
World energy methane emissions near record high in 2024: IEA
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'Tremendous impact'
PARIS: Record fossil fuel production kept planet-heating methane emissions near historic highs last year, the International Energy Agency said Wednesday, warning of a surge in massive leaks from oil and gas facilities.Slashing emissions of methane, second only to carbon dioxide for its contribution to global warming , is essential to meeting international targets on climate change and one of the fastest ways to curb temperature rise.But the IEA warned that countries are considerably underestimating their energy sector methane pollution , estimating that emissions are around 80 percent higher than the total reported by governments to the United Nations.The energy sector is responsible for around a third of the methane emitted by human activities.It leaks from gas pipelines and other energy infrastructure, and is also deliberately released during equipment maintenance.Tackling this is considered one of the easiest ways to lower emissions because plugging leaks can often be done at little or no cost."However, the latest data indicates that implementation on methane has continued to fall short of ambitions," said IEA Executive Director Fatih Birol.The IEA's Global Methane Tracker report said over 120 million tonnes was released from the fossil fuel sector in 2024, close to the record high in 2019.China has the largest energy methane emissions globally, mainly from its coal sector.The United States follows in second, driven by its oil and gas sector, with Russia third.The IEA said its figures are based on measured data where possible, compared to emissions reported by governments, which can be outdated or estimated using information from the energy sector.Global methane emissions are becoming easier to monitor from space, with more than 25 satellites tracking gas plumes from fossil fuel facilities and other sources.The IEA said that Europe's Sentinel 5 satellite, which just sees the very largest leaks, showed that "super-emitting methane events" at oil and gas facilities rose to a record high in 2024.These huge leaks were observed all over the world, but particularly in the United States, Turkmenistan and Russia.Abandoned oil and gas wells, and coal mines are also significant sources of methane leaking into the atmosphere, the IEA said in new analysis for this year's report.When taken together they would be the "world's fourth-largest emitter of fossil fuel methane", accounting for some eight million tonnes last year.Some 40 percent of methane emissions come from natural sources, mainly wetlands.The rest are from human activities, particularly agriculture and the energy sector.Because methane is potent but relatively short-lived it is a key target for countries wanting to slash emissions quickly.More than 150 countries have promised a 30 percent reduction by 2030.Oil and gas firms have meanwhile pledged to slash methane emissions by 2050.The IEA estimated that cutting methane released by the fossil fuel sector would significantly slow global warming, preventing a roughly 0.1 degree Celsius rise in global temperatures by 2050."This would have a tremendous impact, comparable to eliminating all CO2 emissions from the world's heavy industry in one stroke," the report said.Around 70 percent of annual methane emissions from the energy sector could be avoided with existing technologies.But only five percent of global oil and gas meets "near-zero" emissions standards, the IEA said.Energy think tank Ember said the fossil fuel industry needs to reduce methane emissions by 75 percent by 2030 if the world is to meet the target of reducing overall emissions to net zero by the middle of this century.In particular, methane from coal was "still being ignored," said Ember analyst Sabina Assan."There are cost-effective technologies available today, so this is a low-hanging fruit of tackling methane. We can't let coal mines off the hook any longer."

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Time of India
9 hours ago
- Time of India
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Indian Express
a day ago
- Indian Express
Can AI quicken the pace of math discovery?
Artificial intelligence can write a poem in the style of Walt Whitman, provide dating advice and suggest the best way to cook an artichoke. But when it comes to mathematics, large language models like OpenAI's immensely popular ChatGPT have sometimes stumbled over basic problems. Some see this as an inherent limitation of the technology, especially when it comes to complex reasoning. A new initiative from the Defense Advanced Research Projects Agency seeks to account for that shortfall by enlisting researchers in finding ways to conduct high-level mathematics research with an AI 'co-author.' The goal of the new grant-making program, Exponentiating Mathematics, is to speed up the pace of progress in pure (as opposed to applied) math — and, in doing so, to turn AI into a superlative mathematician. 'Mathematics is this great test bed for what is right now the key pain point for AI systems,' said Patrick Shafto, a Rutgers University mathematician and computer scientist who now serves as a program manager in DARPA's information innovation office, known as I20. 'So if we overcome that, potentially, it would unleash much more powerful AI.' He added, 'There's huge potential benefit to the community of mathematicians and to society at large.' Shafto spoke from his office at DARPA's headquarters, an anonymous building in northern Virginia whose facade of bluish glass gives little indication that it houses one of the most unusual agencies in the federal government. Inside the building's airy lobby, visitors surrender their cellphones. Near a bank of chairs, a glass display shows a prosthetic arm that can be controlled by the wearer's brain signals. 'By improving mathematics, we're also understanding how AI works better,' said Alondra Nelson, who served as a top science adviser in President Joe Biden's administration and is a faculty member at the Institute for Advanced Study in Princeton, New Jersey. 