Читать книгу The Innovation Ultimatum - Steve Brown - Страница 45

The argument for stagnation is that deep learning has severe limitations—training needs too many examples and takes too long, and while these AIs pull off some amazing tricks, they have no true understanding of the world. Deep learning is built on algorithms from the mid-1980s and neural network architectures developed in the 1960s. Once we have perfected the implementation of deep learning technology and solved all the problems that we can with it, there are no viable technologies in the pipeline to keep things rolling. The current era of AI deployment will grind to a halt. So goes the stagnation argument.

On the other side of the debate are those who point to promising research that could take AI in new directions and solve a new set of problems.