I would like to discover a development path that would be simple. If
an effective method of abstraction could be developed then artificial
abstractions could be used to create, test, and develop an agi program
on the fly using high-level concepts. The problem is that the process
of abstracting is not easy. It is not like taking characters out of a
string. And it is not easy to use abstractions in reconstructing or
analyzing and projecting the abstractions onto some input. The first
question is whether or not you might use Derrida's deconstruction
method in order to abstract sub-concepts out of high level concepts.
Can I do it myself just writing it down? I think I could but there
would be some trial and error as I tried to refine the relations one
concept and its abstractions have to other parts and abstractions. If
I could get a program to act this way would it be worthwhile? I kind
of doubt it because it sounds like the combinatorial potential would
increase too greatly as you added (through deconstruction and other
ways) more concepts and abstractions into the system. OK, is it
worthwhile in a more open sense in that it might lead to some other
discoveries even if it did not achieve AGI? I am pretty sure it would
be.
Jim Bromer

On Thu, Nov 1, 2018 at 1:32 PM Matt Mahoney via AGI
<[email protected]> wrote:
> 
> Abstraction is not simple. AGI is not simple. I realize that most of
> the interest in AGI on this list is out of scientific curiosity. But
> AGI is expensive so anyone funding your research is going to be
> looking at practical applications like automating human labor or
> extending life. We are already making progress on both fronts, which
> means that the easy parts have already been solved. Automating labor
> has a ROI of world GDP divided by interest rates, or $1 quadrillion.
> Extending life has a ROI of world GDP times life expectancy, or $5
> quadrillion. Google, Amazon, Apple, and Facebook have a combined
> market cap of $3 trillion, so they have a long way to go.
> 
> Automating labor and extending life through uploading both require
> models of the human mind, which includes stuff like vision, language,
> robotics, and art. Basically it is a function that takes sensory input
> and returns a prediction of what a human would do. It is required for
> effective communication between AGI and its human masters so that it
> knows what you want. If you or your loved ones decides to upload you
> after you die, then a model of your mind becomes a component of a
> program for a robot that carries out its predictions of your actions.
> 
> The rest is engineering. We already know that the best vision and
> language models are neural. A human brain sized neural network with
> 10^14 synapses and 10 ms response times requires 10 petaflops and 1
> petabyte. Computers with this capacity require 1 MW of electricity
> costing $100 per hour, which is not competitive with human labor. A
> human body averages 100 watts (of which 20% powers the brain). You
> cannot get there with transistors because they are already not much
> larger than atoms. Computing AGI will require moving atoms or ions,
> not electrons. Is anyone working on that?
> 
> No. Everyone is working on the software, as if they expect it to run
> on their laptop. Sorry. Even if you did have appropriate hardware,
> humans are complex. Your DNA compresses to the size of 300 million
> lines of code. If there was a simple formula for intelligence, we
> would have evolved a lot faster or we would see it in insects and
> bacteria. Legg proved that powerful predictors are necessarily
> complex. Every example of AI supports that proof with more evidence
> that you aren't going to find a simple equation for intelligence.
> Watson was a 30 person-year effort. Google has written 2 billion lines
> of code. Human evolution took 10^48 DNA base copy operations over 3
> billion years.
> 
> So exactly what do you hope to accomplish?
> 
> -- Matt Mahoney, [email protected]

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