Top Mathematics discussions

NishMath

@siliconangle.com //
Google LLC has announced the development of a new AI model designed to improve tropical cyclone forecasting. The model, created through collaboration between Google Research and DeepMind, aims to predict both the path and intensity of these storms days in advance, offering a significant advancement over traditional forecasting methods. This AI-driven approach is accessible through a newly launched website called Weather Lab, providing researchers and experts with access to cutting-edge predictions. According to Google, the algorithm was trained using two datasets. The first described the path, intensity and other key properties of nearly 5,000 cyclones from the past 45 years. The other dataset included information about past weather conditions that was distilled from millions of observations.

The traditional reliance on physics-based weather prediction models often faces limitations, struggling to accurately predict both a cyclone's track and its intensity simultaneously. Google claims its AI model overcomes this hurdle, achieving state-of-the-art accuracy in forecasting both aspects of cyclone behavior. Furthermore, the model can predict other vital details, including a cyclone's formation, size, and shape. In internal tests, Google successfully used the algorithm to predict the paths of four recent cyclones, generating accurate forecasts nearly a week ahead of time for two of the storms, with the capability to predict storms up to 15 days in advance by generating 50 possible scenarios.

The Weather Lab platform allows users to explore and compare predictions from various AI and physics-based models, potentially enhancing the ability of weather agencies and emergency services to anticipate cyclone paths and intensities. DeepMind has also announced a partnership with the U.S. National Hurricane Center, which will incorporate the AI predictions into its operational forecasting workflow for the first time. The company claims this is a major breakthrough in hurricane forecasting, introducing an artificial intelligence system that can predict both the path and intensity of tropical cyclones with unprecedented accuracy which has eluded traditional weather models for decades.

Recommended read:
References :
  • siliconangle.com: Google develops AI model for forecasting tropical cyclones
  • AI News | VentureBeat: Google DeepMind just changed hurricane forecasting forever with new AI model
  • MarkTechPost: Google AI Unveils a Hybrid AI-Physics Model for Accurate Regional Climate Risk Forecasts with Better Uncertainty Assessment
  • www.marktechpost.com: Google AI Unveils a Hybrid AI-Physics Model for Accurate Regional Climate Risk Forecasts with Better Uncertainty Assessment
  • Maginative: Google's AI Can Now Predict Hurricane Paths 15 Days Out — and the Hurricane Center Is Using It
  • SiliconANGLE: Google develops AI model for forecasting tropical cyclones. According to the company, the algorithm was developed through a collaboration between its Google Research and DeepMind units. It’s available through a newly launched website called Weather Lab.
  • www.engadget.com: Google DeepMind is sharing its AI forecasts with the National Weather Service

@quantumcomputingreport.com //
References: thequantuminsider.com , ,
The quantum computing industry is experiencing a surge in activity, marked by significant acquisitions and technological advancements. IonQ has announced its intent to acquire UK-based Oxford Ionics for $1.075 billion in stock and cash, uniting two leaders in trapped-ion quantum computing. This deal aims to accelerate the development of scalable and reliable quantum systems, targeting 256 high-fidelity qubits by 2026 and over 10,000 physical qubits by 2027. The acquisition combines IonQ's quantum computing stack with Oxford Ionics' semiconductor-compatible ion-trap technology, strengthening IonQ's technical capabilities and expanding its European presence. CEO of IonQ, Niccolo de Masi, highlighted the strategic importance of this acquisition, uniting talent from across the world to become the world’s best quantum computing, quantum communication and quantum networking ecosystem.

Recent advancements also include the activation of Europe’s first room-temperature quantum accelerator by Fraunhofer IAF, featuring Quantum Brilliance’s diamond-based QB-QDK2.0 system. This system utilizes nitrogen-vacancy (NV) centers and operates without cryogenic requirements, seamlessly integrating into existing high-performance computing environments. It's co-located with classical processors and NVIDIA GPUs to support hybrid quantum-classical workloads. Moreover, IBM has announced plans to build the world’s first large-scale, error-corrected quantum computer named Starling, aiming for completion by 2028 and cloud availability by 2029. IBM claims it has cracked the code for quantum error correction, moving from science to engineering.

Further bolstering the industry's growth, collaborative projects are demonstrating the potential of quantum computing in various applications. IonQ, in partnership with AstraZeneca, AWS, and NVIDIA, has showcased a quantum-accelerated drug discovery workflow that drastically reduces simulation time for key pharmaceutical reactions. Their hybrid system, integrating IonQ’s Forte quantum processor with NVIDIA CUDA-Q and AWS infrastructure, achieved over a 20-fold improvement in time-to-solution for the Suzuki-Miyaura reaction. Additionally, the Karnataka State Cabinet has approved the second phase of the Quantum Research Park at the Indian Institute of Science (IISc) in Bengaluru, allocating ₹48 crore ($5.595 million USD) to expand the state’s quantum technology infrastructure and foster collaboration between academia, startups, and industry.

Recommended read:
References :
  • thequantuminsider.com: IonQ has announced the results of a collaborative quantum computing project that could accelerate pharmaceutical research timelines by orders of magnitude.
  • : Fraunhofer IAF Activates Europe’s First Room-Temperature Quantum Accelerator from Quantum Brilliance
  • thequantuminsider.com: IonQ Acquires UK-based Oxford Ionics For $1.075 Billion

Sophia Chen@technologyreview.com //
IBM has announced ambitious plans to construct a large-scale, error-corrected quantum computer, aiming for completion by 2028. This initiative, known as IBM Quantum Starling, represents a significant step forward in quantum computing technology. The project involves a modular architecture, with components being developed at a new IBM Quantum Data Center in Poughkeepsie, New York. IBM hopes to make the computer available to users via the cloud by 2029.

