Robot Evolution
RAGLAN...@aol.com
Note: As I read this, assuming what Hans Moravec says is accurate, I
wondered how and if we can genetically keep up with the robot
revolution. Certainly a fourth generation and beyond robot would exceed
us at our current genetic "level". There is talk of the doubling of
power of computers "Moore's Law and the increase in complexity of
robotics but currently nowhere is there a genetic equivalent.
Hans Moravec
Hans Moravec is a Principal Research Scientist at the Carnegie Mellon
University Robotics Institute. Moravec, whose interest in robots
extends back to his childhood, discusses his intriguing and personal
views on robots -- from the current state of technology, to today's
bomb defusing machines, to the capabilities of robots in the next
century. NOVA spoke to Dr. Moravec in October, 1997.
NOVA: Can you give me a good working definition of what a robot is and
how it differs, say, from a machine tool or a computer?
HM: Robots are machines that are able to do things that previously had
been associated only with human beings or animals -- such as the
ability to understand their surroundings and plan their actions.
NOVA: Most hazardous duty and bomb disposal robots seem to be tethered
in some way to a human operator -- either by an actual cable or by a
radio signal. Do they qualify as robots in your mind?
HM: Not really. We call remote control devices "robots" because I guess
they have some of the characteristics of autonomous machines, at least
physically.
The research going towards those machines does contribute to the
research towards autonomous machines, but they're lacking the brains.
They have the bodies of robots, but not the brains.
NOVA: I read an interview in which you said, "Today's best robots can
think at insect level."
HM: A few years ago that was correct. In fact, we're probably a little
above insects now. I make a connection between nervous systems and
computer power that involves the retina of the vertebrate eye. The
human eye has four layers of cells which detect boundaries of light and
dark and motion. The "results" are sent at the rate of ten results per
second down each of those millions of fibers. It takes about a hundred
computer instructions to do a single edge motion detection. So you've
got a million times ten per second, times a hundred instructions;
that's equivalent to a billion calculations per second in the retina.
That is a thousand times as much computer power as we had for most of
the history of robotics and artificial intelligence. So we were working
with one-thousandth the power of the retina, or roughly what you might
find in an insect. What happened by the end of the '80s is the cost of
one MIPS [the standard by which computing power is measured] dropped
down to $10,000 or below. And at that point the power available to
individual research projects started climbing. The climbing rate is
pretty amazing because computers have been doubling in power. It was
once about every 24 months. In the '80s it was once about every 18
months. Recently it's been closer to every 12 months. So we now have in
our research projects machines that can do 300 MIPS, and soon we're
going to have a thousand. So we're at the stage of small vertebrates.
Over the next few decades, the power is going to take us through small
mammals and large mammals. I have a detailed scenario that suggests
that we get to human level, not just in processing power, but in the
techniques by the middle of the next century.
NOVA: Can you walk us through what you think robot evolution will look
like?
HM: Well, I imagine four stages. I think we're just on the verge of
being able to see machines that work well enough that they'll become
the predecessors to the first generation of mass-produced robots --
that are not toys. I have a particular one in mind: a small machine
that could be a robot vacuum cleaner which, with a thousand MIPS of
computing, is able to maintain a very dense three-dimensional map or
image of its surroundings. It will be able to both plan its actions and
to navigate, so that it knows at every moment where it is and is even
able to identify major pieces of furniture and important items around
it. So -- a small machine, small enough to get under things and to find
its own re-charging station and to empty out its accumulated dust from
time to time. That's the research we're doing and I think sometime
within the next five to ten years we'll have something like that -- and
its successors will become a little more capable. They'll have a few
more devices and be programmable for a broader range of jobs until,
eventually, you get a first generation universal robot, which has
mobility and the ability to understand and manipulate what's going on
around it.
NOVA: What do you mean by universal robot?
HM: It's a machine which can be programmed to do many different jobs.
It's analogous to a computer, which is a universal information
processor, except that its abilities extend to the physical world.
NOVA: Okay, so we've got the first generation of universal robot.
HM: Right, the time schedule is around 2010 now. Every single job a
robot needs to know has to be built into the application program and
when you run the program, the robot acts in a pretty inflexible way.
Still, it's perceptual in motor intelligence. It's comparable to maybe
a small lizard.
NOVA: What types of tasks might we expect these robots to do?
HM: Well, things like floor cleaning and perhaps other kinds of dusting
-- delivery. The kinds of factory tasks that robots are now doing
should be possible for universal robots, like the assembly of things.
But because this kind of robot should be mass-produced, the range of
tasks will be probably larger than anything that exists today, and the
machines will be cheap enough to be used in places that you can't use
robots today. I imagine car cleaning tasks and bathroom cleaning and
lots of other things that will depend on the ingenuity of the
programmers.
NOVA: What happens next?
HM: All right, so now we come to a second generation. The second
generation machines will have a computer that is maybe 50 times as
powerful as the first generation and is able to host programs that are
written with alternatives. For example, picking up an object, which
might be part of some big job, could be done with one hand or with
another hand of the robot. Each of the alternatives has associated with
it a number, which is the desirability of doing the step that way, as
opposed to doing it an alternative way. Those desirability numbers are
adjusted based on the robot's experience. And the robot's experience is
defined by a set of independent programs that are called conditioning
modules, which detect whether good things or bad things happen. For
instance, you might have one module that responds to collisions that
the robot undergoes, and produces a signal that says, "Something bad
happened." Another one detects if the task the robot was doing was
finished, or finished particularly quickly, and signals that something
good happened. If the batteries are discharged -- that's bad. Perhaps
if they're kept in a good state -- that's good. Gradually the robot
adapts, because of this internal conditioning, to do things in ways
that work out particularly well and avoid ways that have caused trouble
in the past.
NOVA: So it's capable of rudimentary learning.
HM: Yeah, it's conditioning. And, you can even imagine these
conditioning modules being tied to external advice. For instance, one
module might respond to your saying, "good." And another one to your
saying, "bad." And so you can direct the robot to act a certain way, as
opposed to another. You could, if you wanted to, train it in the way
that you might train a dog -- by repeatedly saying "good." It's
Skinnerian training basically.
NOVA: What sorts of tasks would the second generation robots be able to
do, that the first generation ones wouldn't be able to do?
HM: They'll be used presumably in the same kinds of situations but
they'll be much more reliable there. They'll be much more flexible.
Take a first generation robot that's putting away the dishes. Maybe
it's programmed to always grab certain objects in a certain way. And it
has motion planning and collision avoidance. But still, some things may
have been overlooked in that program. And perhaps in your particular
circumstance, whenever it goes into a certain cabinet, it ends up
always catching its elbow on the door. The first generation robot will
never learn. It will just keep making that same mistake over and over
and over again. The second generation robot will gradually learn to do
things a different way, maybe use a different arm or reach in a
different manner. Essentially the robot will tune itself. And it will
be much more pleasant to have around, because it won't be making lots
of little mistakes that the first generation robot will be.
NOVA: What year is it now?
HM: Approximately 2020. Each one of these generations is about a
decade. The first generation robot may be comparable to a small lizard.
The second generation robot, with it's limited trainability, may be
something comparable to a small mammal. The third generation robot is
the first really interesting one. It's predicated on there already
being quite a large industry, based on these earlier generations, which
is able to support the development of a major module for these machines
-- the ability to model the world, a "world simulator." This simulator
allows the third generation robot to make many mistakes in its mind,
running through scenarios in simulation rather than physically. The
second generation robot learns, but quite slowly. It has to make a
mistake many times before it learns to avoid it. And when it's tuning
up, when it's getting good at something, it has to do it many times
before it really really gets good at it. The third generation robot
runs through the task many times, mentally, and tunes it up there. So
when it first goes to do something physically, it has a good chance of
doing it right.
NOVA: It thinks before it acts.
HM: Right. And, the simulator is a big deal, because it can't be strict
physical simulation. That's why it's still computationally out of
reach, even with the kind of computer power, I imagine for then. What
it needs is something that's closer to folk physics: basically rules of
thumb for every different kind of object that it's likely to encounter
-- because one of the things the robot will have to do is roll into a
new room and make an inventory of everything that it sees around it, so
that it can build a pretty accurate simulation of that room, so it can
then do its mental rehearsals. It will have to identify the objects
that it sees and then call up generic descriptions of what those
objects are and how they behave when they're interacted with, and how
to use them. Building this generic database is a major effort.
NOVA: What sorts of things would it need to know about, say, cutlery?
HM: Well, first of all, where cutlery might probably be located, how to
pick up the individual pieces, roughly how heavy to expect them to be,
you know, how hard they can be gripped. Because, for instance, if it
sees an egg and wants to pick it up, it has to know to be gentle with
it. If it sees a knife, it has to know in advance to pick it up a
little harder because, if it picks it up too lightly, the weight will
cause it to fall down. And, of course, it can learn by making actual
mistakes, but the whole idea of the simulation is to avoid those
mistakes, in the first place, whenever possible. Now, what's really
interesting about the third generation robot is that, besides having
this physical model for things in the world, it will also have to have
a psychological model for actors in the world, particularly human
beings. It should know that poking a sharp stick at a human being will
produce a change in state of the human being. They will probably become
angry and if they're angry, they're likely to do certain things which
will probably interfere with the robot's tasks. And, since these robots
will probably be used, among other things, as servants working among
people, it will be useful for the robot to have an idea of whether its
owners are happy or unhappy and choose actions that improve the
happiness. Basically, machines that make their owners happier are
likely to sell better, so ultimately there'll be market pressure. Third
generation robots should be able to deduce something about the internal
state of the human beings around it -- if a person seems to be in a
hurry or if this person seems to be tired. You can probably deduce that
from a modest observation of body language.
NOVA: They're doing some mood recognition already at the M.I.T. Media
Lab, aren't they?
HM: Yeah, that's right. Another interesting thing that a third
generation robots is able to do, is to provide a description of things.
You should be able, with a small additional amount of programming, to
generate some kind of a narrative. You may ask, "Why did you avoid
going into that room?" --"Because Bob's in there and I know he's upset
and my moving around him will probably irritate him further." Now,
here's a funny twist. You can have conversations with the third
generation robot where it seems to believe that it's conscious; it
talks about its own internal mental life in the same ways that the
people do. And so, I think for practical purposes, it is. So the third
generation robot can analyze. It's comparable to maybe a monkey. When
it simulates the world, it's always in terms of particular objects or
particular sizes and particular locations. It doesn't really have any
ability to generalize. Its ability to understand the world is very
literal.
NOVA: It sounds sort of sweet.
HM: It is. That's right. You wouldn't expect any deviousness at all.
NOVA: Take us to the fourth generation robot.
HM: All right. Basically, if the third generation robot is something
like a monkey, the fourth generation robot becomes something like a
human being -- actually more powerful in some ways. The fourth
generation robot basically marries the third generation robot's ability
to simulate the world with an extremely powerful reasoning program.
Even today, we have reasoning programs that are superior to human
beings in various areas. Deep Blue plays chess better than just about
everybody -- and various expert systems can do their chains of
deductions better than just about anybody. And of course, for a long
time, computers have been able to do arithmetic better than everybody,
for sure. But there is a certain limitation that these programs
embodying intelligence have had, which is they really haven't been able
to interact with the physical world. When a medical diagnosis program
talks about the symptoms of a patient, it's only processing words. And
when it comes up with a recommendation, again, it's more words.
NOVA: But now the reasoning will be connected to physical experience or
understanding.
HM: Right. The reasoning that the fourth generation does is greatly
enhanced by the third generation robot's ability to model the world. So
physical situations that the robot thinks about in its simulation can
now be abstracted into statements about the world. And then inferences
can be drawn from the statements, so that the robot can come to
non-obvious conclusions. For example, it might be able to figure out
from running several examples that if it takes any container of liquid
without a lid and turns it upside down, the liquid will spill out. The
third generation robot would need to figure out not to turn this glass
over, not to turn this jar over, not to turn this pitcher over etc.
whereas the fourth generation robot would be able to infer it. That's
just a example. Fourth generation robots would be able to do much more
complicated tasks -- and do them probably better than humans, because
really deep reasoning involves long deductive chains and keeping track
of a lot of details. Human memory is not that powerful.
NOVA: Can you envision, in the future, a robot being better than a
human at finding and disarming a bomb?
HM: Sure. You can imagine that for the near future. The sensors that a
robot can bring can be tuned for the task. For example, radar can
penetrate various kinds of materials -- depending on the frequency you
use, you can see through walls. So, simply the ability to see into a
package would certainly make a robot a better bomb detector.
NOVA: Can you envision a robot understanding the psychology of a
terrorist better than a human being?
HM: Well, ultimately. Now we're talking 40 or 50 years from now, when
we have these fourth generation machines and their successors, which I
think ultimately will be better than human beings, in every possible
way. But, the two abilities that are probably the hardest for robots to
match, because they're the things that we do the best, that have been
life or death matters for us for most of our evolution, are, one,
interacting with the physical world. You know, we've had to find our
food and avoid our predators and deal with things on a moment to moment
basis. So manipulation, perception, mobility - that's one area. And the
other area is social interaction. Because we've lived in tribes forever
and we've had to be able to judge the intent and probable behavior of
the other members of our tribe to get along. So the kind of social
intuition we have is very powerful and probably uses close to the full
processing power of our brain -- the equivalent of a hundred trillion
calculations per second -- plus a lot of very special knowledge, some
of which is hard-wired, some of which we learned growing up. This is
probably where robots catch up last. But, once they do catch up, then
they keep on going. I think there will come a time when robots will
understand us better than we understand ourselves, or understand each
other. And, you can even imagine the time in the more distant future
when robots will be able to host a very detailed simulation of what's
going on in our brains and be able to manipulate us.
NOVA: Wow.
HM: I see these robots as essentially our off-spring, by unconventional
means. Ultimately, I think they're on their own and they'll do things
that we can't imagine or understand -- you know, just the way children
do.
feedbackdroid
RAGLAN...@AOL.COM wrote:
> Note: As I read this, assuming what Hans Moravec says is accurate, I
> wondered how and if we can genetically keep up with the robot
> revolution. Certainly a fourth generation and beyond robot would exceed
> us at our current genetic "level". There is talk of the doubling of
> power of computers "Moore's Law and the increase in complexity of
> robotics but currently nowhere is there a genetic equivalent.
>
>
Moravec has been saying pretty much the same thing for 20 years, or
more ....
Mind Children: the future of robot and human intelligence, 1988
http://www.frc.ri.cmu.edu/~hpm/hpm.pubs.html
He seems to think the main thing holding back robotics and AI is the
amount of computing power available, and that the way to produce
human-level machine intelligence is to continue as we are, only do it
with more powerful computers [unless he's changed his viewpoint
recently .... seems not] ....
http://www.frc.ri.cmu.edu/~hpm/project.archive/robot.papers/2004/Predictions.html
He seems to always talk about MIPS and MOPS, etc, rather than concepts
and algorithms.
However, after several years of monitoring various AI forums, I don't
think anyone has a real idea of how to produce "general AI". It's not
clear that the concepts of today will simply "scale up" to human
intelligence levels, simply by using more powerful processors, as they
are mainly domain-specific solutions, and not general AI solutions. And
it's not clear that anyone else is on the right track either, at least
given comments on the forums. Mostly people repeat past arguments
interminably, state opinions [like I'm doing here, of course], or
voicing sameold platitudes - [open to revision, it someone actually
steps forward with a critical answer]. It's really kind of a shame.
If you've read about the idea of "The Singularity" by Vernor Vinge, Ray
Kurzweil, or Damien Broderick, you know they talk about a largely
unpredictable future, exactly for the reason stated in the first
sentence by the original poster, namely the problem of "keeping up"
with the rate of acceleration of technology. To wit, advances in
biotechnology, AI, and nanotechnology will make a future much different
from what we see today, since it's so difficult to predict where the
combination of the 3 will take us, especially given their supposedly
"exponential" growth.
However, for my money, AI is lagging behind the others, especially
biotechnology. My opinion is that more processing power is not going to
solve the problem of "general AI". Also, I'm not much worried about
humans genetically keeping up with the rate of advancement of robots,
since - at least today - biotechnology and molecular biology are
advancing at a rate that seems to me to far surpass the genesis of new
ideas in AI. This will continue, at least until people break out of the
ruts of endessly revisiting the old ideas, instead of finding new ones.
So, Michael, by time Moravec's robots ever get here, your grandchildren
or great-grandchildren will probably be manipulating their personal DNA
using at-home kits sold by Edmund Scientific. [I say this only
half-jokingly]
RAGLAN...@aol.com
feedbackdroid:
So, Michael, by time Moravec's robots ever get here, your
grandchildren
or great-grandchildren will probably be manipulating their personal
DNA
using at-home kits sold by Edmund Scientific. [I say this only
half-jokingly]
I don't know much about AI so I can't relate anything new.
"Theoretically" it seems artificial intelligence is possible. As you
state it will take new breakthroughs in AI to help understand it and
advance it. The future is unpredictable in many ways but I'll focus
just on AI here. Even with advances in biotechnology I would assume
there is a qualitative difference between AI and human intelligence.
Although there is much talk about our biology being enhanced i.e.
intelligence and other traits has it occurred to anyone there might be
a limit to what we can enhance or change...built in constraints even
with genetic engineering. Now "theoretically" with AI it could
eventually gain its own "intelligence" (yes it sounds like science
fiction) and it could come to "understand humans" better than we
understand each other. That would be a dangerous situation. Granted
this won't happen soon, certainly not within my lifetime but it is a
possibility to consider. So far the only life form humans have created
is the computer virus-worm. Hawking remarked on how it seemed fitting
humans created another life form in their own image. I think one way
this might be mitigated is by cyborgs...machine-man interface. That way
there is a collaborative process. But far in the future I can see
genetically engineered humans putting limitations on AI for their own
self-survival.
Michael Ragland
Tim Tyler
> Moravec has been saying pretty much the same thing for 20 years, or
> more ....
>
> Mind Children: the future of robot and human intelligence, 1988
> http://www.frc.ri.cmu.edu/~hpm/hpm.pubs.html
>
> He seems to think the main thing holding back robotics and AI is the
> amount of computing power available, and that the way to produce
> human-level machine intelligence is to continue as we are, only do it
> with more powerful computers [unless he's changed his viewpoint
> recently .... seems not] ....
>
> http://www.frc.ri.cmu.edu/~hpm/project.archive/robot.papers/2004/Predictions.html
>
> He seems to always talk about MIPS and MOPS, etc, rather than concepts
> and algorithms.
>
> However, after several years of monitoring various AI forums, I don't
> think anyone has a real idea of how to produce "general AI". It's not
> clear that the concepts of today will simply "scale up" to human
> intelligence levels, simply by using more powerful processors, as they
> are mainly domain-specific solutions, and not general AI solutions. And
> it's not clear that anyone else is on the right track either, at least
> given comments on the forums. [...]
It looks as though the brain consists of a very large numbers of
neurons, following a similar, relatively simple program for
altering their activation thresholds, growing axons, etc.
Nothing a machine would have too much trouble emulating -
with a bit of research.
Machines can /already/ communicate *far* faster than we do. Their
brain size is increasing at an exponential rate, and they are
easy to plug into the global internet, with no HCI bottleneck.
> If you've read about the idea of "The Singularity" by Vernor Vinge, Ray
> Kurzweil, or Damien Broderick, you know they talk about a largely
> unpredictable future, exactly for the reason stated in the first
> sentence by the original poster, namely the problem of "keeping up"
> with the rate of acceleration of technology. To wit, advances in
> biotechnology, AI, and nanotechnology will make a future much different
> from what we see today, since it's so difficult to predict where the
> combination of the 3 will take us, especially given their supposedly
> "exponential" growth.
>
> However, for my money, AI is lagging behind the others, especially
> biotechnology. My opinion is that more processing power is not going to
> solve the problem of "general AI". Also, I'm not much worried about
> humans genetically keeping up with the rate of advancement of robots,
> since - at least today - biotechnology and molecular biology are
> advancing at a rate that seems to me to far surpass the genesis of new
> ideas in AI.
The problem is, that the biological knowledge has so far had practically
no impact on human brain development. It is not being applied.
Computer brains double in size for equivalent cost every 4-5 years -
and the software and AI capabilities are improving too - while human
brain size has - so far - remained practically fixed, and can't easily
be scaled up by simply spending more money on the units.
Humans will need to get their act together if they want their particular
brand of molecular nanotechnology computing substrate to be regarded as
being economically attractive for very much longer.
--
__________
|im |yler http://timtyler.org/ t...@tt1lock.org Remove lock to reply.
Bob Kolker
>
> Humans will need to get their act together if they want their particular
> brand of molecular nanotechnology computing substrate to be regarded as
> being economically attractive for very much longer.
When was the last time a machine made an inspired guess? Do machines
create any mathematics? No. They just manipulate symbols. Machines, even
those whose running is rule based or based on genetic algorithms have
not show any evidence of consociousness nor of -understanding- what it
is they are doing.
The most sophisticated computer is still an algorithmic symbol
manipulator. There is not an iota of evidence that machines grasp
meaning. They may imitate that by comparing strings but it is not the
real thing.
