Modern robotics has been around for more than 20 years and there is no autonomy whatsoever.
It might surprise many people who claim that AIG is 20-30 away from now that in robotics, something as simple as cup stacking is a major problem. If you can figure out how to do that you will get slightly famous.
The brain and a computer work so fundamentally differently that its impossible to provide a single framework to compare them. This is why I used the metric of computational complexity but even that is not at all accurate.
I think due to the tremendous amount of automation we have seen in the past 10-20 years, many programmer have difficulty appreciating the awesomeness of biological intelligence. Rather than view the success of automation as a failure of how human education works.
By fully appreciating how complexity of the biological intelligence can we start to learn from it and append it.
Totally agree! I think we have deviated too far from biological models in order to achieve marketable results. Google is satisfied with probabilistic machine learning because it will still make them plenty of money being able to tag images. This is why I admire the approach Jeff Hawkins is taking by going back to the biological model for more inspiration.
Modern robotics has been around for more than 20 years and there is no autonomy whatsoever.
It might surprise many people who claim that AIG is 20-30 away from now that in robotics, something as simple as cup stacking is a major problem. If you can figure out how to do that you will get slightly famous.
The brain and a computer work so fundamentally differently that its impossible to provide a single framework to compare them. This is why I used the metric of computational complexity but even that is not at all accurate.
I think due to the tremendous amount of automation we have seen in the past 10-20 years, many programmer have difficulty appreciating the awesomeness of biological intelligence. Rather than view the success of automation as a failure of how human education works.
By fully appreciating how complexity of the biological intelligence can we start to learn from it and append it.