X-Message-Number: 25556
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From: Peter Merel <>
Subject: The Singularity Is A Fantasy - replies to Ettinger & Kluytmans
Date: Sun, 16 Jan 2005 16:13:33 +1100

Robert Ettinger writes,

> If a tiny insect or mite  can
> negotiate terrain, how hard can the problem be, in the longer view? If 
> it  has been
> done, it can be done--the Precedent Principle.

Nature's answer to orientation, command and control on the nanoscale 
entails big slow osmotic blobs of protoplasm. I certainly grant that 
biological viri may make a good precedent for MNT, and that some kind 
of intentionally designed self-organization may permit same to operate 
on many scales without big slow osmotic blobs of protoplasm. I can 
readily imagine MNT components that orient, for example, by  means of 
spatial addressing within- within a tesselation of truncated octohedra 
as per Dean & Lucas's Generalized Balanced Ternary. But as far as I 
know the burden of proof for this has not been shouldered by nature - 
it remains with the MNTologists.

Henri Kluytmans writes,

> Indeed, the standard supervised training algorithms seem to be
> very limited in their use. But using genetic methods for evolving
> neural network weights is much less limited. The performance
> of genetic methods does not relate to the complexity of a system.
> As long as there is a way to determine the fitness of a system,
> a genetic method should work.

> [...] Because evolutionary methods do not mind the complexity of a 
> system.

If this assertion were correct we could do away with the supercomputers 
and the quantum computing research and use EC/GA for almost all our 
computing needs. In fact EC/GAs have performance / time complexity 
bounds and tradeoffs, same as any algorithm. The main advantage of 
EC/GA is their applicability to black-box searching. While complexity 
theorists are still assimilating the wealth of experimental evidence 
about performance of EC/GA, see 
http://citeseer.ist.psu.edu/beyer02how.html, and 
http://citeseer.ist.psu.edu/662755.html for general results and 
analysis techniques for search-time vs. success-probability analysis.

> There will be some time in the
> future when the current limitations for genetic methods will have
> been overcome. Nature shows us that genetic methods can work to create
> intelligent neural networks.

As far as we know Nature managed the trick only once, and it took over 
10 billion years on a quantum computer the size of the universe to do 
it. I doubt Drexler's 10^38 machine operations would be enough to 
simulate a good sneeze on such a computer. Consequently Nature actually 
"shows us" that genetic methods are unlikely to create intelligent 
neural networks within the lifetime of the solar system.

Now you may object that, with a working model before us, we can speed 
this up rather a lot. And I'd agree that seems likely. But if that's 
the case you can forget the genetic methods, because the main advantage 
of genetic methods is their ability to perform black box search. If you 
know a lot about some problem domain you can devise *much* more 
efficient methods to search it than GA/EC. You can check out Amerol's 
"Cannibals & Christians" reformulation for the classical AI treatment 
of an NP-hard search problem converted to a linear time one.

>> No one has been able to demonstrate non-biological neural
>> networks that reproduce the behaviors of even the simplest animals.
> I know of some virtual "animals" with simulated neural networks that
> have been evolved to show certain behaviors of biological animals.

Um, yeah, and I have a story about goldilocks and the 3 bears that 
demonstrates bear sentience. You show me an ANN-governed critter that 
can function in any wild environment and I'll happily concede.

> One reseach project was focussed on generating movement in different 
> mediums.
> Another one was trying to generate predator-prey and food competition
> behaviors.

I think it's plain I meant all the behaviors of a single animal. If you 
just want one behavior you can generate both movement and predator/prey 
competition using nothing fancier than the logistic map and a sharp 

> Hugo de Garis PhD thesis was about using neural networks evolved
> by genetic methods and using them as building blocks, whereby
> some NN blocks are used to control others. Also he made simulations
> of neural on top of cellular automata. These simulated neural nets
> can also grow new connections.

Despite decades of beavering away at "building brains", De Garis 
doesn't appear to have shown any significant results. When his initial 
efforts ran into a combinatorial complexity wall he switched to an FPGA 
implementation, and when this project ended without empirically 
significant results in 2001 he switched track to quantum computing. We 
should certainly wish him luck with his new direction, as we do Brooks. 
Still an artfully sculpted Xilinx chassis plus some clumsy cat 
animations hardly seem a proof of concept for a brain.

> The computational time needed for solving [NP-Hard] problems only grows
> in a polynomial way when you want to find the best solution possible.

The complexity bounds of NP-Hard problems are superpolynomial - 
exponential and worse - when you attempt global optima on T/VN 
computers. Unless you've recently proved P==NP. Even with quantum 
computers NP-Complete is only a tiny subset of NP-Hard ...

>  There is no such explosion in computing time if you are only looking
> for a good solution (which can be an approximation of the best 
> solution).
> [...] But it is certainly no NP-hard problem if your satisfied
> with less than the most optimal way to do it.

I've agreed there are approximations that produce good solutions in 
polynomial time for some problems. GA/EC has generated good planar TSP 
solutions, for example. That such approximations are adequate for wild 
MNT orientation, etc, does not follow.

> I do not understand why you think it will be so hugely
> complex to coordinate the activities of a large number of
> nanobots. Most objects you want to make contain a large
> number of repetitive components.

The same can be said for solving large QM problems. The same particles 
over and over again ... each with interdependent position, velocity, 
energy, ... whammo, combinatorial explosion. You understand this is one 
of those problems in the basket we don't try to use supercomputers on. 
Perhaps orienting nanobots in Brownian environments will be easier. Now 
tell me why.

> Furthermore you do not even have to use the nanobot way to make 
> objects using MNT.

I never suggested you can't make assemblers, create factories from 
them, and manufacture objects.

>> Clark relates a suggestion by Drexler that AI be created by simulating
>> a natural environment and then letting life and sentience evolve. 
>> Well,
>> sure, we see sentient life evolving all over the universe all the 
>> time.
>> The Fermi paradox is just a figment of unimaginative minds. :-)
> I happen to support the assumption that no extra-terrestial
> technological civilisations exist in our visible universe.

And that's exactly my argument against your Clark/Drexler post. If only 
one sentience has evolved, we need not hold our breath for 10^38 mops. 
Or 10^38^38. The universe is a *MUCH* bigger and *MUCH* older quantum 
computer than a paltry city-block-full of nanobots. Heck, make your 
large computer out of the sun and you'll still have orders of magnitude 
times solar lifetimes to wait it out.

To give the devil his due, of course, there are explanations for the 
Fermi Paradox in which we are neither the first nor so much as a pimple 
on the behind of the millionth intelligence to evolve in this universe. 
See my old wiki page at 
http://www.c2.com/cgi/wiki?SearchForIntraTerrestrialIntelligence and 
ancillae for reasons why ...

> We can begin with with a neural network and evolve from there. [...] 
> MNT capabilities
> can be used as a perfect tool to analyse biological neurons at a 
> molecular level.

With wild-navigating nanoprobes we can map a sentient brain to produce 
real AI adequate to create wild-navigating nanoprobes ... assumes the 

Peter Merel.

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