The AI That Can Predict Environmental Disasters Before They Strike
Google DeepMind's AlphaEarth is an artificial intelligence model that has the ability to look and forecast changes on the globe as accurately as a satellite. It is not merely a mapping tool - it's a climate science, agriculture, and disaster relief game-changer.
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
India Today
7 hours ago
- India Today
AI godfather warns AI could soon develop its own language and outsmart humans
Geoffrey Hinton, the man many call the Godfather of AI, has issued yet another cautionary note, and this time it sounds like something straight out of a scifi film. Speaking on the One Decision podcast, the Nobel Prizewinning scientist warned that artificial intelligence may soon develop a private language of its own, one that even its human creators won't be able to now, AI systems do what's called 'chain of thought' reasoning in English, so we can follow what it's doing,' Hinton explained. 'But it gets more scary if they develop their own internal languages for talking to each other.'That, he says, could take AI into uncharted and unnerving territory. Machines have already demonstrated the ability to produce 'terrible' thoughts, and there's no reason to assume those thoughts will always be in a language we can track. Hinton's words carry weight. He is, after all, the 2024 Nobel Physics laureate whose early work on neural networks paved the way for today's deep learning models and largescale AI systems. Yet he says he didn't fully appreciate the dangers until much later in his career.'I should have realised much sooner what the eventual dangers were going to be,' he admitted. 'I always thought the future was far off and I wish I had thought about safety sooner.' Now, that delayed realisation fuels his of Hinton's biggest fears lies in how AI systems learn. Unlike humans, who must share knowledge painstakingly, digital brains can copy and paste what they know in an instant.'Imagine if 10,000 people learned something and all of them knew it instantly, that's what happens in these systems,' he explained on BBC collective, networked intelligence means AI can scale its learning at a pace no human can match. Current models such as GPT4 already outstrip humans when it comes to raw general knowledge. For now, reasoning remains our stronghold – but that advantage, says Hinton, is shrinking he is vocal, Hinton says others in the industry are far less forthcoming. 'Many people in big companies are downplaying the risk,' he noted, suggesting their private worries aren't reflected in their public statements. One notable exception, he says, is Google DeepMind CEO Demis Hassabis, whom Hinton credits with showing genuine interest in tackling these for Hinton's highprofile exit from Google in 2023, he says it wasn't a protest. 'I left Google because I was 75 and couldn't program effectively anymore. But when I left, maybe I could talk about all these risks more freely,' he governments roll out initiatives like the White House's new 'AI Action Plan', Hinton believes that regulation alone won't be real task, he argues, is to create AI that is 'guaranteed benevolent', a tall order, given that these systems may soon be thinking in ways no human can fully follow.- Ends
Time of India
2 days ago
- Time of India
Could AI replace expert mathematicians? Here is what OpenAI's Noam Brown says
In a recent YouTube interview with Sonya Huang of Sequoia Capital, a candid conversation unfolded around a topic considered far-fetched: Whether artificial intelligence could eventually replace expert mathematicians in one of the most intellectually demanding tasks, creating International Mathematical Olympiad (IMO) questions. Sitting across from Huang were Alex Wei, Sheryl Hsu, and Noam Brown, a lean three-person team at OpenAI that had already made headlines for achieving gold-level performance on IMO problems. Yet the next challenge, Brown suggested, was not solving Olympiad problems, but creating them, and he believes the gap is closing fast. OpenAI's IMO Team on Why Models Are Finally Solving Elite-Level Math No fundamental barrier: What OpenAI's progress reveals 'These models are really good now at solving these problems,' Brown said during the interview. 'Coming up with them is, you know, still a challenge. But I think it's also worth noting the incredible pace of progress that we're seeing.' Brown's optimism comes with perspective. Not long ago, large language models (LLMs) struggled with basic arithmetic or step-by-step reasoning. Today, cutting-edge systems from OpenAI, Google DeepMind, and Anthropic are not just solving pre-existing questions, they are passing graduate-level math tests, producing proofs, and engaging in mathematical dialogue with a level of consistency once reserved for top-tier students. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like 11 Foods That Help In Healing Knee Pain Naturally Learn More Undo 'Originally when LMs came out, it was like, well, how do we get them to reason? And then we got them to reason. But then how do we get them to reason on hard-to-verify tasks? And now they can reason on hard-to-verify tasks,' Brown explained. 'I think the next hurdle is going to be, okay, how do we get them to come up with these novel questions? Even creating an IMO question is a challenge, and it takes a lot of expert mathematicians a lot of work to do that. But I don't see any fundamental barriers that block us from getting there.' This perspective lands at a time when students across the world are increasingly being taught to view AI not just as a tool, but as a peer in the learning process. If models can eventually generate new problems at Olympiad difficulty, it would radically shift how competitions, instruction, and even research are designed. The role of mathematicians might shift from creation to curation, with AI generating hundreds of complex problems and human experts selecting and refining the most promising ones. To be clear, Brown is not suggesting AI will instantly replace humans at the chalkboard. But his remarks reflect an evolution of AI's relationship with formal disciplines like mathematics. What was once the exclusive domain of human cognition, like proof construction, conceptual abstraction, and elegant question design, is now being nudged forward by machine capabilities. One notable example came last year, when AlphaGeometry, a collaborative project between Google DeepMind and New York University's Computer Science Department, demonstrated notable success in solving Olympiad-level geometry problems, correctly answering 25 out of 30 past IMO questions. It signaled how far AI models had come in mastering not only mathematical reasoning, but also structure and abstraction. What this means for students and the future of mathematics For students and early-career researchers, Brown's comments are both a challenge and an opportunity. The challenge lies in keeping up with systems that are evolving at unprecedented speed. The opportunity is in collaboration, using AI to explore alternate solutions, generate variants of problems, or simulate how different levels of difficulty can be introduced in question design. For example, while designing IMO questions requires originality, structure, and relevance to curriculum, an AI model trained on thousands of previous problems, university-level texts, and proof strategies may soon develop a framework for generating candidate questions. Human evaluators might still be needed to ensure rigor, avoid redundancy, and introduce pedagogical value but the heavy lifting may no longer require weeks of manual effort. Importantly, Brown's comments also reinforce a broader lesson for Science, technology, engineering, and mathematics (STEM) students. The future of work and innovation is not only about what AI can do but how humans decide to work alongside it. In mathematics, a field revered for its purity and precision, AI may not replace the joy of discovery, but it might make that discovery faster, more accessible, and more iterative. As classrooms integrate AI tools into daily learning, and competitions begin exploring model-generated question banks, students entering the field of mathematics in 2025 may find themselves solving questions that were not written by teachers or mentors, but by a machine trained on their thinking patterns. Still, as Noam Brown puts it, 'There's always a next hurdle.' Right now, that hurdle is originality. But the line between what machines can solve and what they can create is fading quietly, rapidly, and without fundamental barriers. TOI Education is on WhatsApp now. Follow us here. Ready to navigate global policies? Secure your overseas future. Get expert guidance now!
Time of India
2 days ago
- Time of India
The AI That Can Predict Environmental Disasters Before They Strike
Synopsis Google DeepMind's AlphaEarth is an artificial intelligence model that has the ability to look and forecast changes on the globe as accurately as a satellite. It is not merely a mapping tool - it's a climate science, agriculture, and disaster relief game-changer.
