1.6 Potential Approaches to AGI 7
likely to come about for a decade or two. So to do brain-emulation AGI seriously, one needs to wait a while until brain scanning technology improves.
Current AI methods like neural nets that are loosely based on the brain, are really not brain- like enough to make a serious claim at emulating the brain’s approach to general intelligence. We don’t yet have any real understanding of how the brain represents abstract knowledge, for example, or how it does reasoning (though the authors, like many others, have made some speculations in this regard [GMIH08]).
Another problem with this approach is that once you’re done, what you get is something with a very humanlike mind, and we already have enough of those! However, this is perhaps not such a serious objection, because a digital-computer-based version of a human mind could be studied much more thoroughly than a biology-based human mind. We could observe its dynamics in real-time in perfect precision, and could then learn things that would allow us to build other sorts of digital minds.
1.6.4 Evolve an AGI
Another approach is to try to run an evolutionary process inside the computer, and wait for advanced AGI to evolve.
One problem with this is that we don’t know how evolution works all that well. There’s a field of artificial life, but so far its results have been fairly disappointing. It’s not yet clear how much one can vary on the chemical structures that underly real biology, and still get powerful evolution like we see in real biology. If we need good artificial chemistry to get good artificial biology, then do we need good artificial physics to get good artificial chemistry?
Another problem with this approach, of course, is that it might take a really long time. Evolution took billions of years on Earth, using a massive amount of computational power. To make the evolutionary approach to AGI effective, one would need some radical innovations to the evolutionary process (such as, perhaps, using probabilistic methods like BOA [Pel05] or MOSES [Loo06] in place of traditional evolution).
1.6.5 Derwe an AGI design mathematically
One can try to use the mathematical theory of intelligence to figure out how to make advanced AGI.
This interests us greatly, but there’s a huge gap between the rigorous math of intelligence as it exists today and anything of practical value. As we'll discuss in Chapter 7, most of the rigorous math of intelligence right now is about how to make AI on computers with dramati- cally unrealistic amounts of memory or processing power. When one tries to create a theoretical understanding of real-world general intelligence, one arrives at quite different sorts of consider- ations, as we will roughly outline in Chapter 10. Ideally we would like to be able to study the CogPrime design using a rigorous mathematical theory of real-world general intelligence, but at the moment that’s not realistic. The best we can do is to conceptually analyze CogPrime and its various components in terms of relevant mathematical and theoretical ideas; and perform analysis of CogPrime’s individual structures and components at varying levels of rigor.
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