Bob Kolker
feedbackdroid
As I tried to indicate last time, apparently unsuccessfully, my take on
listening to AI people is that no one today really has much clue as to
how to make really intelligent machines. And I see no indication that
simply increasing the processing power many fold is going to solve
the problem. AI solutions today tend to be narrow domain, and don't
scale up to solving the practical problems we humans deal with
constantly in everyday life. That may change in 50 years, but it
seems it will require a different paradigm for solving intelligence
than anyone has today.
Maybe part of Michaels's worry is that, in america in general
society, robots have a generally "negative" connotation, largely
due to how they are portrayed by holloywood and in scifi stories.
OTOH, if you look at Japan, the culture there is of robots accepted
both as helpers and as companions, and much of the literature has
them helping to fight evil, rather than being evil. There is an
interesting
new book named "Loving the Machine: The Art and Science of
Japanese Robots" discussing this.
seem...@googlemail.com
> Tim Tyler wrote:
> > Humans will need to get their act together if they want their particular
> > brand of molecular nanotechnology computing substrate to be regarded as
> > being economically attractive for very much longer.
>
> When was the last time a machine made an inspired guess? Do machines
> create any mathematics? No. They just manipulate symbols. Machines, even
> those whose running is rule based or based on genetic algorithms have
> not show any evidence of consociousness nor of -understanding- what it
> is they are doing.
>
> The most sophisticated computer is still an algorithmic symbol
> manipulator. There is not an iota of evidence that machines grasp
> meaning. They may imitate that by comparing strings but it is not the
> real thing.
Moravec's estimate was that our machines are currently around the
level of a small lizard or fish:
``So we're at the stage of small vertebrates.
Over the next few decades, the power is going to take us through
small
mammals and large mammals. I have a detailed scenario that suggests
that we get to human level, not just in processing power, but in the
techniques by the middle of the next century.''
His estimate of when the machines catch up: around 2050.
Of course it's not as simple as that. Machines /already/ out-perform
humans for /some/ tasks - including computation-intensive ones - like
playing chess.
Our brains are what has led to our domination of the planet. Those
brains are now starting to face competition from a source that
currently lags behind in a number of areas - but is also way ahead
in others - and which is not afraid to use engineering techniques to
develop and scale upwards.
IMO, the writing is on the wall for the human brain: shape up or face
obsolescence.
feedbackdroid
seem...@googlemail.com wrote:
>
> IMO, the writing is on the wall for the human brain: shape up or face
> obsolescence.
>
In which case, I hope you make the best for your next 44 years.
BTW, in his latest book "The Lifebox ...", Rudy Rucker predicts it'll
be much longer than what Moravec's 20-year old predictions say before
computer intelligence will match human. Year 2100 before the computer
hardware is powerful enough, and years beyond that to produce (ie,
evolve) the proper software.
RAGLAN...@aol.com
The estimate of 2050 is not important. What is important is what seemy
said, "The writing is on the walls and our brains better shape up or
face obsolescence." He is absolutely right. Obviously, we don't have
the science and technology to shape up our brains yet but we should
develop and implement that before Hans Moravec's predicted future of
human and beyond artificial intelligence materializes. It would very
dangerous to have AI which understands "us" better than we understand
each other. We must remain the masters of AI and not let the opposite
happen.
Michael Ragland
Tim Tyler
> BTW, in his latest book "The Lifebox ...", Rudy Rucker predicts it'll
> be much longer than what Moravec's 20-year old predictions say before
> computer intelligence will match human. Year 2100 before the computer
> hardware is powerful enough, and years beyond that to produce (ie,
> evolve) the proper software.
Others are less pessimistic - e.g. Ray Kurzweil:
"By 2020, $1,000 (£581) worth of computer will equal the processing
power of the human brain," he says. "By the late 2020s, we'll have
reverse-engineered human brains."
[...]
"By 2030 we will have achieved machinery that equals and exceeds human
intelligence'."
- http://www.guardian.co.uk/science/story/0,,1647150,00.html
IMO, the 'tipping point' occurs around the time when companies
are typically spending more money on their machine employees
than they are on their human ones - and the machine senses,
muscles and brains start to outweigh the human ones.
My expectation is that that will happen somewhere around 2050.
Bob Kolker
>
> "By 2020, $1,000 (£581) worth of computer will equal the processing
> power of the human brain," he says. "By the late 2020s, we'll have
> reverse-engineered human brains."
Want to bet?
AI in excess of human intelligence has been fifty years in the future
since 1956. A hundred years from now it will still be fifty years in the
future..
I think of super AI the same way as I think of controlled fusion
reactions to produce cheap energy. Ever and always in the future.
Bob Kolker
feedbackdroid
RAGLAN...@AOL.COM wrote:
> feedbackdroid wrote:
> > seem...@googlemail.com wrote:
> >
> > >
> > > IMO, the writing is on the wall for the human brain: shape up or face
> > > obsolescence.
> > >
> >
> >
> > In which case, I hope you make the best for your next 44 years.
> >
> > be much longer than what Moravec's 20-year old predictions say before
> > computer intelligence will match human. Year 2100 before the computer
> > hardware is powerful enough, and years beyond that to produce (ie,
> > evolve) the proper software.
>
>
> said, "The writing is on the walls and our brains better shape up or
> face obsolescence." He is absolutely right. Obviously, we don't have
> the science and technology to shape up our brains yet but we should
> develop and implement that before Hans Moravec's predicted future of
> human and beyond artificial intelligence materializes. It would very
> dangerous to have AI which understands "us" better than we understand
> each other. We must remain the masters of AI and not let the opposite
> happen.
>
> Michael Ragland
I haven't watched this yet, but it might be germane ....
http://www.iss.whu.edu.cn/degaris/
Tim Tyler
> Tim Tyler wrote:
> > "By 2020, $1,000 (£581) worth of computer will equal
> > the processing power of the human brain," he says.
> > "By the late 2020s, we'll have reverse-engineered
> > human brains."
>
> Want to bet?
>
> AI in excess of human intelligence has been fifty years in
> the future since 1956. A hundred years from now it will
> still be fifty years in the future.
The processing power of the hardware /is/ getting there:
Moravec's estimate for the processing power of the
brain is 100 teraflops.
The fastest commercial supercomputer in public operation
today, IBM's BlueGene/L, uses 65,536 custom PowerPC cores to
achieve 367 teraflops, or around a third of a petaflops.
A one petaflops machine - ten times exceeding Moravec's
model human - is scheduled for construction in 2008:
http://news.zdnet.co.uk/emergingtech/0,1000000183,39276005,00.htm
The PS3 clocks in at 2 teraflops. A couple reportedly cost
about $1000 to make today. That's about 4.65 doublings away
from the human brain's rating. Moore's law has doubling in
about 24 months:
http://en.wikipedia.org/wiki/Moore's_law
That would makes the date of hardware equality at
$1000 around Q1, 2017 - not much different from
the predicitons of Ray Kurzweil and Arthur C Clarke -
who peg the date at around 2020.
I don't know when we will have "reverse-engineered
human brains". The statement seems a bit vague.
> I think of super AI the same way as I think of controlled
> fusion reactions to produce cheap energy. Ever and always in
> the future.
Common use of fusion looks like it is further off.
According to officials from the recent international
$12.8 billion nuclear fusion reactor project:
``Officials involved in the project say 10 percent to 20
percent of the world's energy could come from fusion by
the end of the century.''
- http://www.chron.com/disp/story.mpl/business/energy/4359033.html
Bob Kolker
>
> The processing power of the hardware /is/ getting there:
>
> Moravec's estimate for the processing power of the
> brain is 100 teraflops.
But the brain is not a computer. So how can this number be applicable?
You can cobble together fast computing circuitry and it still will not
do what the organic brain does.
Bob Kolker
feedbackdroid
Tim Tyler wrote:
> Bob Kolker quoted me as quoting:
> > Tim Tyler wrote:
>
> > > "By 2020, $1,000 (£581) worth of computer will equal
> > > the processing power of the human brain," he says.
> > > "By the late 2020s, we'll have reverse-engineered
> > > human brains."
> >
> > Want to bet?
> >
> > AI in excess of human intelligence has been fifty years in
> > the future since 1956. A hundred years from now it will
> > still be fifty years in the future.
>
> The processing power of the hardware /is/ getting there:
>
You can project a few years down the road regards hardware, but Rucker
sees development of the proper software is much more of a problem, and
may take many additional years, as I indicated previously. Human
intelligence-level software is no given. Moravec doesnt seem to much
broach that problem. To Rucker, it's primary.
Also, it's a little surprising to me that evolutionists here seem to be
so worried about robots. As a roboticist, I am much more worried about
biotech and genetic engineering, in terms of possible negative uses by
unscrupulous people, especially given that I perceive the latter
research to be advancing literally light-years faster than AI research.
[discussions on the AI forums just seem to go round in the same old
circles, endlessly]. Does no one else see this?
Tim Tyler
> Tim Tyler wrote:
>> Bob Kolker quoted me as quoting:
>> > Tim Tyler wrote:
>> > > "By 2020, $1,000 (£581) worth of computer will equal
>> > > the processing power of the human brain," he says.
>> > > "By the late 2020s, we'll have reverse-engineered
>> > > human brains."
>> >
>> > Want to bet?
>> >
>> > AI in excess of human intelligence has been fifty years in
>> > the future since 1956. A hundred years from now it will
>> > still be fifty years in the future.
>>
>> The processing power of the hardware /is/ getting there:
>
> You can project a few years down the road regards hardware, but Rucker
> sees development of the proper software is much more of a problem, and
> may take many additional years, as I indicated previously. Human
> intelligence-level software is no given. Moravec doesnt seem to much
> broach that problem. To Rucker, it's primary.
It seems to me that Moravec gives an extra thirty-something years
on top of the time taken for the hardware to get there, to allow
for software development.
The brain's learning algorithm is specified in the genome, which
in total is only 0.791175 Gigibytes uncompressed - probably not
too difficult to emulate.
As to whether copying the brain is a good idea, I'm somewhat
sceptical. Machines seem to be doing pretty well by exploring
some pretty different paths.
> Also, it's a little surprising to me that evolutionists here seem to be
> so worried about robots. As a roboticist, I am much more worried about
> biotech and genetic engineering, in terms of possible negative uses by
> unscrupulous people, especially given that I perceive the latter
> research to be advancing literally light-years faster than AI research.
> [discussions on the AI forums just seem to go round in the same old
> circles, endlessly]. Does no one else see this?
The main problem I see with biotech, is its stunted development,
caused by misguided conservatism. Even the most basic, basic
biological technology - e.g. engineered sterile seeds - is
currently unconditionally banned internationally.
A failure to promptly and rapidly develop our biological heritage
seems likely to lead to a 'rough' takeover by machines, with the
biological heritage getting pretty trampled on in the process.
It seems to me to be likely to be in everyone's interests for any
substrate switching to go as slowly and smoothly as possible - and
for that to happen, biotech developments had better keep pace with
developments in other areas.
It is not enough to develop interfaces to the machine world -
though the narrow pipes through which humans must currently
communicate over the net certainly puts them at a substantial
disadvantage, and bandwidth improvements would be very welcome.
At the end of the day, the different computing substrates will
compete for resources - and if the human brain becomes uneconomical,
it will be replaced.
Tim Tyler
> Tim Tyler wrote:
>> The processing power of the hardware /is/ getting there:
>>
>> Moravec's estimate for the processing power of the
>> brain is 100 teraflops.
>
> But the brain is not a computer. So how can this number be applicable?
The brain is, functionally, a computer. It accept inputs, processes
them and produces outputs.
It's an analog computing device - but any universal analog machine can
be simulated arbitrarily closely by a digital one (and visa versa).
Bob Kolker
>
> The brain is, functionally, a computer. It accept inputs, processes
> them and produces outputs.
It is NOT. It is a complicated gland. It is gooey, it is sticky and it
oozes.
Bob Kolker
Inman Harvey
> Bob Kolker wrote:
>> Tim Tyler wrote:
>
>>> The processing power of the hardware /is/ getting there:
>>>
>>> Moravec's estimate for the processing power of the
>>> brain is 100 teraflops.
>> But the brain is not a computer. So how can this number be applicable?
>
> The brain is, functionally, a computer. It accept inputs, processes
> them and produces outputs.
>
> It's an analog computing device - but any universal analog machine can
> be simulated arbitrarily closely by a digital one (and visa versa).
Here we see the major opposition between the two main approaches in
Cognitive Science and Artificial Intelligence.
The classic approach (GOFAI for Good Old Fashioned AI) did indeed follow
the traditional path of taking the latest technological artefact as a
model for the brain: used to be steam engines, then telephone exchanges,
then from the 1950s the computer became the model.
The opposing view (variously: Nouvelle AI, Dynamical Systems approach,
Enactive, and various other flavours) rejects the notion of the brain as
an input-output device such as a computer.
In the context of s.b.e, you will find that most practitioners of
Evolutionary Robotics, including myself and my colleagues, are very
emphatically in the second camp. Even apart from any other
considerations, digital computers and computational devices are very
brittle, give a very rugged fitness landscape and this simply makes an
evolutionary methodology pretty much a non-starter.
"any universal analog machine can be simulated arbitrarily closely by a
digital one (and vice versa)" -- yes indeed. But the design issues are
totally different. And analog machines do *not* share the crucial
defining factors of digital computers as laid out by e.g. Alan Turing.
Tim Tyler wrote:
.........
> The processing power of the hardware /is/ getting there:
>
> Moravec's estimate for the processing power of the
> brain is 100 teraflops.
These sorts of calculations only seem plausible [though still cannot be
cashed out] to members of the GOFAI school of thought. Can I reassure
biologists that many people in AI and robotics find this kind of
equation of brains with computers to be absurd!
Inman Harvey
--
Inman Harvey >> Evolutionary and Adaptive Systems Group (EASy) <<
COGS/CCNR/CSE >> Informatics, Univ. of Sussex, Brighton BN1 9QH, UK <<
inm...@susx.ac.uk >> www.informatics.susx.ac.uk/users/inmanh/ <<
Perplexed in Peoria
"Inman Harvey" <inm...@cogs.susx.ac.uk> wrote in message news:ekie9i$1s1m$1...@darwin.ediacara.org...
Interesting. What are Turing's 'crucial defining factors'?
I do know that most models of digital computation focus on the issue
of processing sequentially through the steps of an algorithm in order
to solve a problem rather than on the issue of continually maintaining
a control loop.
I took a course on GOFAI once, and more recently worked for a process-control
company. Both viewpoints seem to capture some aspects of animal
intelligence and learning. But is is difficult for me to imagine
the evolutionary transitions when a mechanism evolved to control things
becomes a more self-contained (detached?) inference engine capable of
learning a game like chess and playing it well.
Tim Tyler
> Tim Tyler wrote:
>> Bob Kolker wrote:
>>> Tim Tyler wrote:
>>>> Moravec's estimate for the processing power of the
>>>> brain is 100 teraflops.
>>>
>>> But the brain is not a computer. So how can this number be applicable?
>>
>> The brain is, functionally, a computer. It accept inputs,
>> processes them and produces outputs.
>
> Here we see the major opposition between the two main
> approaches in Cognitive Science and Artificial
> Intelligence.
>
> The classic approach (GOFAI for Good Old Fashioned AI) did
> indeed follow the traditional path of taking the latest
> technological artefact as a model for the brain: used to be
> steam engines, then telephone exchanges, then from the
> 1950s the computer became the model.
>
> The opposing view (variously: Nouvelle AI, Dynamical
> Systems approach, Enactive, and various other flavours)
> rejects the notion of the brain as an input-output device
> such as a computer.
Do you have any references in support of this notion?
My impression is that the only folk who reject the fundamentals
of the brain-computer analogy are people like Roger Penrose
and John Searle - i.e. those whose world view in the area is
totally muddled.
> Even apart from any other considerations, digital computers
> and computational devices are very brittle, give a very
> rugged fitness landscape and this simply makes an
> evolutionary methodology pretty much a non-starter.
The shape of the fitness landscape involved depends on what
you are evolving.
Not everything that can done inside a digital computer
is 'brittle'. A digital computer can simulate anything
you care to mention - including a wide variety of robust
systems.
In the case of computer hardware, what is involved is a
recipe responsible for construction of the computer, held
by the manufacturers involved. They certainly seem to
be able to make modifications to it without breaking it.
Anway, the brittleness or otherwise of the recipe involved
in its production has nothing to do with my original
statement that the brain is functionally, a computer.
A computer is something that computes. Factors such as
whether it breaks when you drop it onto a concrete floor
are a bit of a side issue.
Tim Tyler
> Tim Tyler wrote:
>> Moravec's estimate for the processing power of the
>> brain is 100 teraflops.
>
> These sorts of calculations only seem plausible [though still
> cannot be cashed out] to members of the GOFAI school of thought.
No they don't.
That's the second time you've mentioned GOFAI.
That's widely regarded as the AI equivalent of telling a
fellow researcher that they are stuck back in the 1960s :-(
In this case, it is totally inappropriate - there are
large numbers of modern researchers who wouldn't
/dream/ of labeling themselves as GOFAI practitioners,
yet who nonetheless believe that you can quantify the
human brain's memory in terms of bits and the human
brain's processing power in terms of flops - i.e.
that the brain is broadly equivalent to some digital
computation device.
Tim Tyler
> "any universal analog machine can be simulated arbitrarily closely by a
> digital one (and vice versa)" -- yes indeed. But the design issues are
> totally different. And analog machines do *not* share the crucial
> defining factors of digital computers as laid out by e.g. Alan Turing.
Turing is best known for developing an abstract model
of serial computation.
His model contained various unrealistic factors - such
as perfect reliability and infinite memory - which mean
that modern digital computers don't measure up too well
to it either.
Anyway, what I originally said was:
"The brain is, functionally, a computer."
Note the word 'functionally'. Whether a
computer /actually/ processes signals in analog
or digital is not something which you can
determine by an examination of its outputs -
since any universal analog machine can be
simulated arbitrarily closely by a digital
one (and visa versa).
Similarly, the analog vs digital issue seems
rather irrelevant to the issue of whether you
can measure the processing speed of a
computer in flops - which is normally not
a big deal - since you can simply consider
the speed of a functionally-equivalent
digital computer.
inm...@susx.ac.uk
> "Inman Harvey" <inm...@cogs.susx.ac.uk> wrote in message news:ekie9i$1s1m$1...@darwin.ediacara.org...
> > > Bob Kolker wrote:
> > >> Tim Tyler wrote:
> > >
> > >>>
> > >>> Moravec's estimate for the processing power of the
> > >>> brain is 100 teraflops.
> > >
> > > The brain is, functionally, a computer. It accept inputs, processes
> > > them and produces outputs.
> > >
> > > be simulated arbitrarily closely by a digital one (and visa versa).
> >
> > Cognitive Science and Artificial Intelligence.
> >
> > The classic approach (GOFAI for Good Old Fashioned AI) did indeed follow
> > the traditional path of taking the latest technological artefact as a
> > model for the brain: used to be steam engines, then telephone exchanges,
> > then from the 1950s the computer became the model.
> >
> > The opposing view (variously: Nouvelle AI, Dynamical Systems approach,
> > Enactive, and various other flavours) rejects the notion of the brain as
> > an input-output device such as a computer.
> >
> > Evolutionary Robotics, including myself and my colleagues, are very
> > considerations, digital computers and computational devices are very
> > brittle, give a very rugged fitness landscape and this simply makes an
> > evolutionary methodology pretty much a non-starter.
> >
> > digital one (and vice versa)" -- yes indeed. But the design issues are
> > totally different. And analog machines do *not* share the crucial
> > defining factors of digital computers as laid out by e.g. Alan Turing.
>
In this context, I am pointing primarily at the concept of a 'Halting
State' of a Turing machine -- that signals when the program is finsihed
and the result of a computation can be read out.
As computer users we are so familiar with this that we cease to notice
it -- but it forces the operation of computations into "defined
question" (or input) followed by (provided the computation finishes)
"signalled output"; and an ongoing process may be a (hopefully rapid)
succession of these.
>
> I do know that most models of digital computation focus on the issue
> of processing sequentially through the steps of an algorithm in order
> to solve a problem rather than on the issue of continually maintaining
> a control loop.
Yes indeed. Contrast this with eg a Watt governor (as a classic analog
machine for controlling the output of a steam engine). This operates
genuinely *continuously*, not as a sequence of computations. So the
ideological divide in cognitive science on this issue can be concisely
summarised as: is the brain more like a digital computer or a Watt
governor?
>
> I took a course on GOFAI once, and more recently worked for a process-control
> company. Both viewpoints seem to capture some aspects of animal
> intelligence and learning. But is is difficult for me to imagine
> the evolutionary transitions when a mechanism evolved to control things
> becomes a more self-contained (detached?) inference engine capable of
> learning a game like chess and playing it well.
>
Yes, a profound question. Clearly *some* aspects of human cognition fit
into the "defined question" - "reasoning steps" - "signalled output"
framework, and a game of chess would be a paradigm example. The
evolutionary transition from 'merely' competent survival (finding of
food and mates, avoidance of dangers and predators - that fits more
naturally to the Watt governor approach than the digital computer
approach) to performance of sequential algorithms (reasoning, chess -
that fits more naturally to the digital computer approach, indeed that
was Turing's model) is a fascinating issue.
But it is not the brain that does reasoning -- it is the human that
reasons, using its brain plus all sorts of cultural artefacts such as
language, symbols, pen and paper (and nowadays, computers). This does
not justify modelling the brain as some kind of computer.
