Never say never! We are getting better. Maybe we just need more samples for learning.
The problem is how to collect all the relevant data to train an algorithm.
They are training on a minor subset of all samples which it will occur while finding a solution in real life situations.
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My argument isn't concerning the amount of training data, which, for a machine based on deep-learning, like the one that plays Go, can be prohibitively large. It's more about the flexibility of (good) machine learning methods, which is very low. Again, referring to the specifics of deep learning, it requires a ton of trial-and-error in determining the parameters of the model. This work requires the manual adjustment of expert technicians.
Anyway, my argument was just that generalist machine learning computers, capable of solving the sorts of problems humans do every day, without specific setup and method implementation for each category of task, is way far off.