The company's approach to fault tolerance involves a novel architecture using quantum low-density parity check (qLDPC) codes. This method is projected to drastically reduce the number of physical qubits required for error correction, potentially cutting overhead by around 90% compared to other leading codes. IBM says it's cracked the code to quantum error correction and this will significantly enhance the computational capability of the new machine compared to existing quantum computers. IBM also released two technical papers outlining how qLDPC codes can improve instruction processing and operational efficiency, and describes how error correction and decoding can be handled in real-time using classical computing resources.

IBM anticipates that Starling will be capable of executing 100 million quantum operations using 200 logical qubits. This lays the foundation for a follow-up system, IBM Quantum Blue Jay, which will operate with 2,000 logical qubits and run 1 billion operations. According to IBM, storing the computational state of Starling would require memory exceeding that of a quindecillion (10⁴⁸) of today’s most powerful supercomputers. This project aims to solve real-world challenges and unlock immense possibilities for business in fields such as drug development, materials science, chemistry, and optimisation.

Recommended read:
References :
  • Analytics India Magazine: IBM Plans ‘World’s First’ Fault-Tolerant Quantum Computer by 2029
  • www.technologyreview.com: IBM announced detailed plans today to build an error-corrected quantum computer with significantly more computational capability than existing machines by 2028.
  • ComputerWeekly.com: IBM updates path to fault-tolerant quantum computing
  • www.cxoinsightme.com: IBM unveiled its path to build the world’s first large-scale, fault-tolerant quantum computer, setting the stage for practical and scalable quantum computing.
  • www.newscientist.com: New Scientist reports IBM will build a practical quantum supercomputer by 2029.

Carl Franzen@AI News | VentureBeat //
Mistral AI has launched its first reasoning model, Magistral, signaling a commitment to open-source AI development. The Magistral family features two models: Magistral Small, a 24-billion parameter model available with open weights under the Apache 2.0 license, and Magistral Medium, a proprietary model accessible through an API. This dual release strategy aims to cater to both enterprise clients seeking advanced reasoning capabilities and the broader AI community interested in open-source innovation.

Mistral's decision to release Magistral Small under the permissive Apache 2.0 license marks a significant return to its open-source roots. The license allows for the free use, modification, and distribution of the model's source code, even for commercial purposes. This empowers startups and established companies to build and deploy their own applications on top of Mistral’s latest reasoning architecture, without the burdens of licensing fees or vendor lock-in. The release serves as a powerful counter-narrative, reaffirming Mistral’s dedication to arming the open community with cutting-edge tools.

Magistral Medium demonstrates competitive performance in the reasoning arena, according to internal benchmarks released by Mistral. The model was tested against its predecessor, Mistral-Medium 3, and models from Deepseek. Furthermore, Mistral's Agents API's Handoffs feature facilitates smart, multi-agent workflows, allowing different agents to collaborate on complex tasks. This enables modular and efficient problem-solving, as demonstrated in systems where agents collaborate to answer inflation-related questions.

Recommended read:
References :
  • Simon Willison: Mistral's first reasoning LLM - Magistral - was released today and is available in two sizes, an open weights (Apache 2) 24B model called Magistral Small and an API/hosted only model called Magistral Medium. My notes here, including running Small locally with Ollama and accessing Medium via my llm-mistral plugin
  • Simon Willison's Weblog: Mistral's first reasoning model is out today, in two sizes. There's a 24B Apache 2 licensed open-weights model called Magistral Small (actually Magistral-Small-2506), and a larger API-only model called Magistral Medium.
  • THE DECODER: Mistral launches Europe's first reasoning model Magistral but lags behind competitors
  • AI News | VentureBeat: The company is signaling that the future of reasoning AI will be both powerful and, in a meaningful way, open to all.
  • www.marktechpost.com: How to Create Smart Multi-Agent Workflows Using the Mistral Agents API’s Handoffs Feature
  • TestingCatalog: Mistral AI debuts Magistral models focused on advanced reasoning
  • the-decoder.com: The French start-up Mistral is launching its first reasoning model on the market with Magistral. It is designed to enable logical thinking in European languages.
  • www.artificialintelligence-news.com: Mistral AI has pulled back the curtain on Magistral, their first model specifically built for reasoning tasks.
  • www.infoworld.com: Mistral AI unveils Magistral reasoning model
  • AI News: Mistral AI has pulled back the curtain on Magistral, their first model specifically built for reasoning tasks.
  • Simon Willison: Mistral's first reasoning LLM - Magistral - was released today and is available in two sizes, an open weights (Apache 2) 24B model called Magistral Small and an API/hosted only model called Magistral Medium. My notes here, including running Small locally with Ollama and accessing Medium via my llm-mistral plugin
  • SiliconANGLE: Mistral AI debuts new Magistral series of reasoning LLMs.
  • siliconangle.com: Mistral AI SAS today introduced Magistral, a new lineup of reasoning-optimized large language models. The LLM series includes two algorithms on launch.
  • MarkTechPost: Mistral AI Releases Magistral Series: Advanced Chain-of-Thought LLMs for Enterprise and Open-Source Applications
  • www.marktechpost.com: Mistral AI Releases Magistral Series: Advanced Chain-of-Thought LLMs for Enterprise and Open-Source Applications
  • WhatIs: What differentiates Mistral AI reasoning model Magistral
  • AlternativeTo: Mistral AI debuts Magistral: a transparent, multilingual reasoning model family, including open-source Magistral Small available on Hugging Face and enterprise-focused Magistral Medium available on various platforms.

@www.marktechpost.com //
A new framework called AlphaOne, developed by researchers at the University of Illinois Urbana-Champaign and the University of California, Berkeley, offers AI developers a novel method to modulate the reasoning processes of large language models (LLMs). This test-time scaling technique improves model accuracy and efficiency without requiring costly retraining. AlphaOne essentially provides a new "dial" to control LLM 'thinking,' allowing developers to boost performance on complex tasks in a more controlled and cost-effective manner compared to existing approaches. The framework dynamically manages slow-to-fast reasoning transitions, optimizing accuracy on real-world datasets like AMC23 and LiveCodeBench.

