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@www.microsoft.com
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Microsoft is taking a proactive approach to future cybersecurity threats by integrating post-quantum cryptography (PQC) into its Windows and Linux systems. This move is designed to protect against the potential for quantum computers to break current encryption methods like RSA, which secure online communications, banking transactions, and sensitive data. Quantum computers, leveraging quantum mechanics, can solve complex problems far faster than classical computers, posing a significant threat to existing cryptographic schemes. Microsoft's initiative aims to safeguard data from a "harvest now, decrypt later" scenario, where hackers steal encrypted data today with the intent of decrypting it once quantum technology becomes advanced enough.
Microsoft's PQC implementation includes the addition of two key algorithms: ML-KEM (Module Lattice-Based Key Encapsulation Mechanism) and ML-DSA (Module Lattice-Based Digital Signature Algorithm). ML-KEM, also known as CRYSTALS-Kyber, secures key exchanges and prevents attacks by protecting the start of secure connections. ML-DSA, formerly CRYSTALS-Dilithium, ensures data integrity and authenticity through digital signatures. These algorithms are being introduced in Windows Insider builds (Canary Build 27852+) and Linux via SymCrypt-OpenSSL v1.9.0, allowing developers and organizations to begin testing and preparing for a quantum-secure future. This update to Windows 11 is a critical step in what Microsoft views as a major technological transition. By making quantum-resistant algorithms available through SymCrypt, the core cryptographic code library in Windows, and updating SymCrypt-OpenSSL, Microsoft is enabling the widely used OpenSSL library to leverage SymCrypt for cryptographic operations. The new algorithms, selected by the National Institute of Standards and Technology (NIST), represent a move towards replacing vulnerable cryptosystems like RSA and elliptic curves. This signifies a broader effort to bolster cybersecurity against the emerging threat of quantum computing. Recommended read:
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@www.microsoft.com
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Microsoft is actively preparing for a future where quantum computers pose a significant threat to current encryption methods. The company is exploring Post-Quantum Cryptography (PQC) solutions, with a focus on algorithms like FrodoKEM, to bolster security on Windows and Linux platforms. This move is driven by the understanding that quantum computers, with their ability to solve complex problems exponentially faster than classical computers, could break the cryptographic backbone of today’s digital world, including systems like RSA, Diffie-Hellman, and elliptic curve cryptography. The urgency is underscored by recent advances like Microsoft’s Majorana 1, a quantum processor powered by topological qubits, which marks major steps toward practical quantum computing.
Microsoft's efforts to transition to quantum-resistant cryptographic systems include adding PQC algorithms to their core cryptography library, SymCrypt. Recently, Microsoft has taken the next step by adding PQC support to Windows Insiders (Canary Build 27852+) and to Linux through SymCrypt-OpenSSL v1.9.0. These additions allow companies and developers to start testing and preparing for a quantum-secure future, preventing a potential "harvest now, decrypt later" scenario where hackers collect encrypted data today to decrypt later using quantum computers using quantum computers. This proactive approach aims to safeguard digital lives against the looming quantum threat. The new additions to Windows include ML-KEM (Module Lattice-Based Key Encapsulation Mechanism), also known as CRYSTALS-Kyber, designed for secure key exchange, and ML-DSA (Module Lattice-Based Digital Signature Algorithm), previously known as CRYSTALS-Dilithium, used for digital signatures to ensure data integrity and authenticity. NIST approved three PQC standards which are called FIPS 203, 204, and 205. FIPS 203 is a Module-Lattice-Based Key-Encapsulation Mechanism Standard that specifies a key encapsulation mechanism designed to protect information exchange over public networks, ensuring confidentiality even in the presence of quantum adversaries. FIPS 204 is a Module-Lattice-Based Digital Signature Standard that defines a digital signature scheme that provides authentication and integrity, crucial for verifying identities and securing communications. The FIPS 205:Stateless Hash-Based Digital Signature Standard outlines a stateless hash-based digital signature scheme, offering an alternative approach to digital signatures with strong security assurances. NIST encourages organizations to begin the transition to these new standards to ensure long-term data security. Recommended read:
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Source Asia@Source Asia
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news.microsoft.com
, Microsoft Research
Microsoft Research has unveiled Aurora, a groundbreaking AI foundation model with 1.3 billion parameters, that is set to revolutionize Earth system forecasting. This innovative model outperforms traditional operational forecasts in critical areas such as air quality prediction, ocean wave forecasting, tropical cyclone tracking, and high-resolution weather prediction. Aurora achieves this superior performance at significantly lower computational costs, marking a significant advancement in the field. The model's capabilities extend beyond traditional weather forecasting, positioning it as a versatile tool for addressing a wide range of environmental challenges.
