### [agi] Re: Solomonoff Machines – up close and personal

Hi Ed,

So is the real significance of the universal prior, not its probability
value given in a given probability space (which seems relatively
unimportant, provided is not one or close to zero), but rather the fact that
it can model almost any kind of probability space?

It just takes a binary string as input.  If you can express your problem as
one in which
a binary string represents what has been observed so far, and a continuation
of this
string represents what happens next, then Solomonoff induction can deal with
it.
So you don't have to pick the space.  You do however have to take your
problem
and represent it as binary data and feed it in, just as you do when you put
any kind
of data into a computer.

The power of the universal prior comes from the fact that it takes all
computable
distributions into account.  In a sense it contains all well defined
what the structure in the string could be.  This is a point that is worth
contemplating
for awhile.  If there is any structure in there and this structure can be
described by
a program on a computer, even a probabilistic one, then it's already
factored into
the universal prior and the Solomonoff predictor is already taking it into
account.

How does the Kolmogorov complexity help deal with this problem?

The key thing that Kolmogorov complexity provides is that it assigns a
weighting to
each hypothesis in the universal prior that is inversely proportional to the
complexity
of the hypothesis.  This means that the Solomonoff predictor respects, in
some sense,
the principle of Occam's razor.  That is, a priori, simpler things are
considered more
likely than complex ones.

ED## ??Shane??, what are the major ways programs are used in a
Solomonoff machine?  Are they used for generating and matching patterns? Are
they used for generating and creating context specific instantiations of
behavioral patterns?

Keep in mind that Solomonoff induction is not computable.  It is not an
algorithm.
The role that programs play is that they are used to construct the
universal prior.
Once this is done, the Solomonoff predictor just takes the prior and
conditions on
the observed string so far to work out the distribution over the next bit.
That's all.

Lukasz## The programs are generally required to exactly match in AIXI
(but not in AIXItl I think).
ED## ??Shane??, could you please give us an assist on this one? Is
exact matching required?  And if so, is this something that could be
loosened in a real machine?

Exact pattern matching is required in the sense that if a hypothesis says
that
something cannot happen, and it does, then that hypothesis is effectively

A real machine might have to loosen this, and many other things.  Note that
nobody
I know is trying to build a real AGI machine based on Solomonoff's model.

Isn't there a large similarity between a Solomonoff machine that could learn
a hierarchy of pattern representing programs and Jeff Hawking's hierarchical
learning (as represented in the Serre paper).  One could consider the
patterns at each level of the higherarchy as sub-routines.  The system is
designed to increase its representational efficiency by having
representational subroutines available for use by multiple different
patterns at higher compositional levels.  To the extent that a MOSES-type
evolutionary system could be set to work making such representations more
compact, it would become clear how semi-Solomonoff machines could be made to
work in the practical world.

In think the point is that if you can do really really good general sequence
prediction (via something
impractical like Solomonoff induction, or practical like the cortex) then
you're a long way towards
being able to build a pretty impressive AGI.  Some of Hutter's students are
interested in the latter.

The def of Solomonoff induction on the web and even in Shane Legg's paper
Solomonoff induction make it sound like it is merely Bayesian induction,
using the picking of priors based on Kolmogorov complexity.

Yes, that's all it is.

But statements made by Shane and Lukasz appears to imply that a Solomonoff
machine uses programming and programming size as a tool for pattern
representation, generalization, learning, inference, and more.

All these programs are weighted into that universal prior.

So I think (but I could well be wrong) I know what that means.
Unfortunately I am a little fuzzy about whether NCD would take what
information, what-with-what or binding information, or frequency
information sufficiently into account to be an optimal measure of
similarity.  Is this correct?

NCD is just a computable approximation.  The universal similarity metric (in
the Li and Vitanyi book
that I cited) gives the pure incomputable version.  The pure version
basically takes all effective
similarity metrics into account when working out how similar two things
are.  So if you have some
concept of similarity that you're 

### [agi] Re: How valuable is Solmononoff Induction for real world AGI?

Hello Edward,

I'm glad you found some of the writing and links interesting.  Let me try to

I understand the basic idea that if you are seeking a prior likelihood for
the occurrence of an event and you have no data about the frequency of its
occurrence -- absent any other knowledge -- some notion of the complexity,
in information-theory terms, of the event might help you make a better
guess.  This makes sense because reality is a big computer, and complexity
-- in terms of the number of combined events required to make reality cough
up a given event , and the complexity of the space in which those events are
to be combined -- should to some extent be related to the event's
probability.  I can understand how such complexity could be approximated by
the length of code required in some a theoretical Universal computer to
model such real world event-occurrence complexity.

This seems like a reasonable summary to me.

Ok, let's take your example as I think it captures the essence of what
you are getting at:

So what I am saying is, for example, that if you are receiving a sequence
of bytes from a video camera, much of the complexity in the input stream
might not be related to complexity-of-event-creation- or Occam's-razor-type
issues, but rather to complexity of perception, or similarity understanding,
or of appropriate context selection, factors which are not themselves
necessarily related to complexity of occurrence.

In short, yes.  So, for example, if you hooked up a Solomonoff induction
machine to
a video camera it would first need to, in some sense, understand the
nature of this
input stream.  This may be more complex than what the camera is actually
looking at!

Given that the Solomonoff machine starts from zero knowledge of the world,
other than
a special form of prior knowledge provided by the universal prior, there is
no way around
this problem.  Somehow it has to learn this stuff.  The good news, as
Solomonoff proved,
is that if the encoding of the video input stream isn't too crazy complex (
i.e. there exists
a good algorithm to process the stream that isn't too long), then the
Solomonoff machine
will very quickly work out how to understand the video stream.  Furthermore,
if we put
such a camera on a robot or something wandering around in the world, then it
would not
take long at all before the complexity of the observed world far surpassed
the complexity
of the video stream encoding.

Perhaps what you should consider is a Solomonoff machine that has been
pre-trained
to do, say, vision.  That is, you get the machine and train it up on some
simple vision
input so that it understands the nature of this input.  Only then do you
look at how well
it performs at finding structure in the world though its visual input
stream.

Furthermore, I am saying that for an AGI it seems to me it would make much
more sense to attempt to derive priors from notions of similarity, of
probabilities of similar things, events, and contexts, and from things like
causal models for similar or generalized classes.  There is usually much
from reality that we do know that we can, and do, use when learning about
things we don't know.

Yes.  In essence what you seem to be saying is that our prior knowledge of
the
world strongly biases how we interpret new information.  So, to use your
example,
we all know that people living on a small isolated island are probably
genetically quite
similar to each other.  Thus, if we see that one has brown skin, we will
guess that the
others probably do also.  However, weight is not so closely tied to
genetics, and so if
one is obese then this does not tell us much about how much other islanders
weigh.

Out-of-the-box a Solomonoff machine doesn't know anything about genetics and
weight, so it can't make such inferences based on seeing just one islander.
However,
if it did have prior experience with genetics etc., then it too would
generalise as you
describe using context.  Perhaps the best place to understand the theory of
this
is section 8.3 from An Introduction to Kolmogorov complexity and its
Applications
by Li and Vitanyi.  You can also find some approximations to this theory
that have
been applied in practice to many diverse problems under the title of
Normalized
Compression Distance or NCD.  A lot of this work has been done by Rudi
Cilibrasi.

HOW VALUABLE IS SOLMONONOFF INDUCTION FOR REAL WORLD AGI?

Well ;-)

In a certain literal sense, not much, as it is not computable.  However,
many
practical methods in machine learning and statistics can be viewed as
computable
approximations of Solomonoff induction, and things like NCD have been used
in practice with some success.  And who knows, perhaps some very smart
person will come up with a new version of Solomonoff induction that is much
more practically useful.  Personally, I suspect other approaches will reach
human
level AGI first.

If you are interested in this topic, I'm currently 

### Re: [agi] How can you prove form/behaviour are disordered?


On 6/8/07, Matt Mahoney [EMAIL PROTECTED] wrote:

The author has received reliable information, from a Source who wishes to
remain anonymous, that the decimal expansion of Omega begins

Omega = 0.998020554253273471801908...

For which choice of universal Turing machine?

It's actually  0.00787499699781238...

At least when based on the Turing machine described here:

http://www.emis.de/journals/EM/expmath/volumes/11/11.3/Calude361_370.pdf

Shane

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### Re: [agi] NARS: definition of intelligence


Pei,

Yes, the book is the best source for most of the topics. Sorry for the

absurd price, which I have no way to influence.

It's $190. Somebody is making a lot of money on each copy and I'm sure it's not you. To get a 400 page hard cover published at lulu.com is more like$25.

Shane

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### Re: [agi] Intelligence vs Efficient Intelligence


Matt,

Shane Legg's definition of universal intelligence requires (I believe)

In a universal intelligence test the agent never knows what the environment
it is facing is.  It can only try to learn from experience and adapt in
order to
perform well.  This means that a system which is not adaptive will have a
very low universal intelligence.  Even within a single environment, some
environments will change over time and thus the agent must adapt in order
to keep performing well.

Shane

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### Re: [agi] definitions of intelligence, again?!


Eliezer,

As the system is now solving the optimization problem in a much
simpler way (brute force search), according to your perspective it
has actually become less intelligent?

It has become more powerful and less intelligent, in the same way that
natural selection is very powerful and extremely stupid.

I think the best way to resolve this is to be more specific about what
we are calling powerful or stupid.

At a micro level an individual act of selection, reproduction etc. that
evolution is built upon is not powerful and is extremely stupid.

At a macro level when we consider an entire environment that performs
trillions of trillions of acts of selection, reproduction etc. over billions
of years, that system as a whole is very powerful, intelligent and creative.

The same can be said of the brain.  At a micro level an individual act of
Hebbian learning etc. on a synapse is not very powerful and quite stupid.
However, at a macro level when you consider trillions of trillions of these
acts in a system that has been trained over a couple of decades, the
result is the brain of an adult which is indeed powerful and intelligent.

Shane

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### Re: [agi] definitions of intelligence, again?!


Pei,

This just shows the complexity of the usual meaning of the word

intelligence --- many people do associate with the ability of solving
hard problems, but at the same time, many people (often the same
people!) don't think a brute-force solution show any intelligence.

I think this comes from the idea people have that things like intelligence
and creativity must derive from some very clever process.  A relatively
dumb process implemented on a mind blowingly vast scale intuitively
doesn't seem like it could be sufficient.

I think the intelligent design movement gets its strength from this
intuition.
People think, How could something as complex and amazing as the human
body and brain come out of not much more than random coin flips?!?!?
They figure that the algorithm of evolution is just too simple and therefore
dumb to do something as amazing as coming up with the human brain.
Only something with super human intelligence could achieve such a thing.

The solution I'm proposing is that we consider that relatively simple rules
when implemented on sufficiently vast scales can be very intelligent.  From
this perspective, humans are indeed the product of intelligence, but the
intelligence isn't God's, its a 4 billion year global scale evolutionary
process.

When intelligence is used on human, there is no problem, since few

hard problem can be solved by the human mind by brute-force.

Maybe humans are a kind of brute-force algorithm?  Perhaps the
important information processing that takes place in neurons etc.
is not all that complex, the amazing power of the system largely
comes from its gigantic scale?

At this point, you see capability as more essential, while
I see adaptivity as more essential.

Yes, I take capability as primary.  However, adaptivity is implied
by the fact that being adaptable makes a system more capable.

today, conventional computers

solve many problems better than the human mind, but I don't take that
as reason for them to be more intelligent.

The reason for that, I believe, is because the set of problems that they
can solve is far too narrow.  If they were able to solve a very wide range
of problems, through brute force or otherwise, I would be happy to call
them intelligent.  I suspect that most people, when faced with a machine
that could solve amazingly difficult problems, pass a Turing test, etc...,
would refer to the machine as being intelligent.  They wouldn't really care
if internally it was brute forcing stuff by running some weird quantum XYZ
system that was doing 10^10^100 calculations per second.  They
would simply see that the machine seemed to be much smarter than
themselves and thus would say it was intelligent.

for most people, that will
happen only when my system is producing results that they consider as
impressive, which will not happen soon.

Speaking of which, you're been working on NARS for 15 years!
As the theory of NARS is not all that complex (at least that was my
impression after reading you PhD thesis and a few other papers),
what's the hold up.  Even working part time I would have thought
that 15 years would have been enough to complete the system
and demonstrate its performance.

In Ben's case I understand that psynet/webmind/novamente have
all be fairly different to each other and complex.  So I understand
why it takes so long.  But NARS seems to be much simpler and
the design seems more stable over time?

It seems to me that what you are defining would be better termed
intelligence efficiency rather than intelligence.

What if I suggest to rename your notion universal problem solver?  ;-)

To tell the truth, I wouldn't really mind too much!  After all, once a
sufficiently powerful all purpose problem solver exists I'll simply ask
it to work out what the best way to define intelligence is and then
ask it to build a machine according to this definition.

See, even if my definition is wrong, a solution to my definition would
still succeed in solving the problem.  :-)

but I really don't see how you can put the current AGI projects, which

are as diverse one can image, into the framework you are proposing. If
you simply say that the one that don't fit in are uninteresting to
you, the others can say the same to your framework, right?

Sure, they might not want to build something that is able to achieve an
extremely wide range of goals in an extremely wide range of environments.

All I'm saying is that this is something that is very interesting to me,
and that it also seems like a pretty good definition of intelligence.

Shane

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### Re: [agi] Intelligence vs Efficient Intelligence


Ben,

According to this distinction, AIXI and evolution have high intelligence

but low efficient intelligence.

Yes, and in the case of AIXI it is presumably zero given that the resource
consumption is infinite.  Evolution on the other hand is just efficient
enough
that when implemented on a crazy enough scale the results can be pretty
amazing.

If this hypothesis is correct then AIXI and the like don't really tell us

what matters, which is the achievement of efficient intelligence in
relevant real-world
contexts...

That might well be true.

I don't want to give the impression that I don't care about the efficiency
of
intelligence.  On any given hardware the most intelligent system will be the
one that runs the algorithm with the greatest intelligence efficiency.
Thus,
if I want to see very intelligent systems then I need to care about how
efficient they are.  Nevertheless, it is still the end product raw
intelligence
generated by the system that really excites me, rather than statistics on
its
internal efficiency.

Shane

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### Re: [agi] definitions of intelligence, again?!


Mike,

There is no right way to deal with problematic problems, period (no right
way to write that essay or work out that marketing strategy). There are
reasonably effective and unreasonably uneffective ways, but no right ways,
and you can't be sure in advance which will and which won't be more or less
effective.

In defining universal intelligence I have not assumed that there is a right
way to
write an essay, or work out a marketing strategy, or anything else.  All I
if you define a problem and measure performance according to something, how
well can the system perform with respect to that measure?  How the agent
goes
about doing this is left open.

Shane

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### Re: [agi] definitions of intelligence, again?!