Inman Harvey
--
Inman Harvey >> Evolutionary and Adaptive Systems Group
<<
>> COGS/Informatics, Univ. of Sussex, Brighton BN1 9QH, UK
<<
inm...@cogs.susx.ac.uk >> http://www.cogs.susx.ac.uk/users/inmanh/ <<
inm...@susx.ac.uk
> Inman Harvey wrote:
> > Tim Tyler wrote:
> >> Bob Kolker wrote:
> >>> Tim Tyler wrote:
>
> >>>> brain is 100 teraflops.
> >>>
> >>
> >> The brain is, functionally, a computer. It accept inputs,
> >> processes them and produces outputs.
> >
> > approaches in Cognitive Science and Artificial
> > Intelligence.
> >
> > The classic approach (GOFAI for Good Old Fashioned AI) did
> > indeed follow the traditional path of taking the latest
> > technological artefact as a model for the brain: used to be
> > steam engines, then telephone exchanges, then from the
> > 1950s the computer became the model.
> >
> > The opposing view (variously: Nouvelle AI, Dynamical
> > Systems approach, Enactive, and various other flavours)
> > rejects the notion of the brain as an input-output device
> > such as a computer.
>
In support of "the notion of the brain as an input-output device such
as a computer."? That is your view, not mine, and the majority of
classical AI and cognitive science literature (what we call GOFAI) will
support that view!
But I suspect you were really asking for refernces *against* that
notion, and in favour of Nouvelle AI, Dynamical Systems approach,
Enactive approaches. For example, any works by:
Rodney Brooks
Humberto Maturana
Francisco Varela
Randall Beer
Tim van Gelder
The Sussex School (see my url below)
The Indiana School
Rolf Pfeifer
etc etc etc
Eg 2 specific references --
Mind as Motion: Explorations in the Dynamics of Cognition (Paperback)
by Robert F. Port (Editor), Tim vanGelder (Editor)
Beer, R.D. (2000). Dynamical approaches to cognitive science. Trends in
Cognitive Sciences 4(3):91-99.
>
> My impression is that the only folk who reject the fundamentals
> of the brain-computer analogy are people like Roger Penrose
> and John Searle - i.e. those whose world view in the area is
> totally muddled.
>
Well I would agree that Penrose is totally muddled on this -- and
incidentally completely ignorant of non-GOFAI approaches, as so many
outside the field are. But your impression of people within Cognitive
Science and AI *endorsing* the brain-computer analogy would have been
about 90% true say 15 years ago. Nowadays it is only say 60% true --
this is only a slow change, but people in established positions tend
not to alter their minds on such basic issues, so we just have to wait
until they die off.
We used to be an ignored minority, but are now very much the coming
wave. Eg the Director of the most prominent AI Lab in the world (Rod
Brooks at MIT); the most prominent group within the main university for
AI in the UK (Sussex); CogSci at Indiana University; Zurich AI Lab --
all these would in general dismiss the brain=computer notion. So it is
perhaps time the outside world caught up on this!
>,,,,,
> That's the second time you've mentioned GOFAI.
>
> That's widely regarded as the AI equivalent of telling a
> fellow researcher that they are stuck back in the 1960s :-(
Well yes, it is a term of mild abuse! Stuck back in the 20th Century.
> In this case, it is totally inappropriate - there are
> large numbers of modern researchers who wouldn't
> /dream/ of labeling themselves as GOFAI practitioners,
> yet who nonetheless believe that you can quantify the
> human brain's memory in terms of bits and the human
> brain's processing power in terms of flops - i.e.
> that the brain is broadly equivalent to some digital
> computation device.
Yes, these people do not like the abusive GOFAI label -- but they are
precisely the people we are referring to!
> ......
> Similarly, the analog vs digital issue seems
> rather irrelevant to the issue of whether you
> can measure the processing speed of a
> computer in flops - which is normally not
> a big deal - since you can simply consider
> the speed of a functionally-equivalent
> digital computer.
No. What is the processing speed, in flops, of a Watt governor? A
meaningless question.
You can simulate a Watt governor with a digital computer, to any level
of precision that you feel like -- and the number of flops in your
simulation will depend solely on how precise you choose to be. Ditto
for the brain.
Bringing this back to s.b.e. -- analog devices are inherently more
evolvable than digital devices, and having been in the business of
Evolutionary Robotics for over 15 years I can tell you that we
definitely do not think of brains -- real or artificial - as anything
remotely like computers.
inm...@susx.ac.uk
> Inman Harvey wrote:
> > Tim Tyler wrote:
> >> Bob Kolker wrote:
> >>> Tim Tyler wrote:
>
> >>>> brain is 100 teraflops.
> >>>
Tim Tyler
> Tim Tyler wrote:
>> Similarly, the analog vs digital issue seems
>> rather irrelevant to the issue of whether you
>> can measure the processing speed of a
>> computer in flops - which is normally not
>> a big deal - since you can simply consider
>> the speed of a functionally-equivalent
>> digital computer.
>
> No. What is the processing speed, in flops, of a Watt governor? A
> meaningless question.
>
> You can simulate a Watt governor with a digital computer, to any level
> of precision that you feel like -- and the number of flops in your
> simulation will depend solely on how precise you choose to be. Ditto
> for the brain.
If the simulated brain performs a lot like the real one -
i.e. can hold conversations, get equivalent scores on
IQ tests and passes Turing tests - then that would satisfy
me.
It is true that increased capacity of the simulation would
produce increased accuracy of the results - but beyond a
certain point you are not really simulating brain function
any more - it would be more a case of simulating the details
of quantum physics, in order to get the last significant
figures of the simulation accurate - when in fact the
last significant figures have little to do with brain
function - and are mostly irrelevant noise.
This discussion of brain performance came up when estimating
when machine minds would outstrip human ones - and thus
displace them in the market for cerebral jobs.
To do that, machine mind's don't need to emulate human
minds in *exact* detail. They just need to be broadly
*functionally* similar, and capable of playing the same
role in the business workplace.
From this point of view, the human brain is not like
an *arbitrary* Watt governor. It is more like a
*particular* Watt governor, with the job of, say,
protecting a particular type of boiler from exploding.
As such, it can absolutely be considered to be
functionally equivalent to some digital device
that does much the same job with about the same
success rate.
The digital device doesn't have to use a
zillion decimal places to /exactly/ simulate
the precise function of the original analog
controller to do this. It just has to prevent
about the same percentage of boilers from
exploding.
I.e. it doesn't have to be *exactly* equivalent,
it just needs to be *functionally* equivalent.
Digital phones didn't need to /exactly/ simulate the
original analog signals before they replaced them.
Similarly, digital TV won't need to /exactly/ simulate
the original analog signals in order to replace them.
Just as you can apply much the same strength tests to
machine and human muscles, and the same acuity tests
to machine and human senses, so you can use the same
metrics to measure the speed of the human brain and
computer, and their respective functional memory
capacities.
It doesn't really make any difference that the human
brain is an analog computer - any more than it mattered
that radio signals were originally analog. It is still
perfectly reasonable to discuss metrics such as the
bandwidth of these types of system - in order to compare
them with their digital counterparts and estimate when
the digital system will replace the analog one.
Tim Tyler
I can't claim to have read either of those.
However, I really don't think I'm in disagreement with the
members of your list on this issue.
My position is that the brain plays a role analogous to
"processor" and "memory", in living systems.
The analogy looks roughly like this:
Organic : Machine
muscles : servos, motors
senses : sensors
brain : CPU/RAM/disc (i.e. computer)
Just as the brain transforms the sense data into
motor neuron responses in a living organism, so
the computer performs an analogous task in a robot.
That analogy is what I am referring to by saying:
``The brain is, functionally, a computer. It accept inputs,
processes them and produces outputs.''
AFAICS, that analogy is supported by practically all
modern AI practitioners.
The view has opponents (Penrose, Searle) who reject the
whole idea that a computer can do what a brain does -
but these guys are clearly off their respective rockers -
and I do not regard their views as worth discussing.
That the brain makes more use of analog components
than most modern computers do makes the analogy less
precise - but analog vs digital is an implementation
detail - and seems to me to be a very long way from
destroying the utility of this analogy.
Note that digital devices have a /long/ history of
replacing their analog predecessors.
The situation is especially bad in the case of
computing - analog computing systems tend to have low
fidelity, and have to simulate digital devices using
thresholding if they are to be of any practical use
at all.
The global communications network has already gone
digital. It's now just a matter of time before the
last historical traces of analog computing nodes
are permanently eradicated from the network.
Tim Tyler
> Bringing this back to s.b.e. -- analog devices are inherently more
> evolvable than digital devices, and having been in the business of
> Evolutionary Robotics for over 15 years I can tell you that we
> definitely do not think of brains -- real or artificial - as anything
> remotely like computers.
The genome is almost exclusively digital in both real biology and
virtual biology - and for an obvious reason - analog systems tend
to have poor copying fidelity.
As to whether digital or analog *phenotypes* are more evolvable -
I'm not convinced that is a sufficiently well-defined question
to have an answer. The whole digital vs analog question seems
to depend a great deal on your problem domain.
However, I do think that the world is in the process of going
digital in a big way - and that consequently the planet's
computing systems will all go digital - taking a large number
of the world's problem domains with them in the process.
Essentially, analog computing or signalling systems suck.
Richard Dawkins describes the reason the world is going
digital rather well near the start of 'River out of Eden' -
in the chapter called 'The Digital River'.
Bob Kolker
> It doesn't really make any difference that the human
> brain is an analog computer - any more than it mattered
> that radio signals were originally analog. It is still
> perfectly reasonable to discuss metrics such as the
> bandwidth of these types of system - in order to compare
> them with their digital counterparts and estimate when
> the digital system will replace the analog one.
I can compare the length of a carrot to the length of an elephant's
trunk. Does that mean the carrot and the elephant's trunk are very much
alike?
Bob Kolker
Bob Kolker
>
> The analogy looks roughly like this:
>
> Organic : Machine
>
> muscles : servos, motors
> senses : sensors
> brain : CPU/RAM/disc (i.e. computer)
Which shows the inaptness of analogy. Brains and CPUs do not work on the
same principles.
The analogy should not be stretched since the underlying entities are
greatly different from each other
Bob Kolker
feedbackdroid
inm...@susx.ac.uk wrote:
>
> But I suspect you were really asking for refernces *against* that
> notion, and in favour of Nouvelle AI, Dynamical Systems approach,
> Enactive approaches. For example, any works by:
>
> Rodney Brooks
> Humberto Maturana
> Francisco Varela
> Randall Beer
> Tim van Gelder
> The Sussex School (see my url below)
> The Indiana School
> Rolf Pfeifer
> etc etc etc
>
> Eg 2 specific references --
> Mind as Motion: Explorations in the Dynamics of Cognition (Paperback)
> by Robert F. Port (Editor), Tim vanGelder (Editor)
>
> Beer, R.D. (2000). Dynamical approaches to cognitive science. Trends in
> Cognitive Sciences 4(3):91-99.
>
>
I don't know how the Sussex school feels about these people, but I
would also add the extensive work of Gerald Edelman, and also Walter J.
Freeman, plus
Esther Thelen and Linda B. Smith, A Dynamical systems Approach to the
Development of Cognition and Action
Andy Clark, Being There: Putting Brain, body, and World Together Again
the work of Rolf Pfeifer [mentioned above] and others on
Morpho-functional Machines
These are largely all dynamical and systems-level approaches to
intelligence, and grounded more in real-world biology than in abstract
symbolic processing, GOFAI.
Along these same lines, and opposed I think to Tyler's comments about
how DNA supposedly "works" - ie, simply digital - might even be the
systems-biologists Marc Kirschner and John Gerhart, The Plausibility of
Life : Resolving Darwin's Dilemma. They haven't made a complete jump as
yet, I believe, but their ideas are definitely tending to view the DNA
machine as a large spatial-temporal dynamical system with multiple
interacting feedback loops.
Tim Tyler
> Tim Tyler wrote:
>> The analogy looks roughly like this:
>>
>> Organic : Machine
>>
>> muscles : servos, motors
>> senses : sensors
>> brain : CPU/RAM/disc (i.e. computer)
>
> Which shows the inaptness of analogy. Brains and CPUs do
> not work on the same principles.
I never claimed they did. What I said was:
``The brain is, functionally, a computer. It accept inputs,
processes them and produces outputs.''
Again, note the word 'functionally'. After using that
word, I do not expect to be deluged with complaints
that implementation details are different. The word
'functionally' means that implementation details are
irrelevant.
> The analogy should not be stretched since the underlying
> entities are greatly different from each other.
They share precisely the similarity originally claimed:
both accept inputs, process them and produce outputs.
That is the /main/ role of both systems - both are
/primarily/ information-processing devices.
Tim Tyler
> Along these same lines, and opposed I think to Tyler's comments about
> how DNA supposedly "works" - ie, simply digital - might even be the
> systems-biologists Marc Kirschner and John Gerhart, The Plausibility of
> Life : Resolving Darwin's Dilemma.
IIRC, what I wrote on that front was:
``The genome is almost exclusively digital in both real biology
and virtual biology - and for an obvious reason - analog systems
tend to have poor copying fidelity.''
Not quite such an absolute claim.
Inherited information within real organisms is largely -
but not totally - digital. The digital information tends
to be what matters, though - since only that has much
chance of persisting in the long term.
Kent Paul Dolan
> When was the last time a machine made
> an inspired guess?
The last time Deep Fritz won a tournament
against the best human player alive?
How in the world would you prove otherwise
based on external behavior?
Tim's correct, the race is over, the rest of
the story is a matter of cleaning up the
details.
See, the trick is, machines don't have to be
smarter than we are, _the same way we are
smart_, they just have to be smarter than us
by out-thinking us, full stop.
If we get to an answer by "an inspired guess",
and a machine gets to a better answer, faster,
by computing a huge subset of all possible
cases and picking the best answer it finds,
which is "smarter"?
xanthian.
John Edser
"Kent Paul Dolan" xant...@well.com wrote:-
> See, the trick is, machines don't have to be
> smarter than we are, _the same way we are
> smart_, they just have to be smarter than us
> by out-thinking us, full stop.
JE:=
Computers cannot think. Chess simulations etc are _programmed by humans_ not
computers.
Regards,
John Edser
Independent Researcher
Tim Tyler
> Bob Kolker wrote:
>> When was the last time a machine made
>> an inspired guess?
>
> The last time Deep Fritz won a tournament
> against the best human player alive?
That a computer is chess world champion
tends to be regarded as a consequence of
chess being an easier game than it might
seem these days.
A better yardstick for when machine
intelligence equals our own may be
the game of 'go' - e.g. see:
"To Test a Powerful Computer, Play an Ancient Game"
- http://www.ishipress.com/times-go.htm
The following table of 1997 estimates of when
the go world champion will be a computer
(by a bunch of programmers and experienced
go players) may be of interest:
The ratings are on a log scale. 'Shodan' is
about eight or nine grades above the current
computer champion.
``At the recent FOST Cup I asked the participants two
questions. When do you think a computer will be shodan
level? and when do you think a computer will be able to beat
any human player?
Shodan level is 'international shodan', which is maybe 3 dan
in Japan, 2 dan in America, 1-dan in Europe. [...]
I've ordered the list with most optomistic first.
Professional players, and go programmers have been noted.
Shodan World Champion Name
1999 2005 Mei-Kou Tei 9-dan (programmer)
2000 2010 Darren Cook (programmer)
2000 2010 Chihiro Mizuuchi
2000 2010 Naritatsu Yamamoto
2002 2040 Martin Mueller (programmer)
2005 2050 Hirooka
2005 2100 Amano
2007 2097 Ken Chen (programmer)
2007 2097 Kojima 9-dan
2007 2197 Redmond 8-dan
2010 2023 Shinichi Sei (programmer)
2010 2030 Tristan Cazenave (programmer)
2010 2045 David Fotland (programmer)
2010 2050 Yung Jye Hunag (programmer)
2010 2100 Jun Saito
2010 2150 Kobayashii
2015 2035 David Keeble
2017 2090 Hiroyuki 8-dan
2020 2050 Mick Reiss (programmer)
2020 2100 Jay Burmeister
2020 2100 Chen Zhixing (programmer)
2020 2100 Prof. Hsu
2020 2100 Yoshikawa
2020 2200 Oyama
2030 2050 Masahiro Okazaki
2050 2100 Kim
2100 -- Izuka
2200 2500 Fujisawa
2500 3000 Oyaizu''
FWIW, my guess would be shodan:2020, champion:2050.
Kent Paul Dolan
> That a computer is chess world champion tends to
> be regarded as a consequence of chess being an
> easier game than it might seem these days.
> A better yardstick for when machine intelligence
> equals our own may be the game of 'go'
Sorry Tim, no.
That's purely an instance of goalpost moving to deny
the accomplishments of artificial intelligence.
"Back in the day" the claim was that no computer,
ever, would be chess champion, because the needed
skills ("insight", "flashes of intuition" among
others) were so purely human, that only something
"really intelligent" could ever be chess champion.
Computers prove able to win _without_ "insight" or
"flashes of inspiration", using just pure slogging
hard work done very, very fast, something at which
they are far superior to humans.
Now, we're there, the championship is in a see-saw,
but computers are still getting smarter, human
intelligence is humbled, and suddenly we want to
change the rules so that this win by the computer
"doesn't count", so artificial intelligence is some
"far distant accomplishment"?
Don't go there.
"Go" is a very simple-in-concept game. The only
reason it isn't already dominated by computers is
the computational burden presented by the sheer size
of the board, not the complexity or subtlety of the
game. Play Go on a board the size of a chess board,
say on a 9x9 board instead of the current standard
19x19 Go board and a good Go program with first move
would presumably win over human players every time.
[For the non-Go knowledgeable, the size of the board
has always been odd by odd, and has increased
steadily in step sizes of 2 over the history of the
game as the knowledge base accumulated and human
skills improved.]
[long table]
That table needs a lot more explanation to make
sense to non-players of the game Go.
xanthian.
Just to get back at least in part to the newsgroup
topic, computers can already design computers at
least at the gross level, Carneige Mellon put
together an assembly line configuration program for
DEC midi-computers decades ago, using knowledge
engineering technology of the day.
Today, most VLSI design is so complex, only
computers can do it, but they do it with human
guidance and to achieve human-chosen goals.
Put computers, rather than humans, in charge of
computer evolution, and let each computer generation
take over the task from the prior generation, and a
singularity of intellectual power cannot be far
behind.
Perhaps at that point, topics like "how did
abiogenesis happen" can be solved by pure
computational prowess, rather than endless
speculation and slow grinding laboratory progress.
Kent Paul Dolan
> Computers cannot think.
That's meat-chauvanism nonsense, and a purely theistic stand.
I'm programmed by my DNA.
Does that mean I can't think?
Whatever "thinking" means, it has to be judged
by what it accomplishes, not by how it is brought
about.
xanthian.
John Edser
"Kent Paul Dolan" xant...@well.com wrote:-
> >Tim Tyler wrote:
> > That a computer is chess world champion tends to
> > be regarded as a consequence of chess being an
> > easier game than it might seem these days.
> > A better yardstick for when machine intelligence
> > equals our own may be the game of 'go'
> Sorry Tim, no.
>
> That's purely an instance of goalpost moving to deny
> the accomplishments of artificial intelligence.
JE:-
But the "goalposts" (Galilean constant frame of reference) were moved for
measuring the "accomplishments artificial intelligence" i.e.: a true
comparative measure of the INDUCTIVE intelligence of man and machine was not
completed (or even attempted as far as I can see).
I repeat: computers do not write computer programs only people do. All a
computer can do is RUN the program provided. IOW, the critical INDUCTIVE
intelligence that computers do not have has to be provided by human
intelligence. Once provided as a given program, computers can do comparative
sets of DEDUCTIONS from the nested sets of inductions provided massively
faster than we can allowing machines to beat humans at chess etc.
Intelligence is primarily an exercise in inductive IMAGINATION and not just
an exercise in mechanical deduction (as many mathematicians think that it
is). In the same way, an ordinary dumb digging machine can dig a trench
cheaper and faster than a gang of men with shovels. However, that machine
must be controlled by a human inductive intelligence. This includes a
machine driven by a computer because a human had to firstly create from
human imagination the theories involved and then write a program which can
drive the machine which actually works, i.e. remain based on these theories.
Regards,
John Edser
Independent Researcher
>
Tim Tyler
I was echoing the view of Douglas Hofstadter there.
In Godel, Escher, Bach he predicted that a general
purpose AI would be needed to beat a human at chess.
When a special dedicated machine managed the task
his tune changed - to:
``"It was a watershed event, but it doesn't have to do with
computers becoming intelligent," said Douglas Hofstadter, a
professor of computer science at Indiana University and
author of several books about human intelligence, including
"Godel, Escher, Bach," which won a Pulitzer Prize in 1980,
with its witty argument about the connecting threads of
intellect in various fields of expression. "They're just
overtaking humans in certain intellectual activities that we
thought required intelligence. My God, I used to think chess
required thought. Now, I realize it doesn't. It doesn't mean
Kasparov isn't a deep thinker, just that you can bypass deep
thinking in playing chess, the way you can fly without
flapping your wings."''
