Seems to me, that's pretty much what subsumption and BBR are all about.
How do you tell a rock from a subsumption robot? The rock just sits
there doing nothing [although, as I recall, Curt has some strong
opinions about "rock intelligence" ;-)], while the sub.s-bot both
reacts to and acts upon its environment.
To me [but not to Marvin, given his past comments], the sub.s-bot is
basically a good start at what distinquishes animals from everything
else. Autonomy, sensing, and action. However, as this thread is all
about, the BBR/sub.s approach looks to be basically stalled, and there
doesn't seem to be much [or enough] effort at adding all those levels
in-between the 2 ends Marvin just talked about. I think Brooks' book
Flesh and Machines was really the death-knell of sub.s, and his final
conclusion - there is some "missing stuff" - was totally wrong, as I
mentioned in an earlier post.
I do distinquish the difference between human and animal, in that
humans have "another" level on top that adds high-level symbolic
processing to what the animals have - roughly parallel to the
distinction between higher-order and primary consciousness that Dennett
and Edelman talk about. But you have to start adding those in-between
levels to bridge the gap down to subsumption. Marvin's high-level
commonsense reasoning systems can only work on a proper lower-level
foundation platform that can function successfully in the real world.
For my part, I'm pursuing [and have been for a while] the idea of
adding those in-between levels from the bottom up, which is the same
way organisms evolved intelligence. I just don't think you can do this
from the top down. For one thing, there is too much of an unexplained
gap between the highest-most symbolic levels in humans, and primary
consciousness levels in the other animals. IOW, you don't start with
language first, and then extend that to build the
communications-interaction capability of a monkey. You do it the other
way around. My $0.02.
The band is from Athens, GA, actually. Am I showing my age here?
If you can create an AI system that can make sense of Fred Schneider's
lyrics, than you can create anything!
But what are the leads into these "in-between levels"?
What is the dna, protein and control loop equivalents
for the evolution of intelligent neural systems?
I suspect that high level mimicry of human intelligence
may turn out as unreal as high level mimicry seen in an
animatron. Looks real with lots of clever programming
but lacks something fundamental to duplicate human or
even animal intelligence. All of the intelligence being
a product of the programmer being part of the developmental
loop and forever requiring the programmer in that loop.
The implication in the previous messages was that BBR is essentially
the lowest level, and what needs filling in is the levels in-between
that and the symbolic levels at the top-end. Much higher than DNA and
There are 2 strains of people on this thread. The guys who came over
from c.a.p., with their grandiose ideas about solutions to general AI
[hello, Curt :)], and the guys who are currently building robots which
are essentially at the BBR level. A wide gulf there. I put myself in
the 2nd group. I'm not expecting to add some kind of GOFAI-type
processor - or direct link to CYC, or unstructured neural net, etc - to
my little robot, and expect much. And neither are Randy or David, I
Rather what we need to look at is the "next" step up from the bottom.
Better sensors and perceptual processing, plus memory and learning,
simple goal-planning [cf, chap 6 of Arkin's book]. Our robots are
little better than blind, deaf, and dumb, like Tommy of the Who [now
Gordon's got me doing it too :)].
The first thing is much better sensors and perceptual processing, so
the robot isn't stuck forever in Helen Keller's internal world [see
Curt and Joe's comments]. But this is such a radical step up in cpu
requirements, that it's a problem right now too. Most of our little
bots don't have much more power than PICs or AVRs, and maybe a DSP chip
here or there.
I realize these sorts of comments about stupid little robots always
lets Marvin down, but that's where OUR [this forum's] world currently
is, I think.
I am so pleased to see Marvin grace us with his presence again, I would
hate to think we were letting him down with our interests. Personally,
I think I'm on to something enabling and trancedental concerning
another level of understanding above the plateau bbr has come to rest
upon. But I might be self aggrandizing.
Perhaps I should ask Marvin himself. Would the separation of layered
behaviors from emergent intelligence, with the realization the
behaviors themselves are devoid of intelligence, but rather, the
sequencing of behaviors being the source of intelligence not be a
significant clarifying point for ai? Or, in you opinion, is this all
beneath a professional level of curiousity or research?
