Top Mathematics discussions
NishMath - #Google
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@console.cloud.google.com
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References:
Compute
, BigDATAwire
Google Cloud is empowering global scientific discovery and innovation by integrating Google DeepMind and Google Research technologies with its cloud infrastructure. This initiative aims to provide researchers with advanced, cloud-scale tools for scientific computing. The company is introducing supercomputing-class infrastructure, including H4D VMs powered by AMD CPUs and A4/A4X VMs powered by NVIDIA GPUs, which boast low-latency networking and high memory bandwidth. Additionally, Google Cloud Managed Lustre offers high-performance storage I/O, enabling scientists to tackle large-scale and complex scientific problems.
Google Cloud is also rolling out advanced scientific applications powered by AI models. These include AlphaFold 3 for predicting the structure and interactions of biomolecules, and WeatherNext models for weather forecasting. Moreover, the company is introducing AI agents designed to accelerate scientific discovery. As an example, Google Cloud and Ai2 are investing $20 million in the Cancer AI Alliance to accelerate cancer research using AI, advanced models, and cloud computing power. Google Cloud will provide the AI infrastructure and security, while Ai2 will deliver the training and development of cancer models. In addition to these advancements, Google unveiled its seventh-generation Tensor Processing Unit (TPU), Ironwood. The company claims Ironwood delivers 24 times the computing power of the world’s fastest supercomputer when deployed at scale. Ironwood is specifically designed for inference workloads, marking a shift in Google's AI chip development strategy. When scaled to 9,216 chips per pod, Ironwood delivers 42.5 exaflops of computing power, and each chip comes with 192GB of High Bandwidth Memory. Recommended read:
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@simonwillison.net
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Google has broadened access to its advanced AI model, Gemini 2.5 Pro, showcasing impressive capabilities and competitive pricing designed to challenge rival models like OpenAI's GPT-4o and Anthropic's Claude 3.7 Sonnet. Google's latest flagship model is currently recognized as a top performer, excelling in Optical Character Recognition (OCR), audio transcription, and long-context coding tasks. Alphabet CEO Sundar Pichai highlighted Gemini 2.5 Pro as Google's "most intelligent model + now our most in demand." Demand has increased by over 80 percent this month alone across both Google AI Studio and the Gemini API.
Google's expansion includes a tiered pricing structure for the Gemini 2.5 Pro API, offering a more affordable option compared to competitors. Prompts with less than 200,000 tokens are priced at $1.25 per million for input and $10 per million for output, while larger prompts increase to $2.50 and $15 per million tokens, respectively. Although prompt caching is not yet available, its future implementation could potentially lower costs further. The free tier allows 500 free grounding queries with Google Search per day, with an additional 1,500 free queries in the paid tier, with costs per 1,000 queries set at $35 beyond that. The AI research group EpochAI reported that Gemini 2.5 Pro scored 84% on the GPQA Diamond benchmark, surpassing the typical 70% score of human experts. This benchmark assesses challenging multiple-choice questions in biology, chemistry, and physics, validating Google's benchmark results. The model is now available as a paid model, along with a free tier option. The free tier can use data to improve Google's products while the paid tier cannot. Rates vary by tier and range from 150-2,000/minute. Google will retire the Gemini 2.0 Pro preview entirely in favor of 2.5. Recommended read:
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Maximilian Schreiner@THE DECODER
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Google's Gemini 2.5 Pro is making waves as a top-tier reasoning model, marking a leap forward in Google's AI capabilities. Released recently, it's already garnering attention from enterprise technical decision-makers, especially those who have traditionally relied on OpenAI or Claude for production-grade reasoning. Early experiments, benchmark data, and developer reactions suggest Gemini 2.5 Pro is worth serious consideration.
