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To be clear, optimizing for task-specific skill can be valuable. It gets you somewhere. But now, we're at a stage in the development of AI where generalization has become, inevitably, the bottleneck to skill acquisition.
Of course, if you can amass a sufficiently dense sampling of situations within a sufficiently narrow domain, you can always train a machine model -- but it will break down as soon as it encounters anything it has never seen before. That's modern deep learning.
And for many high-value real-world tasks, that's just about every day. Consider self-driving cars, or domestic robotics. You can't enumerate the set of possible situations a driver might ever encounter -- billions of miles are not nearly enough.
You can't even enumerate the set of possible kitchens a robot might operate in. If you want to ever be able to deploy a L5 self-driven system or a human-level domestic robot, you have to figure out how to implement broad cognitive abilities -- beyond task-specific skills.
Amen to that
Continuous learning is the only way
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