I've not read enough about BBR to know all the typical details yet, but the
little I've picked up so far, I think it's not quite low enough. The
problem is that we (as humans with brains) tend to try and classify
behavior into buckets. We look at an animal and say things like, "oh,
that's wall following behavior", or "that's goal seeking", or "that's light
avoidance". The lowest level we tend to try and understand behavior
informally is the highest level at which our brain is able to detect
When we try to build robots, we tend to think first at similar levels. How
for example might we create wall following behavior, or obstacle avoidance
in a robot. And I suspect (but don't know because of my lack of reading on
the subject), a lot of BBR work has systems for creating similar level
behaviors. For example, our robot hits a wall and keeps spinning it's
wheels, and we decide we need to add wall avoidance which combines some
some combination of sensory triggers and behaviors to make it either
prevent from hitting the wall in the first place (turn before getting too
close), or adjust after a collision (back up and turn). But for the most
part, only one behavior is active at a time, and there is some logic for
selecting which behavior to use in the current situation which might be a
priority interrupt type of logic (drive straight is the default lowest
priory behavior until it's interrupted by a higher priority sensory
condition for turning - like there is a wall near us).
BBR (as far as I can tell) is the attempt to move to using simpler
behaviors with simpler triggers, so that the system will produce the
correct combination of behaviors to fit a wide range of environmental
conditions - so that new and thought about (by the designer) sequences will
emerge and just naturally perform a useful function.
I believe what people end up coding by hand in these systems is typically
not low enough however. The problem needs to be factored down even
further, to even simpler behaviors. The reason they are not I believe, is
simply because it's too hard for us to understand how to do this by hand.
It moves the complexity of the behavior down to a set of more complex
trigger logic. And understanding how to code the complex logic for to
trigger micro behaviors, is something that's not at all intuitive to a
human programmer. It's just too hard for us to understand.
But, for the same reason BBR seems to have an advantage of being more
adaptive to different environments (environments the programmer might not
have thought about), I think factoring the problem down to even simpler
behaviors, is likely to only improve the adaptability and the amount of
emergent behaviors that spontaneously arise from the machine.
The level of behavior I'm talking about is more like "turn right wheel 1
deg clockwise". This is consistent with the level I was trying to attack
the problem with my pulse sorting network (which Dan another others from
c.a.p know about). But it would be very hard to hand code a complex set of
conditional expressions to specify for a typical robot when it should "turn
right wheel 1 deg clockwise". Which is why this level of micro behavior is
not used so much. But, with the correct learning system at work evolving
the complex logic tests used to select micro behaviors, I think you would
get excellent results (and ultimately, use this type of low level micro
behavior selection system, as the foundation for a system that produced
human level behaviors).
But keep in mind, I came here not to continue the discussions we have in
c.a.p. but because I've been building and experimenting with robots to see
if I can apply some of those grandiose ideas to real world problems. So I
want to know more about what had worked in these real world applications -
as I am as people teach me more about ideas like BBR. :)
But, if we could produce a good generic RL trained decision tree (which is
basically what I was trying to create with my "pulse sorting" networks)
which could be plugged in to drive micro behaviors in the simplest of bots,
I think it could be of real value for even these very simple bots. I've
been playing with the vex hardware which has only a small PIC processor but
that's more than enough to test some of basic RL ideas.
As you well know, I think more can be done with general solutions than you
do. I think if we solve the lower level better than we have now, it will
make filling in those higher levels much easier.
The problem with hand-coding instead of learning, is that you have to hand
code all the levels - which means you have to conceptualize how to factor
the problem into levels, and sub-levels, and then fill in each level.
Strong learning systems should do that factoring and filling in
automatically. With a strong learning system to work with, the problem for
the programming becomes one of picking the correct high level goals and
motivations. If you can define them correctly, the learning system will
fill in the implementation details for you.
Well, we don't really have a problem there do we? It's the next step that
has all the problem...