Aurora's architecture, based on Perceiver IO, allows it to efficiently process structured inputs and outputs, making it well-suited for complex Earth system data. Researchers at Microsoft have trained Aurora on an unprecedented volume of atmospheric data, incorporating information from satellites, radar, weather stations, simulations, and forecasts. This extensive training enables Aurora to rapidly generate forecasts and adapt to specific tasks through fine-tuning with smaller, task-specific datasets. The model's flexibility and ability to learn from diverse data sources are key factors in its exceptional forecasting accuracy. The development of Aurora signifies a major step forward in applying AI to Earth science. By demonstrating the potential of foundation models to accurately and efficiently predict various environmental phenomena, Aurora paves the way for new approaches to disaster preparedness, resource management, and climate change mitigation. The publicly available code and weights of Aurora, accessible on GitHub, encourage further research and development in this exciting area. Microsoft's work underscores the transformative power of AI in addressing some of the world's most pressing environmental challenges. Recommended read:
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@www.microsoft.com
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Microsoft is taking a significant step towards future-proofing cybersecurity by integrating post-quantum cryptography (PQC) into Windows Insider builds. This move aims to protect data against the potential threat of quantum computers, which could render current encryption methods vulnerable. The integration of PQC is a critical step toward quantum-resilient cybersecurity, ensuring that Windows systems can withstand attacks from more advanced computing power in the future.
Microsoft announced the availability of PQC support in Windows Insider Canary builds (27852 and above). This release allows developers and organizations to begin experimenting with PQC in real-world environments, assessing integration challenges, performance trade-offs, and compatibility. This is being done in an attempt to jump-start what’s likely to be the most formidable and important technology transition in modern history. The urgency behind this transition stems from the "harvest now, decrypt later" threat, where malicious actors store encrypted communications today, with the intent to decrypt them once quantum computers become capable. These captured secrets, such as passwords, encryption keys, or medical data, could remain valuable to attackers for years to come. By adopting PQC algorithms, Microsoft aims to safeguard sensitive information against this future risk, emphasizing the importance of starting the transition now. Recommended read:
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Source Asia@Source Asia
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Microsoft's Aurora AI model is revolutionizing weather forecasting by providing accurate 10-day forecasts in mere seconds. This AI foundation model, developed by Microsoft Research, has demonstrated capabilities that extend beyond traditional weather prediction, encompassing environmental events such as tropical cyclones, air quality, and ocean waves. Aurora achieves this by training on a massive dataset of over one million hours of atmospheric data from satellites, radar, weather stations, simulations, and forecasts, which Microsoft believes is the largest collection ever assembled for training an AI forecasting model. The model's speed and accuracy have the potential to improve safety and inform decisions across various sectors.
The core strength of Aurora lies in its foundation model architecture. It's not simply limited to weather forecasting; it can be fine-tuned for specific environmental prediction tasks. After initial training on general weather patterns, Aurora can be adapted with smaller datasets to forecast elements like wave height or air quality. The AI does not fully grasp the physical laws governing weather, but its use for environmental prediction tasks and ability to provide accurate forecasts is still significant. This flexibility makes it a versatile tool for understanding and predicting various aspects of the Earth system. Aurora's performance has been noteworthy, beating existing numerical and AI models across 91 percent of forecasting targets when fine-tuned to medium-range weather forecasts. Its rapid processing time, taking seconds compared to the hours required by traditional models, makes it a valuable asset for timely decision-making. Microsoft is leveraging AI technology to make weather forecasting more efficient and accurate. While generative AI is revolutionizing how we do things, integrating it into workflows is making work easier by automating redundant tasks, creating more time to focus on more important tasks. Recommended read:
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Source Asia@Source Asia
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Microsoft's Aurora AI foundation model is revolutionizing weather and environmental forecasting, offering quicker and more accurate predictions compared to traditional methods. Developed by Microsoft Research, Aurora is a large-scale AI model trained on a vast dataset of atmospheric information, including satellite data, radar readings, weather station observations, and simulations. This comprehensive training allows Aurora to forecast a range of environmental events, from hurricanes and typhoons to air quality and ocean waves, with exceptional precision and speed. The model's capabilities extend beyond conventional weather forecasting, making it a versatile tool for understanding and predicting environmental changes.