On 5/17/07, Pei Wang [EMAIL PROTECTED] wrote:

Sorry, it should be I assume you are not arguing that evolution is
the only way to produce intelligence

Definitely not.  Though the results in my elegant sequence prediction
paper show that at some point math is of no further use due to Goedel
incompleteness.  To go beyond that point things like evolution may be
necessary.

Shane

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### Re: [agi] definitions of intelligence, again?!


Pei,

However, in general I do think that, other things being equal, the

system that uses less resources is more intelligent.

Would the following be possible with your notion of intelligence:
There is a computer system that does a reasonable job of solving
some optimization problem.  We go along and keep on plugging
more and more RAM and CPUs into the computer.  At some point
the algorithm sees that it has enough resources to always solve
the problem perfectly through brute force search and thus drops
its more efficient but less accurate search strategy.

As the system is now solving the optimization problem in a much
simpler way (brute force search), according to your perspective it
has actually become less intelligent?

NARS can...
- accept a number as input?
- be instructed to try to maximise this input?
- interact with its environment in order to try to do this?

I assume NARS is able to do all of these things.

Though NARS has the potential to work in the environment you
specified, it is not designed to maximize a reward measurement given
by the environment.

Of course.  If I want a general test, I can't assume that the
systems to be tested were designed with my test in mind.

Cheers
Shane

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### Re: [agi] definitions of intelligence, again?!


Pei,

No. To me that is not intelligence, though it works even better.

This seems to me to be very divergent from the usual meaning
of the word intelligence.  It opens up the possibility that a super
computer that is able to win a Nobel prize by running a somewhat
efficient AI algorithm could be less intelligent than a basic pocket
calculator that is solving optimization problems in a very efficient way.

It seems to me that what you are defining would be better termed
intelligence efficiency rather than intelligence.

Let is also why I think the definition of intelligence in psychology
cannot be directly accepted in AI. For human beings, problem-solving
ability at a certain age can be used approximately to indicate the
learning ability of a system (person),

I don't see this.  Unless they are designed to be highly culture neutral,
such as a Raven test, IQ tests usually test for knowledge as well as
problem solving.  In this way they can estimate how well the individual
was able to learn in the past.

They don't need to have the test in mind, indeed, but how can you

justify the authority and fairness of the testing results, if many
systems are not built to achieve what you measure?

I don't see that as a problem.  By construction universal intelligence
measures how well a system is able to act as an extremely general
purpose problem solver (roughly stated).  This is what I would like to
have, and so universal intelligence is a good measure of what I am
interested in achieving.  I happen to believe that this is also a decent
formalisation of the meaning of intelligence for machines.   Some
systems might be very good at what they have been designed to do,
but what I want to know is how good are they as a general purpose
problem solver?   If I can't give them a problem, by defining a goal
for them, and have them come up with a very clever solution to my
problem, they aren't what I'm interested in with my AI work.

Shane

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### Re: [agi] definitions of intelligence, again?!


Pei,

necessary to spend some time on this issue, since the definition of

intelligence one accepts directly determines one's research goal and
criteria in evaluating other people's work. Nobody can do or even talk

This is exactly why I am interested in the definition of intelligence.
I think the topic deserves far more time and thought in AI than it
currently gets.

Based on the above general consideration, I define intelligence as

the ability to adapt and work with insufficient knowledge and
resources

According to my definition, a thermostat is not intelligence, and nor
is an algorithm that provide optimum solutions by going through all
possibilities and pick the best.

If an optimisation algorithm searches some of a solution space (because
of lack of computer power to search all of it) and then returns a solution,
does this system have some intelligence according to your definition?

Most optimisation algorithms have simple adaption (let's say that it's a
genetic algorithm to make things concrete), and the system has insufficient
knowledge and resources to directly search the whole space.

To criticize his assumption as too far away from reality is a

different matter, which is also why I don't agree with Hutter and
Legg. Formal systems can be built on different assumptions, some of
which are closer to reality than some others.

Both AIXI and universal intelligence are too far away from reality to
be directly implemented.  I think we all agree on that.  In their current
form their main use is for theoretical study.

In the case of AIXI, it seems to me that it would be difficult to build
something that approximates these ideas in a way that produced a
real working AI system.  Or maybe I'm just not smart enough to see
how it would be done.

In the case of universal intelligence I think there is some hope due to
the fact that the C-Test is based on quite similar ideas and this has
been used to construct an intelligence test with sensible results.
Sometime after my thesis I'm going to code up an intelligence test
based on universal intelligence and see how well various AI algorithms
perform.

Cheers
Shane

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### Re: [agi] definitions of intelligence, again?!


Mark,

Gödel's theorem does not say that something is not true, but rather that
it cannot be proven to be true even though it is true.

Thus I think that the analogue of Gödel's theorem here would be something
more like:  For any formal definition of intelligence there will exist a
form of
intelligence that cannot be proven to be intelligent even though it is
intelligent.

Cheers
Shane

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### Re: [agi] definitions of intelligence, again?!


Pei,

Fully agree. The situation in mainstream AI is even worse on this

topic, compared to the new AGI community. Will you write something for
AGI-08 on this?

Marcus suggested that I submit something to AGI-08.  However I'm not
I've finished writing my thesis in a couple of months.

if it searches different parts of the space in a context

and experience sensitive manner, it is intelligent; if it doesn't only
search among listed alternatives, but also find out new alternatives,
it is much more intelligent.

Hmmm.  Ok, imagine that you have two optimization algorithms
X and Y and they both solve some problem equally well.  The
difference is that Y uses twice as many resources as X to do it.
As I understand your notion of intelligence, X would be considered
more intelligent than Y.  True?

Essentially then, according to you intelligence depends on how well
a system can perform per unit of resources consumed?

beside input/output of
the system, you assume the rewards to be maximized come from the
environment in a numerical form, which is an assumption not widely
accepted outside the reinforcement learning community. For example,
NARS may interpret certain input as reward, and certain other input as
punishment, but it depends on many factors in the system, and is not
objective at all. For this kind of systems (I'm sure NARS isn't the
only one), how can your evaluation framework be applied?

NARS can...
- accept a number as input?
- be instructed to try to maximise this input?
- interact with its environment in order to try to do this?

I assume NARS is able to do all of these things.

Shane

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### Re: [agi] Determinism


On 5/14/07, David Clark [EMAIL PROTECTED] wrote:

Even though I have a Math minor from University, I have used next to no
Mathematics in my 30 year programming/design career.

Yes, but what do you program?

I've been programming for 24 years and I use math all the time.
Recently I've been working with Marcus Hutter on a new learning
algorithm based on a rather nasty mathematical derivation.  The
results kick butt.  Another tricky derivation that Hutter did a few
years back is now producing good results in processing gene
expression data for cancer research.  I could list many more...

Anyway, my point is, whether you need math in your programing
or not all depends on what it is that you are trying to program.

Shane

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### Re: [agi] Tommy


Josh,

Interesting work, and I like the nature of your approach.

We have essentially a kind of a pin ball machine at IDSIA
and some of the guys were going to work on watching this
and trying to learn simple concepts from the observations.
I don't work on it so I'm not sure what the current state of
their work is.

When you publish something on this please let the list know!

thanks
Shane

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### Re: [agi] rule-based NL system


On 5/2/07, Mark Waser [EMAIL PROTECTED] wrote:

One of the things that I think is *absolutely wrong* about Legg's
paper is that he only uses more history as an example of generalization.  I
think that predictive power is test for intelligence (just as he states) but
that it *must* include things that the agent has never seen before.  In this
sense, I think that Legg's paper is off the mark to the extent of being
nearly useless (since you can see how it's has poisoned poor Matt's
approach).

Mark,

Why do you think that a Legg-Hutter style intelligence test would
not expose an agent to things it hadn't seen before?

To have a significant level of intelligence an agent must be able
to deal with environments that are full of surprises and unknowns.
Agents that can't do this would only be able to deal with the most
basic environments, and thus would have a relatively low universal
intelligence value.

Cheers
Shane

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Numbers for humans vary rather a lot.  Some types of cells have up to
200,000 connections (Purkinje neurons) while others have very few.
Thus talking about the number of synapses per neuron doesn't make
much sense.  It all depends on which type of neuron etc. you mean.

Anyway, when talking about a global brain average I most often see the
number 1,000.   For rat cortex (which is a bit different to mouse cortex
in terms of thickness and density) I usually see the number 10,000 as
the average (just for cortex, not the whole brain).

Shane

On 4/29/07, Matt Mahoney [EMAIL PROTECTED] wrote:

Does anyone know if the number of synapses per neuron (8000) for mouse
cortical cells also apply to humans?  This is the first time I have seen
an
estimate of this number.  I believe the researchers based their mouse
simulation on anatomical studies.

--- J. Storrs Hall, PhD. [EMAIL PROTECTED] wrote:

In case anyone is interested, some folks at IBM Almaden have run a
one-hemisphere mouse-brain simulation at the neuron level on a Blue Gene
(in

0.1 real time):

http://news.bbc.co.uk/2/hi/technology/6600965.stm
http://ieet.org/index.php/IEET/more/cascio20070425/

Neurobiologically realistic, large-scale cortical and sub-cortical
simulations
are bound to play a key role in computational neuroscience and its
applications to cognitive computing. One hemisphere of the mouse cortex
has
roughly 8,000,000 neurons and 8,000 synapses per neuron. Modeling at
this
scale imposes tremendous constraints on computation, communication, and
memory capacity of any computing platform.
We have designed and implemented a massively parallel cortical
simulator
with
(a) phenomenological spiking neuron models; (b) spike-timing dependent
plasticity; and (c) axonal delays.
We deployed the simulator on a 4096-processor BlueGene/L supercomputer
with

256 MB per CPU. We were able to represent 8,000,000 neurons (80%
excitatory)

and 6,300 synapses per neuron in the 1 TB main memory of the system.
Using a

synthetic pattern of neuronal interconnections, at a 1 ms resolution and
an
average firing rate of 1 Hz, we were able to run 1s of model time in 10s
of
real time!

Josh

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### Re: [agi] Circular definitions of intelligence


Mike,

But interestingly while you deny that the given conception of intelligence

is rational and deterministic.. you then proceed to argue rationally and
deterministically.

Universal intelligence is not based on a definition of what rationality is.
It is based
on the idea of achievement.  I believe that if you start to behave
irrationally (by any
reasonable definition of the word) then your ability to achieve goals will
go down and
thus so will your universal intelligence.

that actually you DON'T usually know what you desire. You have conflicting

desires and goals. [Just how much do you want sex right now? Can you produce
a computable function for your desire?]

Not quite.  Universal intelligence does not require that you personally can
define
your, or some other system's, goal.  It just requires that the goal is well
defined
in the sense that a clear definition could be written down, even if you
don't know
what that would look like.

If you want intelligence to include undefinable goals in the above weaker
sense
then you have this problem:

Machine C is not intelligent because it cannot do X, where X is something
that cannot be defined.

I guess that this isn't a road you want to take as I presume that you think
that
machine intelligence is possible.

And you have to commit yourself at a given point, but that and your

priorities can change the next minute.

A changing goal is still a goal, and as such is already taken care of by the
universal intelligence measure.

And vis-a-vis universal intelligence, I'll go with Ben

According to Ben Goertzel, Ph. D, Since universal intelligence is only
definable up to an arbitrary constant, it's of at best ~heuristic~ value in
thinking about the constructure of real AI systems. In reality, different
universally intelligent modules may be practically applicable to different
types of problems. [8] http://www.sl4.org/archive/0104/1137.html

Ben's comment is about AIXI, so I'll change to that for a moment.  I'm going
to have
to be a bit more technical here.

I think the compiler constant issue with Kolmogorov complexity is in some
cases
important, and in others it is not.  In the case of Solomonoff's continuous
universal
prior (see my Scholarpedia article on algorithmic probability theory for
details) the
measure converges to the true measure very quickly for any reasonable choice
of
reference machine.  With different choices of reference machine the compiler
constant may mean that the system doesn't converge for a few more bytes of
input.
This isn't an issue for an AGI system that will be processing huge amounts
of data
over time.  The optimality of its behaviour in the first hundred bytes of
its existence
really doesn't matter.  Even incomputable super AIs go through an infantile
stage,
albeit a very short one.

You seem to want to pin AG intelligence down precisely, I want to be more

pluralistic - and recognize that uncertainty and conflict are fundamental to
its operation.

Yes, I would like to pin intelligence down as precisely as possible.

I think that if somebody could do this it would be a great step forward.
I believe that issues of definition and measurement are the bedrock of
good science.

Cheers
Shane

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### Re: [agi] Circular definitions of intelligence


Ben,

Are you claiming that the choice of compiler constant is not pragmatically

significant in the definition of the Solomonoff-Levin universal prior, and
in Kolmogorov
complexity?  For finite binary sequences...

I really don't see this, so it would be great if you could elaborate.

In some cases it matters, in others it doesn't.  Solomonoff's prediction
error
theorem shows that the total summed expected squared prediction error is
bounded by a constant when the true generating distribution \mu is
computable.
The constant is (ln 2)/2 K(\mu) bits.  The K term in this bound depends on
the
choice of reference machine.  For a reasonable choice of reference machine
you might be able to push the bound up by something like 1000 bits.  If you
are considering long running systems that will process large amounts of
data,
that 1000 extra bits is tiny.

On the other hand, if you want to know if K(10)  K(147) then your answer
will depend on which reference machine you use.  In short: Kolmogorov
complexity works well for reasonably big objects, it doesn't work well for
small objects.

Probably the best solution is to condition the measure with information
the world.  In which case K(10|lots of world data)  K(147|lots of world
data)
should work the way you expect.  Google complexity works this way.
In the case of Solomonoff induction, you let the predictor watch the world
for
a while before you start trying to get it to solve prediction tasks.

In a practical Novamente context, it seems to make a big difference.  If we

make different choices regarding the internal procedure-representation
language Novamente uses, this will make a big difference in what
internally-generated programs NM thinks are simpler ... which will make a
big difference in which ones it retains versus forgets; and which ones it
focuses its attention on and prioritizes for generating actions.

I think that the universal nature we see in Kolmogorov complexity should
also apply to practical AGI systems.  By that I mean the following:

By construction, things which have high Kolmogorov complexity are complex
with respect to any reasonable representation system.  In essence, the
reference
machine is your representation system.  Once an AGI system has spent some
time learning about the world I expect that it will also find that there are
certain
types of representation systems that work well for certain kinds of
problems.
For example, it might encounter a problem that seems complex, but then it
realises that, say, if it views the problem as a certain type of algebra
problem
then it knows how to find a solution quite easily.  I think that the hardest
part
to finding a solution to a difficult problem often lies in finding the right
way to
view the problem, in order words, the right representation.

