Researchers are debating how difficult it is to estimate labor impacts of autonomous truck driving as companies try to build a reliable product while reducing costs. The question of competitiveness with manually driven vehicles arises, and teleoperation may be a solution for handling vehicle failures.
The implementation of self-driving vehicles will be a crucial realization in the technological world. With the Defense Department, top computer science and robotics departments, and Silicon Valley working on this phenomenon, if self-driving cars fail, it will be the biggest technology failure in human history.
The key to solving the challenge of self-driving cars is to recreate the way humans perceive and drive using optical sensors and neural networks. It is a difficult problem that is yet to be fully understood.
Despite the billions of things that exist in this world, it is amazing how much works as it does, including artificial intelligence, technology, and millions of other inventions, even if there are occasional suboptimal results.
The current hardware and software stack used for driverless vehicles is not well-suited for exponential scaling. Real-world data collection is a valuable asset, but filtering through it at a large scale presents a challenge.
This podcast episode explores the intersection of artificial intelligence and self-driving cars, discussing their benefits and challenges from financial and societal perspectives.