We’ve created the world’s first Spam-detecting AI trained entirely in simulation and deployed on a physical robot.
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Our latest report featuring case studies of how we’re detecting and preventing malicious uses of AI.
We’ve created a robotics system, trained entirely in simulation and deployed on a physical robot, which can learn a new task after seeing it done once.
A quick guide to separating Physical AI from world models, embodied AI, physics AI, and digital twins The post Physical AI: What It Is and What It Is Not appeared first on Towards Data Science .
arXiv:2606. 09499v1 Announce Type: cross Abstract: World models have recently seen a rapid growth in both their popularity and capability as more data efficient tools for generating robot training data or simulating real world environments, with many works proposing their integration into the robot learning pipeline.
arXiv:2607. 04146v1 Announce Type: cross Abstract: This work establishes that trigger-word data poisoning of vision language action models is practical, while at the same time the open-source robotics ecosystem holds trust assumptions about community contributions.
The article reviews the challenges and opportunities of detecting social bots amid the rise of advanced AI chatbots. It highlights gaps in current detection methods, especially regarding AI-generated conversations, and identifies emerging trends such as synthetic data generation, multimodal cross‑platform detection, low‑resource language support, and federated learning approaches. The authors propose these directions as promising avenues for future research.