'So I think it's kind of a virtuous cycle of understanding.' She suggested that, down the road, math-adept AI could enhance cryptography and aid in space exploration. Started after World War II to compete with the Soviet Union in the space race, DARPA is most famous for fostering the research that led to the creation of ARPANET, the precursor to the internet we use today. At the agency's small gift store, which is not accessible to the public, one can buy replicas of a cocktail napkin on which someone sketched out the rudimentary state of computer networks in 1969. DARPA later funded the research that gave rise to drones and Apple's digital assistant, Siri. But it is also responsible for the development of Agent Orange, the potent defoliant used to devastating effect during the Vietnam War. 'I'm sure this isn't 100% innocent,' Andrew Granville, a mathematician at the University of Montreal, said of DARPA's math initiative, although he emphasized that he was only speculating about eventual outcomes. DARPA is, after all, part of the Pentagon, even if it has traditionally operated with enviable independence. The U.S. military is rapidly incorporating AI into its operations, with the aim of not losing out to China and its People's Liberation Army or to Russia, which has been testing out new technologies on the battlefield in Ukraine. At the same time, Granville praised the endeavor, which comes as the Trump administration is cutting funding for scientific research. 'We are in disastrous times for U.S. science,' Granville said. 'I'm very pleased that DARPA is able to funnel money to academia.' A surfer and skateboarder in his free time, Shafto, 49, sat in a sparse conference room one recent afternoon, imagining a future when AI would be as good at solving multistep problems as it is at trying to glean meaning from huge troves of texts, which it does through the use of probability theory. Despite the unseasonably raw weather, Shafto seemed dressed for the beach in a blue-and-white Hawaiian-style shirt, white flannel trousers and sandals, with a trilby hat on the table before him. His vibe was, on the whole, decidedly closer to that of Santa Cruz than of Capitol Hill, largely in keeping with DARPA's traditional disregard for the capital's slow, bureaucratic pace. (The agency sets priorities and funds outside scientists but does not do research on its own; academics like Shafto spend an average of four years as program managers.) 'There are great mathematicians who work on age-old problems,' Shafto said. 'That's not the kind of thing that I'm particularly interested in.' Instead, he wanted the discipline to move more quickly by using AI to save time. 'Problems in mathematics take decades or centuries, sometimes, to solve,' he said in a recent presentation at DARPA's headquarters on the Exponentiating Mathematics project, which is accepting applications through mid-July. He then shared a slide showing that, in terms of the number of papers published, math had stagnated during the last century while life and technical sciences had exploded. In case the point wasn't clear, the slide's heading drove it home: 'Math is sloooowwww. …' The kind of pure math Shafto wants to accelerate tends to be 'sloooowwww' because it is not seeking numerical solutions to concrete problems, the way applied mathematics does. Instead, pure math is the heady domain of visionary theoreticians who make audacious observations about how the world works, which are promptly scrutinized (and sometimes torn apart) by their peers. 'Proof is king,' Granville said. Math proofs consist of multiple building blocks called lemmas, minor theorems employed to prove bigger ones. Whether each Jenga tower of lemmas can maintain integrity in the face of intense scrutiny is precisely what makes pure math such a 'long and laborious process,' acknowledged Bryna R. Kra, a mathematician at Northwestern University. 'All of math builds on previous math, so you can't really prove new things if you don't understand how to prove the old things,' she said. 'To be a research mathematician, the current practice is that you go through every step, you prove every single detail.' Lean, a software-based proof assistant, can speed up the process, but Granville said it was 'annoying, because it has its own protocols and language,' requiring programming expertise. 'We need to have a much better way of communication,' he added. Could artificial intelligence save the day? That's the hope, according to Shafto. An AI model that could reliably check proofs would save enormous amounts of time, freeing mathematicians to be more creative. 'The constancy of math coincides with the fact that we practice math more or less the same: still people standing at a chalkboard,' Shafto said. 'It's hard not to draw the correlation and say, 'Well, you know, maybe if we had better tools, that would change progress.'' AI would benefit, too, Shafto and others believe. Large language models like ChatGPT can scour the digitized storehouses of human knowledge to produce a half-convincing college essay on the Russian Revolution. But thinking through the many intricate steps of a mathematical problem remains elusive. 'I think we'll learn a lot about what the capabilities of various AI protocols are from how well we can get them to generate material that's of interest,' said Jordan S. Ellenberg, a mathematician at the University of Wisconsin-Madison who is part of a team applying for an Exponentiating Mathematics grant. 'We have no intuition yet about which problems are going to be hard and which problems are easy. We need to learn that.' One of the more disconcerting truths about artificial intelligence is that we do not entirely understand how it works. 'This lack of understanding is essentially unprecedented in the history of technology,' Dario Amodei, CEO of the artificial intelligence company Anthropic, wrote in a recent essay. Ellenberg somewhat downplayed that assertion, pointing out that electricity was widely used before its properties were fully understood. Then again, with some AI experts worrying that artificial intelligence could destroy the world, any clarity into its operations tends to be welcome. Nelson, the former White House adviser, acknowledged 'legitimate' concerns about the rapid pace at which artificial intelligence is being integrated into seemingly every sector of society. All the more reason, she argued, to have DARPA on the case. 'There's a much higher benchmark that needs to be reached than whether or not your chatbot is hallucinating if you ask it a question about Shakespeare,' she said. 'The stakes are much higher.'


