Matt, Do you have any statistics about how the complexity of the brain
evolves with the complexity of the body?
And also - care to put an estimate on the relative chances of starting to
emulate a worm with 300 neurons vs a human with billions?
-----Original Message-----
From: Matt Mahoney
Sent: Wednesday, April 03, 2013 4:10 PM
To: AGI
Subject: Re: [agi] Utilizing kickstarter.com?
On Fri, Mar 29, 2013 at 10:30 AM, just camel <[email protected]> wrote:
Why do you always argue via evolution and DNA? DNA requires a vast amount
of
overhead and comes with tons of evolutionary baggage that is irrelevant to
our intelligence but was/is required in order to make the system
evolve/work.
Large software projects also have a lot of useless and redundant code,
test code, code for features that nobody uses, code that doesn't work,
etc. When you measure cost and lines of code, all that code still
counts. You can throw out all the code you don't need, but you still
paid for it.
Living organisms are the same way. Both humans and the microscopic
bacteria eating roundworm C. elegans have the same number of genes,
about 20,000. However, the human genome is 30 times larger, 3 billion
bases vs. 100 million. The difference is that the exome (protein
encoding regions) makes up 2% of the human genome but 70% of C.
elegans.
Let's assume that humans are more complex than C. elegans. Then that
extra information must lie outside the exome. This is not all junk
DNA. Some of it encodes binding sites for gene regulatory proteins.
Some of it encodes genes that are no longer used, but could be
activated by mutations. Many of our genes have multiple copies whose
count can vary. C. elegans' densely packed genome, just like densely
packed optimized code, is much harder to modify without breaking
something. That is why it is not as highly evolved, if we can make
that comparison.
There is a wide range of genome sizes among related species, mostly
due to copying. But in general, the lower bound increases for "higher"
organisms. For mammals, the smallest genome is 2 billion bases.
http://www.genomesize.com/statistics.php
Human DNA has the same information content as 300 million lines of
code. That does not mean we will solve AGI by writing a program that
big. The way we now automate work is to write lots of little programs
to do specific tasks. So it could mean automating 100,000 different
tasks with 3000 lines of code each.
You might point out that Watson is far simpler than my estimate.
Watson does something very close to passing the Turing test. Its
development was a 30 person-year effort, suggesting about 60 K lines,
and certainly no more than 300 K. However Watson cannot see, hear,
move, or make copies of itself. It has a good language model, as long
as the input has no spelling or grammar errors. Watson cannot use
discourse or nonverbal context to aid understanding as humans can. I
question whether it could have defeated its human opponents without
the huge advantage of an 8 ms response time to buzz in.
Also we do not have a lot of the restraints that evolution had
... like being extremely energy efficient or caring that much about heat
dissipation or compactness and mobility.
The power all of our brains consume is 0.4TW (50Watt * 8billion people).
The total power consumption of the human world in 2010 was 16 TW.
A human brain sized neural network requires 10 MW on a 10 petaflop
computer. Running 10 billion of those would raise the Earth's
temperature by 12.5% (from 59 F to 123 F). Reducing transistor sizes
won't fully solve the problem because feature sizes are already down
to about 100 atoms wide. It will probably require a fundamentally new
computer architecture, something other than silicon. Right now we have
no idea what that might be.
Clearly we have way more options than evolution in terms of energy
constraints, in terms of vehicle/body constraints, in terms of a priory
intelligence.
We have already discovered many solutions that improve on evolution.
No bird can fly into space or carry passengers at 600 MPH. A
mechanical adding machine is better at arithmetic than the brain. I
can't prove that there aren't more computationally efficient ways to
do everything that humans can do, just that both evolution and 60
years of AI research, both with enormous incentives, have failed to
find them.
--
-- Matt Mahoney, [email protected]
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