AutoTech - Out-of-this-world Bots
Explore NASA's decade-long development of intelligent robots for space missions, enhancing human-robot teamwork and efficiency in planetary exploration and beyond.
Neural Networks by Example
Explore neural networks' principles, applications, and real-life examples using MNIST dataset. Learn about convolutional networks and their use in image recognition and self-driving cars.
Scenario Extraction for Self-Driving Vehicles - Real World Applications
Explore real-world scenario extraction for self-driving vehicles. Learn methods to analyze and structure time-series data, classify states and events, and assess traffic behavior for autonomous driving validation.
Deep Learning Models for Autonomous Driving
Discover the latest autonomous driving deep learning models and innovations from Silicon Valley, featuring insights from a former Tesla Autopilot R&D expert and current Phantom AI CEO.
How to Create ML Models That Teach Themselves - Unsupervised Learning in Self-Driving Cars
Explore self-driving car technology and unsupervised ML models with Helm.ai. Learn about deep teaching, real-world applications, and future enhancements in autonomous vehicle development.
Machines Behaving Badly: The Morality of AI
Explore ethical considerations and unexpected consequences of AI, including racism in AI assistants, robot rights, self-driving car accidents, and facial recognition limitations.
Convolutional Neural Networks
Explore convolutional neural networks for computer vision, covering feature extraction, convolution operations, and applications like object detection and self-driving cars.
Self-Driving Cars
Explore the future of autonomous vehicles and their impact on energy consumption, lifestyle, and the Internet of Things in this insightful presentation by a distinguished engineering professor.
Cost-Effective Simulation-Based Test Selection in Self-Driving Cars SW
Explore cost-effective simulation-based testing for self-driving cars using SDC-Scissor, a machine learning framework to enhance safety and reduce testing costs.
Deep Learning in Computer Vision
Explore deep learning in computer vision, from basic concepts to advanced architectures. Learn to design networks that understand images, with practical examples and expert insights.
The Modern Trolley Problem
Exploring ethical dilemmas in self-driving car algorithms, demonstrating object recognition, and discussing responsible AI principles and sustainable engineering practices.
The Modern Trolley Problem - Responsible AI Principles
Exploring ethical challenges in AI-driven vehicles, from object recognition to decision-making algorithms. Demonstrates model training and deployment while emphasizing responsible AI principles and sustainable engineering practices.
Deep Learning for Self-Driving Cars
Explore deep learning's role in autonomous driving software, addressing challenges and research in reducing traffic fatalities through AI-driven solutions.
The Modern Trolley Problem - Responsible AI Principals
Explore ethical challenges in AI-driven vehicles, from blame allocation to algorithm design. Discover object recognition, obstacle avoidance, and responsible AI principles for sustainable engineering practices.