First off the bat, you absolutely can create an AGI that is a pure ANN. In fact the most successful early precursor AGI we have - the atari deepmind agent - is a pure ANN. Your claim that ANNs/Deep Learning is not the end of all AGI research is quickly becoming a minority position.
The deepmind agent has no memory, one of the problems that I noted in the first place with naive ANN systems. The deepmind's team's solution to this is the neural Turing machine model, which is a hybrid system between a neural network and a database. It's not a pure ANN. It isn't even neuromorphic.
Improving its performance is going to involve giving it more structure and more specialized components, and not just throwing more neurons and training time at it.
For goodness sake: Geoffrey Hinton, the father of deep learning, believes that the future of machine vision is explicitly integrating the idea of three dimensional coordinates and geometry into the structure of the network itself, and moving away from more naive and general purpose conv-nets.
Source: https://github.com/WalnutiQ/WalnutiQ/issues/157
Your position is not as mainstream as you like to present it.
The real test here would be to take a brain and give it an entirely new sense
Done and done. Next!
If you'd read the full sentence that I wrote, you'd appreciate that remapping existing senses doesn't actually address my disagreement. I want a new sense, to make absolutely sure that the subjects aren't just re-using hard coding from a different system. Snarky, but not a useful contribution to the conversation.
This is nonsense - language processing develops in general purpose cortical modules, there is no specific language circuitry.
This is far from the mainstream linguistic perspective. Go argue with Noam Chomsky; he's smarter than I am. Incidentally, you didn't answer the question about birds and cats. Why can't cats learn to do complex language tasks? Surely they also implement the universal learning algorithm just as parrots do.
What about Watson?
Not an AGI.
AGIs literally don't exist, so that's hardly a useful argument. Watson is the most powerful thing in its (fairly broad) class, and it's not a neural network.
Finally, I don't have the background to refute your argument on the efficiency of the brain (although I know clever people who do who disagree with you).
The correct thing to do here is update. Instead you are searching for ways in which you can ignore the evidence.
No, it really isn't. I don't update based on forum posts on topics I don't understand, because I have no way to distinguish experts from crackpots.
The deepmind's team's solution to this is the neural Turing machine model, which is a hybrid system between a neural network and a database. It's not a pure ANN.
Yes it is a pure ANN - according to my use of the term ANN (arguing over definitions is a waste of time). ANNs are fully general circuit models, which obviously can re-implement any module from any computer - memory, database, whatever. The defining characteristics of an ANN are - simulated network circuit structure based on analog/real valued nodes, and some universal learning algorithm over ...
At some point soon, I'm going to attempt to steelman the position of those who reject the AI risk thesis, to see if it can be made solid. Here, I'm just asking if people can link to the most convincing arguments they've found against AI risk.
EDIT: Thanks for all the contribution! Keep them coming...