Cheers
Shane

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### Re: [agi] Circular definitions of intelligence


Mike,

1) It seems to assume that intelligence is based on a rational,

deterministic program - is that right? Adaptive intelligence, I would argue,
definitely isn't. There isn't a rational, right way to approach the problems
adaptive intelligence has to deal with.

I'm not sure what you mean by this.  The agent that we measure the
intelligence of does
not have to be deterministic, nor does the environment.  Indeed, the agent
doesn't even
have to have a computable probability distribution, it could work by magic
for all we care.

2) It assumes that intelligent agents maximise their rewards. Wrong. You

don't except in extreme situations try to maximise your rewards when you
invest on the stockmarket - or invest in any other action.

In the real world, you have to decide how to invest your time, energy and
resources in taking/solving problematic decisions/problems (like how to
invest on the stockmarket). Those decisions carry rewards, risks and
uncertainty.  The higher the rewards, the higher the risks (nor just of
failure but of all kinds of danger). The lower the rewards, the lower the
risks (and the greater the security).

Let's say that you want to invest money in a low risk way that still has
some minimal
level of return.  In other words, you don't want to simply maximise your
expected
return, rather you want to maximise some balance of return and risk (or any
other
things you also want to take into account such as time and energy).  Take
all these
factors and define a utility function over the possible outcomes.  If you
can't do this
then you don't really know exactly what it is that you desire.  Now simply
consider
the reward signal from the environment to the agent to be exactly the
utility function
that you just defined.   In order to perform well in this setting the agent
must work
out how to balance return on investment against risks etc.  Moreover, this
type of
environment still has a computable measure and thus is already contained in
our
intelligence test.

3) And finally, just to really screw up this search for intelligence
definitions - any definition will be fundamentally ARBITRARY There will
always be conflicting ideals of what intelligent problem solving involves..

There is no such thing as a provably true definition.  However some
definitions are
clearer, more general and more consistent with the informal usage than
others.

So let me put the challenge to you: Can you name one well defined process to
do
with intelligent problem solving that universal intelligence doesn't already
test for?

Shane

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### Re: [agi] Circular definitions of intelligence


Kaj,

(Disclaimer: I do not claim to know the sort of maths that Ben and

Hutter and others have used in defining intelligence. I'm fully aware
that I'm dabbling in areas that I have little education in, and might
be making a complete fool of myself. Nonetheless...)

I'm currently writing my PhD thesis at the moment in which, at Hutter's
request, I am going to provide what should be an easy to understand
explanation of AIXI and the universal intelligence measure.  Hopefully
this will help make the subject more understandable to people outside
the area of complexity theory.  I'll let this list know when this is out.

The intelligence of a system is a function of the amount of different

arbitrary goals (functions that the system maximizes as it changes
over time) it can carry out and the degree by which it can succeed in
those different goals (how much it manages to maximize the functions
in question) in different environments as compared to other systems.

This is essentially what Hutter and I do.  We measure the performance
of the system for a given environment (which includes the goal) and
then sum them up.  The only additional thing is that we weight them
according to the complexity of each environment.  We use Kolmogorov
complexity, but you could replace this with another complexity measure
to get a computable intelligence measure.  See for example the work of
Hernandez (which I reference in my papers on this).  Once I've finished
my thesis, one thing that I plan to do is to write a program to test the
universal intelligence of agents.

This would eliminate a thermostat from being an intelligent system,

since a thermostat only carries out one goal.

Not really, it just means that the thermostat has an intelligence of one
on your scale.  I see no problem with this.  In my opinion the important
thing is that an intelligence measure orders things correct.  For example,
a thermostat should be more intelligent than a system that does nothing.
A small machine learning algorithm should be smarter still, a mouse
smarter still, and so on...

Humans would be

classified as relatively intelligent, since they can be given a wide
variety of goals to achieve. It also has the benefit of assigning
narrow-AI systems a very low intelligence, which is what we want it to
do.

Agreed.

If you want to read about the intelligence measure that I have developed
with Hutter check out the following.
A summary set of talk slides:

http://www.vetta.org/documents/Benelearn-UniversalIntelligence-Talk.pdf

Or for a longer paper:

http://www.vetta.org/documents/ui_benelearn.pdf

Unfortunately the full length journal paper (50 pages) is still in review so
I'm not sure when that will come out.  But my PhD thesis will contain this
material and that should be ready in a few months time.

Cheers
Shane

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### Re: [agi] Growing a Brain in Switzerland


On 4/4/07, Eugen Leitl [EMAIL PROTECTED] wrote:

how do you reconcile the fact that babies are very stupid compared to
difference

The wiring is not determined by the genome, it's only a facility envelope.

Some wiring is genetic, and some is not.  On a large scale genes regulate
how one part of the neocortex is wired to another part (there are even
little
tiny crawler things that do the wiring up during the prenatal development of
the brain that sound totally science fiction and very cool, though the
system
isn't exactly perfect as they kill quite a few neurons when they try to craw
around the brain hooking all the wiring up).

At a micro scale each of the different types of neurons have different
dendritic tree structures (which is genetic), and lie in particular layers
of
cortex (which is also genetic), and various other things.  In short, it's
not
really genetic or due to adaption, it's a complex mixture of genetics and

The models are not complex. The emulation part is a standard numerics

package.

Heh.  Come to Switzerland and talk to the Blue Brain guys at EPFL...
Their model is very complex and definitely not simply some standard
numerics package.  They are working in collaboration with something
like 400 researchers all around the world and the project will be going
for at least several decades.  Simple it is not.

Shane

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### Re: [agi] Growing a Brain in Switzerland


On 4/5/07, Eugen Leitl [EMAIL PROTECTED] wrote:

I forget the exact number, but I think something like 20% of the
human
genome describes the brain.  If somebody is interested in building a

No, it codes for the brain tissue. That's something very different from
describing the brain. See

I didn't mean to imply that all this was for wiring, just that there is a
sizable
about of information used to construct the brain that comes from the genes.
If you want to model the brain then this is the kind of information that you
are going to have to put into your model.

Why does the optic tract project to the lateral geniculate nucleus, the
pretectum
and the superior colliculus and not other places in the brain?  Why does the
lateral genicultate body project to striate and not other parts of cortex?
Why does
the magnocellular pathway project to layer 4Calpha, while the parvocullular
pathway projects to 4A and 4Cbeta?  Why does the cerebral cortex project to
the putamen and caudate nucleus, but not the subthalamic nucleus?  I could
list pages and pages of examples of brain wiring that you were born with and
that came from your genetics, it's basic neuro science.

I don't clam that all wiring in the brain is genetic, or even a sizable
proportion of it.
What I am claiming is that the brain wiring that is genetic is non-trivial
and cannot
be ignored if somebody wants to build a working brain simulation.

You remember the thread: complexity in the code versus complexity in the

data? The Blue Brain complexity is all in the data. This is very different
from the classical AI, which tends to obsessionate about lots of clever
algorithms, but typically does sweep the data (state) under the carpet.

Yes, I agree, it's in the data rather than the code.  But I don't accept
that you can say that their model is simple.

Shane

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### Re: [agi] My proposal for an AGI agenda


On 3/23/07, David Clark [EMAIL PROTECTED] wrote:

Both the code and algorythmn must be good for any computer system to work
and neither is easy.  The bond formula was published for many years but this
particular company certainly didn't have a copy of it inside a program they
could use.  The formula was 1 line of at least 4K lines of code.  The
program wasn't so trivial either :)

The reason AGI doesn't exist yet is not because there aren't enough skilled
programmers in the world.  (Or that these people are using the wrong
operating
system or programming language etc... to address the rest of the discussion
on this thread!)  The problem is that people aren't exactly clear about what
it is
that has to be programmed.  Time and time again in the field people have
thought
that they knew what had to be done, and yet when they finally got around to
coding
it the results weren't what they had hoped for.

Is the research on AI full of Math because there are many Math professors
that publish in the field or is the problem really Math related?  Many PhDs
in computer science are Math oriented exactly because the professors that
deem their work worth a PhD are either Mathematicians or their sponsoring
professor was.

I don't know of any math profs who publish in artificial intelligence,
though no doubt
there are a few that do.  No, thinking about it now I can think of a few.
Even if you
look at the work of my PhD supervisor Marcus Hutter, he's not a math prof,
he's
actually a physics PhD.  His work might look very mathematical to a computer
scientist, but he doesn't actually use much beyond about 4th year university
level
mathematics and statistics in his book.  Indeed he likes to joke about
mathematicians
and how they are overly formal and concerned about details like whether he
has
properly deal with certain special cases on sets of measure 0 :-)

So yeah, the math that you see in the field is almost always coming from
people
who are mathematically inclined, but aren't math profs.  I should also note
that the
number of pure theory people in AI is very small.  For example, I went to
the ALT
conference last year to present some work and there were only 40 people.
This is
the second biggest AI theory conference in the world (after COLT).  Other
areas like
genetic algorithms attract thousands.

Shane

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### Re: [agi] My proposal for an AGI agenda


On 3/23/07, David Clark [EMAIL PROTECTED] wrote:

I have a Math minor from University but in 32 years of computer work, I
haven't used more than grade 12 Math in any computer project yet.

...

I created a bond comparison program for a major wealth investment firm
that
used a pretty fancy formula at it's core but I just typed it in.  I didn't
have to create it, prove it or even understand exactly why it was any
good.

IMO, creating an AGI isn't really a programming problem.  The hard part is
knowing exactly what to program.  The same was probably true of your bond
program: The really hard part was originally coming up with that 'fancy
formula'
which you just had to type in.

Thus far math has proven very useful in many areas of artificial
intelligence,
just pick up any book on machine learning such as Bishop's.  Whether it will
also be of large use for AGI... only time will tell.

Shane

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### Re: [agi] My proposal for an AGI agenda


On 3/21/07, Chuck Esterbrook [EMAIL PROTECTED] wrote:

Sometimes the slowness of a program is not contained in a small

portion of a program.

Sure.  For us however this isn't the case.

Cobra looks nice, very clean to read, even more so than Python.
However the fact that it's in beta and .NET sort of kills it for us.

As we will be going into high performance computing, we have
no choice but to do the core work in plain C running on Linux.

Shane

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### Re: [agi] My proposal for an AGI agenda


Ben, I didn't know you were a Ruby fan...

After working in C# with Peter I'd say that's is a pretty good choice.
Sort of like Java but you can get closer to the metal where needed
quite easily.

For my project we are using Ruby and C.  Almost all the code can
be in high level Ruby which is very fast to code and modify, and then
the few parts of the code that consume 99.9% of the CPU time get
converted into C and carefully optimised.

Shane

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### Re: [agi] one-shot Turing test


On 3/9/07, J. Storrs Hall, PhD. [EMAIL PROTECTED] wrote:

Perhaps the ultimate Turing Test would be to make the system itself act as
the
interviewer for a Turing Test of another system.

It's called an inverted Turing test. See:

Watt, S. (1996) Naive-Psychology and the Inverted Turing Test. Psycoloquy
7(14)

Shane

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### Re: [agi] Numenta (Hawkins) software released


The second scary bit, which I didn't mention above, is made clear in the
blog post from the company CEO, Donna Dubinsky:

Why do we offer you a license without deployment rights? Well, although we
are very excited about the ultimate applications of this technology, we feel
it is too early to expect commercial class applications. We do not want to
mislead you as to the state of the technology.

Perhaps we're wrong! If you find yourself closer to a commercial
application than we expect, let us know. We promise that we'll speed up our
commercial licensing plans!!

which is from http://www.numenta.com/for-developers/blog.php

In other words: Go make cool stuff with our technology, however if you are
to make lots of money, please let us know and we will then calculate how
much
it is going to cost you for a licence.

There is no way I'd ever agree to terms like that, especially given that
they cover not
just code, but also all business ideas etc. related to this technology.

Shane

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### Re: [agi] Numenta (Hawkins) software released


It might however be worth thinking about the licence:

Confidentiality. 1. Protection of Confidential Information. You agree that

all code, inventions, algorithms, business concepts, workflow, ideas, and
all other business, technical and financial information, including but not
limited to the HTM Algorithms, HTM Algorithms Source Code, and HTM
Technology, that you obtain or learn from Numenta in connection with this
Agreement are the confidential property of Numenta (Confidential
Information). Except as authorized herein, you will hold in confidence and
not use, except as permitted or required in the Agreement, or disclose any
Confidential Information and you will similarly bind your employees in
writing. You will not be obligated under this Section 6 with respect to
information that you can document: (i) is or has become readily publicly
available without restriction through no fault of you or your employees or
agents; or (ii) is received without restriction from a third party lawfully
in possession of such information and lawfully empowered to disclose such
information; or (iii) was rightfully in your possession without restriction
prior to its disclosure by Numenta; or (iv) was independently developed by
Information.

Shane

On 3/7/07, J. Storrs Hall, PhD. [EMAIL PROTECTED] wrote:

Just noticed this on Slashdot.
Open source but not free software, for those of you for whom this makes a
difference.
http://www.numenta.com/for-developers/software.php

Josh

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### Re: [agi] Has anyone read On Intelligence


I think On Intelligence is a good book.  It made an impact on
science since then.  Indeed in hindsight is seems strange to me
that I was so interested in AGI and yet I hadn't seriously studied
what is known about how the brain works.  Indeed almost nobody
in AI does, even people working on artificial neural networks.

Anyway, having read a fair amount of neuro science since then
it has become clear to me that while Hawkins' book is a good
understandable summary of a particular view of neuro science,
the claims he makes are all either well known facts, or things
which a fair number of neuro scientists already believe.  So there
isn't anything really new in there that I know of.

The other thing is that he presents a greatly simplified view of how
things really work.  This makes the book readable for non-scientists,
which is great, however nobody really knows how much of all those
details he glosses over are unimportant implementation stuff, and how
much of it is critical to understanding how the whole system behaves.
Of course, nobody will really know this for sure until the brain is fully
understood.

If you're read On Intelligence and are interested in a basic undergraduate
overview of neuroscience I'd recommend the classic text book Essentials
of Neural Science and Behavior by Kandel, Schwartz and Jessell.  Once
you've read that much of the scientific literature in the field is
understandable.

Shane

On 2/21/07, Aki Iskandar [EMAIL PROTECTED] wrote:

I'd be interested in getting some feedback on the book On
Intelligence (author: Jeff Hawkins).

It is very well written - geared for the general masses of course -
so it's not written like a research paper, although it has the feel
of a thesis.

The basic premise of the book, if I can even attempt to summarize it
in two statements (I wouldn't be doing it justice though) is:

1 - Intelligence is the ability to make predictions on memory.
2 - Artificial Intelligence will not be achieved by todays computer
chips and smart software.  What is needed is a new type of computer -
one that is physically wired differently.

I like the first statement.  It's very concise, while capturing a
great deal of meaning, and I can relate to it ... it jives.