Hans India
a day ago
- Hans India
AI and its future: beyond the data-driven era
Artificial intelligence is the science of making machines do things that would require intelligence if done by humans — John McCarthy, who coined the term 'artificial intelligence' and is considered father of AI, said in 1955 Artificial Intelligence is the buzzword that's resonating across boardrooms, classrooms, and coffee shops these days. It is everywhere. From chatbots handling customer service to algorithms curating social media feeds, AI has become the in-thing of our time. Yet despite the widespread adoption and breathless headlines, we're still in the earliest stages of what AI can become. The current reality: data-driven intelligence Today's AI systems, impressive as they may seem, operate on a fundamental principle: processing vast amounts of data to recognize patterns and generate responses. These Large Language Models (LLMs) can write poetry, code software, and answer complex questions, but they're essentially sophisticated pattern-matching engines drawing from enormous datasets. Frankly speaking, what we're experiencing now is just the tip of the iceberg and we're still in the fetal stage of artificial intelligence evolution. However, the current data-driven approach has undeniably been disruptive. Industries from healthcare to finance have scrambled to integrate AI tools, leading to the ubiquitous presence of 'AI-powered' solutions. However, calling these systems true artificial intelligence may be premature - they lack the fundamental cognitive abilities that define genuine intelligence. The next frontier: Artificial General Intelligence The next phase in AI evolution promises something far more sophisticated: Artificial General Intelligence (AGI). Unlike current systems that excel in narrow domains, AGI will possess the ability to understand, learn, and apply intelligence across a broad range of tasks - much like human cognitive flexibility. The key differentiator lies in cognition. Where today's AI relies on statistical analysis of training data, AGI systems will develop the capacity for genuine reasoning and decision-making. This cognitive leap represents a fundamental shift from pattern recognition to actual thinking. AGI won't just process information faster or access more data - it will understand context, make inferences, and adapt to entirely new situations without requiring additional training. This represents a qualitative, not just quantitative, advancement in machine intelligence. The ultimate goal: Absolute Intelligence Beyond AGI lies an even more ambitious target: Absolute Intelligence. This final phase envisions AI systems with fully developed cognitive abilities - machines that can think, reason, and make decisions with the same depth and nuance as human consciousness, potentially surpassing human intellectual capabilities. Absolute Intelligence would mark the point where artificial systems achieve genuine understanding rather than sophisticated mimicry. These systems would possess creativity, intuition, and the ability to grapple with abstract concepts in ways that current AI cannot. Small Language Models: The Future Architecture Contrary to the current trend towards ever-larger models, the future may belong to Small Language Models (SLMs). These more efficient, specialized systems could prove more practical and powerful than their data-hungry predecessors. Small Language Models offer several advantages over massive LLMs: reduced computational requirements, faster processing, greater customization for specific tasks, and the ability to run locally rather than requiring cloud infrastructure. As AI becomes more integrated into daily life, these characteristics will prove increasingly valuable. The shift toward SLMs reflects a maturation of the field - moving from brute-force approaches that require enormous resources toward elegant, efficient solutions that deliver superior performance with less overhead. The Way Forward Rather than dwelling on dystopian scenarios, the AI revolution presents an opportunity to thoughtfully shape the next decade of technological development. The progression from today's data-driven systems through AGI to Absolute Intelligence won't happen overnight. However, the key lies in recognizing that we're not approaching an endpoint but rather embarking on a carefully planned journey. Each phase of AI development builds upon the previous one, creating opportunities to refine our approach, establish ethical frameworks, and ensure that artificial intelligence helps humans. As we stand at this inflection point, the question isn't whether AI will transform our world - it's how we'll guide that transformation. The next ten years will determine whether we harness these emerging capabilities to solve pressing global challenges, enhance human potential, and create a more prosperous future for all. The age of true artificial intelligence is still ahead of us. What we're witnessing today is merely the opening chapter of a much larger story - one that we have the power to write thoughtfully and purposefully. All said and done, the world needs a responsible AI that can enhance our quality of life in all spheres and spaces. That's the bottom line. (Krishna Kumar is a technology explorer & strategist based in Austin, Texas in the US. Rakshitha Reddy is AI developer based in Atlanta, US)