-
http://www.rci.rutgers.edu/~cfs/472_html/Intro/NYT_Intro/ChessMatch/MeanChessPlaying.html
> "Go" is a very simple-in-concept game. The only
> reason it isn't already dominated by computers is
> the computational burden presented by the sheer size
> of the board, not the complexity or subtlety of the
> game. Play Go on a board the size of a chess board,
> say on a 9x9 board instead of the current standard
> 19x19 Go board and a good Go program with first move
> would presumably win over human players every time.
This is certainly not true yet of PC-based go programs.
As well as the larger board size, go has a larger 'branch
factor' than chess does - and there's no easy evaluation
function - a situation that causes computers problems
even on a 9x9 board.
Go does indeed have simple rules - but as we know, simple
rules do not necessarily lead to simple behaviour.
John Edser
"Kent Paul Dolan" xant...@well.com wrote:-
> > JE:-
> > Computers cannot think.
> That's meat-chauvanism nonsense, and a purely theistic stand.
> I'm programmed by my DNA.
> Does that mean I can't think?
JE:-
No, the fact that you misunderstand such means that you are NOT
"programmed" by your DNA. You are self programmed via an entirely unknown
developmental interaction between your DNA/RNA and everything else that is
you as an INDEPENDENT living entity.
Computers do not make mistakes like we do because unlike "meat" metal and
silicon only posses zero INDUCTIVE (imaginative) ability. Machines simply do
exactly as we tell them to do via the program humans (not machines) write.
If we do not tell them what to do at each and every turn a situation can
arise when a condition of logic which the machine is not pre-programmed to
deal with, arises. In this case the dumb machine simply crashes. If you
attempted to prosecute such a machine in a court of law the PROGRAMMER
remains culpable NOT the machine. The legendary buggy software of Microsoft
is not the fault of the machine (as much as Microsoft would just love to be
able to shift the blame, here).
> Whatever "thinking" means, it has to be judged
> by what it accomplishes, not by how it is brought
> about.
JE:-
Yes, where men program machines and not vice versa!
Kent Paul Dolan
> I repeat: computers do not write computer programs only people do.
You keep saying that. How sad for your argument
that the statement has been wrong for decades. I
had computers writing software for me in the early
1990s, and I was a latecomer.
> Independent Researcher
Do a little independent research about computers
writing software, educate yourself, and you'll look
less the idiot the next time you post.
xanthian.
Kent Paul Dolan
> Yes, where men program machines and not vice versa!
Machines have been programming mankind
for decades. Look at any video game, look
at "Dance, Dance, Revolution", look at any
flight simulator, look at the landing strip
indicatator lights for airstrips and aircraft
carriers, look at the indicators on an auto
dashboard.
Again, you base your argument on ignorance,
inflexible agenda, and uninformed opinions.
Your "independent research" is just "isolated
ignorance" in reality. Come join the human
race instead of remaining independent. We're
not nearly as scary as we appear.
xanthian.
feedbackdroid
John Edser wrote:
> "Kent Paul Dolan" xant...@well.com wrote:-
> > >Tim Tyler wrote:
> > > That a computer is chess world champion tends to
> > > be regarded as a consequence of chess being an
> > > easier game than it might seem these days.
> > > A better yardstick for when machine intelligence
> > > equals our own may be the game of 'go'
>
> > Sorry Tim, no.
> >
> > That's purely an instance of goalpost moving to deny
> > the accomplishments of artificial intelligence.
>
> JE:-
> But the "goalposts" (Galilean constant frame of reference) were moved for
> measuring the "accomplishments artificial intelligence" i.e.: a true
> comparative measure of the INDUCTIVE intelligence of man and machine was not
> completed (or even attempted as far as I can see).
>
> I repeat: computers do not write computer programs only people do. All a
> computer can do is RUN the program provided. IOW, the critical INDUCTIVE
> intelligence that computers do not have has to be provided by human
> intelligence.
>
>
this is changing ...
http://www.google.com/custom?q=genetic.programming
John Edser
"feedbackdroid" feedba...@yahoo.com wrote:-
>
> > JE:-
> > But the "goalposts" (Galilean constant frame of reference) were moved
> for
> > measuring the "accomplishments artificial intelligence" i.e.: a true
> > comparative measure of the INDUCTIVE intelligence of man and machine was
> not
> > completed (or even attempted as far as I can see).
> >
> > I repeat: computers do not write computer programs only people do. All a
> > computer can do is RUN the program provided. IOW, the critical INDUCTIVE
> > intelligence that computers do not have has to be provided by human
> > intelligence.
>
> this is changing ...
>
> http://www.google.com/custom?q=genetic.programming
JE:-
From the above link:
http://en.wikipedia.org/wiki/Genetic_programming
In this process Darwinian natural selection is employed, not as a simulation
but as parallel process to biological nature (non evolutionists should take
note). Here machines are NOT writing the program only selection acting on
random heritable variation is. There are only two choices: either man writes
the program using his unique inductive intelligence OR dumb natural
selection has to write it using endless trial and error. My Point: machines
cannot write computer programs.
Regards,
John Edser
Independent Researcher
John Edser
"Kent Paul Dolan" xant...@well.com wrote:-
> > JE:-
> > Yes, where men program machines and not vice versa!
> Machines have been programming mankind
> for decades. Look at any video game, look
> at "Dance, Dance, Revolution", look at any
> flight simulator, look at the landing strip
> indicatator lights for airstrips and aircraft
> carriers, look at the indicators on an auto
> dashboard.
JE:-
Your reasoning remains critically INCOMPLETE. Men "program" other men by
programming machines which "program" men. I REPEAT: Machines do not
"program" men.
> Again, you base your argument on ignorance,
> inflexible agenda, and uninformed opinions.
> Your "independent research" is just "isolated
> ignorance" in reality. Come join the human
> race instead of remaining independent. We're
> not nearly as scary as we appear.
JE:-
This embarrassing-to-yourself rhetorical outburst displays the poverty of
your education. If I were you I would ask for your money back.
John Edser
"Kent Paul Dolan" xant...@well.com wrote:-
> > I repeat: computers do not write computer programs only people do.
> You keep saying that. How sad for your argument
> that the statement has been wrong for decades.
JE:-
This is a science list and not a computer enthusiasts list. You have not
supplied a refutation to what I wrote. Do you know what a refutation is? Do
you know how it critically differs to a non verification? Have you any ideas
as to what a verification means and what it requires? Do you understand
their relative levels of importance? Do you have any idea as to what the
scientific method is?
> I
> had computers writing software for me in the early
> 1990s, and I was a latecomer.
JE:-
Yes, and the computers that wrote this code were programmed by? (guess who)
>snip more rhetoric<
Kent Paul Dolan
> From the above link:
> http://en.wikipedia.org/wiki/Genetic_programming
> Here machines are NOT writing the program only
> selection acting on random heritable variation is.
> There are only two choices: either man writes the
> program using his unique inductive intelligence OR
> dumb natural selection has to write it using
> endless trial and error. My Point: machines cannot
> write computer programs.
My what a sophist idiot you are.
Remove the machine and see how much software gets
written.
Remove the human from a human software writing
enterprise and see how much software gets written.
In both cases, the entity producing the software
_is_ the entity "writing" it.
You don't get to win arguments by redefining what
words mean to suit you.
You don't get to complain about the interior process
used, and discredit the entity whose internal
workings we know exactly while crediting the one
whose internal workings we are only beginning to
understand, but which certainly do not include some
"special magic" in their functioning not capable of
incorporation into a machine.
But then, I've encountered you before, years ago,
and you were equally incapable of learning from new
information then as now.
You opinions are so set in stone, no amount of new
data makes any impression on them.
You are no "researcher" of any type, new information
is of no interest to you at all.
You are instead just an opinionated fool who insists
on remaining in that status in perpetuity, to annoy
anyone who must deal with you.
FWIW
xanthian.
Kent Paul Dolan
> I REPEAT: Machines do not "program" men.
You may repeat your errors until your typing fingers
erode away, that won't improve their truthfulness.
If men were incapable of being programmed by their
machines, that would leave them both incapable of
learning at all how to be machine operators, and
also unable to improve their operating skills with
repeated exposure to those machines.
You continue to try to assign to humans some special
status unachievable by other entities, such as "able
to program", and to deny to humans some special
status easily achievable by other entities, such as
"capable of being programmed".
That promotion of a "special status for humans",
whether an assigned one or a denied one, is pure a
theistic stance, and no proper part of science.
Lose it from your thinking, or remain forever the
butt of jokes from those who understand science
at a gut level, as you do not.
HTH
xanthian.
Kent Paul Dolan
> This is a science list and not a computer
> enthusiasts list.
This is not an email "list", this is a Usenet
newsgroup, an entirely different kind of entity.
It is indicative of your inability to adjust to new
knowledge that you have used Usenet for so long and
still cannot distinguish it properly from other
means of communication.
If the newsgroup were only for scientists, you, as a
perpetual luddite, would long ago have been
excluded. My degree is in math, my profession was
(I'm retired) software development for research in
the sciences and for technical applications of the
sciences. I'm by education and experience and
behavior a scientist, far more claims to the title
than your behavior allows you to make.
> refutation
Your claims are self-refuting, you don't need any
help from me in your pretense of talking science
when all you are engaged in doing is pleading
special cause for humans over all other data
processing entities, a purely theistic enterprise
having no place in discussions of science.
FWIW
xanthian.
William Morse
@darwin.ediacara.org:
> inm...@susx.ac.uk wrote:
>
>> Bringing this back to s.b.e. -- analog devices are inherently more
>> evolvable than digital devices, and having been in the business of
>> Evolutionary Robotics for over 15 years I can tell you that we
>> definitely do not think of brains -- real or artificial - as anything
>> remotely like computers.
>
> The genome is almost exclusively digital in both real biology and
> virtual biology - and for an obvious reason - analog systems tend
> to have poor copying fidelity.
While I am in agreement with most of your arguments on the functional
equivalence of brains and computers, I disagree with the above. At the
level of the codon the genome is digital, but once you get to the next
level of transcription evolution starts to take advantage of whatever
mechanisms give rise to "better" solutions, and that includes chemical
reactions that should be considered analog at the level we are
considering.
> As to whether digital or analog *phenotypes* are more evolvable -
> I'm not convinced that is a sufficiently well-defined question
> to have an answer. The whole digital vs analog question seems
> to depend a great deal on your problem domain.
I agree that digital vs. analog depends on the domain. As noted above, I
would say that digital and analog devices are equally evolvable -
evolution really doesn't care, because evolution works on whatever edge
is available.
> Essentially, analog computing or signalling systems suck.
Humans are essentially analog computing systems. You are a human. Sucks
to be you :-)
William Morse
@darwin.ediacara.org:
Hear, hear!
Yours,
The conscious, loving, dreaming meat AKA Bill Morse
William Morse
@darwin.ediacara.org:
> Kent Paul Dolan wrote:
>> Bob Kolker wrote:
>
>>> When was the last time a machine made
>>> an inspired guess?
>>
>> The last time Deep Fritz won a tournament
>> against the best human player alive?
>
> That a computer is chess world champion
> tends to be regarded as a consequence of
> chess being an easier game than it might
> seem these days.
>
> A better yardstick for when machine
> intelligence equals our own may be
> the game of 'go' - e.g. see:
>
> "To Test a Powerful Computer, Play an Ancient Game"
> - http://www.ishipress.com/times-go.htm
>
> The following table of 1997 estimates of when
> the go world champion will be a computer
> (by a bunch of programmers and experienced
> go players) may be of interest:
The interesting thing is the pessimism of the programmers, and it may be
justified.
The bottleneck is not the complexity of go, or of the human brain, in
relation to computing power - the bottleneck is the development of
software.
We are still using an operating system developed in the 60's (OK that
excludes Windows, but Windows is just now catching up to Unix). Parallel
processing is in its infancy. There has arguably been no fundamentally
new software idea since the spreadsheet. Most of the incredible power of
current hardware is being used to make games more realistic and to
improve our ability to download movies.
But I agree that once software is developed that allows a computer to
learn (especially coupled with a robot that can explore its environment),
we will start to see true machine intelligence.
Yours,
Bill Morse
Tim Tyler
>>> Bringing this back to s.b.e. -- analog devices are inherently more
>>> evolvable than digital devices, and having been in the business of
>>> Evolutionary Robotics for over 15 years I can tell you that we
>>> definitely do not think of brains -- real or artificial - as anything
>>> remotely like computers.
>> The genome is almost exclusively digital in both real biology and
>> virtual biology - and for an obvious reason - analog systems tend
>> to have poor copying fidelity.
>
> While I am in agreement with most of your arguments on the functional
> equivalence of brains and computers, I disagree with the above. At the
> level of the codon the genome is digital, but once you get to the next
> level of transcription evolution starts to take advantage of whatever
> mechanisms give rise to "better" solutions, and that includes chemical
> reactions that should be considered analog at the level we are
> considering.
Transcription is not really part of the genome. IMO, the case
for analog inheritance depends mostly on things like cytoplasmic
factors, methylation and environmental inheritance.
>> Essentially, analog computing or signalling systems suck.
>
> Humans are essentially analog computing systems. You are a human. Sucks
> to be you :-)
Analog systems typically have to simulate digital ones - using
thresholding - if they are to do things like have persistent,
reliable memory. The brain uses thresholding ubiquitously - and
that's probably why our memories can last for as long as they do.
John Edser
"Kent Paul Dolan" xant...@well.com
> > JE:-
> > This is a science list and not a computer
> > enthusiasts list.
> This is not an email "list", this is a Usenet
> newsgroup, an entirely different kind of entity.
JE:-
It is both. I receive sbe as an email list and so can anybody else.
Again, your reasoning remains critically incomplete.
> snip endless rhetoric<
> > refutation
> Your claims are self-refuting, ...
> snip<
JE:-
Obviously, you have no idea what a refutation is. You have not refuted my
proposition that men program machines and not the reverse.
John Edser
"Kent Paul Dolan" <xant...@well.com>
> > I REPEAT: Machines do not "program" men.
JE:-
PLEASE NOTE: this does NOT mean that men CANNOT be programmed in some way by
machines.
>snip rhetoric<
> If men were incapable of being programmed by their
> machines, that would leave them both incapable of
> learning at all how to be machine operators, and
> also unable to improve their operating skills with
> repeated exposure to those machines.
I never claimed that men could not be programmed by machines I claimed that
men program the machines which program men and not the reverse. It seems to
me that you have never been taught how to think.
>snip rhetoric<
John Edser
William Morse wdm...@twcny.rr.com wrote:-
> >> JE:-
> >> Computers cannot think.
> > That's meat-chauvanism nonsense, and a purely theistic stand.
> > I'm programmed by my DNA.
> > Does that mean I can't think?
> > Whatever "thinking" means, it has to be judged
> > by what it accomplishes, not by how it is brought
> > about.
> Hear, hear!
> Yours,
> The conscious, loving, dreaming meat AKA Bill Morse
JE:-
Men "accomplish" machines and not the reverse. The last time I looked men
and women have built ALL the machines including the machines which build
other machines. So far an entirely self programming and self assembling
machine which comes from just a metal and silicon lineage could not have
even attempted to build a man because such a lineage does not even exist.
John Edser
"Kent Paul Dolan" xant...@well.com wrote:-
> > JE_
> > http://en.wikipedia.org/wiki/Genetic_programming
> > Here machines are NOT writing the program only
> > selection acting on random heritable variation is.
> > There are only two choices: either man writes the
> > program using his unique inductive intelligence OR
> > dumb natural selection has to write it using
> > endless trial and error. My Point: machines cannot
> > write computer programs.
>snip embarrassing rhetoric <
> Remove the machine and see how much software gets
> written.
JE:-
Plenty.
I can (and have) written BASIC code on wet sand with just my finger while
taking an inspired stroll on the beach. No silicon and metal machine
anywhere in sight but plenty of written software :-)
> Remove the human from a human software writing
> enterprise and see how much software gets written.
JE:-
None. Entirely self assembled and self coded metal and silicon machines do
not take pleasant strolls on beaches and become inspired to write code on
wet sand :-)
> In both cases, the entity producing the software
> _is_ the entity "writing" it.
JE:-
Your proposition remains tautological nonsense. It makes about as much sense
as Escher's self drawing hands (you can view this famous picture in the
webpage below). Like almost all of Escher and mathematics, your proposition
failed not provide a critical Galilean frame of reference so all you can do
is chase your own tail.
http://en.wikipedia.org/wiki/M.C._Escher
>snip even more embarrassing and useless rhetoric<
Here is a relevant challenge for you: Why don't you attempt write a program
to solve Felsenstein's cost of substitution riddle? No machine that exists
today can solve it (I include machines made of meat). I note that the
program which Malcolm said he would write has not materialized. I claimed
that no such program CAN be written unless a critical Galilean frame of
reference becomes supplied. Felsenstein steadfastly refuses to supply any
frame of reference (or amazingly, just discuss the need for one) for his
false zero cost "solution". Felsenstein's rationale was not correct. However
it appears to have been sycophantically accepted by all the professionals
who post here.
Kent Paul Dolan
> There has arguably been no fundamentally new
> software idea since the spreadsheet.
While I wish I could use evolutionary programming as
a counter-example, I think Holland and Kosa's work
might well have preceded the development of the
spreadsheet.
Still, web spiders, software agents, massive fully
inverted text indices, CDMA signal codecs, particle
swarm and ant colony optimization, LISA-style and
SETI-style distributed computation, and one-way
trapdoor and newer cryptology algorithms might
qualify, and I'm sure there's much more.
I'm getting old and sleepy, but I'm pretty sure
progress is still happening out there despite that
I've pretty much stopped contributing to it.
xanthian.
Kent Paul Dolan
"John Edser" <e...@ozemail.com.au> wrote:
> "Kent Paul Dolan" <xanth...@well.com>
>> John Edser wrote:
>>> I REPEAT: Machines do not "program" men.JE:-
> PLEASE NOTE: this does NOT mean that men
> CANNOT be programmed in some way by
> machines.
Oh, cute dance. You directly contradict your quoted
statement that machines do not program men. You
are shameless in your invincible ignorance: you have
to believe you are right even when you are so
demonstratably wrong you have to reverse course in
a proton's width while the world laughs at your japes.
> I never claimed that men could not be programmed
> by machines
Read your own words above and try to give them any
other meaning, you lying fool.
> It seems to me that you have never been taught
> how to think.
It seems to me that you have never been taught how
to be truthful.
The evidence is on my side.
I've earned my living by writing software for massively
complex algorithms. Whether that counts as "thinking"
is not for a mindless jacknapes such as you to decide.
HTH
xanthian.
Software by me such as implementations of artificial
intelligence using evolutionary algorithms, just to get
back on topic from your theistic drivel for a moment.
It is the case that men can program computers to do
thinking of which men are incapable, simply because
a human lifetime is too short to accomplish the
sheer quantity of thinking computers can do in a day,
at modern speeds. Computers have become the
accessories to our minds that automobiles are to
our legs: a way to go faster than we otherwise might.
But the situation of computers thinking extends well
beyond that. There are well document cases of
computers designing electronic circuits in ways
unthought of by man, of computers discovering
mathematical theorems unthought of by man, and,
in the case of the four color mapping problem, of
computers proving mathematical theorems of which
the human mind is simply incapable of doing the
same due to low memory capacity and slow speed.
What is lacking now, but being avidly sought, is a
_generalized_ way for computers to assemble
existing information to develop new information, in
the way they already do in the above cases and
many more. Once that is achieved, robots will be
able to evolve without further human assistance.
Kent Paul Dolan
> But I agree that once software is developed that allows a computer to
> learn (especially coupled with a robot that can explore its environment),
> we will start to see true machine intelligence.
>From many sources, computers are already able to learn, and to do so
while in motion in an environment. One recent example:
http://news.yahoo.com/s/ap/20061116/ap_on_sc/resilient_robots
which has been discussed beyond the point of tedium, here:
http://groups-beta.google.com/group/comp.ai.philosophy/browse_frm/thread/bf283f425fb9e552
HTH
xanthian.
Phil Roberts, Jr.
>
> Our brains are what has led to our domination of the planet.
Actually, the postulated nonobservable causal substrate
that gives rise to our perception of a brain via one route
and our apperception of thoughts and feelings via another
route is what has led to our domination of the planet.
That, and millenia of cultural evolution.
PR
Phil Roberts, Jr.
>
>>My impression is that the only folk who reject the fundamentals
>>of the brain-computer analogy are people like Roger Penrose
>>and John Searle - i.e. those whose world view in the area is
>>totally muddled.
>>
> Tim Tyler wrote:
>
> Well I would agree that Penrose is totally muddled on this
I have always thought the Godel argument constitutes a pretty
good ARGUMENT against a computational view of the mind. Where
I think Lucas went wrong was in his claim that Godel constitutes
a PROOF against computationalism. You can't prove empirical
assertions, you can only marshall evidence. That's why all
scientific theories are tentatively true until the next
revision.
I can't recall to what
extent Penrose claimed Godel as a proof rather than an argument
against computationalism. But as an argument, I am definitely
in the Lucas/Penrose camp. Can you provide a brief overview of
why you consider Penrose "totally muddled" on this issue?
PR
kramer
parallels in the automotive industry.
Initially only the rich will be able to afford robots, and they will be
inept for any practical purposes and perhaps over sold, but hey its
their money. They will even design houses to incoporate the robots,
the features will of course be angled towards this market.