However, (and although Hawkins backs up the statements fairly
convincingly) I don't like the second set of statements.  As a
software architect (previously at Microsoft, and currently at Charles
Schwab where I am writing a custom business engine, and workflow
system) it scares me.   It scares me because, although I have no
formal training in AI / Cognitive Science, I love the AI field, and
am hoping that the AI puzzle is solvable by software.

So - really, I'm looking for some of your gut feelings as to whether
there is validity in what Hawkins is saying (I'm sure there is
because there are probably many ways to solve these type of
challenges), but also as to whether the solution(s) its going to be
more hardware - or software.

Thanks,
~Aki

P.S.  I remember a video I saw, where Dr. Sam Adams from IBM stated
Hardware is not the issue.  We have all the hardware we need.
This makes sense.  Processing power is incredible.  But after reading
Hawkins' book, is it the right kind of hardware to begin with?

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### Re: [agi] Has anyone read On Intelligence


Sorry, the new version of the book I mentioned (I read the old one) is
called Principles of Neural Science.

With regards to computer power, I think it is very important.  The average
person doing research in AI (i.e. a PhD grad student) doesn't have access
to much more than a PC or perhaps a small cluster of PCs.  So it's all
very well that IBM can build super computers with vast amounts of power,
but most of us don't get access to such machines --- we're many orders
of magnitude behind this.

The other thing is that what is necessary for some algorithm to solve
a problem is very different to what was needed to develop the algorithm
in the first place.  To develop a machine learning algorithm you might want
to test it on 10 different data sets, with various different parameter
settings,
and a few different versions of the algorithm, and then run it many times in
each of these configurations order to get accurate performance statistics.
Then you look at the results and come up with some new ideas and repeat.
Thus, even if algorithm Y is a decent AGI when run on hardware X, you
probably want 100X computer power in order to develop algorithm Y.

Shane

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### Re: [agi] Why so few AGI projects?

Eliezer,Shane, what would you do if you had your headway?Say, you won the
lottery tomorrow (ignoring the fact that no rational person would buy aticket).Not just AGI - what specifically would you sit down and doall day?I've got a list of things I'd like to be working on. For example, I'd like to
try to build a universal test of machine intelligence, I've also got ideas inthe area of genetic algorithms, neural network architectures, and somemore theoretical things related to complexity theory and AI. I also want to
spend more time learning neuroscience. I think my best shot at buildingan AGI will involve bringing ideas from many of these areas together.
Indeed not.It takes your first five years simply to figure out whichway is up.But Shane, if you restrict yourself to results you canregularly publish, you couldn't work on what you really wanted to do,even if you had a million dollars.
If I had a million dollars I wouldn't care so much about my careeras I wouldn't be dependent on the academic system to pay my bills.As such I'd only publish once, or perhaps twice, a year and would
spend more time on areas of research that were more likely to failor would require large time investments before seeing results.Shane

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### Re: [agi] Why so few AGI projects?

This is a question that I've thought about from time to time. The conclusionI've come to is that there isn't really one or two reasons, there are many.Surprisingly, most people in academic AI aren't really all that into AI.
It's a job. It's more interesting than doing database programming ina bank, but at the end of the day it's just a job. They're not out tochange the world or do anything amazing, it's hard enough just trying
to get a paper into conference X or Y. It's true that they are skepticalabout whether AI will make large progress towards human levelintelligence in their life times, however I think the more important pointis that they simply don't even think about this question. They're just not
interested. I'd say that this is about 19 out of every 20 people in academicAI. Of course there are thousands of people working in academic AIaround the world, so 1 out of 19 is still a sizable number of people in total.
Funding is certainly a problem. I'd like to work on my own AGI ideasafter my PhD is over next year... but can I get money to do that? Probablynot. So as a compromise I'll have to work on something else in AI during
the day, and spend my weekends doing the stuff I'd really like to be doing.Currently I code my AI at nights and weekends.Pressure to publish is also a problem. I need results on a regular basisthat I can publish otherwise my career is over. AGI is not really short term
results friendly.Another thing is visibility. Of the academic people I know who are tryingto build a general artificial intelligence (although probably not saying quitethat in their papers), I would be surprised if any of them were known to
anybody on this list. These a non-famous young researchers, and becausethey can't publish papers saying that they want build a thinking machine,you'd only know this if you were to meet them in person.
One thing that people who are not involved in academic AI often don'tappreciate is just how fractured the field is. I've seen plenty of exampleswhere there are two sub-fields that are doing almost the same thing
but which are using different words for things, go to different conferences,and cite different sets of people. I bring this up because I sometimesget the feeling that some people think that academic AI is some sort
of definable group. In reality, most academics lack of knowledge aboutAGI is no different to their lack of knowledge of many other areas of AI.In other words, they aren't ignoring AGI any more than they are ignoring
twenty other areas in the field.Shane

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### Re: [agi] Definitions of intelligence

Pei, I'll correct your definition and add the new ones youcite on Monday.ThanksShaneOn 9/1/06, Pei Wang
[EMAIL PROTECTED] wrote:Shane,Thanks for the great job! It will be a useful resource for all of us.
In my definition, I didn't use the word agent, but system.You may also want to consider the 8 definitions listed in AIMA(http://aima.cs.berkeley.edu/
), page 2.PeiOn 9/1/06, Shane Legg [EMAIL PROTECTED] wrote: As part of some research I've been doing with Prof. Hutter on AIXI and formal definitions of machine intelligence, I've been
collecting definitions of intelligence that have been proposed by psychologists and AI researchers.I now have about 60 definitions.At least to the best of my knowledge, this makes it the largest and most fully referenced collection that there is.
If you're interested I've put it online:http://www.idsia.ch/~shane/intelligence.html Of course to really understand the definitions you will need
to follow the references back to the sources.Nevertheless, this collection provides a compact overview. Corrects etc. are of course welcomed, as are new definitions, provided that they have been published somewhere so that
I can properly cite them.Also the individual must be of a certain significance the area, e.g. be a psychologist, or an academic AI researcher, run an AI company etc. Shane
http://v2.listbox.com/member/[EMAIL PROTECTED]



### Re: [agi] Marcus Hutter's lossless compression of human knowledge prize

Ben,So you think that, Powerful AGI == good Hutter test resultBut you have a problem with the reverse implication,good Hutter test result =/= Powerful AGIIs this correct?
Shane



### Re: [agi] Marcus Hutter's lossless compression of human knowledge prize

That seems clear.Human-level AGI =/= Good Hutter test result

just asHuman =/= Good Hutter test resultMy suggestion then is to very slightly modify the test as follows: Instead of just getting the raw characters, what you get is thesequence of characters and the probability distribution over the
next character as predicted by a standard compressor. You(meaning the algorithm or person being tested) can then chooseto modify this distribution before it is used for compression.So, for example, when the compressor is extremely certain that
the next characters are a href="" then you just let the compressordo its thing. But when the string so far is 3x7= and the compressordoesn't seem to know what the next characters are, you push the
compressor in the right direction.I'm pretty sure that such a combination would easily beat the bestcompressors available when used with a human, or a human levelAGI with world knowledge for that matter. Indeed I think somebody
has already done something like this before with humans. Maybeone of the references that Matt gives above.
However, I am uncertain whether
Amazingly outstanding Hutter test result == powerful AGIAt least I think you'll agree that an amazingly outstanding Huttertest result (possibly on an even larger text corpus that included
conversations etc.) would allow you to then construct a machinethat would pass the Turing test?Shane



### Re: [agi] Marcus Hutter's lossless compression of human knowledge prize

Yes, I think a hybridized AGI and compression algorithm could dobetter than either one on its ownHowever, this might result in
an incredibly slow compression process, depending on how fast the AGIthinks.(It would take ME a long time to carry out this process overthe whole Hutter corpus...)Estimate the average compression by sampling.
Also, not all narrow-AI compression algorithms will necessarily beable to produce output in the style you describe above.Standard LZ
Sure, use a PPM compressor perhaps. At least I think you'll agree that an amazingly outstanding Hutter
test result (possibly on an even larger text corpus that included conversations etc.) would allow you to then construct a machine that would pass the Turing test?I agree ONLY in the context of a vastly larger text corpus --- and I
wonder just how large a corpus would be required ... quite possibly,one much larger than all text ever produced in the history of thehuman race...I don't think it's anywhere near that much. I read at about 2 KB
per minute, and I listen to speech (if written down as plain text)at a roughly similar speed. If you then work it out, buy the timeI was 20 I'd read/heard not more than 2 or 3 GB of raw text.If you could compress/predict everything that I'd read or heard
until I was 20 years old *amazingly well*, then I'm sure you'dbe able to use this predictive model to easily pass a Turing test.Indeed it's trivially true: Just have me sit a Turing test when Iwas 19. Naturally I would have passed it, and thus so would
the compressor/predictor (assuming that it's amazingly good,or at least as good at predicting my responses as I would be).Shane



### Re: [agi] Marcus Hutter's lossless compression of human knowledge prize

But Shane, your 19 year old self had a much larger and more diversevolume of data to go on than just the text or speech that you
ingested...I would claim that a blind and deaf person at 19 could pass aTuring test if they had been exposed to enough information overthe years. Especially if they had the ability to read everything
that was ever spoken to them. So I don't see why you wouldneed a corpus billions of times larger than this, as you suggested.
And, of course, your ability to predict your next verbal response isNOT a good indicator of your ability to adaptively deal with newsituations...All I'm talking about is predicting well enough to pass a Turing
test... that was my claim: That with an amazingly good compressormy life's spoken and written words you could construct a machinethat would pass a Turing test.
I do not assume that an outstanding compressor of your verbal inputsand outputs would necessarily be a great predictor of your futureverbal inputs and outputs -- because there is much more to you thanverbalizations.It might make bad errors in predicting your responses
in situations different from ones you had previously experienced... orin situations similar to situations you had previously experienced butthat did not heavily involve verbiage...But if it can't make good predictions to random questions given to
me in a Turing test, then it's not an amazingly good compressorof the first 20 years of my life. Indeed the first 20 years of my lifewould involve tens of thousands of conversations, and I presume on
all of them my responses would have been good enough to pass aTuring test.Shane



### Re: [agi] [META] Is there anything we can do to keep junk out of the AGI Forum?

Basically, as you can all probably see, Davy has written a chat bot typeof program. If you email him he'll send you a copy --- he says it's a bitover 1.5 MB and runs on XP.It's a bit hard to understand how it works, partly because (by his own
confession) he doesn't know much about AI and so doesn't know toproperly describe what he's doing.The machine translation is made considerably worse by the fact that he'snot writing in proper Italian --- he's using abbreviations for words, not
using standard vowel accents, punctuation, capitalisation etc...ShaneOn 7/26/06, Davy Bartoloni - Minware S.r.l.
[EMAIL PROTECTED] wrote:i Used a very Poor Translator...- Original Message -
From: BillK [EMAIL PROTECTED]To: agi@v2.listbox.comSent: Wednesday, July 26, 2006 4:08 PMSubject: Re: [agi] [META] Is there anything we can do to keep junk out of
the AGI Forum? On 7/26/06, Richard Loosemore wrote:   I am beginning to wonder if this forum would be better off with a  restricted membership policy.
Richard LoosemoreDavy Bartoloni - Minware S.r.l. wrote:   Which thing we want from a IA? , we want TRULY something? thedoubt   rises me that nobody affidera' never to the words of a program, e'
piu' easy   to entrust itself to a book. the book from the emergency, its wordscannot   change, what there e' written e' nearly sure reliable. it isentrusted   to us of piu' to the handbook of the bimby in order making the mousse
of snip It *might* sound a bit better in the original Italian??? But Babelfish / Google translate has made a real mess of it. BillK ---
-- No virus found in this incoming message. Checked by AVG Free Edition. Version: 7.1.394 / Virus Database: 268.10.4/399 - Release Date: 25/07/2006



### Re: [agi] Flow charts? Source Code? .. Computing Intelligence? How too? ................. ping

On 7/25/06, Ben Goertzel [EMAIL PROTECTED] wrote:
Hmmm...About the measurement of general intelligence in AGI's ...I would tend to advocate a vectorial intelligence approachI'm not against a vector approach. Naturally every intelligent
system will have domains in which it is stronger than others.Knowing what these are is useful and important. A single numbercan't capture this.
One might also define a domain-transcending intelligence, measuredby supplying a system with tasks involving learning how to solveproblems in totally new areas it has never seen before.This would be
very hard to measure but perhaps not impossible.However, in my view, this domain-transcending intelligence -- thoughperhaps the most critical part of general intelligence -- should
I think this most critical part, as you put it, is what's missing ina lot of AI systems. It's why people look at a machine that cansolve difficult calculus problems in the blink of a second and say
that it's not really intelligent.This is the reason I think there's value in having an overall generalmeasure of intelligence --- to highlight the need to put the G backinto AI.Shane



### Re: [agi] Flow charts? Source Code? .. Computing Intelligence? How too? ................. ping

James,Currently I'm writing a much longer paper (about 40 pages) on intelligencemeasurement. A draft version of this will be ready in about a month whichI hope to circulate around a bit for comments and criticism. There is also
another guy who has recently come to my attention who is doing verysimilar stuff. He has a 50 page paper on formal measures of machineintelligence that should be coming out in coming months.I'll make a post here when either of these papers becomes available.
Shane



### Re: [agi] Flow charts? Source Code? .. Computing Intelligence? How too? ................. ping

On 7/13/06, Pei Wang [EMAIL PROTECTED] wrote:
Shane,Do you mean Warren Smith?Yes.Shane



### Re: [agi] Universal Test for AI?...... AGI bottlenecks

For a universal test of AI, I would of course suggest universal intelligenceas defined in this report:http://www.idsia.ch/idsiareport/IDSIA-10-06.pdf
ShaneOn Fri, 02 Jun 2006 09:15:26 -500, [EMAIL PROTECTED] [EMAIL PROTECTED]
wrote:What is the universal test for the ability of any given AI SYSTEM
to Perceive Reason and Act?Is there such a test?What is the closest test known to date?Dan Goe



### Re: [agi] Who's watching us?

Jiri,

I would have assumed that to be the case, like what Ben said.

I guess they have just decided that my research is sufficiently
interesting to keep up to date on. Though getting hits from these
people on a daily basis seems a bit over the top. I only publish
something once every few months or so!

