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Researchers at the University of Toronto Institute for Aerospace Studies have developed innovative tools to enhance the safety and reliability of self-driving cars. These tools improve object tracking capabilities, allowing robotic systems to better monitor the movement of vehicles, pedestrians, and cyclists in busy environments. The first tool, Sliding Window Tracker (SWTrack), utilizes additional information over time to prevent missing objects and enhance tracking accuracy.
The SWTrack tool looks back up to five seconds in time to link current detections with past objects, aiding in better understanding and predicting movements. Another tool, UncertaintyTrack, developed by master's student Chang Won (John) Lee and Professor Waslander, uses probabilistic object detection to estimate uncertainty in object detection, crucial for safety-critical tasks. These advancements in object tracking and reasoning could significantly improve the safety and reliability of self-driving cars.
Isomorphic Labs, the AI drug discovery platform that was spun out of Google's DeepMind in 2021, has raised external capital for the first time. The $600
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Sam Altman today revealed that OpenAI will release an open weight artificial intelligence model in the coming months. "We are excited to release a powerful new open-weight language model with reasoning in the coming months," Altman wrote on X.
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