Google DeepMind released the Gemini Robotics 2 model series designed to control humanoid robots and enable them to collaborate on multi-step tasks. The core model, Gemini Robotics ER 2, accepts natural language instructions and can coordinate tasks across multiple robots while tracking progress via camera feeds and recovering from failures without restarting from scratch. The new capabilities allow robots to handle complex chores comprising hundreds of steps and adapt their execution in real time.
Google says it fixed 1,072 security bugs in Chrome's two June releases using internal AI tools, exceeding the 1,036 bugs patched over the previous 23 releases spanning two years. The company attributes this acceleration to applying large language models like Gemini to vulnerability discovery at scale. This shift reflects how AI is fundamentally changing cybersecurity economics, with defenders now able to match or exceed the rate at which attackers can exploit flaws.
Google released Gemini Robotics 2.0, an AI system that enables robots to perform more complex tasks, adapt to changing environments, and coordinate with other robots through improved reasoning models. One of three new sub-models is publicly available to developers starting today via the Gemini Live API. The upgrade allows robots to control complex humanoid bodies with better dexterity, moving closer to the goal of creating generalist machines that can perform diverse physical tasks.
Google launched Gemini Robotics ER 2, an AI model that enables robots to understand video feeds, plan multi-step tasks, and collaborate with other robots in physical environments. The model achieves 57.4% accuracy on progress classification and 91.3% accuracy on moment-finding tasks while operating at sub-second latency required for safe real-world robotics. Developers can now access the model via the Gemini API and Google AI Studio, enabling robots to independently track progress, adapt to failures, and coordinate with other machines to complete complex workflows.
Google DeepMind released Gemini Robotics 2, an AI model that controls humanoid robots' entire bodies including legs and arms, extending beyond its predecessor which only handled upper-body movements. The model enables robots like Apptronik's Apollo 2 to perform full-body tasks such as walking, crouching, bending to pick up objects, and retrieving specific items from shelves. Humanoid robots can now execute a broader repertoire of physical actions previously impossible with upper-body-only control.
Google added a voice command feature to Gemini on macOS that activates by long-pressing the fn key, allowing users to transcribe speech and give commands about what's visible on their screen. The feature automatically cleans up transcripts by removing filler words like "ums" and "ahs," and can execute complex tasks like summarizing PDFs or drafting emails if users enable screen-aware reasoning. The rollout is available in English today worldwide, with support for additional languages planned later this year.
Google launched Gemini 3.5 Flash, a faster mid-tier model with improved agentic capabilities and visual understanding, but at three times the price of its predecessor. The model costs $1.50 per million input tokens (compared to $0.05 for Gemini 3 Flash) and topped benchmarks like APEX-Agents-AA with 47.1% accuracy. Higher per-token prices across major AI providers mean the "Flash" designation no longer signals a cost advantage for developers building agents. Meanwhile, the European Union delayed high-risk AI regulations to December 2027 and December 2026 for other provisions, citing competitiveness concerns, though it strengthened rules banning non-consensual intimate imagery.
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