Shane



### Re: [agi] Who's watching us?


Daniel,

It seems to be a combination of things. For example, my most recent
hits from military related computers came from an air force base just
a few hours ago:
px20o.wpafb.af.mil - - [19/Dec/2005:12:07:41 +] GET /documents/42.pdf HTTP/1.1 200 50543 - Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; .NET CLR
1.1.4322)px20o.wpafb.af.mil - - [19/Dec/2005:12:07:58 +] GET /documents/f92-legg.pdf HTTP/1.1 200 206778 - Mozilla/4.0 (compatible; MSIE
6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)px20o.wpafb.af.mil - - [19/Dec/2005:12:08:02 +] GET /documents/IDSIA-04-04.pdf HTTP/1.1 200 338053 - Mozilla/4.0 (compatible; MSIE
6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)px20o.wpafb.af.mil - - [19/Dec/2005:12:08:10 +] GET /documents/calude99solving.pdf HTTP/1.1 200 192729 - Mozilla/4.0 (compatible; MSIE
6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)px20o.wpafb.af.mil - - [19/Dec/2005:12:08:27 +] GET /documents/96JC-SL-IHW-MDL.pdf HTTP/1.1 200 101489 - Mozilla/4.0 (compatible; MSIE
6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)px20o.wpafb.af.mil - - [19/Dec/2005:12:08:31 +] GET /documents/smith94objective.pdf HTTP/1.1 200 42124 - Mozilla/4.0 (compatible; MSIE
6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)px20o.wpafb.af.mil - - [19/Dec/2005:12:08:51 +] GET /documents/disSol.pdf HTTP/1.1 200 202651 - Mozilla/4.0 (compatible; MSIE
6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)

I'm not sure if that's a spider or not. Some of my other visitors:
hqda.pentagon.mil lanl.gov defence.gov.au robins.af.mil
lockheedmartin.com
The Pentagon looked like a spider as they hit multiple pages and
selected out particular images and files all in one second. Other
patterns of hits look like people with web browsers looking around.

Shane



### Re: [agi] Who's watching us?

After a few hours digging around on the internet, what I found was thata number of popular blogs get hits from military DNSs. The most likelyreason seems to be that some people in the military who have office jobs
spend a lot of time surfing the net. When they find something cool theytell all their other office worker military friends and before you know it,you're getting hits from 10 different military organisations.
I'd never really thought about it before, but I guess people working in thePentagon get bored and read blogs during the day too ;-)I'll take off my tin foil hat now...Shane



### Re: [agi] neural network and AGI

Ben,My suspicion is that in the brain knowledge is often stored on two levels:
* specific neuronal groups correlated with specific informationIn terms of the activation of specific neurons indicating high level concepts,I think there is good evidence of this now. See for example the work of
Gabriel Kreiman.* strange attractors spanning large parts of the brain, correlated
with specific informationThe liquid computation model of Maass, Natschlager and Markram, which wasinspired by neuroscience work on neural microcircuits and cortical columns,shows how complex dynamics can be used to perform useful computations
from random networks. Jaeger has studied how, essentially the same model,can be used as a short term memory using attractors in the system. Thus itseems reasonable that, at least at the columnar scale, attractors could play
an important role in short term memory.Shane



### Re: [agi] neural network and AGI

Hi Pei,Most of our disagreement seems to be about definitions and choicesof words, rather than facts.

(1) My memo is not intend to cover every system labeled as neural network--- that is why I use a whole section to define what I mean by NNmodel discussed in the paper. I'm fully aware of the fact that given
...My strategy is to first discuss the most typical models of the neural
network family (or the standard NN architectures, as Ben put it),My problem is this: At my research institute a large portion of the people workon neural networks. Things like, recurrent LSTM networks for continuous
speech recognition and evolved echo state networks for real time adaptivecontrol problems. I also follow research on computational liquids, biologicallyplausible neural networks, neural microcircuit research, and the ideas of
people like Jeff Hawkins. In my mind, this is all work on neural networks, andthe researchers themselves call it that, and publish in big NN conferences likeICANN, IJCNN and journals like Neural Networks. However none of this work
is like the NN model you have defined. Thus to my mind, your NN modeldoes not represent modern neural network research.
(3) Neuroscience results cannot be directly used to supportartificial neural networksI disagree as a number of the trends and new ideas in artificial neural networksthat I follow are coming from neuroscience research.
If I had to sum up our differences: I'd say that what you call standard neuralnetworks and your NN model, and most of the problems you describe, wouldhave been reasonable in 2000... but not now, 5 to 6 years later.
Shane



### Re: [agi] neural network and AGI

Pei,To my mind the key thing with neural networks is that theyare based on large numbers of relatively simple units thatinteract in a local way by sending fairly simple messages.Of course that's still very broad. A CA could be considered
a neural network according to this description, and indeedto my mind I don't see much difference between the two.Nevertheless, it does rule out many things --- just talk tosomebody who has tried to take a normal algorithm that
does something and turn it into an algorithm that workson a massively parallel architecture using relatively simplecomputations units.As I see it neural networks are more a paradigm of computationrather than any specific AI method or technology. This means
that talking about them in general is difficult.What you seem to be criticising in your memo is what I'd callfeed forward neural networks.Shane



### Re: [agi] neural network and AGI

Hi Pei,As usual, I disagree! I think you are making a straw man argument.The problem is that what you describe as neural networks is just a certainlimited class of neural networks. That class has certain limitations, which
you point out. However you can't then extend those conclusions to neuralnetworks in general. For example...You say, Starting from an initial state determined by an input vector...For recurrent NNs this isn't true, or at least I think that your description
is confusing. The state is a product of the history of inputs, rather thanbeing determined by an input vector. Similarly I also wouldn't say thatNNs are about input-output function learning. Back prop NNs are about
this when used in simple configurations. However this isn't true of NNsin general, in particular its not true of recurrent NNs. See for exampleliquid machines or echo state networks.I also wouldn't be so sure about neurons not being easily mapped to
conceptual units. In recent years neuro scientists have found thatsmall groups of neurons in parts of the human brain correspond to veryspecific things. One famous case is the Bill Clinton neuron. Of course
you're talking about artificial NNs not real brains. Nevertheless, if biologicialNNs can have this quasi-symbolic nature in places, then I can't see howyou could argue that artificial NNs can't do it due to some fundamental
limitation.I have other things to say as well, but my main problem with the paperis what I've described above. I don't think your criticisms apply to neuralnetworks in general.Shane



### [agi] Open AGI?

Hi all,

I'm curious about the general sentiments that people have
about the appropriate level of openness for an AGI project.
My mind certainly isn't made up on the issue and I can see
reasons for going either way.  If a single individual or
small group of people made a sudden break through in AGI
design this would place a huge amount of power in their
hands.  I could easily see this situation being dangerous.
On the other hand I'm not sure that I'd want too many people
knowing how to do it either!  Already the world seems to
have a few too many people who have the detailed knowledge
required to build a working nuclear weapon for example.
What are your thoughts?  Surely this has been debated
many times before I suppose?
Cheers
Shane
---



### Re: [agi] Complexity of Evolving an AGI

Hi Ben,

I understand your perspective and I think it's a reasonable one.
Well your thinking has surely left it's mark on my views ;-)

I think that what you'll get from this approach, if you're lucky, is a kind
of primitive brain, suitable to control something with general
intelligence around that of a reptile or a very stupid mammal.
Then, you can use the structures/dynamics of this primitive brain as raw
materials, for constructing a more powerful general intelligence.
Yes, this is the basic idea.  Some structures and dynamics will tend
to categorize and cluster information, some will tend to recognize
temporal patterns, some might tend to store information, some will
be mixtures of these things.  Exactly what I'll find is hard to know
in advance.  As you say this isn't a powerful brain, but it might
serve as a set of useful building blocks to construct more powerful
general intelligence from.
Your approach seem to be to skip this first step by careful design.
The danger is that it's hard and you might mess it up.  Some of the
needed dynamics could be very subtle and easily missed.  Also it's
not clear just how small the fundamental units of representation
have to be in order to be flexible and dynamic enough.
The danger with my approach is that my theory might be too weak.
This could leave me with a search space that is too big to effectively
search.  Also, if it turns out that my fundamental units of
representation are smaller than what is really needed, I might produce
very inefficient solutions.  I also need to look more closely at
why all the researchers who were evolving neural network dynamics
seem to have given up and are now doing other things... that's not
a good sign!  One of the main guys who was doing this is at a research
institute near here so I might have to go visit him for a chat about it.
Heh, our approaches slowly get closer.  Novamente is greatly simplified
and cleaned up from the 1.5 million lines of code that was webmind, while
vetta has very slowly been increasing in complexity since my webmind days.
Still, this is only a weekend hobby for me --- I need to focus my energies
on getting my PhD done.  Building an AGI might have to wait.
ciao
Shane
---



### Re: [agi] FYI: AAAI Symposium on Human-Level Intelligence

Thanks Pei.

Following the links to the people who are running this I found a
whole bunch of academic AI people who are interested in and working
on general intelligence.  Their approach seems to be very much
based around the idea that powerful systems for vision, sound, speech,
motor skills and so on form the basis for general intelligence.
Somehow I'd managed to miss seeing these people and their projects
in the past.  For example:
http://www.ai.mit.edu/projects/genesis/

http://www.ai.mit.edu/projects/HIE/index.html

http://www.cassimatis.com/

Shane

Pei Wang wrote:

2004 AAAI Fall Symposium Series
Achieving Human-Level Intelligence through Integrated Systems and Research
October 21-24, 2004
Washington D.C.
See http://xenia.media.mit.edu/~nlc/conferences/fss04.html

---

---



### Re: [agi] FYI: AAAI Symposium on Human-Level Intelligence

Also I think this is pretty cool in case you miss it:

http://www.ai.mit.edu/projects/genesis/movies.html

---



### [agi] Two nice non-technical articles


Agi types might like these two articles,

http://www.theregister.co.uk/content/4/33463.html

http://www.theregister.co.uk/content/4/33486.html

Shane

Want to chat instantly with your online friends?  Get the FREE Yahoo!
Messenger http://mail.messenger.yahoo.co.uk

---



### Re: [agi] Complexity of environment of agi agent

Arnoud,

I'm not sure if this makes much sense.  An ideal agent is not going
to be a realistic agent.  The bigger your computer and the better
your software more complexity your agent will be able to deal with.
With an ideal realistic agent I meant the best software we can make on the
best hardware we can make.
In which case I think the question is pretty much impossible to
answer.  Who knows what the best hardware we can make is?  Who
knows what the best software we can make is?

Do I have to see it like something that the value of the nth bit is a
(complex) function of all the former bits? Then it makes sense to me. After
some length l of the pattern computation becomes unfeasible.
But this is not the way I intend my system to handle patterns. It learns the
pattern after a lot of repeted occurences of it (in perception). And then it
just stores the whole pattern ;-) No compression there. But since the
environment is made outof smaller patterns, the pattern can be formulated in
those smaller patterns, and thus save memory space.
This is ok, but it does limit the sorts of things that your system
is able to do.  I actually suspect that humans do a lot of very
simple pattern matching like you suggest and in some sense fake
being able to work out complex looking patterns.  It's just that
we have seen so many patterns in the past and that we are very
good at doing fast and sometimes slightly abstract pattern matching
on a huge database of experience.   Nevertheless you need to be a
little careful because some very simple patterns that don't
repeat in a very explicit way could totally confuse your system:
123.9123

Your system, if I understand correctly, would not see the pattern
until it had seen the whole cycle several times.  Something like
5*100,000*2 = 1,000,000 characters into the sequence and even then
it would need to remember 100,000 characters of information.  A
human would see the pattern after just a few characters with
perhaps some uncertainly as to what will happen after the 9.
The total storage required for the pattern with a human would be
far less than 100,000 characters your system would need too.

Yes, but in general you don't know the complexity of the simplest solution of
the problem in advance. It's more likely that you get to know first what the
complexity of the environment is.
In general an agent doesn't know the complexity of its
environment either.

The strategy I'm proposing is: ignore everything that is too complex. Just
forget about it and hope you can, otherwise it's just bad luck. Of course you
want to do the very best to solve the problem, and that entails that some
complex phenomenon that can be handled must not be ignored a priori; it must
only be ignored if there is evidence that understanding that phenomenon does
not help solving your the problem.
In order for this strategy to work you need to know what the maximum
complexity is an agent can handle, as a function of the resources of the
agent: Cmax(R). And it would be very helpful for making design decisions to
know Cmax(R) in advance. You can then build in that everything above Cmax(R)
should be ignored; 'vette pech' as we say in Dutch if you then are not able
to solve the problem.
Why not just do this dynamically?  Try to look at how much of
the agent's resources are being used for something and how much
benefit the agent is getting from this.  If something else comes
along that seems to have a better ratio of benefit to resource
usage then throw away some of the older stuff to free up resources
for this new thing.
Shane

---



### Re: [agi] Complexity of environment of agi agent

Ciao Arnoud,

Perhaps my pattern wasn't clear enough

1
2
3
4
.
.
.
00099
00100
00101
.
.
.
0
1
.
.
.
8
9
then repeat from the start again.  However each character is
part of the sequence.  So the agent sees 10002300...
So the whole pattern in some sense is 100,000 numbers
each of 5 characters giving a 500,000 character pattern
of digits from 0 to 9.  A human can learn this reasonably
easily but your AI won't.  It would take something more
like a mega byte to store the pattern.  Actually with
the overhead of all the rules it would be much bigger.
Shane

---



### Re: [agi] Complexity of environment of agi agent

arnoud wrote:

How large can those constants be? How complex may the environment be maximally
for an ideal, but still realistic, agi agent (thus not a solomonof or AIXI
agent) to be still succesful? Does somebody know how to calculate (and
formalise) this?
I'm not sure if this makes much sense.  An ideal agent is not going
to be a realistic agent.  The bigger your computer and the better
your software more complexity your agent will be able to deal with.
The only way I could see that it would make sense would be if you
could come up with an algorithm and prove that it made the best
possible usage of time and space in terms of achieving its goals.
Then the constants you are talking about would be set by this
algorithm and the size of the biggest computer you could get.

Not even an educated guess?

But I think some things can be said:
Suppose perception of the environment is just a bit at a time:
...010100010010010111010101010...

In the random case: for any sequence of length l the number of possible
patterns is 2^l. Completely hopeless, unless prediction precision need
decreases also exponentially with l. But that is not realistic. You then know
nothing, but you want nothing also.
Yes, this defines the limiting case for Solomonoff Induction...

in the logarithmic case: the number of possible patterns of length l increases
logarithmically with l: #p  constant * log(l). If the constant is not to
high this environment can be learned easily. There is no need for vagueness
Not true.  Just because the sequence is very compressible in a
Kolmogorov sense doesn't imply that it's easy to learn.  For example
you could have some sequence where the computation time of the n-th
bit take n^1000 computation cycles.  There is only one pattern and
it's highly compressible as it has a pretty short algorithm however
there is no way you'll ever learn what the pattern is.

I suppose the point I'm trying to make is that complexity of the environment
is not all. It's is also important to know how many of the complexity can be
ignored.
Yes.  The real measure of how difficult an environment is is not
the complexity of the environment, but rather the complexity of
the simplest solution to the problem that you need to solve in
that environment.
Shane

P.S. one of these days I'm going to get around to replying to your
other emails to me!!  sorry about the delay!
---



### RE: [agi] Discovering the Capacity of Human Memory


The total number of particles in the whole universe is usually
estimated to be around 10^80.  These guys claim that the storage
of the brain is 10^8432 bits.  That means that my brain has around
10^8352 bits of storage for every particle in the whole universe.