The middle class will want the robots and also build their houses to
accomodate these robots, however most of the homes and robots will be
cast aside when they realize that they were inferiorly designed, and
have no real intelligence. All research will stop along these routes.
The of the people will live in used poorly designed cities and homes
which were built to suit the whims of people who have moved on.
Sorry, I can't stand the over selling of cars by mechanical engineers
and shoddy architects like Frank Lloyd Wright who liked to design
parking garages, generating unecessary dependence on petroleum
products.
-Robin
aatu.kos...@xortec.fi
> I have always thought the Godel argument constitutes a pretty
> good ARGUMENT against a computational view of the mind.
How?
[moderator's note: And how does this relate to evolutionary
biology? Keep it on topic, people. - JAH]
--
Aatu Koskensilta (aatu.kos...@xortec.fi)
"Wovon man nicht sprechen kann, daruber muss man schweigen"
- Ludwig Wittgenstein, Tractatus Logico-Philosophicus
Kent Paul Dolan
> I have always thought the Godel argument
> constitutes a pretty good ARGUMENT against a
> computational view of the mind.
Why on earth so? All Goedel proved was that
mathematics was (necessarily) incomplete, not that
it was useless.
My knowledge of women is similarly incomplete. That
doesn't make that knowledge useless, it just means
that I need to check my work pretty carefully before
building structures that depend on my being correct.
xanthian.
John Edser
"Kent Paul Dolan" xant...@well.com wrote:-
> >>> I REPEAT: Machines do not "program" men.
> > JE:-
> > PLEASE NOTE: this does NOT mean that men
> > CANNOT be programmed in some way by
> > machines.
> snip rhetoric<
> You directly contradict your quoted
> statement that machines do not program men.
>snip rhetoric<
JE:-
Dear oh dear ... it appears you are now refusing to differentiate between
"not" and "cannot".
>snip pointless repeats<
> It is the case that men can program computers to do
> thinking of which men are incapable, simply because
> a human lifetime is too short to accomplish the
> sheer quantity of thinking computers can do in a day,
> at modern speeds.
JE:-
All the above means is that the "thinking" was done by the programmer and
NOT the machine. Thinking is inductive/deductive and not just deductive.
Without a single exception, running a computer program is just a just dumb
act of deduction.
Definitions:-
Induction: from the particular to the general.
Deduction: from the general to the particular.
Machines cannot induce a thing because nobody knows how a mind makes an
induction. Induction remains such an enormous mystery re: intelligence that
Hume preferred to pretend that it doesn't even exist. Without a workable
theory of induction nobody could possibly write a program to tell a machine
how to do it...could they...
> Computers have become the
> accessories to our minds that automobiles are to
> our legs: a way to go faster than we otherwise might.
JE:-
Faster deduction does NOT mean faster machine thinking, it only means faster
applications for people who can think.
> But the situation of computers thinking extends well
> beyond that. There are well document cases of
> computers designing electronic circuits in ways
> unthought of by man, of computers discovering
> mathematical theorems unthought of by man, and,
> in the case of the four color mapping problem, of
> computers proving mathematical theorems of which
> the human mind is simply incapable of doing the
> same due to low memory capacity and slow speed.
JE:-
Thank the programmer and not the machine.
> What is lacking now, but being avidly sought, is a
> _generalized_ way for computers to assemble
> existing information to develop new information, in
> the way they already do in the above cases and
> many more.
JE:-
Machine cannot make inductions. All they can do is deduce from the
inductions allowable via the program that they run. Until a machine can make
an induction all on its own it cannot think. The mantra for computers has
never changed: "garbage in garbage out". The human mind remains unique
because it can produce endless non garbage from endless garbage.
Phil Roberts, Jr.
> Phil Roberts, Jr. wrote:
>
>>I have always thought the Godel argument constitutes a pretty
>>good ARGUMENT against a computational view of the mind.
>
> How?
Godel demonstrated (1931) that for any formal (logical, mathematical,
rule driven, etc.) system capable of simple arithmetic, there is at
least one well-formed sentence or theorem, usually referred to as the
Godel or G sentence, that cannot be proven in the system.
Interestingly enough, because of our ability to attach meaning to the
symbols employed in such a proof, the G sentence is one that we humans
can quite easily “see” to be true in that its semantic interpretation
is simply: ‘This sentence can not be proven in this system’. Since
machines (computers) can prove theorems, but cannot prove the Godel
sentence without a logical contradiction, Godel’s theorem has served
as the basis for an argument, originally championed by J. R. Lucas
(1961) and more recently by the Nobel laureate, Roger Penrose (1989,
1994), that “minds are different from machines” (Lucas).
Interestingly enough, one of the most lucid statements of the
Lucas/Penrose perspective comes from Douglas Hofstadter (1979), who
himself is perhaps one of its most vocal critics:
[quote]
Looked at this way, Godel's proof suggests -- though by no means does
it prove! -- that there could be some high-level way of viewing the
mind/brain, involving concepts which do not appear on lower levels,
and that this level might have explanatory power that does not exist
-- not even in principle -- on lower levels. It would mean that some
facts could be explained on the high level quite easily, but not on
lower levels at all. No matter how long and cumbersome a low-level
statement were made, it would not explain the phenomena in question.
It is analogous to the fact that, if you make derivation after
derivation in [Peano arithmetic], no matter how long and cumbersome
you make them, you will never come up with one for G -- despite the
fact that on a higher level, you can see that [the Godel sentence] is
true.
What might such high-level concepts be? It has been proposed for
eons, by various holistically or "soulistically" inclined scientists
and humanists that consciousness is a phenomenon that escapes
explanation in terms of brain components; so here is a candidate at
least. There is also the ever-puzzling notion of free will. So
perhaps these qualities could be "emergent" in the sense of requiring
explanations which cannot be furnished by the physiology alone
('Godel, Escher, Bach', p. 708).
[unquote]
>
> [moderator's note: And how does this relate to evolutionary
> biology? Keep it on topic, people. - JAH]
>
Because if one assumes, as I do, that 'feelings of worthlessness'
are a maladaptive byproduct of the evolution of rationality,
then they can be construed as an empirical vindication of the
Lucas/Godel perspective on Godel's theorem, i.e., the assertion
that Godel constitutes an argument that rationality can not be
constrained (captured in its entirety) within a formal (e.g.,
mechanical) system and therefore that "minds are different
from machines" (Lucas). Another way of saying this, is that
they can be construed as evidence that Dawkins was right in
maintaining that:
Darwinism is too big a theory to be confined to the
narrow context of the gene....
The contention that 'feelings of worthlessness' constitute
an empirical vindication of Lucas/Godel is based on the
following line of inference:
1. That human beings have been programmed by natural selection to survive.
2. That human beings do not survive by blindly responding to stimuli
with no understanding of the overall objective such mechanisms have
been “designed” to achieve, as is likely the case with most other
species, but as the result of a conscious intention to survive often
involving long range planning.
3. That the basis of the conscious intention to survive, at least when
not under the influence of fear, anger, pain, etc., is the value the
organism places on its own existence, i.e., its self-value.
4. That ‘feelings of worthlessness’ constitute evidence that humans
are beginning to question the value of their existence and therefore
are beginning to question the objective of nature’s most basic program.
5. That the same capacity for “standing outside the system” (Lucas)
that allows us to “see” that the Godel sentence is “true” is what is
responsible for our ability to stand outside of nature’s program and
question (in the guise of ‘feelings of worthlessness’) whether it is
one worth completing.
6. That rationality cannot be constrained (captured in its entirety)
within a formal system, not even by Mother Nature herself.
[quote]
So even if mathematicians are superb cognizers of mathematical truth,
and even if there is no algorithm, practical or otherwise, for
cognizing mathematical truth, it does not follow that the power of
mathematicians to cognize mathematical truth is not entirely
explicable in terms of their brain's executing an algorithm. Not an
algorithm for intuiting mathematical truth – we can suppose that
Penrose has proved that there could be no such thing. What would the
algorithm be for, then? Most plausibly it would be an algorithm --
one of very many – for trying to stay alive... (Dennett, 1989)
[unquote]
Oops! Sorry! Wrong program, old bean! [My response to Dennett’s
failure to notice that, in man, self-worth maximization often trumps
the program “for trying to stay alive”.]
[quote]
I have often felt as though I had inherited all the defiance and all
the passions with which our ancestors defended their Temple and could
gladly sacrifice my life for one great moment in history (Sigmund
Freud).
[unquote]
--
Phil Roberts, Jr.
http://www.rationology.net
Phil Roberts, Jr.
> Phil Roberts, Jr. wrote:
>
>
>>I have always thought the Godel argument
>>constitutes a pretty good ARGUMENT against a
>>computational view of the mind.
>
>
> Why on earth so? All Goedel proved was that
> mathematics was (necessarily) incomplete, not that
> it was useless.
>
Incomplete from the perspective of proving theorems,
but not incomplete from the standpoint of the mind's
ability to "see" truth.
> My knowledge of women is similarly incomplete. That
> doesn't make that knowledge useless, it just means
> that I need to check my work pretty carefully before
> building structures that depend on my being correct.
>
I have no idea how uselessness enters the picture.
The fact that one consider's Godel a pretty good
argument that "minds are different from machines"
(Lucas) is far from useless, particularly if it
turns out to be true, in that it would mean that
we would finally be able to rid ourselves of the
strangehold mechanistic materialism has had on
the soft sciences for the past seventy five years
or so. I think that would be very useful indeed.
PR
Kent Paul Dolan
> evolutionary biology? Keep it on topic, people. -
> JAH]
Actually that's fairly close to topic, Josh.
Understanding at a deep theoretical level the
_inherent_ limitations of computation, assists in
evaluating how good a job evolution has done in
approaching that limit in the brains of animals like
humans, when considered as computational devices, or
how well a robotic emulation of such a brain could
become before it hits a "Goedel wall".
This is very analogous to the idea that having a
deep understanding of the laws of thermodynamics
helps us understand just how efficient it is
possible to make an internal combustion engine or
to evolve the ATP energy cycle in animals.
FWIW
xanthian.
Kent Paul Dolan
> Machines cannot induce a thing because nobody
> knows how a mind makes an induction.
That "John Edser" does not know how a mind makes an
induction does not mean that no one knows.
I know, and so do many others.
Clue: your abyssal ignorance is not some law
of the universe, limiting all others to be
as ignorant as you are. It is all and only a
symptom of your refusal to educate yourself
or allow yourself to be educated by others,
while nevertheless insisting that you are a
world class authority on topics you can
barely spell.
I realize your personality will forever
prevent you from accepting this reality, but
there it still is, will-thee-nil-thee,
gravity works and ignorance like yours is
curable if the patient is cooperative.
To make an induction:
For each fixed value in turn contained in an
observation, make that value a variable
instead, and test whether the observation
still holds true when the variable assumes
its range of possible values, or some
statistically useful subset thereof. If so,
you have induced a new, more generalized
rule.
That wasn't so hard now, was it?
That's precisely how Einstein developed
Special Relativity from the Michelson Morley
experimental results invalidating an "ether
effect" on the speed of light: suppose the
speed of light is constant _everywhere_ not
just between the mountain tops moving with
the earth in its orbit around the sun where
M & M ran their test? Then, logically, what
must follow? The rest was a "simple" matter
of grinding out the math, math so nasty that
Einstein had to get help with it.
And if I can tell you how induction is done, and do
it, so can any computer do it, just turn the above
"how to do an induction" paragraph into code.
Since the rest of your posting depends on your
false premise about induction, the above suffices
to refute you.
HTH
xanthian.
RAGLAN...@aol.com
How sad but true.
Michael Ragland
John Edser
"Phil Roberts, Jr." phi...@ix.netcom.com wrote:-
JE:-
The word "domination" remains emotionally loaded so it is best avoided.
Phil,
Are you a devotee of Hume who decided that because he could not understand
induction then it didn't exist?
Our astoundingly fast biological radiation depended entirely on our unique
ability to make and apply deductively, empirically based INDUCTIONS. The
most importance of these was cognitive mutualised exchange occurring within
interdependent (not dependent) tribal units which subsequently expanded at
an astonishing rate.
Tim Tyler
>> Tim Tyler wrote:
>>> My impression is that the only folk who reject the fundamentals
>>> of the brain-computer analogy are people like Roger Penrose
>>> and John Searle - i.e. those whose world view in the area is
>>> totally muddled. [...]
>
> I have always thought the Godel argument constitutes a pretty
> good ARGUMENT against a computational view of the mind. Where
> I think Lucas went wrong was in his claim that Godel constitutes
> a PROOF against computationalism. You can't prove empirical
> assertions, you can only marshall evidence. That's why all
> scientific theories are tentatively true until the next
> revision.
>
> I can't recall to what
> extent Penrose claimed Godel as a proof rather than an argument
> against computationalism. But as an argument, I am definitely
> in the Lucas/Penrose camp. Can you provide a brief overview of
> why you consider Penrose "totally muddled" on this issue?
John Lucas's 'Godel' argument has been much-criticized - and
Penrose's views in this area are essentially a variation on it.
Brief version of what's wrong:
``A mathematician often makes judgments about what
mathematical statements are true. If he or she is not more
powerful than a computer, then in principle one could write
a (very complex) computer program that exactly duplicated
his or her behavior. But any program that infers
mathematical statements can infer no more than can be proved
within an equivalent formal system of mathematical axioms
and rules of inference, and by a famous result of Godel,
there is at least one true statement that such an axiom
system cannot prove to be true. "Nevertheless we can (in
principle) see that P_k(k) is actually true! This would seem
to provide him with a contradiction, since he aught to be
able to see that also."
This argument won't fly if the set of axioms to which the
human mathematician is formally equivalent is too complex
for the human to understand. So Penrose claims that can't be
because "this flies in the face of what mathematics is all
about! ... each step [in a math proof] can be reduced to
something simple and obvious ... when we comprehend them
[proofs], their truth is clear and agreed by all."
And to reviewers' criticisms that mathematicians are better
described as approximate and heuristic algorithms, Penrose
responds (in BBS) that this won't explain the fact that "the
mathematical community as a whole makes extraordinarily few"
mistakes.
These are amazing claims, which Penrose hardly bothers to
defend. Reviewers knowledgeable about Godel's work, however,
have simply pointed out that an axiom system can infer that
if its axioms are self-consistent, then its Godel sentence
is true. An axiom system just can't determine its own self-
consistency. But then neither can human mathematicians know
whether the axioms they explicitly favor (much less the
axioms they are formally equivalent to) are self-consistent.
Cantor and Frege's proposed axioms of set theory turned out
to be inconsistent, and this sort of thing will undoubtedly
happen again.''
- http://hanson.gmu.edu/penrose.html
I see there's also this:
http://www.paul-almond.com/RefutationofPenroseGodelTuring.htm
As to what this has to do with evolution - if humans can
do things no machine can do - or will ever be able to do -
that may impact the hypothesis that machine-based organisms
may replace humans as the dominant life form on earth over
the next century or so.
However, this particular argment for the qualitative
superiority of humans is simply wrong - and (IMO) rather
obviously so for anyone who knows anything about Godel's
work.
John Edser
"Phil Roberts, Jr." phi...@ix.netcom.com wrote:-
> >>My impression is that the only folk who reject the fundamentals
> >>of the brain-computer analogy are people like Roger Penrose
> >>and John Searle - i.e. those whose world view in the area is
> >>totally muddled.
> > Tim Tyler wrote:
> > Well I would agree that Penrose is totally muddled on this
> I have always thought the Godel argument constitutes a pretty
> good ARGUMENT against a computational view of the mind.
JE:-
Gödel proved that mathematical tautologies remained mindless. IOW, mind is
not based on just an abstract version of reversible logic or deduction, it
remains based on the mystery of INDUCTION. Win a Nobel prize by detailing
the process of induction to sbe readers.
> Where
> I think Lucas went wrong was in his claim that Godel constitutes
> a PROOF against computationalism. You can't prove empirical
> assertions, you can only marshall evidence. That's why all
> scientific theories are tentatively true until the next
> revision.
JE:-
"Until the next revision"? What are the criteria for this supposed
"revision"?
How many ways are there of evading Popper's inevitable requirement for
refutation?
Regards,
John Edser
Independent Researcher
>
> I can't recall to what
> extent Penrose claimed Godel as a proof rather than an argument
> against computationalism. But as an argument, I am definitely
> in the Lucas/Penrose camp. Can you provide a brief overview of
> why you consider Penrose "totally muddled" on this issue?
>
> PR
John Edser
"Kent Paul Dolan" xant...@well.com wrote:-
> > Phil Roberts, Jr. wrote:
> > I have always thought the Godel argument
> > constitutes a pretty good ARGUMENT against a
> > computational view of the mind.
> Why on earth so? All Goedel proved was that
> mathematics was (necessarily) incomplete, not that
> it was useless.
JE:-
WHY mathematics remains critically "incomplete" supplies your answer. Do you
know why?
Glen M. Sizemore
"John Edser" <ed...@ozemail.com.au> wrote in message
news:emc0l6$1p6s$1...@darwin.ediacara.org...
No, but we know some particulars concerning "induction" in animals. Indeed,
a great deal is known about it. Please see the entire history of the
experimental analysis of behavior, some of which can be found in the 50
years of the Journal of the Experimental Analysis of Behavior. One could
say that not only is the study of operant conditioning the study of
intention, it is also the study of much of what we call induction.
Kent Paul Dolan
> WHY mathematics remains critically "incomplete"
> supplies your answer.
No, it doesn't, but then, since I long ago proved you
to be innumerate, that you would think so is
understandable.
> Do you know why?
Exquisitely. Being a mathematician by training, I
long ago (1966 or so) read Goedel's proof, with
understanding, simply for the sheer joy of reading
it. You should make the attempt, it only involves
understanding simple arithmetic.
FYI
xanthian.
Kent Paul Dolan
> Godel demonstrated (1931) that for any formal
> (logical, mathematical, rule driven, etc.) system
> capable of simple arithmetic, there is at least
> one well-formed sentence or theorem, usually
> referred to as the Godel or G sentence, that
> cannot be proven in the system. Interestingly
> enough, because of our ability to attach meaning
> to the symbols employed in such a proof, the G
> sentence is one that we humans can quite easily
> "see" to be true in that its semantic
> interpretation is simply: 'This sentence can not
> be proven in this system'.
But the fallacy in your, and your antecedents'
thinking, is right there and very obvious.
Goedel proved that there are well formed sentences
stating theorems that a computer cannot _prove_ to
be true _or_ to be false, within the same axiomatic
system of arithmetic that the theorem concerns.
In the _same terms in which Goedel worked_, NEITHER
CAN A HUMAN _prove_ that the theorem is true, or is
false, using only the axioms of the system of
arithmetic that the theorem describes.
That the human can "see" the truth or falsehood of
the theorem is an unrelated topic;
[and arguably a fantasy as well; thinking
you know something is not the same thing as
knowing something; like a theisim, "knowing"
something you cannot prove isn't "knowledge"
at all, it's merely _faith_, the same trap
into which theists so consistently fall]
the human is merely working in some other demesne
than the one in which Goedel's machine was working.
In particular, the human is not working in the
demesne of accomplishing that proof as Goedel
described that the proof must be accomplished.
Goedel was more than willing to admit that some
theorem unprovable in one system of arithmetic might
well be provable under a stronger set of axioms, but
he then showed that the stronger set of axioms would
form a system for which exactly the same sort of
unprovable sentence could again be written.
So, all you've proved is that the human mind _may_
employ a stronger set of axioms, not that it is
somehow different in kind.
FWIW
xanthian.
Kent Paul Dolan
> The fact that one consider's Godel a pretty good
> argument that "minds are different from machines"
> (Lucas) is far from useless
Since Goedel's proof demonstrates no such thing,
as I explained at length in a separate posting, then
like any false premise, Lucas' opinion is indeed
useless.
xanthian.
John Edser
"Kent Paul Dolan" xant...@well.com wrote:-
> > JE:-
> > Machines cannot induce a thing because nobody
> > knows how a mind makes an induction.
> That "John Edser" does not know how a mind makes an
> induction does not mean that no one knows.
> I know, and so do many others.
JE:-
Amazing; Kent Paul Dolan claims to know how human minds work.
>snip embarrassing rhetoric<
> To make an induction:
> For each fixed value in turn contained in an
> observation, make that value a variable
> instead, and test whether the observation
> still holds true when the variable assumes
> its range of possible values, or some
> statistically useful subset thereof. If so,
> you have induced a new, more generalized
> rule.
JE:-
Apparently, induction is just a process of turning constants into variables
"in turn" which here is no better than just randomly. I am sure Larry Moran
will be pleased :-) So where have we heard this sort of thing before in
evolutionary theory? It appears the complete works of Shakespeare ARE the
equivalent of billions of monkey's typing randomly...
> That wasn't so hard now, was it?
JE:-
You had better ask each exhausted monkey :-)
> That's precisely how Einstein developed
> Special Relativity from the Michelson Morley
> experimental results invalidating an "ether
> effect" on the speed of light: suppose the
> speed of light is constant _everywhere_ not
> just between the mountain tops moving with
> the earth in its orbit around the sun where
> M & M ran their test? Then, logically, what
> must follow? The rest was a "simple" matter
> of grinding out the math, math so nasty that
> Einstein had to get help with it.
