Why is super-human general AI inevitable?
Do you have references for this 2040 date? I'd love to see who's making this prediction.
Do you have references for this 2040 date? I'd love to see who's making this prediction.
Our current transistor tech is already orders of magnitude smaller, faster, and more efficient than neurons. There's no reason to expect the software stats of the brain are much better.
Here's one survey: http://www.nickbostrom.com/papers/survey.pdf
I don't see any mention of confidence intervals mentioned in the survey, so what are you talking about? Means and standard deviation are given. But they are mostly useless as the distribution is very skewed. They would be infinite if the "never" people were included of course.
Considering that we still have many issues with basic computing - not least working out how to write code that works reliably without constant manual updates - the suggestion that we're going to start building self-improving crash-proof hyperbrains any time soon is wishful thinking.
Realistically, we don't have the first clue how to start solving that problem. As of 2017 "Throw deep learning at it" is more of a fashion statement than a practical solution.
Perhaps not "magical", but the human brain exists in a very unique situation because it's the only one we're trying to understand with other instances of itself.
When I say "massively parallel" I mean that a human brain (forget rest of nervous system) has about 80 G neurons. Each of which has a fan out between 10^4 and 10^5.
Architecturally this is quite different from a transistorized device with a much faster clock speed but a paucity of interconnect. Now few people say that we need to duplicate the brain (Numenta excepted -- although I have been half of a team that made a new brain and it was fun). After all an automobile does not look like a cheetah nor a plane look like an eagle. But this should give you some idea of the complexity of the problem.
(At Leela we are building something that approaches a more "brain" like computation when compared to, say, deep Q learning, and even for us, the equivalent of 2 M neurons requires about 16 TB of RAM).