One persistent issue with large reasoning models is their inability to self-regulate shifts between fast and slow thinking, leading to either premature conclusions or excessive processing. AlphaOne addresses this by providing a universal method for modulating the reasoning process of advanced LLMs. Previous solutions, such as parallel scaling (running a model multiple times) or sequential scaling (modulating thinking during a single run), often lack synchronization between the duration of reasoning and the scheduling of slow-to-fast thinking transitions. AlphaOne aims to overcome these limitations by effectively adapting reasoning processes.

In addition to AlphaOne, Amazon Nova provides a solution for data consistency in generative AI through Text-to-SQL. Businesses rely on precise, real-time insights to make critical decisions, and Text-to-SQL bridges the gap by generating precise, schema-specific queries that empower faster decision-making and foster a data-driven culture. Unlike Retrieval Augmented Generation (RAG) which is better suited for extracting insights from unstructured data and Generative Business Intelligence, Text-to-SQL excels in querying structured organizational data directly from relational schemas and provides deterministic, reproducible results for specific, schema-dependent queries.

Recommended read:
References :
  • learn.aisingapore.org: Build a Text-to-SQL solution for data consistency in generative AI using Amazon Nova
  • AI News | VentureBeat: AlphaOne gives AI developers a new dial to control LLM ‘thinking’ and boost performance
  • www.marktechpost.com: ALPHAONE: A Universal Test-Time Framework for Modulating Reasoning in AI Models
  • MarkTechPost: ALPHAONE: A Universal Test-Time Framework for Modulating Reasoning in AI Models

Emilia David@AI News | VentureBeat //
References: bsky.app , the-decoder.com , Maginative ...
OpenAI has recently launched its newest reasoning model, o3-pro, making it available to ChatGPT Pro and Team subscribers, as well as through OpenAI’s API. Enterprise and Edu subscribers will gain access the following week. The company touts o3-pro as a significant upgrade, emphasizing its enhanced capabilities in mathematics, science, and coding, and its improved ability to utilize external tools.

OpenAI has also slashed the price of o3 by 80% and o3-pro by 87%, positioning the model as a more accessible option for developers seeking advanced reasoning capabilities. This price adjustment comes at a time when AI providers are competing more aggressively on both performance and affordability. Experts note that evaluations consistently prefer o3-pro over the standard o3 model across all categories, especially in science, programming, and business tasks.

O3-pro utilizes the same underlying architecture as o3, but it’s tuned to be more reliable, especially on complex tasks, with better long-range reasoning. The model supports tools like web browsing, code execution, vision analysis, and memory. While the increased complexity can lead to slower response times, OpenAI suggests that the tradeoff is worthwhile for the most challenging questions "where reliability matters more than speed, and waiting a few minutes is worth the tradeoff.”

Recommended read:
References :
  • bsky.app: The OpenAI API is back to running at 100% again, plus we dropped o3 prices by 80% and launched o3-pro - enjoy!
  • the-decoder.com: OpenAI lowered the price of its o3 language model by 80 percent, CEO Sam Altman said.
  • AI News | VentureBeat: OpenAI released the latest in its o-series of reasoning model that promises more reliable and accurate responses for enterprises.
  • Maginative: OpenAI’s new o3-pro model is now available in ChatGPT and the API, offering top-tier performance in math, science, and coding—at a dramatically lower price.
  • THE DECODER: OpenAI has lowered the price of its o3 language model by 80 percent, CEO Sam Altman said. The article appeared first on The Decoder.
  • AI News | VentureBeat: OpenAI's most powerful reasoning model, o3, is now 80% cheaper, making it more affordable for businesses, researchers, and individual developers.
  • www.cnbc.com: The figure includes sales from the company’s consumer products, ChatGPT business products and its application programming interface, or API.
  • Latent.Space: OpenAI just dropped the price of their o3 model by 80% today and launched o3-pro.
  • Simon Willison's Weblog: OpenAI's Adam Groth explained that the engineers have optimized inference, allowing a significant price reduction for the o3 model.
  • siliconangle.com: OpenAI’s newest reasoning model o3-pro surpasses rivals on multiple benchmarks, but it’s not very fast
  • SiliconANGLE: Silicon Angle reports on OpenAI’s newest reasoning model o3-pro surpassing rivals.
  • bsky.app: OpenAI has launched o3-pro. The new model is available to ChatGPT Pro and Team subscribers and in OpenAI’s API now, while Enterprise and Edu subscribers will get access next week. If you use reasoning models like o1 or o3, try o3-pro, which is much smarter and better at using external tools.
  • The Algorithmic Bridge: OpenAI o3-Pro Is So Good That I Can’t Tell How Good It Is
  • bsky.app: OpenAI dropped o3 pricing 80% today and launched o3-pro.

@machinelearning.apple.com //
Apple researchers have released a new study questioning the capabilities of Large Reasoning Models (LRMs), casting doubt on the industry's pursuit of Artificial General Intelligence (AGI). The research paper, titled "The Illusion of Thinking," reveals that these models, including those from OpenAI, Google DeepMind, Anthropic, and DeepSeek, experience a 'complete accuracy collapse' when faced with complex problems. Unlike existing evaluations primarily focused on mathematical and coding benchmarks, this study evaluates the reasoning traces of these models, offering insights into how LRMs "think".

Researchers tested various models, including OpenAI's o3-mini, DeepSeek-R1, and Claude 3.7 Sonnet, using puzzles like the Tower of Hanoi, Checker Jumping, River Crossing, and Blocks World. These environments allowed for the manipulation of complexity while maintaining consistent logical structures. The team discovered that standard language models surprisingly outperformed LRMs in low-complexity scenarios, while LRMs only demonstrated advantages in medium-complexity tasks. However, all models experienced a performance collapse when faced with highly complex tasks.

