Technology News

China’s Photonic Quantum Chip Aims for 1,000x Speedup in AI Tasks Researchers in China have unveiled a new photonic quantum chip that promises to accelerate complex calculations by over a thousand times, marking a significant leap for next-generation computing. Developed by Chip Hub for Integrated Photonics Xplore (CHIPX) and Turing Quantum, the chip is claimed to process specific AI tasks 1,000 times faster than high-end Nvidia GPUs. Its design utilizes co-packaging technology for photons and electronics, integrating over 1,000 optical components on a single 6-inch silicon wafer. This allows for dense optical integration and is supported by a pilot production line capable of producing 12,000 wafers annually. ...

Bipartisan AI Task Force Formed to Address AI Risks and Innovation A bipartisan group of state attorneys general has established a new Artificial Intelligence Task Force to navigate the complexities of AI development. Chaired by North Carolina Attorney General Jeff Jackson and Utah Attorney General Derek Brown, the initiative includes key partnerships with OpenAI and Microsoft. The task force’s core mission is to stay informed on AI advancements, protect the public—especially children—from potential AI-related harms, and develop foundational industry safeguards. This initiative aims to provide crucial consumer protection and a forum for coordination in the absence of comprehensive federal AI legislation. ...

Google DeepMind Reveals AI Agents with Human-Like Computer Skills Google’s DeepMind has made a significant advancement in agentic AI, developing agents that can interact with computers in a manner similar to humans. This development is seen as a major step forward in creating more capable and autonomous AI systems. Researchers at the Bay Area Machine Learning Symposium also discussed the future of AI, with a focus on creating models that can learn through interaction and exploration rather than just being trained on massive datasets. The goal is to achieve systematic generalization, allowing AI to combine known elements into novel meanings and learn with significantly less data than current models require. Google DeepMind’s work on models for more intelligent robots, including the recently released Gemini Robotics 1.5 and E.R. 1.5, further illustrates this push towards more interactive and intelligent AI. ...

NVIDIA Unveils RLP: A New Era of Reinforcement Learning for AI Pretraining NVIDIA researchers have developed a groundbreaking training objective called Reinforcement Learning Pretraining (RLP), which integrates reinforcement learning directly into the pretraining stage of AI model development. The core concept of RLP treats a short chain-of-thought as an action that is rewarded based on the information gain it provides for the model’s next-token prediction. This innovative method creates a verifier-free and dense reward system that can be applied at scale to standard text streams. Unlike previous methods that depend on sparse signals or external verifiers, RLP’s approach allows for updates at every single token position. Experiments demonstrate that RLP can yield significant improvements in a model’s reasoning capabilities, with one model showing a substantial increase in its average score on scientific reasoning benchmarks. ...