Search headlines, summaries and articles to connect the dots.
A deep dive into the tech craze, asking how machines might rewrite the underlying code of civilization. This episode is No. 07 in the “Beneath the Boiling Point” series. Text by Boyang | Editor: Xu Qingyang Technological forecasting typically oscillates between two extremes: either vague grand narratives or linear extrapolations...
arXiv:2610.32069v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) can perceive a traffic scene, reason about it, and propose a driving action within a single model. However, existing MLLM-based drivers reason from scratch at each frame and commit to a single explanation, discarding earlier hypotheses about the scene. When the scene is ambiguous, the agent locks in on the wrong interpretation and acts on it before evidence accumulates. In this paper, we propose DriveMind, an MLLM-based driving framework that keeps several hypotheses about the scene alive at the same time and updates their weights as new frames arrive. Inspired by particle filtering, DriveMind propagates each hypothesis through a VLM, scores it against the current observation, and resamples to m
Ride-hailing platform inDrive has signed memorandums of understanding with Pony.ai and NVIDIA, planning to launch Gen-7 autonomous taxi trials in Astana, the capital of Kazakhstan, in the first half of 2027. Initially, these vehicles will be equipped with safety drivers, with the ultimate goal of achieving fully driverless operation; NVIDIA will provide AI technology support, including the Hyperion platform and the Halos safety system.
An encoder (optical system) maps objects to noiseless images, which noise corrupts into measurements. Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects. Many imaging systems produce measurements that humans never see or cannot interpret directly. Your smartphone processes raw sensor data through algorithms before producing the final photo. MRI scanners collect frequency-space measurements that require reconstruction before doctors can view them. Self-driving cars process camera and LiDAR data directly with neural networks. What matters in these systems is not how measurements look, but how much useful information they contain. AI can extract this information even when it is encoded in ways that humans cannot