The study suggests that the so-called reasoning of LRMs may be more akin to sophisticated pattern matching, which is fragile and prone to failure when challenged with significant complexity. Apple's research team identified three distinct performance regimes: low-complexity tasks where standard models outperform LRMs, medium-complexity tasks where LRMs show advantages, and high-complexity tasks where all models collapse. Apple has begun integrating powerful generative AI into its own apps and experiences. The new Foundation Models framework gives app developers access to the on-device foundation language model.

Recommended read:
References :
  • THE DECODER: LLMs designed for reasoning, like Claude 3.7 and Deepseek-R1, are supposed to excel at complex problem-solving by simulating thought processes.
  • machinelearning.apple.com: Apple machine learning discusses Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
  • PPC Land: PPC Land reports on Apple study exposes fundamental limits in AI reasoning models through puzzle tests.
  • the-decoder.com: The Decoder covers Apple's study, highlighting the limitation in thinking abilities of reasoning models.
  • felloai.com: In a breakthrough paper, Apple researchers reveal the uncomfortable truth about large reasoning models (LRMs): their internal “thought processes” might be nothing more than performative illusions.
  • Gadgets 360: Apple Claims AI Reasoning Models Suffer From ‘Accuracy Collapse’ When Solving Complex Problems
  • futurism.com: Apple Researchers Just Released a Damning Paper That Pours Water on the Entire AI Industry
  • The Register - Software: Apple AI boffins puncture AGI hype as reasoning models flail on complex planning
  • www.theguardian.com: Advanced AI suffers ‘complete accuracy collapse’ in face of complex problems, study finds
  • chatgptiseatingtheworld.com: Apple researchers cast doubt on AI reasoning models of other companies
  • www.livescience.com: AI reasoning models aren’t as smart as they were cracked up to be, Apple study claims
  • www.computerworld.com: Apple warns: GenAI still isn’t very smart
  • Fello AI: Apple's research paper, "The Illusion of Thinking," argues that large reasoning models face a complete accuracy collapse beyond certain complexities, highlighting limitations in their reasoning capabilities.
  • WIRED: Apple's research paper challenges the claims of significant reasoning capabilities in current AI models, particularly those relying on pattern matching instead of genuine understanding.
  • Analytics Vidhya: Apple Exposes Reasoning Flaws in o3, Claude, and DeepSeek-R1
  • www.itpro.com: ‘A complete accuracy collapse’: Apple throws cold water on the potential of AI reasoning – and it's a huge blow for the likes of OpenAI, Google, and Anthropic
  • www.tomshardware.com: Apple says generative AI cannot think like a human - research paper pours cold water on reasoning models
  • Digital Information World: Apple study questions AI reasoning models in stark new report
  • www.theguardian.com: A research paper by Apple has taken the AI world by storm, all but eviscerating the popular notion that large language models (LLMs, and their newest variant, LRMs, large reasoning models) are able to reason reliably.
  • AI Alignment Forum: Researchers at Apple released a paper provocatively titled “The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexityâ€, which “challenge[s] prevailing assumptions about [language model] capabilities and suggest that current approaches may be encountering fundamental barriers to generalizable reasoningâ€.
  • Ars OpenForum: New Apple study challenges whether AI models truly “reason†through problems
  • 9to5Mac: New paper pushes back on Apple’s LLM ‘reasoning collapse’ study
  • AI News | VentureBeat: Do reasoning models really “think†or not? Apple research sparks lively debate, response
  • www.marktechpost.com: Apple Researchers Reveal Structural Failures in Large Reasoning Models Using Puzzle-Based Evaluation
  • MarkTechPost: Apple Researchers Reveal Structural Failures in Large Reasoning Models Using Puzzle-Based Evaluation

@medium.com //
References: medium.com , medium.com , medium.com ...
Medium is currently hosting a series of articles that delve into the core concepts and practical applications of cryptography. These articles aim to demystify complex topics such as symmetric key cryptography, also known as secret key or private key cryptography, where a single shared key is used for both encryption and decryption. This method is highlighted for its speed and efficiency, making it suitable for bulk data encryption, though it primarily provides confidentiality and requires secure key distribution. The resources available are designed to cater to individuals with varying levels of expertise, offering accessible guides to enhance their understanding of secure communication and cryptographic systems.

The published materials offer detailed explorations of cryptographic techniques, including AES-256 encryption and decryption. AES-256, which stands for Advanced Encryption Standard with a 256-bit key size, is a symmetric encryption algorithm renowned for its high level of security. Articles break down the internal mechanics of AES-256, explaining the rounds of transformation and key expansion involved in the encryption process. These explanations are presented in both technical terms for those with a deeper understanding and in layman's terms to make the concepts accessible to a broader audience.

In addition to theoretical explanations, the Medium articles also showcase the practical applications of cryptography. One example provided is the combination of OSINT (Open Source Intelligence), web, crypto, and forensics techniques in CTF (Capture The Flag) challenges. These challenges offer hands-on experience in applying cryptographic principles to real-world scenarios, such as identifying the final resting place of historical figures through OSINT techniques. The series underscores the importance of mastering cryptography in the evolving landscape of cybersecurity, equipping readers with the knowledge to secure digital communications and protect sensitive information.

Recommended read:
References :
  • medium.com: Understanding AES-256 Encryption and Decryption: A Detailed Guide for All Levels
  • medium.com: Understanding Cryptography: The Art of Secure Communication
  • mraviteja9949.medium.com: Symmetric Key Cryptography
  • medium.com: Zero-knowledge proofs (ZKPs) let a saver prove that funds follow a rule — such as “stay locked for six monthsâ€â€Šâ€” without showing the 
  • medium.com: Article on how cryptographic hash functions actually work.
  • medium.com: Quantum-Resistant Cryptography: Preparing Your Code for Post-Quantum Era
  • medium.com: News story about Demystifying ECC, Web3 Cryptography and Their Evolving Threats
  • medium.com: Hello everyone! I’m a pen tester and today we will discuss about cryptography.
  • renanikeda.medium.com: The Diffie-Hellman Key Exchange is one of the most interesting mathematical techniques to guarantee that both parties share the same…

@www.quantamagazine.org //
References: StartsWithABang , Ray Lee , Ray Lee ...
Fermilab has announced the final results from its Muon g-2 experiment, aiming to resolve a long-standing anomaly regarding the magnetic moment of muons. This experiment delves into the quantum realm, exploring how short-lived particles popping in and out of existence influence the magnetic properties of muons. The initial results from this experiment suggested that the Standard Model of physics might be incomplete, hinting at the presence of undiscovered particles or forces.