Right. There are plenty of cheap high quality high bandwidth sensors we
can put on robots (vision, sound, vibration sensors), but the problem is
that we don't have good processing systems to deal with complex high
bandwidth temporal data sources like these. Think about how useful sound
is for example if you have brain like ours to process it. You could add
low cost directional microphones and D/A converters to a any of our cheap
robots but yet it's not done (except in the very special and limited
application of sonar distance sensors). It's because extracting useful
trigger data from an N-channel microphone system is beyond what we easily
know how to do.
If we were driving the bot, we would learn to hear the sound of the bot
hitting a wall and of the wheels spinning on the ground and the sound of
the motors straining. These are the type of clues that exist buried in an
audio data stream that you don't expect a human to be able to easily
program to trigger the switch to a different behavior (we need to back up
and turn left because our wheels sound like they are slipping on this
surface). This is the type of thing a learning system needs to be able to
find, and extract from complex data streams. It needs to recognize the
correlations between a sound, and the fact that a goal is not being
And I think a big part of why current learning systems don't do this very
well, is because we don't yet have the right data processing algorithms -
which I believe you think of as a perception problem, but I tend to see
more of as a behavior problem. But either way, we don't have good enough
systems for finding, and extracting, useful information from complex
sensory data streams - which I think agrees with the point you are making.
I think this ability is the number one most important ability to improve
I think if done correctly, it won't be as radical as it seems to be. And
though many robots use very cheap low power CPUs, we have plenty of high
power cpus that really are still affordable. And, I think better
algorithms for the automatic processing of complex data can be of use even
for simple sensors with much smaller processors. For example, we can
create fairly low bandwidth audio signals that any human could make very
good use of (I'm thinking even less than phone bandwidth). Or just tactile
vibration sensors which need not be more than single bit sensors. But the
problem is always not knowing what to do with the data because it's too
complex for us to understand how to transform that data into useful
I think stupid little robots are likely to be an important testing ground
for the ideas that will lead us to the high level processing levels Marvin
(and many of us) want to create.
And it had layers so that higher-level behaviors could suppress or
encourage lower level behaviors.
I also prefer to call this "reactive programming" because "behaviors"
rings wrong for me. This type of programming uses short sections of
code that react to sensory data.
I like this. I think that the lower-level behaviors should be pure motor
functions (power X to right motor), and "wall-following" would be a
behavior that supressed/encouraged the proper lower behaviors.
As a future modification the wall-following behavior could be programmed
with a learning algorithm of some type.
Pardon me, but I missed the abbreviation "RL"? I do agree that some sort
of decision tree would be useful and quicker than a neural network.
This is what DSPs are made for. I believe that there are special purpose
chips to do limited voice-recognition. It seems to me possible to use
a DSP for the purpose of identifying and remembering these clues.
Yes, algorithms tend to give a bigger improvement than better hardware,
but don't overlook brute force when necessary.
Yes, there are DSP chips and ARM chips which are almost as affordable as
the PICs and AVRs and such.
D. Jay Newman ! Author of:
email@example.com ! _Linux Robotics: Programming Smarter Robots_
That was one of the things that has really excited me about that approach.
It was orders of magnitude faster than the typical neural nets I had spent
years playing with and it had excellent scaling characteristics. There was
no exponential explosion as the dimensions increased. At worse, it seemes
to be only N Log N.
That's because the few people in those areas have been banging
their head against the wall for decades now. Is there any
"reflective, meaning-based, knowledge-using" system in production
use anywhere? (I don't think we can consider Cyc a production
Certainly, our language behaviors are what distinguish us from other
animals. I'm not sure what you mean by "reflective" however, unless this
is just a reference to our power to generate language behavior internally
(our thoughts), and at the same time react to them by producing more
internal language (aka reflect on our thoughts). I do strongly suspect
that our brain features which support this type of activity (private
behaviors) is quite a bit stronger than all the other animals.
I've not seen any evidence however to make me believe that the systems that
control our language behavior is different in any significant way from the
systems that control all our behavior. Producing language behavior shares
all the same problems in common with producing all behavior. The brain
must, at all times, be constantly generating a continuous sequence of
behaviors. Whether that is mouth and lung motions for the purpose of
producing spoken words, or whether it's a complex orchestration of limb
movements to make ourselves a sandwich and eat it, the problem is basically
the same - how does the brain determine what behavior to produce next?
