The idea that AI agents need ‘eyes’ – real visual information from the real world, and not just paperwork and dashboards, have become a marker of advancement in construction technology. Yet mostly what has been referred to as an “agent” today is not actually acting based on what it sees. It narrates and alerts while…
# Small But Powerful
Running a 70B model in production can be expensive, slow, and, for many tasks, unnecessary. If you're building a focused pipeline like a document classifier or a multilingual support responder, a well-trained 3B model will match or beat the 70B on your specific task at a fraction of the…
In this tutorial, we explore adaptive experimentation using Meta’s Ax with the modern Client API. We work through a complete workflow where we tune a RandomForest model on a synthetic classification dataset while balancing predictive accuracy against model footprint. We begin by defining a mixed search space with integer, float, log-scaled, and categorical parameters, then…
Developers and customers building production AI agents need higher token efficiency, lower latency, and more reliable performance. Our Flash series of models is built to meet the sweet spot of efficiency and quality to enable scaling agentic workflows. Building on Gemini 3.5 Flash, we’re introducing new Gemini models: 3.6 Flash: Our workhorse model that delivers…
Genesis AI released Genesis World 1.0. The platform consists of four components: the Genesis World physics engine, Nyx (a real-time path-traced renderer), Quadrants (a Python-to-GPU compiler), and a simulation interface. It is designed to accelerate robotics foundation model development through simulation-based evaluation.
Robotics model development has two bottlenecks: data and iteration speed. The field has…
# Introduction
There's a specific kind of blog post every AI lab publishes eventually: the one where an engineering team explains why their new architecture exists, admits what it costs, and tells you when not to use it. Most of these read like marketing wearing a lab coat. MiniMax published one on May…
Onton, a San Francisco-based search and discovery company, has released Ontology 1, a neurosymbolic model for complex, conversational, multimodal product search. On a 90-query benchmark scored by three independent LLM judges, Ontology 1 reached a mean precision@10 of 0.630, against 0.543 for Google Shopping and 0.469 for Amazon. It did this while indexing roughly 1%…
For robots to assist humans in everyday environments, accurate spatial reasoning is not enough. Robots must also think fast, timing their decisions and reasoning with the real-time speed of the physical world. That’s why today we’re launching Gemini Robotics ER 2, our most capable “embodied reasoning” model for robotics. Think of Gemini Robotics ER 2…
Google DeepMind has released Gemini Robotics 2, the intelligence layer for its next generation of robots. The release moves the stack past table-top manipulation into whole body control, five finger dexterity and multi robot teamwork. It ships as three separate models with three different access tiers.
Most robots today are pre-programmed or tele-operated for narrow,…
# Introduction
You do not need to become a data scientist to benefit from data science and artificial intelligence (AI). However, you should understand what these technologies can do, where they can fail, and how to evaluate their outputs.
After working with data science and AI tools for several years, I have noticed…