> thousands of lifetimes of other data with many modes of interaction and different media would be better.
But why not just 1 lifetime of different kinds of data? Heck, why not an environment of 3 years of multi-media data that a child would experience? That wouldn't cost insane amounts of money (or probably anything even close to what we've spent on deep learning as a species).
A corpus limited to the experiences of a single agent would create a very compelling case for intelligence if at the end of that training there was something that sounded and acted smart. It couldn't "jump the gun" as it were, by a lookup of some very intelligent statement that was made somewhere else. It would imply the agent was creatively generating new models as opposed to finding pre-existing ones. It'd even be generous to plain-ol'-AI as well as deep learning, because it would allow both causal models to explain learned explicit knowledge (symbolic), or interesting tacit behavior (empirical ML).
> But why not just 1 lifetime of different kinds of data? Heck, why not an environment of 3 years of multi-media data that a child would experience? That wouldn't cost insane amounts of money (or probably anything even close to what we've spent on deep learning as a species).
How would you imagine creating such an environment in a way that allows you to train models quickly?
But why not just 1 lifetime of different kinds of data? Heck, why not an environment of 3 years of multi-media data that a child would experience? That wouldn't cost insane amounts of money (or probably anything even close to what we've spent on deep learning as a species).
A corpus limited to the experiences of a single agent would create a very compelling case for intelligence if at the end of that training there was something that sounded and acted smart. It couldn't "jump the gun" as it were, by a lookup of some very intelligent statement that was made somewhere else. It would imply the agent was creatively generating new models as opposed to finding pre-existing ones. It'd even be generous to plain-ol'-AI as well as deep learning, because it would allow both causal models to explain learned explicit knowledge (symbolic), or interesting tacit behavior (empirical ML).