Many people in Nashville say they don't trust artificial intelligence chatbots to give them unbiased information amid the backlash Google faces over its Gemini program. On Tuesday, the Department of Education's Office for Civil Rights (OCR) released presidentially-mandated guidance that lays out how schools' use of artificial intelligence (AI) can be discriminatory toward minority and transgender students, "likely" opening them up to federal investigations. President Biden signed Executive Order 14110 last year mandating that the Education Department develop resources, policies and guidance regarding AI in schools to help ensure responsible and non-discriminatory use, "including the impact AI systems have on vulnerable and underserved communities." "The growing use of AI in schools, including for instructional and school safety purposes, and AI's ability to operate on a mass scale can create or contribute to discrimination," the Education Department's guidance states. "This resource provides information regarding federal civil rights laws in OCR's jurisdiction and includes examples of types of incidents that could, depending on the facts and circumstances, present OCR with sufficient reason to open an investigation."

Sampling-based planning algorithms like Rapidly-exploring Random Tree (RRT) are versatile in solving path planning problems. RRT* offers asymptotical optimality but requires growing the tree uniformly over the free space, which leaves room for efficiency improvement. To accelerate convergence, informed approaches sample states in an ellipsoidal subset of the search space determined by current path cost during iteration. Learning-based alternatives model the topology of the search space and infer the states close to the optimal path to guide planning. We combine the strengths from both sides and propose Neural Informed RRT* with Point-based Network Guidance. We introduce Point-based Network to infer the guidance states, and integrate the network into Informed RRT* for guidance state refinement. We use Neural Connect to build connectivity of the guidance state set and further boost performance in challenging planning problems. Our method surpasses previous works in path planning benchmarks while preserving probabilistic completeness and asymptotical optimality. We demonstrate the deployment of our method on mobile robot navigation in the real world.