The experiment's findings continue to show a discrepancy between experimental measurements and the predictions of the Standard Model. However, the statistical significance of this discrepancy has decreased due to improvements in theoretical calculations. This implies that while the Standard Model may not fully account for the behavior of muons, the evidence for new physics is not as strong as previously thought. The result is at 4.2σ (standard deviations) away from what's calculated using the Standard Model, which is a bit short of the 5 sigma normally used to declare a discovery. There's about a 1 in 40,000 chance that this is a fluke.

Despite the reduced statistical significance, the results remain intriguing and motivate further research. The possibility of undiscovered particles influencing muons still exists, pushing physicists to explore new theoretical models and conduct additional experiments. Fermilab shared first results from their "g-2" experiment showing the Standard Model of physics is even more incomplete than we thought. If the universe includes particles we don't yet know about, these too will show up as fluctuations around particles, influencing the properties we can measure.

Recommended read:
References :
  • StartsWithABang: Anomaly no more! “Muon g-2†puzzle resolved at last Can theory and experiment agree on the magnetic moment of the muon? At last, a new theory initiative paper coupled with final, world's best experimental results point to the resolution.
  • Ray Lee: Fermilab is announcing final results from the muon g-2 experiment today! I'm heading out the door, but the results will be at 10am CT. Quoting myself from April 7th, 2021: Fermilab shared first results from their "g-2" experiment showing the Standard Model of physics is even more incomplete than we thought.
  • bigthink.com: Anomaly no more! “Muon g-2†puzzle resolved at last Can theory and experiment agree on the magnetic moment of the muon? At last, a new theory initiative paper coupled with final, world's best experimental results point to the resolution.
  • Ray Lee: I should add, there have been various papers since this announcement back in 2021 that claim the calculations were incomplete and newer methods, such as brute-forcing the calculation via SM lattice methods on supercomputers, has pushed the discrepancy with experiment down to less than 2 sigma. Today we'll learn more! 3/3
  • physics.aps.org: Link to the stream: A rather nice cartoon explainer of all this by Jorge Cham: An accessible and slightly more scientific walkthrough over at Quanta Magazine from 2021: And the below graphic, showing how one particle physicist (who's name escapes me), viewed the tension in the results, four years ago. 2/3

@www.quantamagazine.org //
Recent breakthroughs have significantly advanced the "Core of Fermat's Last Theorem," a concept deeply rooted in number theory. Four mathematicians have extended the key insight behind Fermat's Last Theorem, which states there are no three positive integers that, when raised to a power greater than two, can be added together to equal another number raised to the same power. Their work involves applying this concept to other mathematical objects, notably elliptic curves. This extension represents a major step towards building a "grand unified theory" of mathematics, a long-sought goal in the field.

This achievement builds upon the groundwork laid by Andrew Wiles's famous 1994 proof of Fermat's Last Theorem. Wiles, with assistance from Richard Taylor, demonstrated that elliptic curves and modular forms, seemingly distinct mathematical entities, are interconnected. This discovery revealed a surprising "modularity," where these realms mirror each other in a distorted way. Mathematicians can now leverage this connection, translating problems about elliptic curves into the language of modular forms, solving them, and then applying the results back to the original problem.

This new research goes beyond elliptic curves, extending the modularity connection to more complicated mathematical objects. This breakthrough defies previous expectations that such extensions would be impossible. The Langlands program, a set of conjectures aiming to develop a grand unified theory of mathematics, hinges on such correspondences. The team's success provides strong support for the Langlands program and opens new avenues for solving previously intractable problems in various areas of mathematics, solidifying the power and reach of the "Core of Fermat's Last Theorem."

Recommended read:
References :
  • Computational Complexity: The research discussed in this cluster is part of a broader effort to build a unified theory of mathematics, and it involves the extension of the key insight behind Fermat's Last Theorem to include the study of other mathematical objects, such as elliptic curves.
  • Terence Tao: The research discussed in this cluster is part of a broader effort to build a unified theory of mathematics, and it involves the extension of the key insight behind Fermat's Last Theorem to include the study of other mathematical objects, such as elliptic curves.
  • nLab: The research discussed in this cluster is part of a broader effort to build a unified theory of mathematics, and it involves the extension of the key insight behind Fermat's Last Theorem to include the study of other mathematical objects, such as elliptic curves.
  • Quanta Magazine: The research discussed in this cluster is part of a broader effort to build a unified theory of mathematics, and it involves the extension of the key insight behind Fermat's Last Theorem to include the study of other mathematical objects, such as elliptic curves.

@www.linkedin.com //
Nvidia's Blackwell GPUs have achieved top rankings in the latest MLPerf Training v5.0 benchmarks, demonstrating breakthrough performance across various AI workloads. The NVIDIA AI platform delivered the highest performance at scale on every benchmark, including the most challenging large language model (LLM) test, Llama 3.1 405B pretraining. Nvidia was the only vendor to submit results on all MLPerf Training v5.0 benchmarks, highlighting the versatility of the NVIDIA platform across a wide array of AI workloads, including LLMs, recommendation systems, multimodal LLMs, object detection, and graph neural networks.