JE:-
I agree: that Einstein reduced Newton's constant _assumptions_ of m and t to
just variables and then replaced them with a new and quite unexpected
constant c which under Newton was previously assumed to be just another
variable in order to explain the results of Michelson Morley experiment. I
have been posting these same details to sbe for over 5 years now. Such
events rigorously vindicate Popper because the Newtonian constants, which
acted as critical Galilean frames of reference, were not just irrefutable.
If they were then Einstein could never have replaced Newton. Note also,
reducing all constants to variables requires other constants WHICH MAY
REMAIN UNKNOWN to take their place otherwise you only end up with a mindless
tautology (which of course mathematicians are more than happy with: viz:
Felsenstein's oversimplified (no constant term existed) "solution"
incorrectly offered as a valid solution to the cost of substitution
problem).
I disagree: that this evolutionary lineage of rational thought was
accomplished by just a reduction of constants to variables "in turn" where
all that was required was to "test whether the observation still holds true
when the variable assumes its range of possible values". If you reduce a
constant to a variable you have to replace it with something else to
maintain a frame of reference. This may or may not be an existing variable
promoted to become a new constant. IOW at some point constants have to be
conjured out of nothing.
As an exercise why don't you explain how Darwin made his inductive inference
using your model?
>snip<
John Edser
"Kent Paul Dolan" wrote:-
> > JE:-
> > WHY mathematics remains critically "incomplete"
> > supplies your answer.
> No, it doesn't, ...
JE:-
I disagree. Please refer to the argument below.
>snip relentless rhetoric<
> > JE:-
> > Do you know why?
> Exquisitely.
> Being a mathematician by training, I
> long ago (1966 or so) read Goedel's proof, with
> understanding, simply for the sheer joy of reading
> it. You should make the attempt, it only involves
> understanding simple arithmetic.
JE:-
No answer was provided (just rhetorical assertions).
Here is my answer:
Mathematics has always been critically incomplete because the propositions
of non mathematics which can make it complete are whatever constitutes
constant terms within the mathematics because only these can represent valid
EMPIRICAL frames of reference. Mathematics has no interest in what is and
what is not empirically based simply because all it does is logically
process each and every term no matter what they represent. Quite obviously,
each and very definition of an empirical constant term has to be imported
from outside of mathematics. IOW mathematics has to import any empirical
frames of reference. Without at least one, mathematics remains NON
empirically based. Without any constants at all, mathematics becomes reduced
to a tautology (just circular logic) so mathematics always remains
critically "incomplete". Being able to define what exactly represents an
empirical constant term within a mathematically based logic was and remains
entirely an act of human induction (no machine can make such an induction).
Circular logic (represented by reversible set intersection) becomes
transformed into vital contesting lines of logic (represented by
irreversible nested sets) depending on what was assumed to remain
empirically constant and what was not within each _different_ and therefore
_contesting_ lineal (not circular) assumption. Science (which always was and
will always remain empirically based) rigorously requires each and every
assumed constant term to remain empirically refutable allowing some frames
of reference to be discarded in favor of others but ONLY after testing
against NATURE in an entirely UNBIASED way. The net result: empirically
based reasoned arguments (theories) evolve in a Darwinian way where
refutation replaces natural selection. As one frame of reference replaces
another via Popperian refutation _absolutely lager_ outer nested sets
encompasses all the others expanding the truth domain of a theory of science
(the theory can explain more and more in a refutable way). These can modeled
as an expanding series of concentric circles representing expanding nested
sets. Each refutation (the edge of each nested circle) requires an even
better induction than the last where this critical scientific lineage of
human inductive inferences cannot be made by any known machine.
John Edser
aatu.kos...@xortec.fi wrote:-
> > Phil Roberts, Jr. wrote:
> > Godel demonstrated (1931) that for any formal (logical, mathematical,
> > rule driven, etc.) system capable of simple arithmetic, there is at
> > least one well-formed sentence or theorem, usually referred to as the
> > Godel or G sentence, that cannot be proven in the system.
> > Interestingly enough, because of our ability to attach meaning to the
> > symbols employed in such a proof, the G sentence is one that we humans
> > can quite easily "see" to be true in that its semantic interpretation
> > is simply: 'This sentence can not be proven in this system'.
> An elementary, if widespread, confusion. Gödel's incompleteness
> theorem (or, rather, Rosser's strenghtening of it), which is a
> mathematical theorem no more and no less doubtful than, say, the
> infinitude of primes, only tells us that given a formal theory T, there
> exists, constructible by a mechanical procedure, a sentence G_T, such
> that G_T is true and unprovable in T just in case T is consistent. The
> only cases we can "see" that G_T is true are those in which we are
> able, for whatever reasons, to see that T is consistent. For some
> theories T we can indeed "see" this, in the sense of having perfectly
> good reasons to conclude that T is consistent, and for some other T we
> just don't have the slightest idea whether T is consistent. Consider,
> for example, the theory K ) Peano airthmetic + Goldbach's conjecture.
JE:-
Since this is a science group and not a mathematics group explaining this
example in more detail would help. What appears to be your most important
statement: " ..there exists, constructible by a mechanical procedure, a
sentence G_T, such that G_T is true and unprovable in T just in case T is
consistent" does not make sense to me. I cannot fathom: "...just in case T
is consistent".
It seems to me that what you are stating is that mathematics is not real
(remains NON empirically based). Unfortunately, mathematics has to get real
(become empirically based) because human thinking remains entirely based on
_testable_ theories of nature. Mathematics is required to import assumed
constants of nature just to be able to break out of any of the enormous
number of abstract tautological assumptions (circular arguments such as
axioms, definitions of equality etc) which dominate mathematics.
Mathematical tautologies (which are not valid theories) do not exist in
isolation to non tautologous assumptions (which alone can be regarded as
valid theories). This is because without exception, all tautologous
assumptions have to be deduced from non tautologies. Mathematical circular
arguments can only exist as a deduction from NON tautologous assumptions all
of which are EMPIRICAL, i.e. all non tautologies remain outside of
mathematics. Only an identified empirical non tautology can possibly remove
mathematical incompleteness by introducing into mathematics an empirically
testable frame of reference from which all the circular arguments assumed
within mathematics can be deduced.
Kent Paul Dolan
> Amazing; Kent Paul Dolan claims to know how human
> minds work.
Even more amazing, "independent researcher" John
Edser claims that he _doesn't_ know how human minds
work doing induction, despite that the mechanisms
are well documented and very familiar to most
educated humans, and are taught to freshmen in
college.
[ship tedious blowhardism]
> As an exercise why don't you explain how Darwin
> made his inductive inference using your model?
Umm, your usual ritual of descent into an infinity
of recursive requests for "more proof" is rejected
at the onset, as the premium symptom of your idiocy
and intellectual dishonesty which it is.
You've been proved wrong, full stop.
Cope.
Einstein's work was more than sufficient as an
example of how induction is done.
If you cannot convince yourself that you know how
induction works in the human mind from that single
well known example, you will simply have to remain
forever ignorant.
Your perpetual ignorance on this one topic, is not
my problem to cure, it is yours.
You've made that choice in this case just as for
all the hundreds to thousands of other topics for
which you have also made, in public, that same
choice for retaining fiercely your right to
perpetual ignorance.
Progress in understanding evolution slows to a
crawl every time you drag your ignorance forward
to put it on display here, diverting the rest of the
groups to trying to teach you past your obdurate
idiocy.
Ours is not to reason why you make such poor
choices, ours is but to watch you and ridicule you,
as you prance and caper amusingly to avoid
learning.
If you want, you are more than welcome to live in
lifelong fear of ever learning anything that
contradicts your incorrect opinions.
That's no skin off my nose.
HTH
xanthian.
Phil Roberts, Jr.
>
> John Lucas's 'Godel' argument has been much-criticized - and
> Penrose's views in this area are essentially a variation on it.
I concede that there is a clear majority who
disagree with the Lucas/Penrose position. On
the other side of the equation, however, we have:
a. Hofstadter, Dennett, Penrose, Clarke and Chaitin,
in various ways acknowledging that Godel at least
SUGGESTS a disconnect between formalism and
mathematical reasoning.
b. Little unanimity as to what exactly is wrong with
the Godel argument, with dozens and dozens of different
sorts of objections, many based on impenetrable
confabulations.
c. Papers still being published criticizing the Godel
argument against mechanism almost 80 years after Godel
first published his theorem.
d. The universal abandonment of Hilbert's program of
formalizing mathematical reasoning by mathematicians
all over the world subsequent to Godel's proof.
e. Intersubjectively reproducible empirical evidence
(feelings of worthlessness) suggesting that not even
Mother Nature herself seems to be able to constrain
rationality within a formalism (the program for
"trying to stay alive").
f. Evidence (e.g., Parfit, 'Reasons
and Persons', p. 12) that any theory that attempts
to constrain rationality within a formal structure
(e.g., a fixed objective) can be shown to sanction
rational irrationality (i.e., can be shown to be
self-defeating).
> Brief version of what's wrong:
>
> ``A mathematician often makes judgments about what
> mathematical statements are true. If he or she is not more
> powerful than a computer, then in principle one could write
> a (very complex) computer program that exactly duplicated
> his or her behavior.
Assumes what is being questioned.
> But any program that infers
Programs don't infer, they model logical relations that
have been found to underly human inferences on most
occasions. As to whether these relations are actually
being followed or simply EMBEDDED IN our inferences remains
to be seen.
> mathematical statements can infer no more than can be proved
> within an equivalent formal system of mathematical axioms
> and rules of inference,
True, but Lucas/Penrose assumes we can go beyond this, that
the intuiting of mathematical truth is not simply a matter
of logical proof:
The immediate consequence is that truth cannot be
defined in terms of provability. In any serious
intellectual endeavor we shall be able to formulate
simple mathematical arguments, and thus shall be
subject to Godel's incompleteness theorem. However
far we go in formalizing our canons of proof, we
shall be able to devise propositions which are not,
according to those canons, provable, but are none
the less, true. So it is one thing to be provable,
and a different thing to be true. Truth outruns
provability. (J.R. Lucas).
>
> This argument won't fly if the set of axioms to which the
> human mathematician is formally equivalent is too complex
> for the human to understand.
What is the basis for the assumption that the intuiting
of mathematical truth is based on a set of axioms, let
alone that they must be too complex to understand?
>
> These are amazing claims, which Penrose hardly bothers to
> defend. Reviewers knowledgeable about Godel's work, however,
> have simply pointed out that an axiom system can infer that
> if its axioms are self-consistent,
An axiom system can infer?
> then its Godel sentence
> is true. An axiom system just can't determine its own self-
> consistency. But then neither can human mathematicians know
> whether the axioms they explicitly favor (much less the
> axioms they are formally equivalent to) are self-consistent.
> Cantor and Frege's proposed axioms of set theory turned out
> to be inconsistent, and this sort of thing will undoubtedly
> happen again.''
Agreed. But we can nonetheless "know" them to be true in
the sense that we all agree we have good reason to believe.
>
> As to what this has to do with evolution - if humans can
> do things no machine can do - or will ever be able to do -
> that may impact the hypothesis that machine-based organisms
> may replace humans as the dominant life form on earth over
> the next century or so.
More importantly, it would mean that there is reason to
suspect that E. O. Wilson may have gotten it wrong in
asserting genetic determinism:
Can the cultural evolution of higher ethical values
gain a direction and momentum its own and completely
replace genetic evolution? I think not. The genes
hold culture on a leash. The leash is very long, but
inevitably values will be constrained in accordance
with their effects on the human gene pool (E. O.
Wilson).
and that Dawkins may have actually gotten it right in
asserting the converse:
We, alone on earth, can rebel against the tyranny of
the selfish replicators" (Dawkins, 1976, p. 215).
>
> However, this particular argment for the qualitative
> superiority of humans is simply wrong - and (IMO) rather
> obviously so for anyone who knows anything about Godel's
> work.
Why then is one of the papers you referenced
written in 2004? Shouldn't this have all been over and
done with decades ago for a flaw that is so "obvious"?
[quote from Penrose]
The many arguments that computationalists and
other people have presented for wriggling around
Godel's original argument have become known to me
only comparatively recently; perhaps we act and
perceive according to an unknowable algorithm,
perhaps our mathematical understanding is
intrinsically unsound, perhaps we could know the
algorithms according to which we understand
mathematics, but are incapable of knowing the
actual roles that these algorithms play. All
right, these are logical possibilities. But are
they really plausible explanations?
For those who are wedded to computationalism,
explanations of this nature may indeed seem
plausible. But why should we be wedded to
computationalism? I do not know why so many
people seem to be. Yet, some apparently hold to
such a view with almost religious fervour.
(Indeed, they may often resort to unreasonable
rudeness when they feel this position to be
threatened!) Perhaps computationalism can indeed
explain the facts of human mentality -- but perhaps
it cannot. It is a matter for dispassionate
discussion, and certainly not for abuse!
I find it curious, also, that even those who argue
passionately may take for granted that
computationalism in some form -- at least for the
objective physical universe -- HAS to be correct.
Accordingly, any argument which seems to show
otherwise MUST have a "flaw" in it. Even Chalmers,
in his carefully reasoned commentary, seeks out
"the deepest flaw in the Godelian arguments".
There seems to be the presumption that whatever
form of the argument is presented, it just HAS
to be flawed. Very few people seem to take
seriously the slightest possibility that the
argument might perhaps, at root, be correct!
This I certainly find puzzling;.
Nevertheless, I know of many who (like myself) do
find the simple "bare" form of the Godelian
argument to be very persuasive. To such people,
the long and sometimes tortuous arguments that I
have provided in 'Shadows of the Mind' may not
add much to the case -- in fact, some have told
me that they think that they effectively weaken
it! It might seem that if I need to go to
lengths such as that, the case must surely be a
flimsy one. (Even Feferman, from his own
particular non-computational standpoint, argues
that my diligent efforts may be "largely
wasted!) Yet, I would claim that some progress
has been made. I am struck by the fact that none
of the present commentators has chosen to dispute
my conclusion G (in 'Shadows', p. 76) that "Human
mathematicians are not using a knowably sound
algorithm in order to ascertain mathematical
truth". (Roger Penrose, 'Psyche' Vol 2)
Phil Roberts, Jr.
>
> Goedel was more than willing to admit that some
> theorem unprovable in one system of arithmetic might
> well be provable under a stronger set of axioms, but
> he then showed that the stronger set of axioms would
> form a system for which exactly the same sort of
> unprovable sentence could again be written.
Yes. Lucas addressed this issue, in response to
Whitely and Bannaceraf as I recall:
Banacerraf protests that "It is conceivable that
another machine [or formal system] could do that
as well." Of course. But that other machine was
not the machine that the mechanist was claiming
that I was. It is the machine that I am alleged
to be that is relevant: and since I can do
something that it cannot, I cannot be it. Of
course It is still open for the mechanist to
alter his claim and say, now, that I am that
other machine which, like me, could do what the
first machine could not. Only, if he says that,
then I shall ask him "Which other machine?" and
as soon as he has specified it, proceed to find
something else which that machine cannot do and
I can. I can take on all comers, provided only
they come one by one in the sense of each being
individually specified as being the one that it
is: and therefore I can claim to have tilted at
and laid low all logically possible machines.
An idealized person, or mind, may not be able to
do more than all logically possible machine can,
between them, do: but for each logically
possible machine there is something which he can
can do and it cannot; and therefore he cannot be
the same as any logically possible machine.
(J. R. Lucas, 'The Monist', vol 52, pp 145-158)
>
> So, all you've proved is that the human mind _may_
> employ a stronger set of axioms, not that it is
> somehow different in kind.
Or maybe no axioms at all, at least not in any strict
sense of the terms. Perhaps "all forms of reasoning
are nothing but comparing", as Hume has maintained
and, as such, actually ANAlogical (nonlogical):
One should not think of analogy-making as a special
variety of reasoning (as in the dull and uninspiring
phrase "analogical reasoning and problems solving,"
a long-standing cliché in the cognitive science world),
for that is to do analogy a terrible disservice. After
all, reasoning and problem-solving have (at least I
dearly hope!) been at long last recognized as lying
far indeed from the core of human thought. If analogy
were merely a special variety of something that in
itself lies way out on the peripheries, then it would
be but an itty bitty blip in the broad blue sky of
cognition. To me, however, analogy is anything but
a bitty blip -- rather, ITS THE VERY BLUE THAT FILLS
THE WHOLE SKY OF COGNITION -- ANALOGY IS EVERYTHING...
(Douglas Hofstadter) [emphasis mind].
Phil Roberts, Jr.
> "John Edser" <ed...@ozemail.com.au> wrote in message
>>
>>Definitions:-
>>Induction: from the particular to the general.
>>Deduction: from the general to the particular.
>>
>>Machines cannot induce a thing because nobody knows how a mind makes an
>>induction.
>
>
> No, but we know some particulars concerning "induction" in animals. Indeed,
> a great deal is known about it. Please see the entire history of the
> experimental analysis of behavior, some of which can be found in the 50
> years of the Journal of the Experimental Analysis of Behavior. One could
> say that not only is the study of operant conditioning the study of
> intention, it is also the study of much of what we call induction.
>
In my own introspectively based attempt to get a
handle on "reasoning", I found it helpful to begin
by dividing the cognitive realm into two broad
divisions, higher cognition and lower cognition.
And I felt it was important to focus on the
process that leads to an increase in knowledge,
or understanding, or rationality, or whatever term
you prefer. And in this regard I came up with the
following two categories:
Higher Cognition ("Reasoning"):
The cognition of abstruse similarity and difference.
Example:
Electricity is like water flowing in a pipe.
Lower Cognition (Conditioning):
The cognition of obvious similarity and difference.
Example:
This A + B sequence is like ones previously observed
(e.g., Pavlov's dogs).
Here is what I had to say on these two categories in my
paper, 'Rehabilitating Introspection' available at my
website:
Higher Cognition:
Not uncommonly, deductive syllogisms such as ‘Socrates is a
man, all men are mortal, therefore Socrates is mortal’, are
offered as examples of reasoning. This is not how I am
employing the term in the phylogeny, which is why it appears
in quotation marks. I mean for it to refer to whatever
thought process lies at the heart of ampliative inference, a
process often associated with ‘Aha!’ or ‘Eureka!’
experiences, but commonly falling below the threshold of an
identifiable event in which much, if not most, of the
processing is not introspectively available. Even so, by
applying a bit of the abstraction and generalization
prescribed by our procedure (and in contrast to the Nisbett
and Wilson approach to the study of “higher order, inference
based responses”), I believe enough is available for us to
make a reasonable guess that the cognition of similarity and
difference (analogical/metaphorical “reasoning”) does most
of the heavy lifting. But then I am hardly the first
introspectionist to arrive at that conclusion:
[quote]
All kinds of reasoning consist in nothing but a
comparison and a discovery of those relations either
constant or inconstant, which two or more objects
bear to each other (Hume, 1739).
Lower Cognition:
My unorthodox definition of conditioning as ‘the cognition
of obvious similarity and difference’ stems from my
unorthodox definition of reasoning as ‘the cognition of
abstruse similarity and difference’ which, when combined
with the former, offers a number of explanatory advantages:
1. It allows for continuity between the two concepts and, as
such, allows for an appreciation of how “reasoning” might
have evolved from conditioning. In this view, the ability
to understand electricity by comparing it to how water flows
in a pipe is just an extension of the process that underlies
an organism’s ability to understand a currently observed A +
B sequence (e.g., Pavlov’s dogs) by comparing it to ones
previously observed.
2. It allows one to forego syllogistic deduction (‘Socrates
is a man…”, etc.) as a paradigm for reasoning in that, based
on the analogy with conditioning, concluding that Socrates
is mortal can be viewed as analogous to a conditioned mouse
remembering it must go left at the fourth fork in a maze.
In much the manner the mouse’s recollection would be
construed as more a manifestation of conditioning that has
already occurred, we might also conclude that deducing
Socrates is mortal is more a manifestation of reasoning
which has already occurred, and perhaps closer to
remembering than reasoning, at least in an ampliative sense
of coming to a deeper understanding of the nature of
reality, and thereby serving to produce a net increase in
one’s rationality.
[quote]
If analogy were merely a special variety of something
that in itself lies way out on the peripheries, then
it would be but an itty bitty blip in the broad blue
sky of cognition. To me, however, analogy is anything
but a bitty blip -- rather, it’s the very blue that
fills the whole sky of cognition – analogy is
everything… (Hofstadter, 2001).
3. It allows for a naturalistic indeterminism in that one
can surmise that once an event sequence or feature has
become cognized it is easy to appreciate how one might then
have the option of following the sequence or conforming to
the feature or not, and thereby becoming less determined by
it, i.e., aware of more options than prior to the cognition.
Another way of saying this is that it lends itself to the
suspicion that there might well be an inverse correlation
between ‘being cognizant’ or ‘being rational’ and ‘being
determined’.
4. It affords a linkage between “reasoning” in the
ampliative sense and rationality, in that rationality could
be construed simply as ‘the psychical product of “reasoning”
(ampliative inference)’ with the Latin/Greek origin of
‘ratio’ meaning ‘to compare’.
