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Google has unveiled a new AI model designed to forecast tropical cyclones with improved accuracy. Developed through a collaboration between Google Research and DeepMind, the model is accessible via a newly launched website called Weather Lab. The AI aims to predict both the path and intensity of cyclones days in advance, overcoming limitations present in traditional physics-based weather prediction models. Google claims its algorithm achieves "state-of-the-art accuracy" in forecasting cyclone track and intensity, as well as details like formation, size, and shape.
The AI model was trained using two extensive datasets: one describing the characteristics of nearly 5,000 cyclones from the past 45 years, and another containing millions of weather observations. Internal testing demonstrated the algorithm's ability to accurately predict the paths of recent cyclones, in some cases up to a week in advance. The model can generate 50 possible scenarios, extending forecast capabilities up to 15 days. This breakthrough has already seen adoption by the U.S. National Hurricane Center, which is now using these experimental AI predictions alongside traditional forecasting models in its operational workflow. Google's AI's ability to forecast up to 15 days in advance marks a significant improvement over current models, which typically provide 3-5 day forecasts. Google made the AI accessible through a new website called Weather Lab. The model is available alongside two years' worth of historical forecasts, as well as data from traditional physics-based weather prediction algorithms. According to Google, this could help weather agencies and emergency service experts better anticipate a cyclone’s path and intensity. Recommended read:
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@www.iansresearch.com
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The increasing capabilities of quantum computers are posing a significant threat to current encryption methods, potentially jeopardizing the security of digital assets and the Internet of Things. Researchers at Google Quantum AI are urging software developers and encryption experts to accelerate the implementation of next-generation cryptography, anticipating that quantum computers will soon be able to break widely used encryption standards like RSA. This urgency is fueled by new estimates suggesting that breaking RSA encryption may be far easier than previously believed, with a quantum computer containing approximately 1 million qubits potentially capable of cracking it. Experts recommend that vulnerable systems should be deprecated after 2030 and disallowed after 2035.
Last week, Craig Gidney from Google Quantum AI published research that significantly lowers the estimated quantum resources needed to break RSA-2048. Where previous estimates projected that cracking RSA-2048 would require around 20 million qubits and 8 hours of computation, the new analysis reveals that it could be done in under a week using fewer than 1 million noisy qubits. This more than 95% reduction in hardware requirements is a seismic shift in the projected timeline for "Q-Day," the hypothetical moment when quantum computers can break modern encryption. RSA encryption, used in secure web browsing, email encryption, VPNs, and blockchain systems, relies on the difficulty of factoring large numbers into their prime components. Quantum computers, leveraging Shor's algorithm, can exponentially accelerate this process. Recent innovations, including Approximate Residue Arithmetic, Magic State Cultivation, Optimized Period Finding with Ekerå-Håstad Algorithms, and Yoked Surface Codes & Sparse Lookups, have collectively reduced the physical qubit requirement to under 1 million and allow the algorithm to complete in less than 7 days. Recommended read:
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@medium.com
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Google Quantum AI has published a study that dramatically lowers the estimated quantum resources needed to break RSA-2048, one of the most widely used encryption standards. The study, authored by Craig Gidney, indicates that RSA cracking may be possible with fewer qubits than previously estimated, potentially impacting digital security protocols used in secure web browsing, email encryption, VPNs, and blockchain systems. This breakthrough could significantly accelerate the timeline for "Q-Day," the point at which quantum computers can break modern encryption.