I thought I was feeling smarter than usual this morning!

Possible explanations:

1) The quote to totally wrong the the ^ should be a , ?

2) They got confused and thought it was 1 April

3) They are actually doing research into just how flaky AI
researchers really are and how easy it is to publish
mathematical nonsense in Mind and Brain Journal

4) The scientists somehow managed to get their PhDs without
understanding how numbers work

5) They concluded that the brain is really analogue and so they
worked out the volume of the skull at the Planck scale (actually
that doesn't work either as the Planck length is far far far to
large at 1.6 x 10^-35 m)

and so on...

Does anybody have a better explanation?

Shane

--- Amara D. Angelica [EMAIL PROTECTED] wrote:
http://www.kurzweilai.net/news/news_printable.html?id=2417

Discovering the Capacity of Human Memory

Brain and Mind, August 2003

The memory capacity of the human brain is on the order of 10^8432 bits,
three scientists have estimated.

Writing in the August issue of Brain and Mind, their OAR cognitive
model asserts that human memory and knowledge are represented by a
network of relations, i.e., connections of synapses between neurons,
rather than by the neurons themselves as in the traditional
information-container model (1 neuron = 1 bit).

This explains why the magnitude of neurons in an adult brain seems
stable; however, huge amount of information can be remembered throughout
the entire life of a person, they point out.

Based on the projected computer memory capacity of 8 x 10^12 bits in the
next ten years, Yingxu Wang et al. conclude that the memory capacity of
a human brain is equivalent to at least 10^8419 modern
computersThis tremendous difference of memory magnitudes between
human beings and computers demonstrates the efficiency of information
representation, storage, and processing in the human brains.

They also conclude that this new factor has revealed the tremendous
quantitative gap between the natural and machine intelligence and that
next-generation computer memory systems may be built according to their
relational model rather than the traditional container metaphor because
the former is more powerful, flexible, and efficient, and is capable of
generating a mathematically unlimited memory capacity by using limited
number of neurons in the brain or hardware cells in the next generation
computers.

Brain and Mind 4 (2): 189-198, August 2003

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### Re: [agi] Discovering the Capacity of Human Memory



The thing is that they are talking about the number of BITS not the
number of POSSIBLE STATES.  Given x bits the number of possible
states is 2^x.  For example with 32 bits you can have 2^32 different
states... or about 4,000,000,000 possible states.

Thus, if the brain has 10^8432 bits of storage as they claim, then
the number of possible states is 2^(10^8432).

To make things even worse, even if they realise their error and
decided that they didn't understand what a bit is and that they
actually meant possible states, the number of bits in this case
then becomes just log_2 (10^8432) = 8432 * log_2 (10) = 28,010 bits
or about 3.5 kilo bytes of storage.  I'd like to think that I have
more than a 3.5 Kb brain!!

They really should have sanity checked their results.

Shane

--- Pei Wang [EMAIL PROTECTED] wrote:  The paper can be accessed at
http://www.enel.ucalgary.ca/People/wangyx/Publications/Papers/BM-Vol4.2-HMC.pdf

Their conclusion is based on the assumptions that there are 10^11 neurons
and their average synapses number is 10^3. Therefore the total potential
relational combinations is
(10^11)! / (10^3)! ((10^11)! - (10^3)!), which is approximately 10^8432.

The model is obviously an oversimplification, and the number is way too big.

Pei

- Original Message -
From: shane legg [EMAIL PROTECTED]
To: [EMAIL PROTECTED]
Sent: Tuesday, September 16, 2003 6:24 AM
Subject: RE: [agi] Discovering the Capacity of Human Memory

The total number of particles in the whole universe is usually
estimated to be around 10^80.  These guys claim that the storage
of the brain is 10^8432 bits.  That means that my brain has around
10^8352 bits of storage for every particle in the whole universe.

I thought I was feeling smarter than usual this morning!

Possible explanations:

1) The quote to totally wrong the the ^ should be a , ?

2) They got confused and thought it was 1 April

3) They are actually doing research into just how flaky AI
researchers really are and how easy it is to publish
mathematical nonsense in Mind and Brain Journal

4) The scientists somehow managed to get their PhDs without
understanding how numbers work

5) They concluded that the brain is really analogue and so they
worked out the volume of the skull at the Planck scale (actually
that doesn't work either as the Planck length is far far far to
large at 1.6 x 10^-35 m)

and so on...

Does anybody have a better explanation?

Shane

--- Amara D. Angelica [EMAIL PROTECTED] wrote:
http://www.kurzweilai.net/news/news_printable.html?id=2417

Discovering the Capacity of Human Memory

Brain and Mind, August 2003

The memory capacity of the human brain is on the order of 10^8432 bits,
three scientists have estimated.

Writing in the August issue of Brain and Mind, their OAR cognitive
model asserts that human memory and knowledge are represented by a
network of relations, i.e., connections of synapses between neurons,
rather than by the neurons themselves as in the traditional
information-container model (1 neuron = 1 bit).

This explains why the magnitude of neurons in an adult brain seems
stable; however, huge amount of information can be remembered throughout
the entire life of a person, they point out.

Based on the projected computer memory capacity of 8 x 10^12 bits in the
next ten years, Yingxu Wang et al. conclude that the memory capacity of
a human brain is equivalent to at least 10^8419 modern
computersThis tremendous difference of memory magnitudes between
human beings and computers demonstrates the efficiency of information
representation, storage, and processing in the human brains.

They also conclude that this new factor has revealed the tremendous
quantitative gap between the natural and machine intelligence and that
next-generation computer memory systems may be built according to their
relational model rather than the traditional container metaphor because
the former is more powerful, flexible, and efficient, and is capable of
generating a mathematically unlimited memory capacity by using limited
number of neurons in the brain or hardware cells in the next generation
computers.

Brain and Mind 4 (2): 189-198, August 2003

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### RE: [agi] Discovering the Capacity of Human Memory

Yeah, it's a bit of a worry.

By the way, if anybody is trying to look it up, I spelt the guy's
name wrong, it's actually Stirling's equation.  You can find
it in an online book here:

http://www.inference.phy.cam.ac.uk/mackay/itprnn/book.html

It's a great book, about 640 pages long.  The result I
used is equation 1.13 which is on page 2.

Shane

--- Brad Wyble [EMAIL PROTECTED] wrote:

It's also disconcerting that something like this can make it through the
review process.

Transdisciplinary is oftentimes a pseudonym for combining half-baked and
ill-formed ideas from multiple domains into an incoherent mess.

This paper is an excellent example.  (bad math + bad neuroscience != good
paper)

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### [agi] Robert Hecht-Nielsen's stuff

A while back Rob Sperry posted a link to a video
of a presentation by Robert Hecht-Nielsen.
( http://inc2.ucsd.edu/inc_videos/ )
In it he claims to have worked out how the brain
thinks :)  I didn't look at it at the time as it's
150MB+ and only had a dial up account, but checked
it out the other day with my new ADSL account.
Aside from his, well, rather over the top style of
presentation, what do people think of this?  I haven't
seen any comment to this list and don't know much
neuroscience myself either.  He has a book too, has
Cheers
Shane
---



### Re: [agi] Robert Hecht-Nielsen's stuff

Brad Wyble wrote:
Well the short gist of this guy's spiel is that Lenat is on
the right track.

My understanding was that he argues that Lenat is on the wrong
track!  Lenat is trying to accumulate a large body of relatively
high level logical rules about the world.  This is very hard to
do and requires a vast amount of human labour to do.
Hecht-Nielsen argues that the brain doesn't in fact logically reason
(at least not directly or course) but rather does something simpler.
Essentially some kind of pattern matching and association as I
understand it.  Also, the knowledge representation system is at a
sub-logical being made up of relatively simple associations formed
on the most part by experience.
This is the complete opposite of Cyc.  Indeed, he attributes much
of the failure of AI to be due to the assumption that intelligent
systems should work on some kind of logic and use logical expressions
to represent knowledge.  Surely Cyc is in this tradition and so,
from Hecht-Nielsen's point of view, must be on the wrong track?
Shane

---



### Re: [agi] Request for invention of new word

Semiborg?

:)

Shane

Ben Goertzel wrote:
Hi ,

For a speculative futuristic article I'm writing (for a journal issue edited
by Francis Heylighen), I need a new word: a word to denote a mind that is
halfway between an individual mind and a society of minds.
Not a hive-mind, but rather a community of minds that exchange
thoughts/ideas directly rather than thru language, and hence derive a degree
of synergetic mutual intelligence much greater than that achievable in a
society of separate minds
I'm reviewing two possible future examples of such minds

1) a community of Novamente AI engines

2) a community of human minds that are enhanced with neurochips or related
technologies and linked into a Net enabled with Kazaa for
thought-complex-swapping
Any suggestions?

Also, any reference to prior (serious or science-fictional) developments
of this theme?
Thanks,
Ben G
---

---



### Re: [agi] more interesting stuff

Kevin wrote:
Kevin's random babbling follows:

Is there a working definition of what complexity exactly is?  It seems to
be quite subjective to me.  But setting that aside for the moment...
I think the situation is similar to that with the concept of
intelligence in the sense that it means different things
to different people.  Indeed there are many formal definitions
of various kinds of complexity, each measuring different things.
For example, is a book on number theory complex?  Well in the everyday
sense of the word most people would say that it is.  In the Kolmogorov
sense it's actually quite simple as the theorems all follow from a
small set of axioms and hence it is quite compressible.  In the logical
depth sense (that is, how many computation cycles would be required to
reproduce the work) the complexity is quite high.  Another example
might be breaking prime number based encryption systems -- there is
little information complexity but a lot of computation time complexity.
Anyway, something can be very complex in one sense while being very
simple in another.  This just seems to show that our intuitive vague
notion of complexity seems to cover a number of very loosely related
things.  Which is a problem when people write about complexity without
one of the reasons that I gave up reading non-mathematical papers
on complexity and systems.

Strictly speaking, the noumenal and the phenomenal cannot be separated or
thought of distinctly IMO.  From this viewpoint, complexity is merely
apparent and not fundamentally real complexity...
I'd agree to an extent.  Though if all viewpoints have some common
deep down underlying basis of reference such as Turing computation
then perhaps this is THE ultimate viewpoint with which to view the
problem?  This is essentially the approach Kolmogorov compelexity
takes.  As we move away from UTMs towards complex AGI systems the
apparent complexity of things will of course start to change in the
sense that you suggest.  However many of the more fundamental kinds
of complexity (like Kolmogorov and logical depth above) will still
hold firm for very complex things.  In other words: just because
your AGI is really smart doesn't mean that it can now do a large NP
hard problem in a small amount of time --- the problem is likely
to remain very complex in some sense (excluding weird stuff like
finding new laws of physics and computation etc... of course).
Cheers
Shane
---



### Re: [agi] A probabilistic/algorithmic puzzle...


Hi Cliff and others,

As I came up with this kind of a test perhaps I should
say a few things about its motivation...

The problem was that the Webmind system had a number of
proposed reasoning systems and it wasn't clear which was
the best.  Essentially the reasoning systems took as input
a whole lot of data like:

Fluffy is a Cat
Snuggles is a Cat
Tweety is a Bird
Cats are animals
Cats are mamals
Cats are dogs

and so on...  This data might have errors, it might be
very bias in its sample of the outside world, it might
contain contradictions and so on... nevertheless we
would expect some basic level of consistency to it.

The reasoning systems take this and come up with all
sorts of conclusions like: Fluffy is an animal based
on the fact that Fluffy is a Cat and Cats seem to be
animals...  In a sense the reasoning system is trying
to fill in the gaps in our data by looking at the
data it has and drawing simple conclusions.

So what I wanted to do is to some how artificially
generate test sets that I could use to automatically
test the systems against each other.  I would vary the
number of entities in the space (Fluffy, Cat, Bird...)
the amount of noise in the data set, the number of
data points and so on...

Now the problem is that you can't just randomly generate
any old data points, you actually need at least some kind
of consistency which is a bit tricky when you have some
A's being B's and most B's being C's and all B's not being
D's but all D's being A's.  Before long your data is totally
self contradictorary are are basically just feeding your
reasoning system complete junk and so it isn't a very
interesting test of the system's ability.

So my idea was basically to create a virtual Venn diagram
using randomly placed rectangles as the sets used to compute
the probability for each entity in the space and the conditional
probabilities of their various intersections.  This way your
fundamental underlying system has consistent probabilities
which is a good start.  You can then randomly sample points
from the space or directly compute the probabilities from the
rectange areas (actually n dimensional rectanges as this gives
more interesting intersection possibilities) and so on to get
your data sets.  You can then look at how well the system is
able to approximate the true probabilities based on the
incomplete data that it has been given (you can compute the
true probabilities directly as you know the recatangle areas).

I think I proposed about 6 or so basic variations on this
theme to test the reasoning system's ability with deal with
various level or noise and missing data...  you can come up
with all sorts of interesting variations with a bit of thought.

Yeah, just a fancy Venn diagram really used to generate
reasonably consistent data sets.

Cheers
Shane

---



### Re: [agi] AIXI and Solomonoff induction


The other text book that I know is by Cristian S. Calude, the Prof. of
complexity theory that I studied under here in New Zealand.  A new
version of this book just recently came out.  Going by the last version,
the book will be somewhat more terse than the Li and Vitanyi book and
thus more appropriate for professional mathematicans who are used to
that sort of style.  The Li and Vitanyi book is also a lot broader in
its content thus for you I'd recommend the Li and Vitanyi book which
is without doubt THE book in the field, as James already pointed out.

There should be a new verson (third edition) of Li and Vitanyi sometime
this year which will be interesting.  Li and Vitanyi have also written
quite a few introductions to the basics of the field many of which you
should be able to find on the internet.

Cheers
Shane

The Li and Vitanyi book is actually intended to be a graduate-level text in
theoretical computer science (or so it says on the cover) and is formatted
like a math textbook.  It assumes little and pretty much starts from the
beginning of the field; you should have no problems accessing the content.

It is a well-written book, which is a good thing since it is sort of THE
text for the field with few other choices.

Cheers,

-James Rogers
[EMAIL PROTECTED]

---

---



### Re: [agi] AIXI and Solomonoff induction


Hi Cliff,

Sorry about the delay... I've been out sailing watching the America's
Cup racing --- just a pity my team keeps losing to the damn Swiss! :(

Anyway:

Cliff Stabbert wrote:

SL This seems to be problematic to me.  For example, a random string
SL generated by coin flips is not compressible at all so would you
SL say that it's alive?

No, although it does take something living to flip the coins; but
presumably it's non-random (physically predictable by observing
externals) from the moment the coin has been flipped.  The decision to
call heads or tails however is not at all as *easily* physically
predictable, perhaps that's what I'm getting at.  But I understand

Well I could always build a machine to flip coins...  Or pulls lottery
balls out of a spinning drum for that matter.

