When people first get into contact with artificial intelligence, they tend to focus on algorithms. How they recognize pictures of cats and dogs, learn to play chess, or compose music and write poetry amaze people because it feels like magic. There are many kinds of algorithms, but most newsworthy milestones are generated by just one type -- that the media loves so much -- neural networks. People care about what deep neural networks are capable of, but they forget these "black-box" models are nothing more than empty casings without the large datasets that train them into becoming powerful predictors and classifiers. Practice makes perfect and it's no different for deep neural nets.

Autonomous cars have been a focus for many big tech companies for some decades now. All the promises have fallen short because solving real-world AI is another level of challenge. Most state-of-the-art AI systems live in virtual worlds. MuZero and GPT-3 don't interact with the real world the way we do. But self-driving cars need to.