The at-scale submissions used two AI supercomputers powered by the NVIDIA Blackwell platform: Tyche, built using NVIDIA GB200 NVL72 rack-scale systems, and Nyx, based on NVIDIA DGX B200 systems. Nvidia collaborated with CoreWeave and IBM to submit GB200 NVL72 results using a total of 2,496 Blackwell GPUs and 1,248 NVIDIA Grace CPUs. The GB200 NVL72 systems achieved 90% scaling efficiency up to 2,496 GPUs, improving time-to-convergence by up to 2.6x compared to Hopper-generation H100.

The new MLPerf Training v5.0 benchmark suite introduces a pretraining benchmark based on the Llama 3.1 405B generative AI system, the largest model to be introduced in the training benchmark suite. On this benchmark, Blackwell delivered 2.2x greater performance compared with the previous-generation architecture at the same scale. Furthermore, on the Llama 2 70B LoRA fine-tuning benchmark, NVIDIA DGX B200 systems, powered by eight Blackwell GPUs, delivered 2.5x more performance compared with a submission using the same number of GPUs in the prior round. These performance gains highlight advancements in the Blackwell architecture and software stack, including high-density liquid-cooled racks, fifth-generation NVLink and NVLink Switch interconnect technologies, and NVIDIA Quantum-2 InfiniBand networking.

Recommended read:
References :
  • NVIDIA Newsroom: NVIDIA Blackwell Delivers Breakthrough Performance in Latest MLPerf Training Results
  • NVIDIA Technical Blog: NVIDIA Blackwell Delivers up to 2.6x Higher Performance in MLPerf Training v5.0
  • IEEE Spectrum: Nvidia’s Blackwell Conquers Largest LLM Training Benchmark
  • NVIDIA Technical Blog: Reproducing NVIDIA MLPerf v5.0 Training Scores for LLM Benchmarks
  • AI News | VentureBeat: Nvidia says its Blackwell chips lead benchmarks in training AI LLMs
  • MLCommons: New MLCommons MLPerf Training v5.0 Benchmark Results Reflect Rapid Growth and Evolution of the Field of AI
  • www.aiwire.net: MLPerf Training v5.0 results show Nvidia’s Blackwell GB200 accelerators sprinting through record time-to-train scores.
  • blogs.nvidia.com: NVIDIA is working with companies worldwide to build out AI factories — speeding the training and deployment of next-generation AI applications that use the latest advancements in training and inference. The NVIDIA Blackwell architecture is built to meet the heightened performance requirements of these new applications. In the latest round of MLPerf Training — the
  • mlcommons.org: New MLCommons MLPerf Training v5.0 Benchmark Results Reflect Rapid Growth and Evolution of the Field of AI
  • NVIDIA Newsroom: NVIDIA RTX Blackwell GPUs Accelerate Professional-Grade Video Editing
  • ServeTheHome: The new MLPerf Training v5.0 are dominated by NVIDIA Blackwell and Hopper results, but we also get AMD Instinct MI325X on a benchmark as well
  • AIwire: This is a news article on nvidia Blackwell GPUs lift Nvidia to the top of MLPerf Training Rankings
  • www.servethehome.com: MLPerf Training v5.0 is Out
  • IEEE Spectrum: Nvidia’s Blackwell Conquers Largest LLM Training Benchmark

@medium.com //
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:
References :
  • medium.com: Last week, Craig Gidney from Google Quantum AI published a breakthrough study that redefines the landscape of cryptographic security. His 
  • www.theguardian.com: Google working on AI email tool that can ‘answer in your style’
  • The Official Google Blog: We’re investing for a cleaner energy future with TAE Technologies, a leading nuclear fusion company.
  • medium.com: Google’s quantum leap just changed everything: They can now break encryption 20x faster than 

@quantumcomputingreport.com //
References: medium.com , medium.com , medium.com ...
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:
References :
  • medium.com: Should Post-Quantum Cryptography Start Now? The Clock Is Ticking
  • medium.com: Google’s quantum leap just changed everything: They can now break encryption 20x faster than…
  • quantumcomputingreport.com: Significant Theoretical Advancement in Factoring 2048 Bit RSA Integers
  • medium.com: Last week, Craig Gidney from Google Quantum AI published a breakthrough study that redefines the landscape of cryptographic security.
  • www.microsoft.com: The recent advances in quantum computing offer many advantages—but also challenge current cryptographic strategies. Learn how FrodoKEM could help strengthen security, even in a future with powerful quantum computers.
  • medium.com: Securing the Internet of Things: Why Post-Quantum Cryptography Is Critical for IoT’s Future
  • medium.com: Quantum Resilience Starts Now: Building Secure Infrastructure with Hybrid Cryptography
  • medium.com: Quantum-Resistant Cryptography: Preparing Your Code for Post-Quantum Era

@aasnova.org //
JWST is currently being used to study exoplanets, particularly sub-Neptunes, providing valuable data on their atmospheric composition. A recent study utilized JWST spectroscopy to analyze the atmosphere of the sub-Neptune GJ 3090b. This planet orbits a late-type, low-mass star and its radius places it at the outer edge of the radius valley. Sub-Neptunes are the most common type of planet in the Milky Way, however their formation and composition are not well understood, making these studies especially important.

The JWST's observations of GJ 3090b revealed a low-amplitude helium signature, suggesting a metal-enriched atmosphere. The presence of heavy molecules like water, carbon dioxide, and sulfur further contributes to the understanding of the planet's atmospheric properties. These atmospheric observations help clarify how hydrogen and helium may be escaping the planet’s atmosphere, with the presence of metals slowing down mass loss and weakening the helium signature.

While JWST is making significant contributions to exoplanet research, it won't find the very first stars. Other telescopes will be needed to make those observations. JWST however contains some of the latest discoveries, including the new cosmic record-holder for the most distant galaxy, MoM-z14.

