Imitating the nematode’s nervous system to process information efficiently, this new intelligent system is more robust, more interpretable, and faster to train than current deep neural network architectures with millions of parameters.
In a perfect world, AI should be developed to avoid unethical issues, but that may be unlikely since those issues cannot always be predicted. In an automated society, human beings will have the responsibility to support and protect each other more than today.
How Autonomous Vehicles will redefine the concept of mobility. They are already among us and will transform the entire automotive industry.
Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving. Localizing dynamic objects and estimating the camera ego-motion in 3D space are crucial tasks for autonomous driving.
The applications of deep learning has been explored in various components throughout the autonomous driving stack, for example, in perception, prediction, and planning. Deep learning can also be used in mapping, a critical component for higher-level autonomous driving.
Autonomous vehicles (AVs) essentially refers to driving without any human interaction and would revolutionize the way we work and live.
The intermediate phase of autonomy with remotely human-controlled vehicles will show incredible market values. The biggest cost for gig driving economics right now is the driver time.
Deep Reinforcement Learning for autonomous vehicles with OpenAI Gym, Keras-RL in AirSim simulator. Autonomous vehicles become popular nowadays, so does deep reinforcement learning.
The current crisis is an inflection point for the strategies of AV companies. It changes the course of the driverless vehicle industry.