Gemini 2.5 Pro distinguishes itself with its transparent, structured reasoning. Google's step-by-step training approach results in a structured chain of thought that provides clarity. The model presents ideas in numbered steps, with sub-bullets and internal logic that's remarkably coherent and transparent. This breakthrough offers greater trust and steerability, enabling enterprise users to validate, correct, or redirect the model with more confidence when evaluating output for critical tasks. Recommended read:
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Maximilian Schreiner@THE DECODER
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Google DeepMind has announced Gemini 2.5 Pro, its latest and most advanced AI model to date. This new model boasts enhanced reasoning capabilities and improved accuracy, marking a significant step forward in AI development. Gemini 2.5 Pro is designed with built-in 'thinking' capabilities, enabling it to break down complex tasks into multiple steps and analyze information more effectively before generating a response. This allows the AI to deduce logical conclusions, incorporate contextual nuances, and make informed decisions with unprecedented accuracy, according to Google.
The Gemini 2.5 Pro has already secured the top position on the LMArena leaderboard, surpassing other AI models in head-to-head comparisons. This achievement highlights its superior performance and high-quality style in handling intricate tasks. The model also leads in math and science benchmarks, demonstrating its advanced reasoning capabilities across various domains. This new model is available as Gemini 2.5 Pro (experimental) on Google’s AI Studio and for Gemini Advanced users on the Gemini chat interface. Recommended read:
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Amir Najmi@unofficialgoogledatascience.com
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Data scientists and statisticians are continuously exploring methods to refine data analysis and modeling. A recent blog post from Google details a project focused on quantifying the statistical skills necessary for data scientists within their organization, aiming to clarify job descriptions and address ambiguities in assessing practical data science abilities. The authors, David Mease and Amir Najmi, leveraged their extensive experience conducting over 600 interviews at Google to identify crucial statistical expertise required for the "Data Scientist - Research" role.
Statistical testing remains a cornerstone of data analysis, guiding analysts in transforming raw numbers into actionable insights. One must also keep in mind bias-variance tradeoff and how to choose the right statistical test to ensure the validity of analyses. These tools are critical for both traditional statistical roles and the evolving field of AI/ML, where responsible practices are paramount, as highlighted in discussions about the relevance of statistical controversies to ethical AI/ML development at an AI ethics conference on March 8. Recommended read:
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Maximilian Schreiner@THE DECODER
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Google has unveiled Gemini 2.5 Pro, its latest and "most intelligent" AI model to date, showcasing significant advancements in reasoning, coding proficiency, and multimodal functionalities. According to Google, these improvements come from combining a significantly enhanced base model with improved post-training techniques. The model is designed to analyze complex information, incorporate contextual nuances, and draw logical conclusions with unprecedented accuracy. Gemini 2.5 Pro is now available for Gemini Advanced users and on Google's AI Studio.
Google emphasizes the model's "thinking" capabilities, achieved through chain-of-thought reasoning, which allows it to break down complex tasks into multiple steps and reason through them before responding. This new model can handle multimodal input from text, audio, images, videos, and large datasets. Additionally, Gemini 2.5 Pro exhibits strong performance in coding tasks, surpassing Gemini 2.0 in specific benchmarks and excelling at creating visually compelling web apps and agentic code applications. The model also achieved 18.8% on Humanity’s Last Exam, demonstrating its ability to handle complex knowledge-based questions. Recommended read:
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Carl Franzen@AI News | VentureBeat
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Google has recently launched a Gemini-powered Data Science Agent on its Colab Python platform, aiming to revolutionize data analysis. This AI agent automates various routine data science tasks, including importing libraries, cleaning data, running exploratory data analysis (EDA), and generating code. By handling these tedious processes, the agent allows data scientists to focus on more strategic and insightful aspects of their work, such as uncovering patterns and building predictive models.