Recommended read:
References :
  • StartsWithABang: Earlier this week, I gave a talk about JWST to the RASC Toronto audience through York University, and it has the latest and greatest of its discoveries inside, including the new cosmic record-holder for most distant galaxy: MoM-z14. Check it out!
  • aasnova.org: Abundant but Ambiguous: Understanding the Atmospheres of Sub-Neptunes with JWST

@medium.com //
References: RunPod Blog , TheSequence
DeepSeek's latest AI model, R1-0528, is making waves in the AI community due to its impressive performance in math and reasoning tasks. This new model, despite having a similar name to its predecessor, boasts a completely different architecture and performance profile, marking a significant leap forward. DeepSeek R1-0528 has demonstrated "unprecedented levels of demand" shooting to the top of the App Store past closed model rivals and overloading their API with unprecedented levels of demand to the point that they actually had to stop accepting payments.

The most notable improvement in DeepSeek R1-0528 is its mathematical reasoning capabilities. On the AIME 2025 test, the model's accuracy increased from 70% to 87.5%, surpassing Gemini 2.5 Pro and putting it in close competition with OpenAI's o3. This improvement is attributed to "enhanced thinking depth," with the model using significantly more tokens per question, engaging in more thorough chains of reasoning. This means the model can check its own work, recognize errors, and course-correct during problem-solving.

DeepSeek's success is challenging established closed models and driving competition in the AI landscape. DeepSeek-R1-0528 continues to utilize a Mixture-of-Experts (MoE) architecture, now scaled up to an enormous size. This sparse activation allows for powerful specialized expertise in different coding domains while maintaining efficiency. The context also continues to remain at 128k (with RoPE scaling or other improvements capable of extending it further.) The rise of DeepSeek is underscored by its performance benchmarks, which show it outperforming some of the industry’s leading models, including OpenAI’s ChatGPT. Furthermore, the release of a distilled variant, R1-0528-Qwen3-8B, ensures broad accessibility of this powerful technology.

Recommended read:
References :
  • RunPod Blog: The 'Minor Upgrade' That's Anything But: DeepSeek R1-0528 Deep Dive
  • TheSequence: The Sequence Radar #554 : The New DeepSeek R1-0528 is Very Impressive

Dashveenjit Kaur@TechHQ //
Dell Technologies has secured a contract with the U.S. Department of Energy to construct the next-generation NERSC-10 supercomputer, a project powered by NVIDIA's Vera Rubin architecture. This new system, dubbed "Doudna" after Nobel laureate Jennifer Doudna, a pioneer in CRISPR gene-editing technology, is poised to be a major federal investment in scientific computing infrastructure. Energy Secretary Chris Wright announced the contract during a visit to Lawrence Berkeley National Laboratory, emphasizing that the deployment in 2026 is crucial for maintaining American technological leadership amidst increasing global competition in AI and quantum computing.

The "Doudna" supercomputer, also known as NERSC-10, aims to significantly accelerate scientific research across multiple domains, including fusion energy, astronomy, and life sciences. Designed to serve 11,000 researchers, it represents an integration of artificial intelligence, quantum workflows, and real-time data streaming from experimental facilities. Unlike traditional supercomputers, Doudna’s architecture emphasizes coherent memory access between CPUs and GPUs, facilitating efficient data sharing between heterogeneous processors which is essential for modern AI-accelerated scientific workflows.

The Doudna system is expected to deliver a 10x increase in scientific output compared to its predecessor, Perlmutter, while only consuming 2-3x the power, translating to a 3-5x improvement in performance per watt. Nick Wright, advanced technologies group lead and Doudna chief architect at NERSC, stated, "We’re not just building a faster computer, we’re building a system that helps researchers think bigger and discover sooner." NVIDIA's Vera Rubin platform introduces hardware-level optimizations specifically designed for the convergence of simulation, machine learning, and quantum algorithm development, marking a significant advancement in cutting-edge research capabilities.

Recommended read:
References :
  • blogs.nvidia.com: Ready for a front-row seat to the next scientific revolution? That’s the idea behind Doudna — a groundbreaking supercomputer announced today at Lawrence Berkeley National Laboratory in Berkeley, California.
  • insidehpc.com: The new system, due in 2026, is named after Jennifer Doudna, the Berkeley Lab-based biochemist who won the 2020 Nobel Prize for Chemistry for her work on gene-editing technology.
  • TechHQ: Nvidia Vera Rubin supercomputer to serve researchers in fusion energy, astronomy, and life sciences.
  • techxplore.com: A new supercomputer named after a winner of the Nobel Prize in chemistry will help power artificial intelligence technology and scientific discoveries from a perch in the hills above the University of California, Berkeley, federal officials said Thursday.
  • insidehpc.com: DOE Announces “Doudna†Dell-NVIDIA Supercomputer at NERSC
  • techhq.com: Nvidia Vera Rubin supercomputer to serve researchers in fusion energy, astronomy, and life sciences. Dell’s system targets 10x performance, 3-5x better power efficiency, to be deployed in 2026.

@www.quantamagazine.org //
Researchers are making strides in AI reasoning and efficiency, tackling both complex problem-solving and the energy consumption of these systems. One promising area involves reversible computing, where programs can run backward as easily as forward, theoretically saving energy by avoiding data deletion. Michael Frank, a researcher interested in the physical limits of computation, discovered that reversible computing could keep computational progress going as traditional computing slows due to physical limitations. Christof Teuscher at Portland State University emphasized the potential for significant power savings with this approach.

An evolution of the LLM-as-a-Judge paradigm is emerging. Meta AI has introduced the J1 framework which shifts the paradigm of LLMs from passive generators to active, deliberative evaluators through self-evaluation. This approach, detailed in "J1: Incentivizing Thinking in LLM-as-a-Judge via Reinforcement Learning," addresses the growing need for rigorous and scalable evaluation as AI systems become more capable and widely deployed. By reframing judgment as a structured reasoning task trained through reinforcement learning, J1 aims to create models that perform consistent, interpretable, and high-fidelity evaluations.

