Collaborating with experts on 18 research problems, an advanced version of Gemini Deep Think helped resolve long-standing bottlenecks across algorithms, ML and combinatorial optimization, information theory, and economics. Highlights from our “Accelerating Research with Gemini” paper include (corresponding section numbers in paper): Crossing mathematical borders for network puzzles: Progress on classic computer science problems like …

Redefining the Future of Scientific Research — Google DeepMind Read More »

of the new things I’ve come across recently, while researching command-line-based coding assistants, is the mention and use of a tool I hadn’t heard of before. That tool is called Tmux, which stands for Terminal Multiplexer.  In the simplest possible terms, Tmux allows you to split up a single terminal window into a number of …

A beginner’s guide to Tmux: a multitasking superpower for your terminal Read More »

Image by Editor   Contents# Introduction# Understanding Vertex AI Search# Core Architecture and Components# Implementation Steps# Building Conversational Interfaces# Relevance Tuning and Optimization# Performance Considerations# Security and Access Control# Monitoring and Evaluation# Common Challenges and Solutions# Integration Patterns# Best Practices# Conclusion # Introduction  Vertex AI Search, formerly known as Enterprise Search on Google Cloud, represents a significant evolution in how organizations can implement intelligent search capabilities within their applications. …

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Today, we’re releasing a major upgrade to Gemini 3 Deep Think, our specialized reasoning mode, built to push the frontier of intelligence and solve modern challenges across science, research, and engineering. We updated Gemini 3 Deep Think in close partnership with scientists and researchers to tackle tough research challenges — where problems often lack clear …

AI model update designed for science Read More »

I with countless organizations that are surrounded by more data than they know what to do with. Metrics flood in from every direction, from website traffic numbers to ad impressions and conversion rates. Yet somehow, the decisions still feel like guesswork. The problem is not lack of data; it is that data alone does not …

How to Leverage Explainable AI for Better Business Decisions Read More »

Image by Editor   Contents# Introduction# What SMOTE is and How it Works# Implementing SMOTE Correctly in Python# Common Misuses of SMOTE# Concluding Remarks # Introduction  Getting labeled data — that is, data with ground-truth target labels — is a fundamental step for building most supervised machine learning models like random forests, logistic regression, or neural network-based classifiers. Even though one …

Why Most People Misuse SMOTE, And How to Do It Right Read More »

New research shows that reorganizing a model’s visual representations can make it more helpful, robust and reliable “Visual” artificial intelligence (AI) is everywhere. We use it to sort our photos, identify unknown flowers and steer our cars. But these powerful systems do not always “see” the world as we do, and they sometimes behave in …

Teaching AI to See the World More Like Humans Do — Google DeepMind Read More »

as the years before: fireworks across the globe. People greeted the new year with new resolutions and new goals. Someone, somewhere, surely said: “2026 is going to be THE year.” Then January happened. As the weeks went on, the end of the month came closer. And for people in ML research, late January has a …

The Machine Learning Lessons I’ve Learned Last Month Read More »

Image by Editor   Contents# The Fragile Pipeline# 1.Solving Data Input Bottlenecks: The Hungry GPU Problem# 2. Paying the Preprocessing Tax# 3. Right-Sizing Compute to the Problem# 4. Evaluation Rigor vs. Feedback Speed# 5. Solving for Inference Constraints Early# Conclusion: Efficiency Is a Feature # The Fragile Pipeline  The gravitational pull of state of the art in modern machine learning is immense. Research …

Is Your Machine Learning Pipeline as Efficient as it Could Be? Read More »

Acknowledgements This research was developed by the SIMA 2 team: Maria Abi Raad, John Agapiou, Frederic Besse, Andrew Bolt, Sarah Chakera, Harris Chan, Jeff Clune, Alexandra Cordell, Martin Engelcke, Ryan Faulkner, Maxime Gazeau, Arne Olav Hallingstad, Tim Harley, Ed Hirst, Drew Hudson, Laura Kampis, Sheleem Kashem, Thomas Keck, Matija Kecman, Oscar Knagg, Alexander Lerchner, Bonnie …

A Gemini-Powered AI Agent for 3D Virtual Worlds — Google DeepMind Read More »