all AI news
On the Road to Clarity: Exploring Explainable AI for World Models in a Driver Assistance System
April 29, 2024, 4:42 a.m. | Mohamed Roshdi, Julian Petzold, Mostafa Wahby, Hussein Ebrahim, Mladen Berekovic, Heiko Hamann
cs.LG updates on arXiv.org arxiv.org
Abstract: In Autonomous Driving (AD) transparency and safety are paramount, as mistakes are costly. However, neural networks used in AD systems are generally considered black boxes. As a countermeasure, we have methods of explainable AI (XAI), such as feature relevance estimation and dimensionality reduction. Coarse graining techniques can also help reduce dimensionality and find interpretable global patterns. A specific coarse graining method is Renormalization Groups from statistical physics. It has previously been applied to Restricted Boltzmann …
abstract arxiv autonomous autonomous driving black boxes cs.cv cs.lg cs.ma driver driver assistance system driving explainable ai feature however mistakes networks neural networks safety systems transparency type world world models xai
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US