Brooks' 'Intelligence Without Representation' (http://people.csail.mit.edu/brooks/papers/representation.pdf) starts with a pretty strong argument imo against the story of 'stick-together' AGI you're describing.

I think Brooks' Cog initiative was an attempt to 'ground' the robot's perceptions of the physical world into forming a rich scalable representation model. But it looks like that line of investigation ended ~2003 with Brooks' retirement. Too bad, given the seeming suitability of using deep nets to implement it.

http://www.ai.mit.edu/projects/humanoid-robotics-group/cog/o...

Thanks for the link to this interesting paper.

I think we're seeing some recapitulation of those arguments WRT 'ensembles of DL models' approaches.

I agree. Google has come out with some papers that are, to put it harshly, basic gluing together of DL models followed by loads of training on their compute resources.

Not just Google. The FractalNet paper comes to mind.

This approach has always interested me. I can train an decent Cats Vs. Dogs classifier in a few minutes. But real human intelligence takes many years of continuous and varied input to develop.

Are there systems out there that are taking influence from newborns being exposed to the world? An unsupervised learning system with a huge array of inputs running for years?

All these current examples of AI and ML are just a very small fraction of what we mean by intelligence so I'm not surprised by the pessimistic posts that hit HN from time to time.

Training systems with rich real world experiences sounds something that Open AI should be developing. It's probably not something that you can do over a weekend plus it takes serious of funding and wetware so it's probably the reason it's not there yet.

See: Kant on a priori notions of space-time.