Machine Learning

  # Introduction  Mocking Internet of Things (IoT) sensor data that would be otherwise difficult to gather at scale can constitute a valuable approach to facilitate experimental analyses, projects, and studies. However, it requires much more than random value generation: it necessitates a chronological timeline, device metadata, and a need to reflect natural environmental fluctuations or …

Mocking a Year of IoT Sensor Time Series Data with Mimesis Read More »

For centuries, the scientific method has been the greatest engine of human progress. At Google, our mission is deeply rooted in building tools to accelerate it. We believe that a new era of discovery won’t come from narrow, specialized models, but general agents that empower researchers across every scientific field. That’s why we are introducing …

New AI Tools for the Future of Science Read More »

into the world of generative AI adoption for almost the past three years now. We’ve spent the last three years learning how to talk to AI, but what if I told you that the next big shift will be learning how not to let AI think for us?! With the growing exposure of AI in …

Meta-Cognitive Regulation Might Be the Most Important AI Skill Nobody Is Talking About Read More »

  # Introduction  For a long time, running transformer models meant maintaining a Python server, paying for GPU time, and routing every inference request through an API. The user typed something, it left their machine, touched your infrastructure, and came back as a prediction. That architecture made sense when the models were too large to run …

Practical NLP in the Browser with Transformers.js Read More »

Introducing Google Antigravity 2.0

coding agents sequentially and not in multiple runs in parallel, you’re losing out. One of the key benefits of coding agents is that you can start completing work in parallel, something that was never really possible before when working on software engineering tasks. However, when I start running a lot of parallel coding sessions, it’s …

How to Effectively Run Many Claude Code Sessions in Parallel Read More »

  # Introduction  Training a machine learning model and observing the loss decrease is a feeling of progress, until the validation accuracy reaches a plateau or the loss begins to spike, and you’re not sure what caused it. At that point, most people add more logging or start tuning hyperparameters, hoping something changes. What most analysts …

Visual Debugging Tools for Machine Learning Workflows Read More »

Last year, Nano Banana brought Gemini’s intelligence to image generation and editing. Since then, it’s helped millions of people restore old photos, design from sketches and visualize ideas in ways that weren’t possible before. From the start we built Gemini to be natively multimodal from the ground up, and now we’re taking the next step. …

Introducing Gemini Omni Read More »

Introduction to AI Agents of the decade. You hear it everywhere on job descriptions, tech companies’ profiles, freelancers’ projects, etc. As overwhelming as it may sound, building an AI Agent is not that difficult. On the contrary, you can easily build a simple AI Agent in a couple of minutes. This is what we will …

The Ultimate Beginners’ Guide to Building an AI Agent in Python Read More »

  # Introduction  In a recent article on Machine Learning Mastery, we built a tool-calling agent that reached outward, that is pulling weather, news, currency rates, and time from public APIs. That article covered the synthesis half of the pattern nicely, but it left the more interesting half on the table: an agent that reasons about …

Easy Agentic Tool Calling with Gemma 4 Read More »