Is such a thing predictable, at least in theory?  I have read about
this sort of thing before but to be perfectly honest I don't recall
the details... perhaps the Heisenberg principle makes it impossible
even in theory.  You would need to ask a quantum physicist I suppose.

more and more quickly: the tides are more predictable than the
behaviour of an ant, the ants are more predictable than a wolf, the
wolves are more predictable than a human in 800 B.C., and the human in
800 B.C. is more predictable than the human in 2003 A.D.

In that sense, Singularity Theory seems to be a statement of the
development of life's (Kolmogorov?) complexity over time.

Well it's hard to say actually.  An ant is less complex than a human,
but an ant really only makes sense in the context of the nest that it
belongs to and, if I remember correctly, the total neural mass of some
ants nests is about the same as that of a human brain.  Also whales
have much larger brains than humans and so are perhaps more complex
in some physical sense at least.

A lot of people in complexity believed that there was an evolutionary
pressure driving system to become more complex.  As far as I know there
aren't any particularly good results in this direction -- though I don't

Cheers
Shane

---



### Re: [agi] AIXI and Solomonoff induction


Hi Cliff,

So Solomonoff induction, whatever that precisely is, depends on a
somehow compressible universe.  Do the AIXI theorems *prove* something
along those lines about our universe,

AIXI and related work does not prove that our universe is compressible.
Nor do they need to.  The sun seems to come up most days, the text in
this email is clearly compressible, laws of chemistry, biology, physics,
economics and so on seem to work.  So in short: our universe is VASTLY
compressible.

or do they *assume* a
compressible universe (i.e. do they state IF the universe is somehow
compressible, these algorithms (given infinite resources) can figure
out how)?

They assume that the environment (or universe) that they have to deal
with is compressible.  If it wasn't they (and indeed any computer based
AI system) would be stuffed.  However that's not a problem as the real
world is clearly compressible...

Assuming the latter, does that mean that there is a mathematical
definition of 'pattern'?  As I stated I'm not a math head, but with
what little knowledge I have I find it hard to imagine pattern as a
definable entity, somehow.

Yes, there is a mathematical definition of 'pattern' (in fact there
are a few but I'll just talk about the one that is important here).
It comes from Kolmogorov complexity theory and is actually quite
simple.  Essentially it says that something is a pattern if it has
an effective description (i.e. computer program for a Turing machine)
that is significantly shorter than just describing the thing in full
bit by bit.  So for example:

For x = 1 to 1,000,000,000,000
print 1
Next

Describes a string of a trillion 1's.  The description (i.e. the
lenght of this program above in bits) is vastly shorter than a
trillion and so a string of a trillion 1's is highly compressible
and has a strong pattern.

On the other hand if I flipped a coin a trillion times and used
that to generate a string of 0's and 1's, it would be exceedingly
unlikely that the resulting string would have any description much
shorter than just listing the whole thing 01000010010010101...
Thus this is not compressible and has no pattern -- it's random.

There is a bit more to the story than that but not a lot more.

OK, let's say you reward it for winning during the first 100 games,
then punish it for winning / reward it for losing during the next 100,
reward it for winning the next 100, etc.  Can it perceive that pattern?

Clearly this pattern is computationally expressible as so it's no
problem at all.  Of course it will take the AI a while to work out
the rules of the game and on game 101 it will be surprised to be
punished for winning.  And probably for games 102 and a few more.
After a while it will lose a game and realise that it needs to start
losing games.  At game 201 is will probably again get a surprise
when it's punished for losing and will take a few games to realise
that it needs to start winning again.  By game 301 is will suspect
that it need to start losing again and will switch over very quickly.
By game 401 it would probably switch automatically as it will see
the pattern.  Essentially this is just another rule in the game.

Of course these are not exact numbers, I'm just giving you an idea
of what would in fact happen if you had an AIXI system.

Given infinite resources, could it determine that I am deciding to
punish or reward a win based on a pseudo-random (65536-cyclic or
whatever it's called) random number generator?

Yes.  It's pseudo-random and thus computationally expressible
and so again it's no problem for AIXI.  In fact AIXItl would
solve this just fine with only finite resources.

And if the compressibility of the Universe is an assumption, is
there a way we might want to clarify such an assumption, i.e., aren't
there numerical values that attach to the *likelihood* of gravity
suddenly reversing direction; numerical values attaching to the
likelihood of physical phenomena which spontaneously negate like the
chess-reward pattern; etc.?

This depends on your view of statistics and probability.  I'm a
Bayesian and so I'd say that these things depend on your prior
and how much evidence you have.  Clearly the evidence that gravity
stays the same is rather large and so the probability that it's
going to flip is extremely super hyper low and the prior doesn't
matter to much...

In fact -- would the chess-reward pattern's unpredictability *itself*
be an indication of life?  I.e., doesn't Ockham's razor fail in the
case of, and possibly *only* in the case of, conscious beings*?

I don't see what you are getting at here.  You might need to explain
some more. (I understand Ockham's razor, you don't need to explain
that part; actually it comes up a lot in the theory behind Solomonoff
induction and AIXI...)

Cheers
Shane

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### Re: [agi] Breaking AIXI-tl

Eliezer S. Yudkowsky wrote:

Has the problem been thought up just in the sense of What happens when
two AIXIs meet? or in the formalizable sense of Here's a computational
challenge C on which a tl-bounded human upload outperforms AIXI-tl?

I don't know of anybody else considering human upload vs. AIXI.

Cheers
Shane

---



### Re: [agi] AIXI and Solomonoff induction

Cliff Stabbert wrote:

[On a side note, I'm curious whether and if so, how, lossy compression
might relate.  It would seem that in a number of cases a simpler
algorithm than expresses exactly the behaviour could be valuable in
that it expresses 95% of the behaviour of the environment being
studied -- and if such an algorithm can be derived at far lower cost
in a certain case, it would be worth it.  Are issues like this
addressed in the AIXI model or does it all deal with perfect
prediction?]

Yes, stuff like this comes up a lot in MDL work which can be viewed
as a computable approximation to Solomonoff induction.  Perhaps at
some point a more computable version of AIXItl might exist that is
similar in this sense.

Some results do exist on the relationship between Kolmogorov complexity
and lossy compression but I can't remember much about it off the top of
my head (I'm only just getting back into the whole area myself after a
number of years doing other things!)

What I'm getting at is an attempt at an external definition or at
least telltale of conscious behaviour as either that which is not
compressible or that which although apparently compressible for some
period, suddenly changes later or perhaps that which is not
compressible to less than X% of the original data where X is some
largeish number like 60-90.

This seems to be problematic to me.  For example, a random string
generated by coin flips is not compressible at all so would you
say that it's alive?  Back in the mid 90's when complexity theory
was cool for a while after chaos theory there was a lot of talk
about the edge of chaos.  One way to look at this is to say that
alive systems seem to have some kind of a fundamental balance between
randomness and extreme compressibility.  To me this seems obvious and
I have a few ideas on the matter.  Many others investigated the subject
but as far as I know never got anywhere.

Chaitin, one of the founders of Kolmogorov complexity theory did
some similar work some time ago,

http://citeseer.nj.nec.com/chaitin79toward.html

The reason I'm thinking in these terms is because I suspected Ockham's
razor to relate to the compressibility idea as you stated; and I've

Sounds to me like you need to read Li and Vitanyi's book on
Kolmogorov complexity theory :)

http://www.cwi.nl/~paulv/kolmogorov.html

Cheers
Shane

---



### Re: [agi] Godel and AIXI


Which is more or less why I figured you weren't going to do
a Penrose on us as you would then fact the usual reply...

Which begs the million dollar question:

Just what is this cunning problem that you have in mind?

:)

Shane

Eliezer S. Yudkowsky wrote:

Shane Legg wrote:

Eliezer S. Yudkowsky wrote:

An intuitively fair, physically realizable challenge with important
real-world analogues, solvable by the use of rational cognitive
reasoning inaccessible to AIXI-tl, with success strictly defined by
reward (not a Friendliness-related issue).  It wouldn't be
interesting otherwise.

Give the AIXI a series mathematical hypotheses some of which are
Godelian like statements and asking the AIXI it if each statement
is true and then rewarding it for each correct answer?

I'm just guessing here... this seems too Penrose like, I suppose
you have something quite different?

Indeed.

Godel's Theorem is widely misunderstood.  It doesn't show that humans
can understand mathematical theorems which AIs cannot.  It does not even
show that there are mathematical truths not provable in the Principia
Mathematica.

Godel's Theorem actually shows that *if* mathematics and the Principia
Mathematica are consistent, *then* Godel's statement is true, but not
provable in the Principia Mathematica.  We don't actually *know* that
the Principia Mathematica, or mathematics itself, is consistent.  We
just know we haven't yet run across a contradiction.  The rest is
induction, not deduction.

The only thing we know is that *if* the Principia is consistent *then*
Godel's statement is true but not provable in the Principia.  But in
fact this statement itself can be proved in the Principia.  So there are
no mathematical truths accessible to human deduction but not machine
deduction.  Godel's statement is accessible neither to human deduction
nor machine deduction.

Of course, Godel's statement is accessible to human *induction*.  But it
is just as accessible to AIXI-tl's induction as well.  Moreover, any
human reasoning process used to assign perceived truth to mathematical
theorems, if it is accessible to the combined inductive and deductive
reasoning of a tl-bounded human, is accessible to the pure inductive
reasoning of AIXI-tl as well.

In prosaic terms, AIXI-tl would probably induce a Principia-like system
for the first few theorems you showed it, but as soon as you punished it
for getting Godel's Statement wrong, AIXI-tl would induce a more complex
cognitive system, perhaps one based on induction as well as deduction,
that assigned truth to Godel's statement.  At the limit AIXI-tl would
induce whatever algorithm represented the physically realized
computation you were using to invent and assign truth to Godel
statements.  Or to be more precise, AIXI-tl would induce the algorithm
the problem designer used to assign truth to mathematical theorems;
perfectly if the problem designer is tl-bounded or imitable by a
tl-bounded process; otherwise at least as well as any tl-bounded human
could from a similar pattern of rewards.

Actually, humans probably aren't really all that good at spot-reading
Godel statements.  If you get tossed a series of Godel statements and
you learned to decode the diagonalization involved, so that you could
see *something* was being diagonalized, then the inductive inertia of
you to blindly declare the truth of your own unidentified Godel
statement, thus falsifying it.  Thus I'd expect AIXI-tl to far
outperform tl-bounded humans on any fair Godel-statement-spotting
tournament (arranged by AIXI, of course).

---



### Re: [agi] AIXI and Solomonoff induction


Hi Cliff,

I'm not good at math -- I can't follow the AIXI materials and I don't
know what Solomonoff induction is.  So it's unclear to me how a
certain goal is mathematically defined in this uncertain, fuzzy
universe.

In AIXI you don't really define a goal as such.  Rather you have
an agent (the AI) that interacts with a world and as part of that
interaction the agent gets occasional reward signals.  The agent's
job is to maximise the amount of reward it gets.

So, if the environment contains me and I show the AI chess positions
and interpret its outputs as being moves that the AI wants to make
and then give the AI reward when ever it wins... then you could say
that the goal of the system is to win at chess.

Equally we could also mathematically define the relationship between
the input data, output data and the reward signal for the AI.  This
would be a mathematically defined environment and again we could
interpret part of this as being the goal.

Clearly the relationship between the input data, the output data and
the reward signal has to be in some sense computable for such a system
to work (I say in some sense as the environment doesn't have to be
deterministic it just has to have computaionally compressible
regularities).  That might see restrictive but if it wasn't the case
then AI on a computer is simply impossible as there would be no
computationally expressible solution anyway.  It's also pretty clear
that the world that we live in does have a lot of computationally
expressible regularities.

What I'm assuming, at this point, is that AIXI and Solomonoff
induction depend on operation in a somehow predictable universe -- a
universe with some degree of entropy, so that its data is to some
extent compressible.  Is that more or less correct?

Yes, if the universe is not somehow predicatble in the sense of
being compressible then the AI will be screwed.  It doesn't have
to be prefectly predictable; it just can't be random noise.

And in that case, goals can be defined by feedback given to the
system, because the desired behaviour patterns it induces from the
feedback *predictably* lead to the desired outcomes, more or less?

Yeah.

I'd appreciate if someone could tell me if I'm right or wrong on this,
or point me to some plain english resources on these issues, should
they exist.  Thanks.

The work is very new and there aren't, as far as I know, alternate
texts on the subject, just Marcus Hutter's various papers.
I am planning on writing a very simple introduction to Solomonoff
Induction and AIXI before too long that leaves out a lot of the
maths and concentrates on the key concepts.  Aside from being a good
warm up before I start working with Marcus soon, I think it could
be useful as I feel that the real significance of his work is being
missed by a lot of people out there due to all the math involved.

Marcus has mentioned that he might write a book about the subject
at some time but seemed to feel that the area needed more time to
mature before then as there is still a lot of work to be done and
important questions to explore... some of which I am going to be
working on :)

I should add, the example you gave is what raised my questions: it
seems to me an essentially untrainable case because it presents a
*non-repeatable* scenario.

In what sense is it untrainable?  The system learns to win at chess.
It then start getting punished for winning and switches to losing.
I don't see what the problem is.

If I were to give to an AGI a 1,000-page book, and on the first 672
pages was written the word Not, it may predict that on the 673d page
will be the word Not..  But I could choose to make that page blank,
and in that scenario, as in the above, I don't see how any algorithm,
no matter how clever, could make that prediction (unless it included
my realtime brainscans, etc.)

Yep, even an AIXI super AGI isn't going to be psychic.  The thing is
that you can never be 100% certain based on finite evidence.  This is
a central problem with induction.  Perhaps in ten seconds gravity will
suddernly reverse and start to repel rather than attract.  Perhaps
gravity as we know it is just a physical law that only holds for the
first 13.7 billion years of the universe and then reverses?  It seems
very very very unlikely, but we are not 100% certain that it won't
happen.

Cheers
Shane

---



### Re: [agi] An Artificial General Intelligence in the Making


Daniel,

An ARFF file is just a collection of n-tuple data items where each tuple
dimension has defined type information.  It also has a dimension that
is marked as being the class of the data item.  So because it's
basically just a big table of data you could in theory put any kind of
information you like in there provided that you are a little creative
in the encoding.

However while you could do something like that with an ARFF file it
probably doesn't make much sense.  ARFF files carry with them the
implicit assumption that the data items are more or less i.i.d. and
that you suspect that there is some sort of explicit relationship
between the dimensions; in particular you usually are interested in
the abilty to predict the class dimension using the other dimensions.
This is how Weka classifiers interpret the files.

So in short:  I'm sure you could jam KNOW data into an Arff file but
I don't really see why doing so would make much sense.