The Data Science Agent, accessible within Google Colab, operates as an intelligent assistant that executes tasks autonomously, including error handling. Users can define their analysis objectives in plain language, and the agent generates a Colab notebook, executes it, and simplifies the machine learning process. In addition, Google is expanding the capabilities of its Gemini AI model, which will soon allow users to ask questions about content displayed on their screens. This enhancement, part of Google's Project Astra, enables real-time interaction and accessibility by identifying screen elements and responding to user queries through voice. Recommended read:
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Emily Forlini@PCMag Middle East ai
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Google DeepMind has announced the pricing for its Veo 2 AI video generation model, making it available through its cloud API platform. The cost is set at $0.50 per second, which translates to $30 per minute or $1,800 per hour. While this may seem expensive, Google DeepMind researcher Jon Barron compared it to the cost of traditional filmmaking, noting that the blockbuster "Avengers: Endgame" cost around $32,000 per second to produce.
Veo 2 aims to create videos with realistic motion and high-quality output, up to 4K resolution, based on simple text prompts. While it's not the cheapest option compared to alternatives like OpenAI's Sora, which costs $200 per month, Google is targeting filmmakers and studios with larger budgets. The primary customers for Veo are filmmakers and studios, who typically have bigger budgets than film hobbyists. They would run Veo throughVertexAI, Google's platform for training and deploying advanced AI models."Veo 2 understands the unique language of cinematography: ask it for a genre, specify a lens, suggest cinematic effects and Veo 2 will deliver," Google says. Recommended read:
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@Talkback Resources
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Google Cloud has launched quantum-safe digital signatures within its Cloud Key Management Service (Cloud KMS), now available in preview. This cybersecurity enhancement prepares users against future quantum threats by aligning with the National Institute of Standards and Technology’s (NIST) post-quantum cryptography (PQC) standards. The upgrade provides developers with the necessary tools to protect encryption.
Google's implementation integrates NIST-standardized algorithms FIPS 204 and FIPS 205, enabling signing and validation processes resilient to attacks from quantum computers. By incorporating these protocols into Cloud KMS, Google enables enterprises to future-proof authentication workflows, which is particularly important for systems requiring long-term security, such as critical infrastructure firmware or software update chains. This allows organizations to manage quantum-safe keys alongside classical ones, facilitating a phased migration. Recommended read:
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vishnupriyan@Verdict
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Google's AI mathematics system, known as AlphaGeometry2 (AG2), has surpassed the problem-solving capabilities of International Mathematical Olympiad (IMO) gold medalists in solving complex geometry problems. This second-generation system combines a language model with a symbolic engine, enabling it to solve 84% of IMO geometry problems, compared to the 81.8% solved by human gold medalists. Developed by Google DeepMind, AG2 can engage in both pattern matching and creative problem-solving, marking a significant advancement in AI's ability to mimic human reasoning in mathematics.
This achievement comes shortly after Microsoft released its own advanced AI math reasoning system, rStar-Math, highlighting the growing competition in the AI math domain. While rStar-Math uses smaller language models to solve a broader range of problems, AG2 focuses on advanced geometry problems using a hybrid reasoning model. The improvements in AG2 represent a 30% performance increase over the original AlphaGeometry, particularly in visual reasoning and logic, essential for solving complex geometry challenges. Recommended read:
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@physics.aps.org
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References:
IEEE Spectrum
, thequantuminsider.com
Google's quantum simulator has challenged the conventional understanding of magnetism, specifically the Kibble-Zurek mechanism, which is widely used to predict the behavior of magnets during phase transitions. By employing a hybrid analog-digital approach, Google's simulator has revealed that this mechanism doesn't always hold true, suggesting that magnetism may function differently than previously thought. This discovery highlights the potential of quantum simulators to uncover new physics and challenge existing theories.
Researchers combined analog and digital quantum computing utilizing 69 superconducting qubits and a high-fidelity calibration scheme to simulate complex quantum systems. With an impressively low error rate of 0.1% per qubit, simulations at this fidelity would take over a million years on the Frontier exascale supercomputer. This breakthrough demonstrates the potential of quantum simulation to tackle problems that are currently intractable for even the most powerful classical computers, opening doors to new discoveries in materials science and other fields. Recommended read:
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