Aurora's unique architecture enables it to be fine-tuned for specific tasks using modest amounts of additional data. This "fine-tuning" process allows the model to generate forecasts in seconds, demonstrating its efficiency and adaptability. Researchers have shown that Aurora outperforms existing numerical and AI models in 91% of forecasting targets when fine-tuned for medium-range weather forecasts. Its ability to accurately predict hurricane trajectories and other extreme weather events highlights its potential to improve disaster preparedness and response efforts, ultimately saving lives and mitigating damage. Senior researchers Megan Stanley and Wessel Bruinsma emphasized Aurora's broader impact on environmental science, noting its potential to revolutionize the field. In a paper published in Nature, they highlighted Aurora's ability to correctly forecast hurricanes in 2023 more accurately than operational forecasting centers, such as the US National Hurricane Center. Aurora also demonstrated its capabilities when correctly forecasting where and when Doksuri would hit the Philippines four days in advance. These findings underscore the transformative potential of AI in addressing complex environmental challenges and paving the way for more effective climate modeling and environmental event management. Recommended read:
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@www.microsoft.com
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Microsoft is making significant strides in enhancing digital security and technological advancement on multiple fronts. The company is actively pushing passkeys as a simpler and safer alternative to traditional passwords. Marking the shift, Microsoft joined the FIDO Alliance in celebrating the first "World Passkey Day," and has pledged to increase the adoption of passkeys, which offer a phishing-resistant authentication method utilizing face, fingerprint, or PIN. This initiative aims to combat the rising tide of password-based cyberattacks, which have surged to an alarming 7,000 attacks per second. Microsoft introduced Windows Hello as a way to sign into accounts without a password and this laid the ground work for an entirely new era of authentication.
Microsoft Vice Chair and President Brad Smith is also urging the United States and its allies to intensify their efforts in quantum computing. Smith emphasizes the increasing competition from countries like China and highlights the importance of bolstering investment, workforce development, and supply chain security to maintain U.S. technological leadership. Quantum computing promises transformative advancements in fields like medicine, energy, and national security. In addition to its quantum efforts, Microsoft has announced new European digital commitments, recognizing the importance of trans-Atlantic ties for economic growth. These commitments include datacenter operations in 16 countries and a Digital Resilience Commitment, reflecting the company's deep economic reliance on Europe. Recommended read:
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Matthias Bastian@THE DECODER
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Microsoft has launched three new additions to its Phi series of compact language models: Phi-4-reasoning, Phi-4-reasoning-plus, and Phi-4-mini-reasoning. These models are designed to excel in complex reasoning tasks, including mathematical problem-solving, algorithmic planning, and coding, demonstrating that smaller AI models can achieve significant performance. The models are optimized to handle complex problems through structured reasoning and internal reflection, while also being efficient enough to run on lower-end hardware, including mobile devices, making advanced AI accessible on resource-limited devices.
Phi-4-reasoning, a 14-billion parameter model, was trained using supervised fine-tuning with reasoning paths from OpenAI's o3-mini. Phi-4-reasoning-plus enhances this with reinforcement learning and processes more tokens, leading to higher accuracy, although with increased computational cost. Notably, these models outperform larger systems, such as the 70B parameter DeepSeek-R1-Distill-Llama, and even surpass DeepSeek-R1 with 671 billion parameters on the AIME-2025 benchmark, a qualifier for the U.S. Mathematical Olympiad, highlighting the effectiveness of Microsoft's approach to efficient, high-performing AI. The Phi-4 reasoning models show strong results in programming, algorithmic problem-solving, and planning tasks, with improvements in logical reasoning positively impacting general capabilities such as following prompts and answering questions based on long-form content. Microsoft employed a data-centric training strategy, using structured reasoning outputs marked with special tokens to guide the model's intermediate reasoning steps. The open-weight models have been released with transparent training details and are hosted on Hugging Face, allowing for public access, fine-tuning, and use in various applications under a permissive MIT license. Recommended read:
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Carl Franzen@AI News | VentureBeat
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Microsoft has announced the release of Phi-4-reasoning-plus, a new small, open-weight language model designed for advanced reasoning tasks. Building upon the architecture of the previously released Phi-4, this 14-billion parameter model integrates supervised fine-tuning and reinforcement learning to achieve strong performance on complex problems. According to Microsoft, the Phi-4 reasoning models outperform larger language models on several demanding benchmarks, despite their compact size. This new model pushes the limits of small AI, demonstrating that carefully curated data and training techniques can lead to impressive reasoning capabilities.