Cheers
Shane

Daniel Colonnese wrote:

For those of us who are following the KNOW thread, could somebody
comment on the capabilities of KNOW beyond existing knowledge
representation language such as the ARFF format for the popular WEKA system.

I've input data into such a system before and while existing systems
have extensive grammar for representing logical relations they have very
limited capabilities for more ambiguous  knowledge.

The KNOW document Ben posted a link too says:

Syntax to semantics mapping in the natural language module, in which
the final result should be represented in this language;

This kind of capabilities would certainly be a huge advance over
something like ARFF.  If anyone works with ARFF, could he or she comment
on the possibilities of such translation with the ARFF grammar?  Does
anyone who's familiar with the technical workings of knowledge
representation language have any idea on how this kind of mapping could
be accomplished?

-Daniel

---



### Re: [agi] C-T Thesis (or a version thereof) - Is it useable as anin-principle argument for strong AI?


Hi,

This isn't something that I really know much about, but I'll
put my understanding of the issue down in the hope that if
I'm missing something then somebody will point it out and
I'll learn something :)

The literal Church-Turing thesis states that all formal models
of what constitutes a well defined process are in fact equivalent
to the Turing machine model.  This thesis came about after it
was discovered that all the various formal models (lambda
calculus, recursive function theory and many others) that
had been proposed were provably equivalent to the TM model.
It is worth noting that nobody has actually proven that this
claim is true, it's more the case that all efforts to find
formal model of well defined processes that's more powerful
than a Turing machine model have all failed and so people
assume that the thesis probably true.

Some people take this a step further and claim that not only
are all formal models of well defined processes equivalent,
but in fact all well defined physical processes are also
equivalent to the Turing machine model.  This appears to be
supported by the fact that no well defined physical process
has ever been found that is more powerful than the Turing
machine model.  Thus in a sense this claim is very similar
to the one above as it essentially rests on empirical evidence
rather than hard proof.

If this physical interpretation of the Church-Turing thesis
is accepted then it follows that if the physical brain and its
operation is a well defined process then it must be possible
to implement the process that the brain carries out on a Turing
machine.  This is the claim of Strong AI.

Does that sounds correct to people?

Cheers
Shane

Anand AI wrote:

Hi everyone,

After having read quite a bit about the the C-T Thesis, and its different
versions, I'm still somewhat confused on whether it's useable as an
in-principle argument for strong AI.  Why or why isn't it useable?  Since I
suspect this is a common question, any good references that you have are
appreciated.  (Incidentally, I've read Copeland's entry on the C-T Thesis in
SEoC (plato.standford.edu).)

I'll edit any answers for SL4's Wiki (http://sl4.org/bin/wiki.pl?HomePage),
and thanks very much in advance.

Best wishes,

Anand

---



### Re: [agi] AI and computation (was: The Next Wave)

Pei Wang wrote:

In my opinion, one of the most common mistakes made by people is to think AI
in terms of computability and computational complexity, using concepts like
Turing machine, algorithm, and so on.  For a long argument, see
are welcome.

It's difficult for me to attack a specific point after reading
through your paper because I find myself at odds with your views
in many places.  My views seem to be a lot more orthodox I suppose.

Perhaps where our difference is best highlighted is in the
following quote that you use:

something can be computational at one level,
but not at another level [Hofstadter, 1985]

To this I would say: Something can LOOK like computation
at one level, but not LOOK at computation at another level.
Nevertheless it still is computation and any limits due to
the fundamental properties of computation theory still apply.

Or to use an example from another field: A great painting
involves a lot more than just knowledge of the physical
properties of paint.  Nevertheless, a good painter will know
the physical properties of his paints well because he knows
that the product of his work is ultimately constrained by these.

That's one half of the story anyway; the other part is that I
believe that intelligence is definable at a pretty fundamental
level (i.e. not much higher than the concept of universal Turing
computation) but I'll leave that part for now and focus on this
first issue.

Shane

---



### Re: [agi] Early Apps.

Alan Grimes wrote:

According to my rule of thumb,

If it has a natural language database it is wrong,

I more or less agree...

Currently I'm trying to learn Italian before I leave
New Zealand to start my PhD.  After a few months working
through books on Italian grammar and trying to learn lots
of words and verb forms and stuff and not really getting
very far, I've come to realise just how complex language is!

Many of you will have learnt a second language as an adult
yourselves and will know what I mean - natual languages are
massively complex things.  I worked out that I know about
25,000 words in English, many with multiple means, many
having huges amounts of symbol grounding information and
complex relationships with other things I know, then there
is spelling information and grammar knowledge and I'm
told that English grammar isn't too complex, but my Italian
grammar reference book is 250 pages of very dense information
on irregular verbs and tenses etc... and of course even that
is only a high level ridged structure description not how the
language is actually used.

Natural languages are hard - really hard.  Humans have special
brain areas that are set up to solve just this kind of problem
and even then it takes a really long time to get good at it,
perhaps ten years!  To work something that complex out using
a general intelligence rather than specialised systems would
require a computer that was amazingly smart in my opinion.

One other thing; if one really is focused on natural language
learning why not make things a little easier and use an artificial
language like Esperanto?  Unlike like highly artificial languages
like logic based or maths based etc. languages, Esperanto is just
like a normal natural language in many ways.  You can get novels
written in it, you can speak it, some children have even grown
up speaking it as one of their first languages along side other
natural languages.  However the language is extremely regular
compared to a real natural language.  For example there are only
16 rules of grammar - they can fit onto an single sheet of paper!
All the verbs and adverbs and pronouns and so on obey neat and tidy
patterns and rules.  I'm told that after two weeks somebody can
become comfortable enough with the grammar to be able to hold a
conversation and then after a few months of learning more words
is able to communicate quite freely and read books and so on.

Why not aim at this and make the job much easier?  If you ever
did build a computer that could hold a good conversation in
Esperanto I'm sure moving to a natural language would only be
a matter of taking what you already had and increasing the level
of complexity to deal with all the additional messiness required.

Enough rating for today!  :)

Shane

---



### Re: [agi] Language and AGI (was Re: Early Apps)



I suspect that Esperanto will not be much more difficult to tackle
than any current existing language, or at best a *tiny* bit easier.
The greatest difficulty of language is not grammar, or spelling,
punctuation, etc.  To get an AGI to the point of using _any_ language
naturally on the level humans use it is the big challenge.  It can
be ancient Greek or Latin with all its declensions and exceptions; the
difficulty lies in the use of language per se.

In case my position isn't clear, I think that any language
be focused on playing a wide range of simple games instead.

However I have been really struck by the fact that Esperanto
(and no doubt many other artificial languages) can be equal
to a natural language in terms of the role they play and yet
are something like ten times less complex than a real natural
language in terms of language structure.

I'm sure a reasonably powerful AGI would be able to infer the
Esperanto rule for forming the plural of a noun (you add j
to the end of the word) but I think it would struggle to work
out how to do it in Italian (it's about six pages of rules in
my Italian grammar book and than doesn't cover all the weird
cases like when a word changes gender conditionally when forming
a plural depending on the context).

Sure, getting a computer to speak Esperanto would still be
*really* hard, but having hundreds of pages of grammar rules
that serve no real purpose other than to add a truck load of
complexity to an already difficult problem just seems absurd.

I guess people continue to do AI with languages like English
because that is what is of practical use and where more money
is likely to be.

Shane

---



### Re: [agi] TLoZ: Link's Awakening.


I don't think this is all that crazy an idea.  A reasonable
number of people think that intelligence is essentailly about
game playing in some sense, I happen to be one.

I actually used to play The Legend of Zelda many years back.
Not a bad game from what I remember.  However I'm not convinced
that this is the best game for this purpose as, if I remember
correctly, there were quite a few things in the game that had
meaning to me as a player because they related to things in
the external world.  I'm talking about different sorts of objects
etc. that you could pick up and use.  Thus as a player I had a
reasonable amount of background knowledge and understanding of
what various objects were for and what their properties were
likely to be that was based on my knowledge of the real world.
An AGI wouldn't have this and so playing the game would be a
lot harder.

Perhaps then PacMan would be a better game?  When you walk into
a wall that hurts (pain), when you eat a dot (?) that's pleasure,
eating a cherry is lots of pleasure and running into a ghost and
losing a life is lots of pain.  With a little experimentation the
AGI would be able to quickly figure all this out without needing

My other point is that an AGI has to be a General Intelligence.
So being able to just play PacMan isn't really enough, what we
would really need is huge collection games like this that
exercised the AI's brain in all sorts of slightly different ways
with different types of simple learning problems.  We need somebody
to build a collection of simple games with a common simple API.
A standard AGI test bed of sorts.

(in case those with a theoretical bent are wondering: yes, I'm
very much an RL, AIXI model of intelligence kind of a guy, in
fact it's my PhD area)

Cheers
Shane

Alan Grimes wrote:

In 1986 Nintendo released a game called The Legend of Zelda.
It remained on the top-10 list for the next five years.

So why do I mention this totally irrelevant game on this list?

Well, I'ts become apparent that I am well suited for a niche on
list-ecology that is responsible for throwing up a semi-crazy idea and
provoking useful discussion. This aims to be such a posting.

The basic problem of a baby AI mind is that you want to give it some
interactive environment that is heavy on feedback but doesn't require it
to understand abstract relationships right off the bat. A game such as
Dragon Warrior would not be good at all because it relies heavily on
textual clues.

A game such as the legend of Zelda, however, is excelent because you
hardly have to be literate at all to begin to play it. There may be a
game that better-maches this criterian but lets stick to this one.

The game's ROM was only 160k and the NES is easily emulated on a PC. As
there are open-source interpriters available, it should be feasable to
adapt it to serve an AI's needs.

One would need to hack the rom a bit to lay down traps for certain
events such as bumping into something but that shouldn't be to terrably
hard.

The idea is to then take all the IO+hacks, and then map them onto your
AI's simulated spinal chord.

If Link bumps into something, the event is trapped and sent to the AI's
mind and thus it learns... (It would also corelate this experience with
the audio and visual feedback).

The output would be the directional buttons, A, B, [select] and [start].

This approach is rather limiting as it doesn't give the AI any
real-world capabilities but it would serve quite well for demonstration
purposes.

The AI would need to demonstrate basic planning skills (ie: you should
restore your health and pick up some potions before attempting a big
level), as well as navigation using the map systems.

My godforsaken develment machine (if it ever works) should be well
suited to this type of experament.

Currently I am planning an AI based on an architecture that I call
mind-2. It is an attempt at a high-level brain emulation. It will not
use neurons but rather vectors and registers to achieve functional
equivalence to the apparent CAM organization of the brain.

This Mind-2 architecture is not a strong AI but it should be no less
general than the human brain. I've shifted my focus to it because it
doesn't require nearly as deep an understanding of the function of the
brain as would a strong AI. The mere fact that we have no AI at present
makes it a useful project.

A mind-2 architecture for Link can be greatly simplified next to the
complexity required for dealing with the real world. The organization of
this can be a small fraction of the size of a real-world intelligence.

---



### Re: [agi] TLoZ: Link's Awakening.



Shane Legg wrote:

An AGI wouldn't have this and so playing the game would be a
lot harder.

Of course and AGI *could* have this... but you need to build a
big knowledge base into your system and that's a big big job...
or custom build a knowledge base for this particular game into
your system, but is that cheating?

Cheers
Shane

---



### Re: [agi] AI on TV

maitri wrote:

The second guy was from either England or the states, not sure.  He was
working out of his garage with his wife.  He was trying to develop robot
AI including vision, speech, hearing and movement.

This one's a bit more difficult, Steve Grand perhaps?

http://www.cyberlife-research.com/people/steve/

Shane

---



### Re: [agi] AI on TV

Gary Miller wrote:

On Dec. 9 Kevin said:

It seems to me that building a strictly black box AGI that only uses
text or graphical input\output can have tremendous implications for our
society, even without arms and eyes and ears, etc.  Almost anything can
be designed or contemplated within a computer, so the need for dealing
needed to have a complete, human like AI.  It may even be better that
these first AGI systems will not have vision and hearing because it will
make it more palatable and less threatening to the masses

My understanding is that this current trend came about as follows:

Classical AI system where either largely disconnected from the physical
world or lived strictly in artificial mirco worlds.  This lead to a
number of problems including the famous symbol grounding problem where
the agent's symbols lacked any grounding in an external reality.  As a
reaction to these problems many decided that AI agents needed to be
more grounded in the physical world, embodiment as they call it.

Some now take this to an extreme and think that you should start with
robotic and sensory and control stuff and forget about logic and what
thinking is and all that sort of thing.  This is what you see now in
many areas of AI research, Brooks and the Cog project at MIT being
one such example.

Shane

---



### Re: [agi] AI on TV


I think my position is similar to Ben's; it's not really what you
ground things in, but rather that you don't expose your limited
little computer brain to an environment that is too complex --
context free languages, could well be too rich for a baby AI.
Trying to process 3D input is far too complex.  Better then to
The A2I2 project by Peter Voss is taking a similar approach.

Once very simple concepts and relations have been formed at this
level then I would expect an AI to be better able to start dealing
with richer things like basic language using what it learned
previously as a starting point.  For example, relating simple
patterns of language that have an immediate and direct relation
from there.

Shane

---



### Re: [agi] Inventory of AGI projects


I think the key fact is that most of these projects are currently
relatively inactive --- plenty of passion out there, just not a
lot of resources.

The last I heard both the HAL project and the CAM-brain project
where pretty much at a stand still due to lack of funding?

Perhaps a good piece of information to add to a list of AGI projects
would be an indication of the level of resources that the project has.

(I'm currently between places and only on the internet via cafes...
So I won't be very active on this list for a few weeks at least)

I suppose I should give a short who-am-I for those who don't know:
I'm a New Zealand mathematician/AI kind of a guy, worked for Ben
for a few years on Webmind and spend most of this year working for
Peter Voss on the A2I2 project.  I'm into complexity and intelligence
and am starting a PhD with Marcus Hutter at IDSIA in a few months
working on a mathematical definition of intelligence that he's come
up with.

Cheers
Shane

--- Ben Goertzel [EMAIL PROTECTED] wrote:

Hi,

Inspired by a recent post, here is my attempt at a list of serious AGI
projects underway on the planet at this time.

If anyone knows of anything that should be added to this list, please let me
know.

. Novamente ...

· Pei Wangs NARS system

· Peter Vosss A2I2 project

· Jason Hutchens intelligent chat bots, an ongoing project that for a while
was carried out at www.a-i.com

· Doug Lenats Cyc project

· The most serious traditional AI systems: SOAR and ACT-R

· Hugo de Gariss artificial brain

· James Rogers information theory based AGI effort

· Eliezer Yudkowskys DGI project

· Sam Adams experiential learning project at IBM

· The algorithmic information theory approach to AGI theory, carried out by
Juergen Schmidhuber and Marcus Hutter at IDSIA

. The Cog project at MIT

-- Ben

---