resource efficiency,' Ren said. 'Other industries consume a lot of energy too, but it's not a reason to suggest AI's environmental impact is not a problem. We should definitely pay attention.'

Yahoo

3 hours ago

• Yahoo

Meet the tiny Australian Moth that travels 1,000 km and navigates using the stars

An Australian moth follows the stars during its yearly migration, using the night sky as a guiding compass, according to a new study. When temperatures heat up, nocturnal Bogong moths fly about 1,000 kilometres to cool down in caves by the Australian Alps. They later return home to breed and die. Birds routinely navigate by starlight, but the moths are the first known invertebrates, or creatures without a backbone, to find their way across such long distances using the stars. Scientists have long wondered how the moths travel to a place they've never been. A previous study hinted that Earth's magnetic field might help steer them in the right direction, along with some kind of visual landmark as a guide. Related Does cutting off rhinos' horns protect them from poachers? New study supports controversial approach Since stars appear in predictable patterns each night, scientists suspected they might help lead the way. They placed moths in a flight simulator that mimicked the night sky above them and blocked out the Earth's magnetic field, noting where they flew. Then they scrambled the stars and saw how the moths reacted. When the stars were as they should be, the moths flapped in the right direction. But when the stars were in random places, the moths were disoriented. Their brain cells also got excited in response to specific orientations of the night sky. The findings were published Wednesday in the journal Nature. It 'was a very clean, impressive demonstration that the moths really are using a view of the night sky to guide their movements,' said Kenneth Lohmann, who studies animal navigation at the University of North Carolina at Chapel Hill and was not involved with the new research. Researchers don't know what features of the night sky the moths use to find their way. It could be a stripe of light from the Milky Way, a colourful nebula or something else entirely. Whatever it is, the insects seem to rely on that, along with Earth's magnetic field, to make their journey. Related Rare snail that can 'slurp up earthworms like noodles' caught on camera laying an egg from its neck Scientists use special 'squeezing' and electrical probes to collect sperm from endangered kākāpō Other animals harness the stars as a guide. Birds take celestial cues as they soar through the skies, and dung beetles roll their remains short distances while using the Milky Way to stay on course. It's an impressive feat for Bogong moths, whose brains are smaller than a grain of rice, to rely on the night sky for their odyssey, said study author David Dreyer with Lund University in Sweden. 'It's remarkable that an animal with such a tiny brain can actually do this,' Dreyer said.

CNN

5 hours ago

• CNN

How your AI prompts could harm the environment

AI Sustainability Climate change EconomyFacebookTweetLink Follow Sign up for CNN's Life, But Greener newsletter. Our limited newsletter series guides you on how to minimize your personal role in the climate crisis — and reduce your eco-anxiety. Whether it's answering work emails or drafting wedding vows, generative artificial intelligence tools have become a trusty copilot in many people's lives. But a growing body of research shows that for every problem AI solves, hidden environmental costs are racking up. Each word in an AI prompt is broken down into clusters of numbers called 'token IDs' and sent to massive data centers — some larger than football fields — powered by coal or natural gas plants. There, stacks of large computers generate responses through dozens of rapid calculations. The whole process can take up to 10 times more energy to complete than a regular Google search, according to a frequently cited estimation by the Electric Power Research Institute. So, for each prompt you give AI, what's the damage? To find out, researchers in Germany tested 14 large language model (LLM) AI systems by asking them both free-response and multiple-choice questions. Complex questions produced up to six times more carbon dioxide emissions than questions with concise answers. In addition, 'smarter' LLMs with more reasoning abilities produced up to 50 times more carbon emissions than simpler systems to answer the same question, the study reported. 'This shows us the tradeoff between energy consumption and the accuracy of model performance,' said Maximilian Dauner, a doctoral student at Hochschule München University of Applied Sciences and first author of the Frontiers in Communication study published Wednesday. Typically, these smarter, more energy intensive LLMs have tens of billions more parameters — the biases used for processing token IDs — than smaller, more concise models. 'You can think of it like a neural network in the brain. The more neuron connections, the more thinking you can do to answer a question,' Dauner said. Complex questions require more energy in part because of the lengthy explanations many AI models are trained to provide, Dauner said. If you ask an AI chatbot to solve an algebra question for you, it may take you through the steps it took to find the answer, he said. 'AI expends a lot of energy being polite, especially if the user is polite, saying 'please' and 'thank you,'' Dauner explained. 'But this just makes their responses even longer, expending more energy to generate each word.' For this reason, Dauner suggests users be more straightforward when communicating with AI models. Specify the length of the answer you want and limit it to one or two sentences, or say you don't need an explanation at all. Most important, Dauner's study highlights that not all AI models are created equally, said Sasha Luccioni, the climate lead at AI company Hugging Face, in an email. Users looking to reduce their carbon footprint can be more intentional about which model they chose for which task. 'Task-specific models are often much smaller and more efficient, and just as good at any context-specific task,' Luccioni explained. If you are a software engineer who solves complex coding problems every day, an AI model suited for coding may be necessary. But for the average high school student who wants help with homework, relying on powerful AI tools is like using a nuclear-powered digital calculator. Even within the same AI company, different model offerings can vary in their reasoning power, so research what capabilities best suit your needs, Dauner said. When possible, Luccioni recommends going back to basic sources — online encyclopedias and phone calculators — to accomplish simple tasks. Putting a number on the environmental impact of AI has proved challenging. The study noted that energy consumption can vary based on the user's proximity to local energy grids and the hardware used to run AI partly why the researchers chose to represent carbon emissions within a range, Dauner said. Furthermore, many AI companies don't share information about their energy consumption — or details like server size or optimization techniques that could help researchers estimate energy consumption, said Shaolei Ren, an associate professor of electrical and computer engineering at the University of California, Riverside who studies AI's water consumption. 'You can't really say AI consumes this much energy or water on average — that's just not meaningful. We need to look at each individual model and then (examine what it uses) for each task,' Ren said. One way AI companies could be more transparent is by disclosing the amount of carbon emissions associated with each prompt, Dauner suggested. 'Generally, if people were more informed about the average (environmental) cost of generating a response, people would maybe start thinking, 'Is it really necessary to turn myself into an action figure just because I'm bored?' Or 'do I have to tell ChatGPT jokes because I have nothing to do?'' Dauner said. Additionally, as more companies push to add generative AI tools to their systems, people may not have much choice how or when they use the technology, Luccioni said. 'We don't need generative AI in web search. Nobody asked for AI chatbots in (messaging apps) or on social media,' Luccioni said. 'This race to stuff them into every single existing technology is truly infuriating, since it comes with real consequences to our planet.' With less available information about AI's resource usage, consumers have less choice, Ren said, adding that regulatory pressures for more transparency are unlikely to the United States anytime soon. Instead, the best hope for more energy-efficient AI may lie in the cost efficacy of using less energy. 'Overall, I'm still positive about (the future). There are many software engineers working hard to improve resource efficiency,' Ren said. 'Other industries consume a lot of energy too, but it's not a reason to suggest AI's environmental impact is not a problem. We should definitely pay attention.'