Previous estimates, including Gidney's 2019 study, suggested that cracking RSA-2048 would require around 20 million qubits and 8 hours of computation. However, the new analysis reveals it could be done in under a week using fewer than 1 million noisy qubits. This reduction in hardware requirements is attributed to several technical innovations, including approximate residue arithmetic, magic state cultivation, optimized period finding with Ekerå-Håstad algorithms, and yoked surface codes & sparse lookups. These improvements minimize the overhead in fault-tolerant quantum circuits, enabling better scaling. Google's researchers have discovered that, thanks to new error correction tricks and smarter algorithms, the encryption could be broken with under 1 million qubits and in less than a week, given favorable assumptions like a 0.1% gate error rate and a 1-microsecond gate time. This significantly faster encryption breaking capability, potentially 20x faster than previously anticipated, raises concerns about the security of Bitcoin wallets and other financial systems that rely on RSA encryption. The findings could potentially make Bitcoin wallets and financial systems vulnerable much sooner than expected. Recommended read:
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@quantumcomputingreport.com
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The rapid advancement of quantum computing poses a significant threat to current encryption methods, particularly RSA, which secures much of today's internet communication. Google's recent breakthroughs have redefined the landscape of cryptographic security, with researchers like Craig Gidney significantly lowering the estimated quantum resources needed to break RSA-2048. A new study indicates that RSA-2048 could be cracked in under a week using fewer than 1 million noisy qubits, a dramatic reduction from previous estimates of around 20 million qubits and eight hours of computation. This shift accelerates the timeline for "Q-Day," the hypothetical moment when quantum computers can break modern encryption, impacting everything from email to financial transactions.
This vulnerability stems from the ability of quantum computers to utilize Shor's algorithm for factoring large numbers, a task prohibitively difficult for classical computers. Google's innovation involves several technical advancements, including approximate residue arithmetic, magic state cultivation, optimized period finding with Ekerå-Håstad algorithms, and yoked surface codes with sparse lookups. These improvements streamline modular arithmetic, reduce the depth of quantum circuits, and minimize overhead in fault-tolerant quantum circuits, collectively reducing the physical qubit requirement to under 1 million while maintaining a relatively short computation time. In response to this threat, post-quantum cryptography (PQC) is gaining momentum. PQC refers to cryptographic algorithms designed to be secure against both classical and quantum attacks. NIST has already announced the first set of quantum-safe algorithms for standardization, including FrodoKEM, a key encapsulation protocol offering a simple design and strong security guarantees. The urgency of transitioning to quantum-resistant cryptographic systems is underscored by ongoing advances in quantum computing. While the digital world relies on encryption, the evolution to AI and quantum computing is challenging the security. Professionals who understand both cybersecurity and artificial intelligence will be the leaders in adapting to these challenges. Recommended read:
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@deepmind.google
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Google DeepMind has unveiled AlphaEvolve, a groundbreaking AI agent designed for algorithmic and scientific discovery. This innovative agent combines the power of large language models (LLMs) like Gemini Pro, evolutionary search frameworks, and automated evaluation methods to evolve superior algorithms. Unlike systems that merely generate plausible code, AlphaEvolve iteratively refines entire codebases, optimizing across multiple performance metrics and grounding itself in actual code execution results, effectively sidestepping hallucinations. Terence Tao also collaborated with the DeepMind team on AlphaEvolve, highlighting its significance in the field.
AlphaEvolve's capabilities extend to a range of algorithmic and scientific challenges. It has optimized Google's data center scheduling, recovering 0.7% of Google's compute capacity, simplified hardware accelerator circuit designs, and accelerated the training of its own underlying LLM, offering a glimpse into AI self-improvement. Notably, AlphaEvolve cracked a problem unchanged since 1969, devising a more efficient method for multiplying two 4x4 complex matrices using only 48 scalar multiplications, surpassing Strassen's algorithm after 56 years. The agent also tackled over 50 other open mathematical problems, often matching or exceeding the state of the art. In parallel, Google has launched "Jules," a new coding agent powered by Google's Gemini 2.5 Pro model and designed to assist developers with repetitive tasks such as bug-fixing, documentation, test generation, and feature building. Jules operates in a secure cloud environment, breaking down complex tasks into smaller steps and adapting to user instructions. The agent automatically creates pull requests with audio summaries, streamlining the code review process. This move signifies the rapid maturation of AI in software development and a broader trend towards AI agents becoming trusted engineering partners. Recommended read:
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