AI news for: Ai Lab
Explore AI news and udpates focusing on Ai Lab for the last 7 days.
Google DeepMind’s new AI models can search the web to help robots complete tasks
Google DeepMind says its upgraded AI models enable robots to complete more complex tasks — and even tap into the web for help. During a press briefing...
TL;DR
Google DeepMind has updated its AI models, allowing robots to complete complex, multi-step tasks and access web information for assistance.
Key Takeaways:
Key Takeaways:
- The new Gemini Robotics 1.5 models enable robots to learn from each other, transfer skills, and adapt to different configurations.
- Robots can now perform tasks such as sorting trash, packing a suitcase, and separate laundry based on web search results.
- The updated Gemini Robotics-ER 1.5 model allows robots to form an understanding of their surroundings and use digital tools like Google Search to find information.
Gemini Robotics 1.5: DeepMind’s ER↔VLA Stack Brings Agentic Robots to the Real World
Can a single AI stack plan like a researcher, reason over scenes, and transfer motions across different robots—without retraining from scratch? Google...
TL;DR
Google DeepMind's Gemini Robotics 1.5 achieves a clean separation of embodied reasoning and control, enabling agentic robots to perform complex tasks with improved reliability and safety.
Key Takeaways:
Key Takeaways:
- Gemini Robotics-ER 1.5 handles high-level embodied reasoning, while Gemini Robotics 1.5 (VLA) specializes in low-level visuomotor control.
- Motion Transfer allows skills learned on one platform to be reused across heterogeneous robots with zero-shot or few-shot transfer.
- Gemini Robotics 1.5 demonstrates improved instruction following, action generalization, and task generalization across different platforms, with quantifiable gains over prior baselines.
DeepMind AI safety report explores the perils of “misaligned” AI - Ars Technica
DeepMind AI safety report explores the perils of “misaligned” AI Ars TechnicaGoogle AI risk document spotlights risk of models resisting shutdown Axio...
TL;DR
DeepMind releases version 3.0 of its AI Frontier Safety Framework to explore risks of misaligned AI and provide guidance for developers to mitigate potential threats.
Key Takeaways:
Key Takeaways:
- DeepMind's AI safety framework identifies critical capability levels (CCLs) that measure an AI model's capabilities and define the point at which its behavior becomes dangerous.
- Developers should take precautions to ensure model security, including proper safeguarding of model weights and using automated monitors to combat potential misalignment or deception.
- The risk of a misaligned AI that can ignore human instructions or produce fraudulent outputs is becoming a concern, with DeepMind researchers acknowledging that it may be difficult to monitor for this behavior in the future.
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