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NishMath - #Google

Maximilian Schreiner@THE DECODER //
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:
References :
  • SiliconANGLE: Google LLC said today it’s updating its flagship Gemini artificial intelligence model family by introducing an experimental Gemini 2.5 Pro version.
  • The Tech Basic: Google's New AI Models “Think” Before Answering, Outperform Rivals
  • AI News | VentureBeat: Google releases ‘most intelligent model to date,’ Gemini 2.5 Pro
  • Analytics Vidhya: We Tried the Google 2.5 Pro Experimental Model and It’s Mind-Blowing!
  • www.tomsguide.com: Google unveils Gemini 2.5 — claims AI breakthrough with enhanced reasoning and multimodal power
  • Google DeepMind Blog: Gemini 2.5: Our most intelligent AI model
  • THE DECODER: Google Deepmind has introduced Gemini 2.5 Pro, which the company describes as its most capable AI model to date. The article appeared first on .
  • intelligence-artificielle.developpez.com: Google DeepMind a lancé Gemini 2.5 Pro, un modèle d'IA qui raisonne avant de répondre, affirmant qu'il est le meilleur sur plusieurs critères de référence en matière de raisonnement et de codage
  • The Tech Portal: Google unveils Gemini 2.5, its most intelligent AI model yet with ‘built-in thinking’
  • Ars OpenForum: Google says the new Gemini 2.5 Pro model is its “smartest†AI yet
  • The Official Google Blog: Gemini 2.5: Our most intelligent AI model
  • www.techradar.com: I pitted Gemini 2.5 Pro against ChatGPT o3-mini to find out which AI reasoning model is best
  • bsky.app: Google's AI comeback is official. Gemini 2.5 Pro Experimental leads in benchmarks for coding, math, science, writing, instruction following, and more, ahead of OpenAI's o3-mini, OpenAI's GPT-4.5, Anthropic's Claude 3.7, xAI's Grok 3, and DeepSeek's R1. The narrative has finally shifted.
  • Shelly Palmer: Google’s Gemini 2.5: AI That Thinks Before It Speaks
  • bdtechtalks.com: What to know about Google Gemini 2.5 Pro
  • Interconnects: The end of a busy spring of model improvements and what's next for the presumed leader in AI abilities.
  • www.techradar.com: Gemini 2.5 is now available for Advanced users and it seriously improves Google’s AI reasoning
  • www.zdnet.com: Google releases 'most intelligent' experimental Gemini 2.5 Pro - here's how to try it
  • Unite.AI: Gemini 2.5 Pro is Here—And it Changes the AI Game (Again)
  • TestingCatalog: Gemini 2.5 Pro sets new AI benchmark and launches on AI Studio and Gemini
  • Analytics Vidhya: Google DeepMind's latest AI model, Gemini 2.5 Pro, has reached the #1 position on the Arena leaderboard.
  • AI News: Gemini 2.5: Google cooks up its ‘most intelligent’ AI model to date
  • Fello AI: Google’s Gemini 2.5 Shocks the World: Crushing AI Benchmark Like No Other AI Model!
  • Analytics India Magazine: Google Unveils Gemini 2.5, Crushes OpenAI GPT-4.5, DeepSeek R1, & Claude 3.7 Sonnet
  • Practical Technology: Practical Tech covers the launch of Google's Gemini 2.5 Pro and its new AI benchmark achievements.
  • Shelly Palmer: Google's Gemini 2.5: AI That Thinks Before It Speaks
  • www.producthunt.com: Google's most intelligent AI model
  • Windows Copilot News: Google reveals AI ‘reasoning’ model that ‘explicitly shows its thoughts’
  • AI News | VentureBeat: Hands on with Gemini 2.5 Pro: why it might be the most useful reasoning model yet
  • thezvi.wordpress.com: Gemini 2.5 Pro Experimental is America’s next top large language model. That doesn’t mean it is the best model for everything. In particular, it’s still Gemini, so it still is a proud member of the Fun Police, in terms of …
  • www.computerworld.com: Gemini 2.5 can, among other things, analyze information, draw logical conclusions, take context into account, and make informed decisions.
  • www.infoworld.com: Google introduces Gemini 2.5 reasoning models
  • Maginative: Google's Gemini 2.5 Pro leads AI benchmarks with enhanced reasoning capabilities, positioning it ahead of competing models from OpenAI and others.
  • www.infoq.com: Google's Gemini 2.5 Pro is a powerful new AI model that's quickly becoming a favorite among developers and researchers. It's capable of advanced reasoning and excels in complex tasks.
  • AI News | VentureBeat: Google’s Gemini 2.5 Pro is the smartest model you’re not using – and 4 reasons it matters for enterprise AI
  • Communications of the ACM: Google has released Gemini 2.5 Pro, an updated AI model focused on enhanced reasoning, code generation, and multimodal processing.
  • The Next Web: Google has released Gemini 2.5 Pro, an updated AI model focused on enhanced reasoning, code generation, and multimodal processing.
  • www.tomsguide.com: Surprise move comes just days after Gemini 2.5 Pro Experimental arrived for Advanced subscribers.
  • Composio: Google just launched Gemini 2.5 Pro on March 26th, claiming to be the best in coding, reasoning and overall everything. But I The post appeared first on .
  • Composio: Google's Gemini 2.5 Pro, released on March 26th, is being hailed for its enhanced reasoning, coding, and multimodal capabilities.
  • Analytics India Magazine: Gemini 2.5 Pro is better than the Claude 3.7 Sonnet for coding in the Aider Polyglot leaderboard.
  • www.zdnet.com: Gemini's latest model outperforms OpenAI's o3 mini and Anthropic's Claude 3.7 Sonnet on the latest benchmarks.
  • www.tomsguide.com: Gemini 2.5 is free, but can it beat DeepSeek?

