These limitations mean that a lot of automation will prove more elusive than AI hyperbolists imagine. “A self-driving car can drive millions of miles, but it will eventually encounter something new for which it has no experience,” explains Pedro Domingos, the author of The Master Algorithm and a professor of computer science at the University of Washington. “Or consider robot control: A robot can learn to pick up a bottle, but if it has to pick up a cup, it starts from scratch.” In January, Facebook abandoned M, a text-based virtual assistant that used humans to supplement and train a deep learning system, but never offered useful suggestions or employed language naturally.

https://www.wired.com/story/greedy-brittle-opaque-and-shallow-the-downsides-to-deep-learning/

https://thebestschools.org/magazine/limits-of-modern-ai/

Machines are here to help humans, but there is so much to do before they can do it as well as we expect them to: building smart computer programs able to perceive the world the way that humans do, understanding how complex ANNs work, developing supercomputers which will outlive even the death of Moore’s law – these are only some of the things scientists are interested in and currently working on. They are also the best possible evidence that ML is not there yet.

Meanwhile, it would be nice if we could, at least once in a while, focus on using our brains for more delicate things, and let AI-powered programs do what we have problems to and they are best at: process large amounts of data rapidly and accurately.

The best, we hope, is yet to come.