Reinforcement learning is making a comeback and making notable progress
such as the success of the TD-Gammon game in the early 90's (based on
temporal difference learning algorithms). Algorithms like Q-learning have
been developed in the past 20 years. Though I don't follow the research
closely, it's reported that there's been an explosion in the field in the
past 10 to 20 years.
I came across this interesting 1 hour video today which is a talk about
fairly recently (the past 5 years) discoveries on animal learning using
Temporal Difference computer models which led to the discovery and
understanding of yet another piece of the puzzle in what the brain is
How Do We Predict the Future: Brains, Rewards and Addiction
To me, reinforcement learning, the work you are known for publishing some
of the first papers on in the context of computers and artificial
intelligence, is the only foundation that can explain why, and how, humans
Humans use language to direct all our high level behaviors. We make plans,
and follow our dreams, by talking about them, either with others, or just
But, how does he brain learn language, and how does it select what language
to generate, and when? Why do we suddenly stop what we are doing, and
start talking to ourselves? What triggers that reaction? What controls
it? Why do we suddenly generate language in the presence of another human?
How does the brain select what language to generate? How does it know when
to stop talking and start doing something else?
These are the high level human unique behaviors we must understand and
build machines to copy.
We can record knowledge in a book by filling the book up with words. And
likewise, we can fill a computer with knowledge in many different ways.
But what is the purpose of the knowledge in the machine? There is only one
ultimate purpose - to allow the machine, to know what behavior to generate
next, at all times. This is the only knowledge that exists in a human
brain - the knowledge about what to do next for any given environmental
context - about what behavior is the next "note" to play next in our life
long symphony (to use the metaphor someone else brought up here).
When we read a book, we can absorb knowledge from the book, into our brain.
But how does this happen? How is it stored?
If the ultimate goal is to build a knowledge storing machine that can read
books, and talk to other humans, and absorb knowledge through this
interaction, then it's obvious you want to build a knowledge database. And
you want to give it the power to learn from it's interactions. And, we
would like it to have the power to interact with itself, to gain further
understanding of it's own knowledge (talk to itself to discover, and
create, new knowledge) (which might be the "reflective" thing you talked
So I agree with your ultimate desire to duplicate high level language
behavior in a machine, for the purpose of duplicating our must human of
behaviors - with the hope of duplicating our most humans of powers at the
same time. If we can make a talking machine which can interact with us
like a human would, one which would do much better on the Turing test for
example than any machine has yet done, that would be fantastic. I would
love to have a computer I could ask to go do research for me on the
Internet and report back to me what it had learned. I don't need it to
have arms and legs or vision.
But, a machine with nothing but the ability to receive, and generate words
through a communication channel, has the same fundamental behavior problems
to solve, that all animals (and robots - trying not to forget what group
this is) needs to solve - what should it do next. You can ask this
question many ways - such as, what is the purpose of the machine, or what
is its goal, or how does it pick its own goals? How does it demonstrate
creativity? How does it demonstrate adaptability.
What makes human behavior intelligent, is that all our behavior is directed
towards a purpose. Without a purpose, the machine has now way to know what
to do next. Without a purpose, there is now way for the machine to
evaluate which behavior is "better". It wouldn't care what it did next -
anything would be just as good as the next. What makes us creative, is
that we can find new behaviors on our own. How do we do this? By having a
system which can understand the value of a behavior never before seen.
Any computer program which is going to attempt to produce human level
intelligence, and creative, language behavior, is going to need an
evaluation system which assigns value to all behaviors. Without this, the
machine won't know a great idea when it has it. It won't know which idea to
pursue, and develop further, and which to drop.
Likewise, humans don't have photographic memories. We selectively extract,
and keep, the knowledge which we sense as being important from the
environment. We follow ideas just like we follow bread crumbs to find food
- we seek out what we believe is valuable.