The Phi-4 reasoning family, consisting of Phi-4-reasoning, Phi-4-reasoning-plus, and Phi-4-mini-reasoning, is specifically trained to handle complex reasoning tasks in mathematics, scientific domains, and software-related problem solving. Phi-4-reasoning-plus, in particular, extends supervised fine-tuning with outcome-based reinforcement learning, which is targeted for improved performance in high-variance tasks such as competition-level mathematics. All models are designed to enable reasoning capabilities, especially on lower-performance hardware such as mobile devices. Microsoft CEO Satya Nadella revealed that AI is now contributing to 30% of Microsoft's code. The open weight models were released with transparent training details and evaluation logs, including benchmark design, and are hosted on Hugging Face for reproducibility and public access. The model has been released under a permissive MIT license, enabling its use for broad commercial and enterprise applications, and fine-tuning or distillation, without restriction. Recommended read:
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@arstechnica.com
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arstechnica.com
, www.techrepublic.com
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Microsoft researchers have achieved a significant breakthrough in AI efficiency with the development of a 1-bit large language model (LLM) called BitNet b1.58 2B4T. This model, boasting two billion parameters and trained on four trillion tokens, stands out due to its remarkably low memory footprint and energy consumption. Unlike traditional AI models that rely on 16- or 32-bit floating-point formats for storing numerical weights, BitNet utilizes only three distinct weight values: -1, 0, and +1. This "ternary" architecture dramatically reduces complexity, enabling the AI to run efficiently on a standard CPU, even an Apple M2 chip, according to TechCrunch.
The development of BitNet b1.58 2B4T represents a significant advancement in the field of AI, potentially paving the way for more accessible and sustainable AI applications. This 1-bit model, available on Hugging Face, uses a novel approach of representing each weight with a single bit. While this simplification can lead to a slight reduction in accuracy compared to larger, more complex models, BitNet b1.58 2B4T compensates through its massive training dataset, comprising over 33 million books. The reduction in memory usage is substantial, with the model requiring only 400MB of non-embedded memory, significantly less than comparable models. Comparisons against leading mainstream models like Meta’s LLaMa 3.2 1B, Google’s Gemma 3 1B, and Alibaba’s Qwen 2.5 1.5B have shown that BitNet b1.58 2B4T performs competitively across various benchmarks. In some instances, it has even outperformed these models. However, to achieve optimal performance and efficiency, the LLM must be used with the bitnet.cpp inference framework. This highlights a current limitation as the model does not run on GPU and requires a proprietary framework. Despite this, the creation of such a lightweight and efficient LLM marks a crucial step toward future AI that may not necessarily require supercomputers. Recommended read:
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Matt Marshall@AI News | VentureBeat
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References:
Microsoft Security Blog
, www.zdnet.com
Microsoft is enhancing its Copilot Studio platform with AI-driven improvements, introducing deep reasoning capabilities that enable agents to tackle intricate problems through methodical thinking and combining AI flexibility with deterministic business process automation. The company has also unveiled specialized deep reasoning agents for Microsoft 365 Copilot, named Researcher and Analyst, to help users achieve tasks more efficiently. These agents are designed to function like personal data scientists, processing diverse data sources and generating insights through code execution and visualization.
Microsoft's focus includes securing AI and using it to bolster security measures, as demonstrated by the upcoming Microsoft Security Copilot agents and new security features. Microsoft aims to provide an AI-first, end-to-end security platform that helps organizations secure their future, one example being the AI agents designed to autonomously assist with phishing, data security, and identity management. The Security Copilot tool will automate routine tasks, allowing IT and security staff to focus on more complex issues, aiding in defense against cyberattacks. Recommended read:
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