Soheil Feizi, an associate professor at the University of Maryland, has received a $1 million federal grant to advance foundational research in reasoning AI models. This funding, stemming from a Presidential Early Career Award for Scientists and Engineers (PECASE), will support his work in defending large language models (LLMs) against attacks, identifying weaknesses in how these models learn, encouraging transparent, step-by-step logic, and understanding the "reasoning tokens" that drive decision-making. Feizi plans to explore innovative approaches like live activation probing and novel reinforcement-learning designs, aiming to transform theoretical advancements into practical applications and real-world usages.

Recommended read:
References :

@www.marktechpost.com //
DeepSeek has released a major update to its R1 reasoning model, dubbed DeepSeek-R1-0528, marking a significant step forward in open-source AI. The update boasts enhanced performance in complex reasoning, mathematics, and coding, positioning it as a strong competitor to leading commercial models like OpenAI's o3 and Google's Gemini 2.5 Pro. The model's weights, training recipes, and comprehensive documentation are openly available under the MIT license, fostering transparency and community-driven innovation. This release allows researchers, developers, and businesses to access cutting-edge AI capabilities without the constraints of closed ecosystems or expensive subscriptions.

The DeepSeek-R1-0528 update brings several core improvements. The model's parameter count has increased from 671 billion to 685 billion, enabling it to process and store more intricate patterns. Enhanced chain-of-thought layers deepen the model's reasoning capabilities, making it more reliable in handling multi-step logic problems. Post-training optimizations have also been applied to reduce hallucinations and improve output stability. In practical terms, the update introduces JSON outputs, native function calling, and simplified system prompts, all designed to streamline real-world deployment and enhance the developer experience.

Specifically, DeepSeek R1-0528 demonstrates a remarkable leap in mathematical reasoning. On the AIME 2025 test, its accuracy improved from 70% to an impressive 87.5%, rivaling OpenAI's o3. This improvement is attributed to "enhanced thinking depth," with the model now utilizing significantly more tokens per question, indicating more thorough and systematic logical analysis. The open-source nature of DeepSeek-R1-0528 empowers users to fine-tune and adapt the model to their specific needs, fostering further innovation and advancements within the AI community.

Recommended read:
References :
  • Kyle Wiggers ?: DeepSeek updates its R1 reasoning AI model, releases it on Hugging Face
  • AI News | VentureBeat: VentureBeat article on DeepSeek R1-0528.
  • Analytics Vidhya: New Deepseek R1-0528 Update is INSANE
  • MacStories: Testing DeepSeek R1-0528 on the M3 Ultra Mac Studio and Installing Local GGUF Models with Ollama on macOS
  • www.analyticsvidhya.com: New Deepseek R1-0528 Update is INSANE
  • www.marktechpost.com: DeepSeek Releases R1-0528: An Open-Source Reasoning AI Model Delivering Enhanced Math and Code Performance with Single-GPU Efficiency
  • NextBigFuture.com: DeepSeek R1 has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training.
  • MarkTechPost: DeepSeek Releases R1-0528: An Open-Source Reasoning AI Model Delivering Enhanced Math and Code Performance with Single-GPU Efficiency
  • Pandaily: In the early hours of May 29, Chinese AI startup DeepSeek quietly open-sourced the latest iteration of its R1 large language model, DeepSeek-R1-0528, on the Hugging Face platform .
  • www.computerworld.com: Reports that DeepSeek releases a new version of its R1 reasoning AI model.
  • techcrunch.com: DeepSeek updates its R1 reasoning AI model, releases it on Hugging Face
  • the-decoder.com: Deepseek's R1 model closes the gap with OpenAI and Google after major update
  • Simon Willison: Some notes on the new DeepSeek-R1-0528 - a completely different model from the R1 they released in January, despite having a very similar name Terrible LLM naming has managed to infect the Chinese AI labs too
  • Analytics India Magazine: The new DeepSeek-R1 Is as good as OpenAI o3 and Gemini 2.5 Pro
  • RunPod Blog: The 'Minor Upgrade' That's Anything But: DeepSeek R1-0528 Deep Dive
  • simonwillison.net: Some notes on the new DeepSeek-R1-0528 - a completely different model from the R1 they released in January, despite having a very similar name Terrible LLM naming has managed to infect the Chinese AI labs too
  • TheSequence: This article provides an overview of the new DeepSeek R1-0528 model and notes its improvements over the prior model released in January.
  • Kyle Wiggers ?: News about the release of DeepSeek's updated R1 AI model, emphasizing its increased censorship.
  • Fello AI: Reports that the R1-0528 model from DeepSeek is matching the capabilities of OpenAI's o3 and Google's Gemini 2.5 Pro.
  • felloai.com: Latest DeepSeek Update Called R1-0528 Is Matching OpenAI’s o3 & Gemini 2.5 Pro
  • www.tomsguide.com: DeepSeek’s latest update is a serious threat to ChatGPT and Google — here’s why

@www.microsoft.com //
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:
References :
  • www.microsoft.com: FrodoKEM: A conservative quantum-safe cryptographic algorithm
  • medium.com: Welcome to the Quantum Era, where even the strongest locks we use to protect our digital lives might soon be breakable. However, don’t…
  • arstechnica.com: Here’s how Windows 11 aims to make the world safe in the post-quantum era
  • medium.com: Quantum Computing and Encryption Breakthroughs in 2025: A New Era of Innovation
  • medium.com: Cracking RSA with Fewer Qubits: What Google’s New Quantum Factoring Estimate Means for…
  • medium.com: Google’s quantum leap just changed everything: They can now break encryption 20x faster than…
  • medium.com: On August 13, 2024, the U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) announced the approval of…
  • medium.com: As our world becomes increasingly interconnected, the Internet of Things (IoT) is transforming industries, homes, and entire cities. From…
  • : Post-Quantum Cryptography Coalition (PQCC) Publishes Comprehensive Roadmap for Post-Quantum Cryptography Migration
  • www.techradar.com: Breaking encryption with quantum computers may be easier than we thought