Carl Franzen@AI News | VentureBeat //
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:
References :
  • AI News | VentureBeat: Google launches free Gemini-powered Data Science Agent on its Colab Python platform
  • Analytics Vidhya: How to Access Data Science Agent in Google Colab?
  • Developer Tech News: Google deploys Data Science Agent to Colab users
  • SiliconANGLE: Google Cloud debuts powerful new AI capabilities for data scientists and doctors
  • TechCrunch: Google upgrades Colab with an AI agent tool
  • Maginative: Google Introduces “AI Mode” in Search, Expanding AI Overviews with Gemini 2.0

Emily Forlini@PCMag Middle East ai //
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:
References :
  • Shelly Palmer: Shelly Palmer discusses Google’s Veo 2, an AI video generator priced at 50 cents a second.
  • www.livescience.com: LiveScience reports Google's AI is now 'better than human gold medalists' at solving geometry problems.
  • PCMag Middle East ai: Google's Veo 2 Costs $1,800 Per Hour for AI-Generated Videos
  • THE DECODER: Google Deepmind sets pricing for Veo 2 AI video generation
  • Dataconomy: Google Veo 2 pricing: 50 cents per second of AI-generated video
  • TechCrunch: Reports Google’s new AI video model Veo 2 will cost 50 cents per second.

vishnupriyan@Verdict //
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:
References :
  • Shelly Palmer: Google’s Veo 2 at 50 Cents a Second: Priced Right—for Now
  • www.livescience.com: 'Math Olympics' has a new contender — Google's AI now 'better than human gold medalists' at solving geometry problems
  • Verdict: Google expands Deep Research tool for workspace users
  • www.sciencedaily.com: Google's second generation of its AI mathematics system combines a language model with a symbolic engine to solve complex geometry problems better than International Mathematical Olympiad (IMO) gold medalists.

@Talkback Resources //
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:
References :
  • gbhackers.com: Google Introduces Quantum-Safe Digital Signatures in Cloud KMS
  • BleepingComputer: Google Cloud has introduced quantum-safe digital signatures to its Cloud Key Management Service (Cloud KMS), making them available in preview.
  • Talkback Resources: Google Cloud KMS Adds Quantum-Safe Digital Signatures to Defend Against Future Threats [cloud] [crypto]
  • gbhackers.com: Google Cloud has unveiled a critical cybersecurity upgrade: quantum-safe digital signatures via its Key Management Service (Cloud KMS), now available in preview.
  • www.bleepingcomputer.com: BleepingComputer reports on Quantum-Safe Digital Signatures.
  • The Quantum Insider: Google Expands Post-Quantum Cryptography Support with Quantum-Safe Digital Signatures

@physics.aps.org //
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:
References :
  • IEEE Spectrum: Article discussing Google's quantum simulator revealing new facets of magnetism.
  • thequantuminsider.com: A Google-led team of researchers used a hybrid digital-analog approach with a quantum processor containing 69 superconducting qubits to simulate how quantum systems naturally progress toward thermal equilibrium, a key process in statistical mechanics.

Amir Najmi@unofficialgoogledatascience.com //
References: medium.com , medium.com , medium.com ...
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:
References :
  • medium.com: Data Science: Bias-Variance Tradeoff
  • medium.com: Six Essential Statistics Concepts Every Data Scientist Should Know
  • www.unofficialgoogledatascience.com: Quantifying the statistical skills needed to be a Google Data Scientist
  • medium.com: These are the best Udemy Courses you can join to learn Mathematics and statistics in 2025
  • medium.com: Python by Examples: Quantifying Predictor Informativeness in Statistical Forecasting