No knowledge based approach to AI that I've seen, seems to understand these
two fundamental issues. First, the only knowledge we have, is knowledge
about what is the best behavior to produce in a given context, and second,
we have an intrinsic value system which is able to judge the value of all
behaviors. It's this value system, that allows the brain, to determine
what it should do next, and when a new behavior emerges, to recognize (and
reward) its value.
I agree completely that we need to build knowledge systems and solve the
problems of high level language production, but I think that most people
working in that field have totally missed the mark on what they should be
building. They have structured their systems more like electronic books,
than like brains. Books are not intelligent. They lay there and do
nothing until an intelligent agent interacts with it. We need to build a
machine that duplicates the function of the brain reading the book, not the
book. And humans make behavior choices (do I read this book, or that other
book) based on the perceived value of each behavior.
Reinforcement learning is all about the creation of value based behavior
systems. They explain how humans evolve their complex "intelligent"
behaviors, and they explain what intelligent behavior is. They explain how
it is possible for humans to be creative and inventive. They also all use
a knowledge database to direct all their behaviors. But that knowledge
database doesn't just store associations between facts. It instead creates
a knowledge database in the form a value function which answers the
question of how valuable different behaviors are, in different contexts.
That's the type of knowledge database you must build, in order to direct a
machine to act intelligently.
And it makes no difference if you chose to limit the machine to only being
able to produce language behaviors, or if it's a robot with arms and legs
and eyes and ears. If you want it to be intelligent, it has to be a
reinforcement learning machine which directs behavior though a value
system, and which also, has the power to evolve it's value system through
The advantage to working with robots first, is that we can learn to produce
strong reinforcement learning systems for behavior problems which are
simpler than the full human language problem first. If you can't build a
reinforcement learning machine that can learn to find food in a maze, then
you aren't going to get a language machine to work. This is because the
maze problem, like language, requires the machine to understand a long
history of context (what is the history of turns I've made so far), and
produce the correct next behavior, based on that long temporal context.
Language, is the same, but even worse. Because the next word I need to
speak, might be based on a long context of the last 500 words I just heard.
If you can't build a mouse that can learn to correctly react to a small
context created by a small maze, there's now way you are going to get a
machine to correctly react to a long string of language.
I'm not aware of any robot mouse that can learn how to get itself out of
different mazes, or to find food in different mazes, through generic
learning techniques (aka a mouse that wasn't hard-coded just to solve
mazes). But yet, this is a problem you looked at over 50 years ago. It's
the same problem that wasn't solved then, and hasn't yet been solved, but
needs to be solved before we are ever going to make a machine use language
like humans do. It's an easier version of the same problem and the type of
problem we should solve first.
I've been working in this same general areas for 25+ years. If you call it
work - it's really more like play. I didn't pick this direction because it
was popular - I picked it because it was the only one that looked like the
approach that could explain how to create an intelligent machine.
I don't know what motivates the bulk of the AI community, but I never had
to make a living, or build a career, so my decisions were not biased by
needs like that. I simply wanted to figure out how to build an intelligent
machine for the sheer intellectual challenge it presented (and OK, for the
potential glory that it might bring if I succeeded in creating something
significant before anyone else). But I had no downside to fear - no need
to cover my bet to make sure I could keep eating.
Many that are making their living doing AI research, are no doubt highly
motivated by projects they believe they can get funding for, and for which
they believe they can create a success - to allow them to get funding for
the next project. This no doubt motivates people to work on stuff that is
currently "popular" in the eyes of the people with funding power. Mostly,
because the lack of any significant breakthroughs in the past 40 years, I
suspect this has caused people to set there sights far lower - to do simple
things, and ignore the hard problems.
Creating new significant algorithms, is hard, and very risky. But I think
that's what needs to be done. Instead, people probably look for projects
that are only a small step forward - lets build a faster chess machine, or
lets build an autonomous car that can drive a little faster.
But trying to understand how to create a value based knowledge data base
for open ended high dimension problems like making a robot that can learn
to find its way though a maze, requires some new insight into how to
structure the database that might come after a 5 year project to look for
it, or the 5 year project might produce nothing - greatly reducing the odds
the researcher will get any more funding.
