On 24/10/2014 20:42, Matt Mahoney via AGI wrote:

You may have seen my estimates of the cost of AGI. Once Moore's Law
brings down the cost of hardware, the most expensive component will be
human knowledge collection. Some human knowledge can be sucked off the
internet, but there is still a lot that isn't written down, including
how to do all the jobs that you want to automate. I estimate 10^17
bits worldwide, and it all has to be extracted through speech and
writing.

Human knowledge is both socially-transmitted and individually-learned.
Machines might make a lot of use of individual learning - which would
reduce the need for social transmission from humans.  Rather than
bugging humans, it will often be easier and simpler for them to figure
things out for themselves.  Once one machine has figured something
out, all future machines can use social learning from other machines
in order to learn it.

Humans learn all the relevant material during their lifespans in each 
generation.
It does seem inevitable that humans will spend a lot of time programming and
teaching machines to perform the tasks they want to automate. On the positive
side, if you can teach one machine how to do a task, it can communicate that
knowledge to other similarly-architected machines.  On the negative side,
getting a machine to perform a task often requires a different strategy from
the one used by a human. For example, we didn't make 3D printers by
transferring knowledge from human potters, carpenters and matalworkers.

At the moment, transferring knowledge isn't really the main problem.
It is more that our computers are too stupid to learn properly. Once
computer learning capacities improve to more human-like levels, it
seems as though the costs associated with teaching them will go down.
At that stage you will be able to just show them what to do, and use
'ordinary' teaching methods - instead of hiring expensive computer
programmers to instruct them.
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 |im |yler  http://timtyler.org/  [email protected]  Remove lock to reply.



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