PR
John Edser
Aatu Koskensilta aatu.kos...@xortec.fi wrote:-
> > JE:-
> > Since this is a science group and not a mathematics group explaining
> this
> > example in more detail would help. What appears to be your most
> important
> > statement: " ..there exists, constructible by a mechanical procedure, a
> > sentence G_T, such that G_T is true and unprovable in T just in case T
> is
> > consistent" does not make sense to me. I cannot fathom: "...just in
> case T
> > is consistent".
> A formal theory T is said to be consistent if there is no sentence A
> such that both A and the negation of A are formally provable in T.
JE:-
Hello Astu,
I hope you bear with us for a little longer because sbe badly needs the
council of a logician who is also interested in evolutionary theory.
To express what you wrote in just a simple way: contradictions are not
allowed within any consistent theory. Is this correct?
> Gödel's first incompleteness theorem shows that, for formal theories T
> meeting certain criteria, it is possible to find a formula G_T with the
> property that G_T is true just in case T is consistent, i.e. G_T is true
> if T is consistent, and false if T is inconsistent.
JE:-
It appears G is deducible from T but not the reverse. G remains predicated
on T which cannot contain a contradiction (must remain consistent). If this
is not the case then G-T only remains a tautology. In the non tautologous
sense: if the T predicate does contain a contradiction (remains
inconsistent) then it is false so that any deduction from it, in this case
G, must also be false. I can't see what is new here because one of the
basics of reasoning is that if the predicate is false then any deduction
from it must also be false. However, if the subject is true it is entirely
possible to induce a false predicate from it (please refer to my post on
material implication).
> Your comments beyond the above were not in any way related to anything I
> said.
JE:-
I disagree. Evolutionary theory is a SCIENCE yet most gene centric Neo
Darwinistic reasoning remains predicated on just tautologous propositions of
mathematics. In my opinion these Neo Darwinist models have been misused.
Importing tautologies from mathematics into evolutionary science renders any
mathematical model of evolutionary science, oversimplified. This allows a
reverse of cause and effect within the model compared to the theory it was
oversimplified from. My detailed example here (there are many others) is
Hamilton's rule: rb>c. This rule does not define a single constant term
providing no frame of reference. Yet, it has been employed for over 50 years
to allow the evolution of organism fitness altruism which Darwinism
prohibits as a refutation of Darwinism. Without a testable frame of
reference Hamilton's Rule has no basis in science, only mathematics.
I claim that Hamilton's Rule (or any other mathematical tautology imported
into evolutionary science) can only be broken using a constant term
_necessarily_ imported from _outside_ of mathematics. IOW what Gödel was
driving at as far as the epistemology of science was concerned is that
mathematics remains entirely deducible from non mathematics and not the
reverse. This is what connects Gödel to most evolutionary theory arguments.
Logicians may not be very interested but evolutionary theorists have to be
because offering oversimplified mathematical models in place of their parent
theory constitutes an enormous error of science no matter that these models
remain mathematically valid.
> I just wished to make the rather trivial observation that the
> incompleteness theorem establishes an implication, "if T is consistent,
> then the Gödel sentence of T is true but unprovable in T", ..
JE:-
As I understand this: If T contains no contradiction then it remains a
valid induction, the truth of which cannot be proven. This is because T on
its own remains just one isolated inductive assumption. Karl Popper argued
that because the sciences are empirically based the truth of T can be tested
because T is absolutely required to remain refutable against nature via
every possible valid deduction from T. As an example, Newton's assumption of
m and t as constants (providing a necessary frame of reference for Newtonian
Mechanics) was empirically refuted via the Michelson-Morley experiment which
proved that the velocity of light was not just a variable.
http://en.wikipedia.org/wiki/Michelson-Morley_experiment
> ...and unless we
> happen to know of some particular T that it is consistent, no way of
> "seeing" the truth of the Gödel sentence is to be found in the proof of
> the first incompleteness theorem.
JE:-
Gödel appears to have outlined the mystery of the inductive process. Nobody
knows how the brain makes an inspired (or just any) guess, i.e. produces a
inductive inference. Within the sciences any induction is allowed (the more
the better), if and only if, they remain refutable against nature allowing
some to be discarded in favor of others. The problem is, most people (even
scientists) are easily seduced by claims of irrefutability because this
seems to provide an illusion of certainty. Many of these certainties are
just tautologous assumptions of mathematics.
John Edser
"Kent Paul Dolan" xant...@well.com wrote:_
> > Amazing; Kent Paul Dolan claims to know how human
> > minds work.
> Even more amazing, "independent researcher" John
> Edser claims that he _doesn't_ know how human minds
> work doing induction,..
JE:-
That is correct. I continue to claim that you do not know "how human minds
work doing induction".
>... despite that the mechanisms
> are well documented and very familiar to most
> educated humans, and are taught to freshmen in
> college.
JE:-
Well, it seems to me that you are not far from being a "freshmen in college"
who has suffered badly under Post Modern professorships. If I were you I
would ask for your money back...
> [ship tedious blowhardism]
JE:-
What you decided to evade via the above-embarrassing-to-you-rhetoric was
this:
If induction is the mechanical process that you describe, what allowed the
FIRST induction? If your answer is: any random induction then the complete
works of Shakespeare was indeed written by a monkey typing randomly. If you
are only prepared to contribute peanuts then you end up with just a monkey
:-)
http://en.wikipedia.org/wiki/Infinite_monkey_theorem
http://poe.perl.org/?Distributed_Monkey_Project
Previously: when you steadfastly refused to acknowledge the critical
difference between "not" and "cannot" what you were attempting to get away
with is: assuming just a tautological loop for who programs who.
1) machines <---> men.
In the above tautology the double headed arrow means: "program".
This allowed just another "what came first, the chicken or the egg ",
conundrum. As just a mindless tautology it has no answer.
You decided to sever this circular tautological loop at only the place which
was most _convenient_ for you:-
2) machines ---> men
Now: just a single headed arrow means "program".
You cannot legally break any tautological loop within the sciences unless
you can provide an empirical refutable constant term because only this
allows a critical empirical frame of reference for any lineal logic which
results. Here, TWO CONTRADICTORY LINEAL propositions are produced when this
loop becomes severed and not just the one:
a) machines ---> men
b) men ---> machines
So, what was YOUR missing constant term which allowed (b) to stand
empirically refuted?
Your breaking of the loop remained ENTIRELY ARBITRARY. In turn, this allowed
just an arbitrary lineal proposition which you then proceeded to incorrectly
tout as meaningful.
> > JE:-
> > As an exercise why don't you explain how Darwin
> > made his inductive inference using your model?
> Umm, your usual ritual of descent into an infinity
> of recursive requests for "more proof" is rejected
> at the onset, as the premium symptom of your idiocy
> and intellectual dishonesty which it is.
JE:-
The above empty rhetoric means: despite all your arrogant bravado YOU SIMPLY
CANNOT DO WHAT YOU ARGUED YOU COULD DO.
Only idiots attempt to evade critical tests of what they claim they can do
because only idiots think that evasion can beat reason.
>snip even more rhetorical assertions<
Kent Paul Dolan
> What appears to be your most important
> statement: " ..there exists, constructible
> by a mechanical procedure, a
> sentence G_T, such that G_T is true and
> unprovable in T just in case T is
> consistent" does not make sense to me.
> I cannot fathom: "...just in case T
> is consistent".
However, your ignorance is _not_ a formal proof that
the statement is incorrect.
Your ignorance is merely an informal proof (as if
more were needed) that you are an innumerate dolt,
and a dolt insistent on remaining obdurately
innumerate, and a dolt insist on presenting your
ignorance as a law of the universe on a par with the
law of gravity or special relativity, a law which
you expect every other participant somehow to
circumvent on your behalf, at great cost in trying
to educate you, when you consistently represent
yourself as a dolt personally insistent on being
incapable of being educated.
The statement as written is precisely correct, and
is stated in precisely correct mathematical
terminology.
Cope.
Any failures involved are entirely your own, and
neither failures of the prior poster nor of the
science of mathematics.
If you need to understand Goedel's proof, go read it
in a textbook, and shut up on the topic and topics
dependent on that proof until you have reduced your
ignorance of Goedel's proof to zero.
I'm betting your lifetime will not suffice,
of course, and that much needed peace and
quiet would suddenly spring forth from your
quarter, to the delight of all.
To do otherwise is rudely to waste the time of every
participant here.
HTH
xanthian.
Kent Paul Dolan
> Tim Tyler wrote:
>> John Lucas's 'Godel' argument has been
>> much-criticized - and Penrose's views in this
>> area are essentially a variation on it.
> I concede that there is a clear majority who
> disagree with the Lucas/Penrose position. On the
> other side of the equation, however, we have:
> a. Hofstadter, Dennett, Penrose, Clarke and
> Chaitin, in various ways acknowledging that Godel
> at least SUGGESTS a disconnect between formalism
> and mathematical reasoning.
Well, no. Goedel proved a very limited thing about
limitations on the power of proof productions
generated from systems of axioms "at least as
powerful as the Peano postulates". That proof in and
of itself had nothing to convey about how _humans_
accomplish mathematical reasoning.
That humans would _like_ to appear somehow superior
to such productions lacks a first demonstration to
satisfy that liking.
So far, no human has _ever_ proved any mathematical
proposition that can be proved _unprovable_ using
Goedel's mechanism.
Quite on the contrary, again and again humans employ
the work done by Goedel to identify _other_
unprovable propositions.
In each case, as with the Hilbert program or, after
the fact, Frege's work, such a "proof of
unprovability" serves as an excellent "stop wasting
time here" signpost [not that a universe of kooks
won't keep trying to square the circle or trisect
the angle, not all are literate enough to read "give
up, what you seek cannot be done" and take it as
solid advice].
> b. Little unanimity as to what exactly is wrong
> with the Godel argument, with dozens and dozens of
> different sorts of objections, many based on
> impenetrable confabulations.
There's nothing "wrong with" the Goedel arguement,
unless you and I are talking at cross purposes and
you mean something other than Goedel's proof.
That proof is simple enough to follow that anyone
with a minimally competent education in mathematics
can work through it in an evening, and once worked
through, it becomes "self-evident"; too simple to
challenge.
If on the contrary you are using "the Goedel
argument" as the false hypothesis that humans are
doing something "super-computational" in their
mathematical reasoning, I'll just refer you back to
my prior posting: no, we aren't.
> c. Papers still being published criticizing the
> Godel argument against mechanism almost 80 years
> after Godel first published his theorem.
So? Twenty years ago or more, "special relativity
scientists" supposedly universally agreed to stop
using "mass" to describe the combination of rest
mass and energy of motion represented as mass
[because the term used that way is dependent on the
frame of reference of the observer], yet just last
year there was a paper published arguing that
"scientists" should do just what, for the most part,
they have done, change their terminology.
"Publish or perish" is a terrible blight, since it
results in so much re-publication of what is already
known.
> d. The universal abandonment of Hilbert's program
> of formalizing mathematical reasoning by
> mathematicians all over the world subsequent to
> Godel's proof.
Right -- Goedel proved that the goal was
unattainable, everyone who could read his proof with
understanding immediately realized that he was
correct, and diverted their attention elsewhere.
This in NO WAY argues for the superiority of human
mathemaatical reasoning to computational
mathematical reasoning.
To the contrary, each mathematician who changed
courses was thereby agreeing that indeed his/her
work was _not_ going to somehow "beat Goedel", and
that trying to do so is a fool's game.
Each such diversion was a vote by an intelligent and
well informed participant that human reasoning _was_
limited to the same limits as "computational
reasoning".
> e. Intersubjectively reproducible empirical
> evidence (feelings of worthlessness) suggesting
> that not even Mother Nature herself seems to be
> able to constrain rationality within a formalism
> (the program for "trying to stay alive").
That's not, and never has been, how nature
"programs" species. Evolution doesn't work that way.
In fact, it cannot, since it works at the level of
gene allele frequencies of occurrance in an entire
population.
Protection and propagation of genes shared in common
with ones own genome can lead to some wonderfully
counterintuitive behaviors. Study any good writeup
on the genetic basis of altruism for more details.
> f. Evidence (e.g., Parfit, 'Reasons and Persons',
> p. 12) that any theory that attempts to constrain
> rationality within a formal structure (e.g., a
> fixed objective) can be shown to sanction rational
> irrationality (i.e., can be shown to be
> self-defeating).
But that is still agreement with Goedel's proof,
recast as "there are some good behaviors that cannot
be proven (computationally) to be good behaviors".
Again, no argument that humans are "better than
computational", more an argument for some findings
of game theory, that sometimes the only way to win
is to randomize your choices in some careful way.
If you always jink left in fleeing from the
tiger, the tiger will learn the shortcut
that makes you lunch. If you randomize your
choices between left and right options, the
tiger cannot improve over following your
trail as you followed it, and your chance of
making the tiger into a robe improves.
>> Brief version of what's wrong:
>> "A mathematician often makes judgments about
>> what mathematical statements are true. If he
>> or she is not more powerful than a computer,
>> then in principle one could write a (very
>> complex) computer program that exactly
>> duplicated his or her behavior.
> Assumes what is being questioned.
No, it doesn't. That's the subjective portion of a
longer argument.
>> But any program that infers
> Programs don't infer,
Before correcting the language of someone speaking
in his own field of expertise, you'd be well advised
to check your knowledge of the meanings a word can
take. Computer programs doing mathematical
reasoning, which for the most part use formal
deductive logic to draw conclusions, very much _do_
"infer":
<quote>
infer
v 1: reason by deduction; establish by deduction [syn: deduce,
deduct, derive]
2: draw from specific cases for more general cases [syn:
generalize,
generalise, extrapolate]
3: conclude by reasoning; in logic [syn: deduce]
4: guess correctly; solve by guessing; "He guessed the right
number of beans in the jar and won the prize" [syn: guess]
5: believe to be the case; "I understand you have no previous
experience?" [syn: understand, gather]
</quote>
http://dict.die.net/infer/
> they model logical relations that have been found
> to underly human inferences on most occasions.
Nonsense.
> As to whether these relations are actually being
> followed or simply EMBEDDED IN our inferences
> remains to be seen.
Bafflegab.
>> mathematical statements can infer no more
>> than can be proved within an equivalent
>> formal system of mathematical axioms and
>> rules of inference,
> True,
Then why did you intrude the above line noise?
> but Lucas/Penrose assumes we can go beyond this,
> that the intuiting of mathematical truth is not
> simply a matter of logical proof:
So? They're wrong, or so all those who voted with
their feet when they abandoned the Hilbert program
concluded.
Continuing to cite their arguments, without
balancing them with the known rebuttals, doesn't
seem particularly useful or integrous when they are
fairly universally considered to be incorrect.
> The immediate consequence is that truth cannot
> be defined in terms of provability.
Nonsense.
The issue isn't "we must take as truth what we
really only have on faith". The issue is (and it is
good for humankind to be so humbled) that the list
of things we can _know_ to be "true" [identical for
intellectually honest persons to the list of things
we can _prove_ to be "true"] is severely
circumscribed, and there's no wriggle room to go
around that circumscription.
We have, for example, no way to prove that the
lights in the sky will not spell out "Drink Coca
Cola" starting tomorrow, but we can, if we are sane,
live our lives in perfect peace without such a proof.
The supposition that pretty much everything should
be subject to being known is an especially
pernicious form of hubris in a universe where
Heisenberg's uncertainty principle holds sway.
> In any serious intellectual endeavor we shall
> be able to formulate simple mathematical
> arguments, and thus shall be subject to
> Godel's incompleteness theorem. However far
> we go in formalizing our canons of proof, we
> shall be able to devise propositions which are
> not, according to those canons, provable, but
> are none the less, true. So it is one thing
> to be provable, and a different thing to be
> true. Truth outruns provability. (J.R.
> Lucas).
Lucas makes the mistake of assuming that truth
exists separate from provability. This is of course
the error of theism. What Goedel proved is that
there are propositions whose truth _or_ falsehood
cannot be determined.
The assumption that some of those propositions must
therefore be "true ones" misunderstands what "true"
should mean.
Lucas wants it to have a separate meaning from
"provable", but I cannot see how allowing such a
separate meaning can be anything but incredibly
dangerous.
Allowing that, allows frauds and mountebanks to
assign the token "true" to any proposition which
they can cast as a Goedel-style unprovable
proposition, merely on their self-interested say-so.
>> This argument won't fly if the set of axioms
>> to which the human mathematician is formally
>> equivalent is too complex for the human to
>> understand.
> What is the basis for the assumption that the
> intuiting of mathematical truth is based on a set
> of axioms, let alone that they must be too complex
> to understand?
That was the assumption to which you objected above
as:
> Assumes what is being questioned.
on which this onging argument is being _still_ being
argued by the prior poster.
"If human reasoning about mathematics is
computational, then the argument that the human can
somehow understand and then by computational means
exceed his own axiom set is incorrect if that axiom
set is too complex for the human to understand" --
to summarize badly.
[It's worth noticing that any computational
device, including the human mind, is
probably storage-limited from entirely
understanding its own operation, in any
case. It would probably need to know, for
example, the bit by bit storage reliability
(probability of failure) of its entire
storage mechanism to accomplish such a
task, more than a bit of data per bit of
available storage.]
>> These are amazing claims, which Penrose hardly
>> bothers to defend. Reviewers knowledgeable
>> about Godel's work, however, have simply
>> pointed out that an axiom system can infer
>> that if its axioms are self-consistent,
> An axiom system can infer?
That's shorthand. The longhand is something like
"any correct production of sentential calculus using
the rules of logic and axioms of a logical system
can infer". Live with the usual form, please, rather
than arguing vocabulary to distract from the
weakness of your arguments.
>> then its Godel sentence is true.
That's a very strong claim of equivalence.
>> An axiom system just can't determine its own
>> self- consistency.
That's just another way of stating what Goedel
proved (as Tim already knows, I'm just saying that
for the rest of the audience).
>> But then neither can human mathematicians know
>> whether the axioms they explicitly favor (much
>> less the axioms they are formally equivalent
>> to) are self-consistent.
That's a bit overstated; _if_ you can derive a
contradiction from them, they definitely _are_
inconsistent. Once proved inconsistent in that way,
one _knows_ them to be inconsistent.
The problem is that no _guaranteed_ plan for
deriving such a contradiction exists; to claim that
one does violates Goedel's proof.
To the best of my understanding, the situation with
which one is confronted is that mathematics becomes
just another branch of science whose theorems are
subject to issues of falsifiability.
_If_ what you proved leads to a contradiction, _and_
if your proof is formally correct when cast in terms
of your base axioms, _then_ your system of axioms is
inconsistent and what is derived from it is _all_
cast into suspicion.
The nationfulls of mansions of mathematics
that will fall _if_ the Riemann hypothesis
proves incorrect would be hard to count by
today, I'd guess, but mathematicians have,
slowly, gotten used to dealing with such
uncertainty.
>> Cantor and Frege's proposed axioms of set
>> theory turned out to be inconsistent, and this
>> sort of thing will undoubtedly happen again.''
> Agreed.
Why then do you go on to say:
> But we can nonetheless "know" them to be true in
> the sense that we all agree we have good reason to
> believe.
You are undergoing a massive failure to understand
what it meant for Frege's system of axioms to be
"inconsistent".
That's not just something we can, or he could, or
his colleagues would, let slide by with a "but we
know it's true anyway, nudge, nudge, wink, wink".
Starting from an inconsistent set of axioms,
anything at all can be proved.
That his axioms proved to be inconsistent, meant
that his life's work crumbled like a house of
cards, and he died self-perceived to be a failure
[despite that today is he greatly respected for
having established much of the logical and
philosophical foundation of the concept of "number",
up until then a concept with no particular rigor
attached to it].
That partial success in achieving rigor didn't leave
the set of his axioms that proved inconsistent the
least bit acceptable to mathematicians, then or now.
>> As to what this has to do with evolution - if
>> humans can do things no machine can do - or will
>> ever be able to do - that may impact the
>> hypothesis that machine-based organisms may
>> replace humans as the dominant life form on earth
>> over the next century or so.
And on the contrary, if humans are "computational"
in a very rich sense, then a steady program of
replicationg those computational capabilities into
integrated mechanisms is a reasonable prospect to
accomplish, if not our replacement, then our
augmentation by peer much faster intellects,
especially as such a program can feed on its own
successes as it goes along, by using its outputs
as intellect augmenters to create the next
generation of outputs.
> More importantly, it would mean that there is
> reason to suspect that E. O. Wilson may have
> gotten it wrong in asserting genetic determinism:
> Can the cultural evolution of higher ethical
> values gain a direction and momentum its own
> and completely replace genetic evolution? I
> think not. The genes hold culture on a leash.
> The leash is very long, but inevitably values
> will be constrained in accordance with their
> effects on the human gene pool (E. O.
> Wilson).
That is a wholly separate issue, a discussion whose
demesne is meme-space, not cyberspace.
Cultural evolution is not about robots [except
perhaps by coincidence of mechanisms likely to be
employed by cultural evolution], but about evolution
at the level of ideas despite slowing (by medical
advances and pacification of the environment) of
genetic evolution to something running short on
forces of natural selection.
I find that Wilson is perhaps correct that we cannot
become more moral than our genome will allow, but
long term confinement of the most massively immoral
of our population is an extremely effective
contraceptive.
So, that particular kind of genetic evolution may
perhaps be among the most strongly "unnaturally"
selected ones which humankind is presently
experiencing [though I don't know quite how well
"chop off the distribution tail" works in natural
selection as opposed to "bias the whole
population distribution" mechanisms].