I've pre-ordered a copy. I don't like reading on-line all that much. :)
Ultimately, we have to bridge the gap from the top to the bottom. It
really makes no difference whether we build from the top down, or the
bottom up, as long as the end result is a complete bridge.
So far, I think most the top down approaches have been lost not knowing
where they were headed (or headed in a direction that I think was a dead
end). Knowing for example that we need to build a knowledge database is a
top down issue. Knowing how to structure it, is the problem of not knowing
where we are headed. This is because when we look into ourselves, we can't
see the mechanisms that create our intelligence, we only see the top level
end product - our behavior. So the top level problem is obvious - we need
to build a machine to receive, and generate language - strings of words.
Reinforcement learning is one of the bottom up approaches that seemed
hopeful very early on, but which never got very far and was given up before
much of anything was built, simply because no one could see how to build
the bridge any further - there were many problems, and no answers.
But, I think after many different top down, and bottom up approaches have
all failed to close the gap, some people are beginning to see the light.
Reinforcement learning is the only bottom up approach that explains human
behavior. No matter how many problems are left unanswered in how this is
implemented, that's the path we must take from the bottom, and it's the
point any top-down approach, much close in on. Any top down approach which
isn't headed towards creating a reinforcement learning machine, isn't
creating machine intelligence - they are just creating yet another type of
computer tool (like a game playing program, or a logic reasoning engine,
expert system, etc.).
Machine intelligence, requires the type of creativity that only comes from
critic based learning systems that can evolve their own complex future
looking value prediction functions. Machines can't be intelligently
creative, if they can't recognize the value of their own behaviors.
TD-Gammon, is a good example of a machine that can do this. It recognized,
and learned on it's own (by playing itself - a very "reflective" behavior)
a opening in the backgammon game that none of the expert players used. But
after seeing TD-Gammon use the opening, the expert humans analyzed it, and
decided it was the best way to play that opening, and now they use it.
TD-Gammon showed how machines can be intelligently creative - to create
things that no human has created. Other evolution based systems have done
the same - (GA or evolution based learning systems are just another type of
At the low level, we know how to build "intelligent" machines, like
TD-gammon. At the high level, we have built some interesting word and idea
manipulation machines - but none that I know of have been built with a
reinforcement learning core directing it's behavior - which is why the high
end solutions don't look at all "intelligent" no matter how much they seem
to "know". They are just not creative purpose driven machines yet.
What we don't know, is how to build a high end reinforcement learning
language machine - one which is able to learn open ended language behavior,
instead of just playing in a very limited environment like a board game.
That's the gap we have to close.
I think playing with robots is a great way to help close the gap from the
bottom up. From the top down, no one is going to get anywhere unless they
realize where they need to head - which means they need to figure out how
to build a knowledge database structured for the purpose of producing
constantly changing language behavior, based on a reinforcement learning
core. The knowledge database must be structured to answers to the only
question the machine ever needs to answer - which is - "What behavior is
most likely to produce the most value for the current situation?". It
produces a constant string of intelligent behaviors, by continually
answering that question (and constantly learning - aka changing its
predictions of values based on experience).
A neuron does what neurons do and a computer does what computers do.
Getting one to do what the other does is a matter of approximation and
interpretation. They are never one and the same.
On 29 Aug 2006 17:58:44 GMT, firstname.lastname@example.org (Curt Welch) wrote:
Except, and correct me if I'm wrong, haven't they discovered micro
structures (microtubules) within each neuron that act like biological
quantum computers? So instead of being a simple input output circuit,
the individual neurons have some significant processing power in their
own right. The problem just got more complicated by a few orders of
"I like to be organised. A place for everything. And everything all over the
"Microtubuar consciousness" is the brainchild of the quantum-mechanic
Roger Penrose, but I really don't think many in the neuroscience
community, outside of possible Karl Pribram, take this idea very
seriously. Pribram, BTW, was the brain-master of the
hologram-in-the-brain hypothesis, which is somewhat debunked now, I
My feeling towards the genesis of Penrose's idea has always been ...
