[YeGoblynQueenne]

An even bigger assumption is that intelligence in computers will require the same amount of computational power that it requires in humans. The AI we have so far is completely different than human intelligence (e.g. machine learning requires vast amounts of data; human learning can learn from single examples, etc). Computers themselves have completely different abilities than humans. Intelligence on the human brain is just not a very good model for intelligence on the computer.

[yonkshi]

I agree a true general AI will probably be fairly different on computer than human. Although I want to mention that humans can learn from one example is mostly because we already have a large a priori from our life experience. We spend years learning how to talk, communicate, write and read. Through which we have built very structured symbolic logic system, which is _learnt_.

A example of this would be mathematics. If a person is never taught mathematics, she/he are only limited to basic math operations. It would take the person years of learning and practice in order to comprehend a mathematical literature. Once we have a symbolic logic network built for a certain aspect of our life, we can rapidly retrieve information based on previous logical patterns, thus allowing us to learn from one example.

[s1dechnl]

Both of you are correct. One need only have understanding. You must maintain a yet to be discovered understanding of the human equivalent and have a depth of understanding in computational systems. While the understanding is non-trivial, the translation effort from one domain to the other isn't much effort. Computing resource capability scales with $$. I decided to do something unorthodox and start with limited computational resources. It drives the innovative spirit ^_-. If your processor is to 'slow', you can simply create a simulated abstraction of time and go from there. Computational power doesn't bog down/limit the effort, one's own understanding of the problem space/domains does.