> and that Dawkins may have actually gotten it
> right in asserting the converse:
> We, alone on earth, can rebel against the
> tyranny of the selfish replicators" (Dawkins,
> 1976, p. 215).
The most immediate question would be: as a
beneficiary of the dice tosses of the selfish
replicators, how wise or secure should we feel in
engaging in such a rebellion?
I know that there are many, many misfeatures
in selfish replication. Forced sex will
easily stand for the lot as it is fairly
easy to comprehend.
But we need to be very careful in what we
are willing to give away in forbidding the
best rapists their reproductive spoils:
bigger, stronger, smarter, healthier,
sneakier, more dexterous -- how much of that
can humanity afford to forego promoting in
its genome?
That's flame bait, and I have a daughter and
a granddaughter that make that an
uncomfortable issue to raise, but still, it
exists whether we discuss it or not.
The other side of that issue of course is
that the target who successfully resists
forced sex gets (usually "her") choice of
mates, and may select one on other, better
criteria for "best genes to propagate in a
cooperative society", since we humans are
the biggest part of our own environment
these days.
We, as a society, may (and usually do)
choose to remove forcers of sex from the
gene pool, but we should be doing that
advisedly, with evolutionary as well as
social consequences fully exposed to
discussion.
It would be ironic if humankind in rebelling against
its "selfish replicators" hastened its own
extinction by losing competition to species choosing
and undergoing no such rebellion, species like fire
ants or killer bees, for example.
>> However, this particular argument for the
>> qualitative superiority of humans is simply wrong
>> - and (IMO) rather obviously so for anyone who
>> knows anything about Godel's work.
> Why then is one of the papers you referenced
> written in 2004? Shouldn't this have all been over and
> done with decades ago for a flaw that is so "obvious"?
See above in my response to your item "c". This
situation is rife throughout science. Second raters
rewrite what first raters first publish in more
arcane terms, and sometimes decades later.
Sometimes, also, papers merely summarize the "state
of an art" and are a more convenient citation than
are the original sources.
> [quote from Penrose]
> The many arguments that computationalists and
> other people have presented for wriggling around
> Godel's original argument have become known to me
> only comparatively recently; perhaps we act and
> perceive according to an unknowable algorithm,
> perhaps our mathematical understanding is
> intrinsically unsound, perhaps we could know the
> algorithms according to which we understand
> mathematics, but are incapable of knowing the
> actual roles that these algorithms play. All
> right, these are logical possibilities. But are
> they really plausible explanations?
> For those who are wedded to computationalism,
> explanations of this nature may indeed seem
> plausible. But why should we be wedded to
> computationalism?
Primarily because the alternative seems to be
mysticism, and descents into mysticism are the
historical stopping point of scientific progress.
> I do not know why so many people seem to be.
> Yet, some apparently hold to
> such a view with almost religious fervour.
Typical theistic BS, trying to call "atheism" a
religion, trying to call maintaining that "humans
compute like machines do" a religious claim.
Each is of course the exact opposite, but tarring
them with the historical record of abject idiocy of
theism reduces them in the minds of naive observers
of the discussion to "mere alternatives" rather than
the "deliberate diametric opposites" they are.
> (Indeed, they may often resort to unreasonable
> rudeness when they feel this position to be
> threatened!)
Like Penrose accusing anyone who opposes _his_ view
of exercising "religous fervor"? The irony is
intense here.
> Perhaps computationalism can indeed explain the
> facts of human mentality -- but perhaps it
> cannot. It is a matter for dispassionate
> discussion, and certainly not for abuse!
Yet when only mysticism is proposed as an
alternative, why would Occam's Razor not come into
immediate employment?
> I find it curious, also, that even those who
> argue passionately may take for granted that
> computationalism in some form -- at least for
> the objective physical universe -- HAS to be
> correct. Accordingly, any argument which seems
> to show otherwise MUST have a "flaw" in it.
Certainly any argument whose only basis is mysticism
_does_ have a flaw in it, by that very fact.
Why would Penrose contend otherwise?
> Even Chalmers, in his carefully reasoned
> commentary, seeks out "the deepest flaw in the
> Godelian arguments". There seems to be the
> presumption that whatever form of the argument
> is presented, it just HAS to be flawed. Very
> few people seem to take seriously the slightest
> possibility that the argument might perhaps, at
> root, be correct! This I certainly find
> puzzling.
Okay, for sure here he is using "Goedelian
arguments" to be shorthand standing for "because
Goedel is correct, therefore human (mathematical)
reasoning must be other (and better) than
computational".
That's incredibly sloppy usage, seemingly
attributing to Goedel something he had no part in
formulating, but merely inspired by work elsewhere.
As to his finding opposition to descent into
mysticism among scientists (mathematicians, here)
"puzzling", he need merely re-read a history of the
end of the life of Galileo to cure his puzzlement.
Mysticism is the avowed enemy of science, and not
the least bit shy when allowed to acquire power
about imposing the death penalty for use of science
in preference to mysticism.
> Nevertheless, I know of many who (like myself)
> do find the simple "bare" form of the Godelian
> argument to be very persuasive. To such
> people, the long and sometimes tortuous
> arguments that I have provided in 'Shadows of
> the Mind' may not add much to the case -- in
> fact, some have told me that they think that
> they effectively weaken it! It might seem that
> if I need to go to lengths such as that, the
> case must surely be a flimsy one. (Even
> Feferman, from his own particular
> non-computational standpoint, argues that my
> diligent efforts may be "largely wasted!) Yet,
> I would claim that some progress has been made.
Why? Mysticism has been promoted, benefiting exactly
whom?
> I am struck by the fact that none of the
> present commentators has chosen to dispute my
> conclusion G (in 'Shadows', p. 76) that "Human
> mathematicians are not using a knowably sound
> algorithm in order to ascertain mathematical
> truth". (Roger Penrose, 'Psyche' Vol 2)
Nothing humans do is "knowably sound" [among other
reasons, because there is no absolute metric for
"right behavior"]. Why should mathematics be an
exception?
The success of evolutionary algorithms in optima
search shows beyond refutation that "looking for an
answer by wandering around lost" is a perfectly
functional mechanism if one applies appropriate
biases to the process.
Peer review, collegeal cooperation, and
"backtracking search on perceived failure" are
several splendid such biasing mechanisms that turn a
random search methodology into a frequently
converging one.
That's how meme evolution happens, among many other
ways.
FWIW
xanthian.
Kent Paul Dolan
> No answer was provided (just rhetorical
> assertions).
I didn't bother to read your answer, because you
failed to bother to comprehend mine.
My reason that mathematics is incomplete cites a
precise _mathematical_ reason that mathematics is
incomplete, a reason that was published by Kurt
Goedel in the form of a formal mathematical proof
that completely changed the future course of
mathematics, a proof well known to anyone with even
a minimal grasp of college mathematics.
Yours, since you rejected Goedel's proof due to your
being unable to comprehend it or even recognize it
by name, is left being mere uninformed opinionated
innumerate blather by someone incompetent to do
mathematics, you, about why mathematics is
incomplete.
That you prefer your innumerate blather to Goedel's
formal mathematical proof speaks volumes about your
failures to do the research you claim as your
profession, about your gross pan-situational
incompetence, and about your pathetic vastly
self-documented innumeracy.
But, of course, you ENJOY providing such proofs of
your status as an incorrigible professional Time
Wasting Moron.
It would be mean spirited of me indeed to resist
providing future opportunities (read "bait") for you
to use to make a fool of yourself in public on
other topics.
You do that demonstration of your membership in the
league of bell capped caperers, so well, and so
consistently, too, and you just cannot resist rising
to the bait.
However, you have taken this discussion clear off
topic for this newsgroup, so I'll be ignoring your
future unceasingly INFANTILE attempts to prove
yourself correct by sheer droning relentless
repetitive imbecility when, as ever, you are
abjectly the opposite of "correct" and seen so by
everyone concerned or looking on except yourself.
Your insanity in this regard is NOT MY PROBLEM.
HTH, HAND, JNOMCS
xanthian.
William Morse
news:emangh$160a$1...@darwin.ediacara.org:
> On Dec 18, 12:54 pm, William Morse <wdmo...@twcny.rr.com> wrote:
>
>> But I agree that once software is developed that allows a computer to
>> learn (especially coupled with a robot that can explore its
>> environment), we will start to see true machine intelligence.
>
>>From many sources, computers are already able to learn, and to do so
> while in motion in an environment. One recent example:
>
> http://news.yahoo.com/s/ap/20061116/ap_on_sc/resilient_robots
>
> which has been discussed beyond the point of tedium, here:
>
> http://groups-beta.google.com/group/comp.ai.philosophy/browse_frm/threa
> d/bf283f425fb9e552
Thanks for the reference. The yahoo article had expired but googling
"resilient robot" worked.
Yours,
Bill Morse
John Edser
"Phil Roberts, Jr." phi...@ix.netcom.com:-
> > John Lucas's 'Godel' argument has been much-criticized - and
> > Penrose's views in this area are essentially a variation on it.
> I concede that there is a clear majority who
> disagree with the Lucas/Penrose position. On
> the other side of the equation, however, we have:
>
> a. Hofstadter, Dennett, Penrose, Clarke and Chaitin,
> in various ways acknowledging that Godel at least
> SUGGESTS a disconnect between formalism and
> mathematical reasoning.
>
> b. Little unanimity as to what exactly is wrong with
> the Godel argument, with dozens and dozens of different
> sorts of objections, many based on impenetrable
> confabulations.
>
> c. Papers still being published criticizing the Godel
> argument against mechanism almost 80 years after Godel
> first published his theorem.
>
> d. The universal abandonment of Hilbert's program of
> formalizing mathematical reasoning by mathematicians
> all over the world subsequent to Godel's proof.
>
> e. Intersubjectively reproducible empirical evidence
> (feelings of worthlessness) suggesting that not even
> Mother Nature herself seems to be able to constrain
> rationality within a formalism (the program for
> "trying to stay alive").
>
> f. Evidence (e.g., Parfit, 'Reasons
> and Persons', p. 12) that any theory that attempts
> to constrain rationality within a formal structure
> (e.g., a fixed objective) can be shown to sanction
> rational irrationality (i.e., can be shown to be
> self-defeating).
JE:-
Typically, Karl Popper who provided the missing Darwinian key just remains
ignored:-
http://philsci-archive.pitt.edu/archive/00002662/
Incompleteness is based on the fact that all we can do about ANYTHING is
make competing but entirely refutable guesses and _continuously_ evolve
them. Each _rational_ guess must refute in favor of a better one providing a
lineage of guesses which can explain more and more in an entirely testable
way. It is the mystery of INDUCTION which sits at the heart of this matter.
The truth about ANYTING, including the truth about truth (it always remains
incomplete because it is always based on just an inductive guess) including
what language can express truth (which logic can support the latest testable
against nature theory) do the following:
1) Perceive patterns.
2) Explain the perceived pattern using competing inductions (more than just
the one guess).
3) Reformulate each induction into empirically testable theories which can
be
i) Verified
ii) Non verified
iii) Refuted
by defining at least one different frame of reference for each contesting
theory (each theory employed to explain the same or greater set of facts).
If the proposed theory is just a tautology (circular logic) then it is
proven not to be a theory of anything, e.g. mathematics. If just any two of
the above three exist then only a model of a theory has been provided via
the process of simplification/oversimplification of a valid theory. No model
can validly contest or replace the theory from which is was
simplified/oversimplified.
4) Test all theories on the table until just one is left.
5) When this stands refuted goto 1.
> > Brief version of what's wrong:
> >
> > ``A mathematician often makes judgments about what
> > mathematical statements are true. If he or she is not more
> > powerful than a computer, then in principle one could write
> > a (very complex) computer program that exactly duplicated
> > his or her behavior.
> Assumes what is being questioned.
JE:-
Mathematics and programming are not at all the same thing. Mathematics
remains a tautology (based on axioms) but computer programs are not they
remain based on human inductions. Put another way: mathematics remains based
on just reversible set intersection while computer programming and what we
call language also requires non eversible _set nesting_.
> > But any program that infers
> Programs don't infer, they model logical relations that
> have been found to underly human inferences on most
> occasions. As to whether these relations are actually
> being followed or simply EMBEDDED IN our inferences remains
> to be seen.
JE:-
Put more simply: programs supply the most basic inductive inferences. A
machine must be minimally supplied with the largest nested set. All it can
ever do is mechanically deduce from this (and any others provided).
> > mathematical statements can infer no more than can be proved
> > within an equivalent formal system of mathematical axioms
> > and rules of inference,
> True, but Lucas/Penrose assumes we can go beyond this, that
> the intuiting of mathematical truth is not simply a matter
> of logical proof:
>
> The immediate consequence is that truth cannot be
> defined in terms of provability.
JE:-
I contend that refutability and "provability" remain exactly the same thing.
Something can only be refuted when a testable frame of reference becomes
replaced by another, i.e. no refutation exists in a vacuum. Each refuted
idea has to be replaced by another with a larger truth domain (the set of
refutable but non refuted deductions which can flow from it must be larger).
A theory can be considered proven when it provides a UNIQUE verification.
This verification will also constitute a refutation of the old theory, e.g.
Einstein's unique verification of c (the maximal velocity of light in a
vacuum) necessarily refuted Newton's m and t (mass and time).
> In any serious
> intellectual endeavor we shall be able to formulate
> simple mathematical arguments, and thus shall be
> subject to Godel's incompleteness theorem.
JE:-
Reasoning is NOT based on just mathematical tautologies, these were and
remain based entirely on reasoning.
> However
> far we go in formalizing our canons of proof, we
> shall be able to devise propositions which are not,
> according to those canons, provable, but are none
> the less, true. So it is one thing to be provable,
> and a different thing to be true. Truth outruns
> provability. (J.R. Lucas).
JE:-
Induction outruns deduction via a continuous evolution of inductions via the
Popperian process of refutation.
> >
> > This argument won't fly if the set of axioms to which the
> > human mathematician is formally equivalent is too complex
> > for the human to understand.
> What is the basis for the assumption that the intuiting
> of mathematical truth is based on a set of axioms, let
> alone that they must be too complex to understand?
JE:-
It has no basis. Mathematical axioms are just tautologies which have become
expanded. All of these must be deducible from rational inductions which
alone can form the basis of any empirically testable theories. IOW non
empirical mathematics is entirely deducible from empirical NON mathematics.
> >
> > These are amazing claims, which Penrose hardly bothers to
> > defend. Reviewers knowledgeable about Godel's work, however,
> > have simply pointed out that an axiom system can infer that
> > if its axioms are self-consistent,
> An axiom system can infer?
JE:-
Tautologies can only be expanded.
> > then its Godel sentence
> > is true. An axiom system just can't determine its own self-
> > consistency.
JE:-
Yes, because no tautology is rational, i.e. they remain logical but not
rational.
> > But then neither can human mathematicians know
> > whether the axioms they explicitly favor (much less the
> > axioms they are formally equivalent to) are self-consistent.
> > Cantor and Frege's proposed axioms of set theory turned out
> > to be inconsistent, and this sort of thing will undoubtedly
> > happen again.''
JE:-
Yes: what is the set of all possible sets? Answer: any rational induction
that can evolve via the process of refutation.
> Agreed. But we can nonetheless "know" them to be true in
> the sense that we all agree we have good reason to believe.
JE:-
No, belief is NOT required, just self consistency relative to at least one
frame of reference which can be empirically tested to refutation.
> > As to what this has to do with evolution - if humans can
> > do things no machine can do - or will ever be able to do -
> > that may impact the hypothesis that machine-based organisms
> > may replace humans as the dominant life form on earth over
> > the next century or so.
JE:-
Machines cannot replace humans unless they can think for themselves
(write their own programs _from scratch_)
> More importantly, it would mean that there is reason to
> suspect that E. O. Wilson may have gotten it wrong in
> asserting genetic determinism:
>
> Can the cultural evolution of higher ethical values
> gain a direction and momentum its own and completely
> replace genetic evolution? I think not. The genes
> hold culture on a leash. The leash is very long, but
> inevitably values will be constrained in accordance
> with their effects on the human gene pool (E. O.
> Wilson).
JE:-
Genes (epistatic dependent genetic combinations and NOT independent single
genes) can only limit cultures. OTOH cultures can control (select) genes.
> and that Dawkins may have actually gotten it right in
> asserting the converse:
>
> We, alone on earth, can rebel against the tyranny of
> the selfish replicators" (Dawkins, 1976, p. 215).
JE+-
This is entirely a false gene centric notion (the misuse of an
oversimplified theory). No "selfish replicators" exist in nature
(not a single empirical additive gene fitness has ever been verified in
nature no matter how you define fitness). Only degrees of fertile organism
fitness mutualism empirically exist. These have been chronically mistaken
for "selfishness" and "altruism" providing utterly irrational evolutionary
theories (theories which cannot be tested to refutation. i.e. they remain
"irrefutable" dictates).
> > However, this particular argment for the qualitative
> > superiority of humans is simply wrong - and (IMO) rather
> > obviously so for anyone who knows anything about Godel's
> > work.
> Why then is one of the papers you referenced
> written in 2004? Shouldn't this have all been over and
> done with decades ago for a flaw that is so "obvious"?
JE:-
If "this particular argument for the qualitative superiority of humans is
simply wrong" then it could NEVER have been written in the first place.
>snip<
John Edser
"Kent Paul Dolan" xant...@well.com wrote:-
> > JE:-
> > What appears to be your most important
> > statement: " ..there exists, constructible
> > by a mechanical procedure, a
> > sentence G_T, such that G_T is true and
> > unprovable in T just in case T is
> > consistent" does not make sense to me.
> > I cannot fathom: "...just in case T
> > is consistent".
> However, your ignorance is _not_ a formal proof that
> the statement is incorrect.
JE:-
Dear oh dear...I never claimed that the statement was "incorrect".
All I asked for was a CLARIFICATION which was subsequently supplied.
What type of a person prohibits the asking of a question? Answer: those
who wish to dictate. In this day and age why would anybody be stupid enough
to wish to dictate what nature is? Answer: those who have been mentally
mangled by Post Modern epistemology. Go sue your teachers....
>snip endless rhetoric<
Regards,
John Edser
"Kent Paul Dolan" xant...@well.com wrote:-
> So far, no human has _ever_ proved any mathematical
> proposition that can be proved _unprovable_ using
> Goedel's mechanism.
JE:-
What a classic. The above should be entered into the Post
Modernist "everything is relative to just nothing at all"
award. I am sure it must win....
John Edser
"Kent Paul Dolan" xant...@well.com wrote:-
> > No answer was provided (just rhetorical
> > assertions).
> I didn't bother to read your answer, because you
> failed to bother to comprehend mine.
JE:-
There was nothing to "comprehend". Your answer was just your _rhetorical
assertion_ that you understand Gödel. At _best_, rhetoric constitutes a non
verification.
>snip<
aatu.kos...@xortec.fi
> > such that both A and the negation of A are formally provable in T.
>
> allowed within any consistent theory. Is this correct?
It's not a question of allowing or prohibiting. The definition of a
consistent theory in mathematical logic is that a consistent theory is
such that there is no sentence A such that both A and the negation of A
are formally provable in T.
> > Gödel's first incompleteness theorem shows that, for formal theories T
> > meeting certain criteria, it is possible to find a formula G_T with the
> > property that G_T is true just in case T is consistent, i.e. G_T is true
> > if T is consistent, and false if T is inconsistent.
>
> It appears G is deducible from T but not the reverse.
No. If T is consistent, then G_T is unprovable but true, and if T is
inconsistent, then G_T is provable (since everything is provable in an
inconsistent theory) but false. Here G_T is a sentence in the formal
language of T, expressing, by means of certain technical coding tricks,
that "G_T is not formally provable in T".
> IOW what Gödel was driving at as far as the epistemology of science was
> concerned is that mathematics remains entirely deducible from non mathematics
> and not the reverse.
What does it mean to say that "mathematics remains entirely deducible
from non mathematics and not the reverse"? It appears in any case to
have absolutely no connection to the mathematical content of the
incompletenss theorems. The same goes for your other remarks. Your
reflections might be immensely significant, but perhaps it would be
better to leave poort old Kurt out of it all?
> > I just wished to make the rather trivial observation that the
> > incompleteness theorem establishes an implication, "if T is consistent,
> > then the Gödel sentence of T is true but unprovable in T", ..
>
> JE:-
> As I understand this: If T contains no contradiction then it remains a
> valid induction, the truth of which cannot be proven.
It makes no sense to say of a formal theory that "it remains a valid
induction". A formal theory T is just a bunch of expressions in a
mathematically defined language. Such a theory might, or might not,
have some connection to our actual mathematical practice or theories,
depending on the theory in question, but that calls for a further
argument in each specific case.
--
Aatu Koskensilta (aatu.kos...@xortec.fi)
"Wovon man nicht sprechen kann, daruber muss man schweigen"
- Ludwig Wittgenstein, Tractatus Logico-Philosophicus
Kent Paul Dolan
> Thanks for the reference. The yahoo article had expired but googling
> "resilient robot" worked.
And thanks back again, the first hit from that search
was a _much_ better explanation of how the robot's
self-modelling worked than the ones I'd seen.
http://www.astrobio.net/news/article2174.html
xanthian.