"Well, I'm a quantum mechanic, and quantum weirdness underlies
everything, therefore it must underly consciousness too, so what can I
find in the brain that looks quantum? Ahh, microtubules!" Gakk!
In short, there are MANY MANY different "theories" of brain operation,
with many different [and small] groups of adherents, and only marginal
evidence to support any of them, as yet.
Regards individual neurons, they are much more than simple input-output
ckts, as they have large dendritic trees, which act like complex
distributed analog computing elements, the output of which triggers
action potentials [digital outputs] in the cell axons which can
propagate for many cm's. We've had many discussions of this on google
c.a.p. [comp.ai.philosophy]. Curt's use of the word "easy" above is
I've been debating these sort of AI ideas with dan for years on c.a.p. I'm
will known for my belief that AI will be simple or easy once we understand
the correct big picture, or the correct approach, or the correct algorithm.
I'm also tend to be more optimistic than most about how long it will take
to uncover this "simplicity". I made a 10 year bet back in the 70's which
I lost, but I've recently made another 10 year bet with the same old
friend, which will end on 2015. I don't intend to lose this time. :)
Dan (as well as most other people) don't share my vision of simplicity.
They see the brain, and AI, as complex machines solving complex problems
(as far as I can tell). I believe the theory behind what it's doing is
fairly simple, and the complexity only comes from it's size, and the
implementation details which do make us what we are. I believe creating
intelligent machines will, in time, be easy. Duplicating all the nuances
of human behavior and human personality, however will always be very
complex and time consuming.
I think AI is like trying to understand the orbits of the planets. On the
surface, it looks too complex to understand. Only after a lot of careful
documentation and collection of data, and study, could the true simplicity
be uncovered and expressed by Kepler's three laws of planetary motion. And
then later, simplified even more by newton's laws of gravity and motion.
What started out as something only the Gods could understand, translated to
something as simple as F = G m1 m2 / r^2 to explain the force of gravity
and F = ma to explain all motion from force. From these simple concepts,
the motions of everything in the sky can be explained.
I believe machine intelligence will turn out to be just as simple at the
core. Other's accuse me of greedy reductionism. Time will tell who's
I'm not familiar with this discovery but I've think I've seen mention of
it. But yes, for a long time now, the more they study neurons, the more
complex they get. It makes it only harder to try and understand what it's
I believe there are some fundamental simple ideas behind what the brain is
doing what we don't fully understand yet. Just like there's simple ideas
behind all complex machines. You can understand the basics of an airplane,
by playing with a simple rubber band powered balsa wood toy plane. But
yet, tear apart a modern jet fighter, and all you find is unlimited amounts
of complexity in all the small parts. Seeing the big picture is very had
when you get lost looking at all the small parts.
Understanding the big picture is the trick to cracking the secrets of the
brain. It's being approached on both the theoretical fronts of computer
science and mathematics as well as the experimental front of neuroscience
research. Together, they will at some point, uncover a clear big picture
understanding of what the brain is doing. Once we have that, we will
probably be able to re-implement the same ideas, using digital technology.
Most likely, we won't end up with anything that looks like a neuron when we
are done, just like you can't find feathers or flapping wings on our flying
machines. Feathers are extremely complex things, just like neurons are
extremely complex things, and though a feather is an important part of a
birds flying features (take away their feathers and they can't fly), we
don't find them in planes. Likewise, I don't really care how complex
neurons are, it's unlikely we are going to find anything like them in our
Oops, sorry for coming in late and replying to a reply. But as someone
with a master's in neuroscience, and a practicing software engineer with
a great deal of interest in neuron emulation , I can tell you that
that microtubule-quantum-computer story is complete and utter bunk.
Roger Penrose is looking to find God in the microtubules, that's all.
He is a brilliant mathematician, but he is NOT a neuroscientist and
should quit trying to play one on TV.
Real neuroscientists can simulate neurons to pretty much any desired
degree of accuracy, even predicting their outputs to a given set of
inputs. They do this with compartmental  or kernel-based models, and
quantum mechanics has nothing to do with it.
 http://www.ibiblio.org/jstrout/uploading /
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