Piaget Modeler,
Thanks for telling me. Just from what you said, I'd reply the same I replied
to Alan. This work seems to be traditional narrow AI, not AGI.
PIAGET MODELER> Breaking channel (modality) dependence just means creating
rules for intermodel associations. That also can be programmed.
SERGIO> Who makes the rules? A human analyst? You are using human
intelligence to create man-made rules. Programming them makes no difference.
Ray Solomonoff has said: "A heuristic programmer would try to discover how
he himself would solve (some problem) - then write a program to simulate
himself." The only visible intelligence is the analyst's, which she used to
create the program. But the program does not know how or what created it,
and contains no intelligence. Proof: how do you make changes to the program?
You call the analyst, right?
By contrast, in emergent inference, there is a host/guest architecture. The
host has no program, and no human analyst is necessary, so it does not rely
on human intelligence at all. If the host runs on a computer, then there
must be a program of course, but it only manages I/O and minimizes the
functional. The program is always the same, never changed, no matter what
the associations are, or what the problem is. Just like our brains.
The guest is the knowledge acquired from the world. It makes no difference
how it is acquired, by sensors or from a teacher. The host processes the
guest and creates the response algorithm (perhaps the program we were
talking about), without any supervision.
PIAGET MODELER> In the PAM-P2 system, there are Associator processes which
create associations. The only human necessary is the designer.
SERGIO> Creating associations ("binding the qualia") is a century-old
problem. Many of the greatest of the 20th century (Turing and Church among
them) were involved with it. It is still very active, and there must be
other "associators" around (just guessing). The problem is that you need
intelligence to make associations, not the other way around. Intelligence
comes first, then the associations. Proof: why do you need a human designer
for your Associator? Answer: because you need the designer's intelligence,
and you don't have any intelligence in the program.
PIAGET MODELER> PAM-P2 is under construction. Stay tuned.
SERGIO> Thanks.
Sergio
From: Piaget Modeler [mailto:[email protected]]
Sent: Friday, June 29, 2012 7:09 PM
To: AGI
Subject: RE: [agi] Building high-level features using large scale
unsupervised learning
Sergio,
In the PAM-P2 system, there are Associator processes which create
associations. The only human necessary is the designer.
Breaking channel (modality) dependence just means creating rules for
intermodel associations. That also can be programmed.
PAM-P2 is under construction. Stay tuned.
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> From: [email protected]
> To: [email protected]
> Subject: RE: [agi] Building high-level features using large scale
unsupervised learning
> Date: Fri, 29 Jun 2012 10:52:11 -0500
>
> Alan,
>
> You make the same traditional mistake that many make. You offer a
> substantial scientific contribution obtained by humans using their human
> intelligence, but never explain the process in their brains that made it
> possible for them to do all that. Jeff Hawkins (On Intelligence) does the
> same. His entire book revolves around certain "invariant representations"
> but he never explains where those come from. Your proposal is the usual
> mixture of man-made intelligence and promises. It does not contribute to
> artificial intelligence.
>
> The keywords are "association" and "emerge." You write:
>
> "The next challenge is that you need to break channel dependence and
> introduce associations between patterns ie with faces and the various
> representations of the word "face". I suspect that once channel dependence
> is fixed, then, at some high level in the network, these associations will
> emerge on their own. "
>
> "Introduce associations" means, in your mind, that you or some other human
> will be using their human intelligence, not artificial intelligence, to
find
> those associations and introduce them in the program. "Emerge" indicates
you
> realize that something is still missing and blame something else that you
> can't explain. You are not alone.
>
>
> Sergio
>
>
> -----Original Message-----
> From: Alan Grimes [mailto:[email protected]]
> Sent: Friday, June 29, 2012 8:20 AM
> To: AGI
> Subject: Re: [agi] Building high-level features using large scale
> unsupervised learning
>
> Ben Goertzel wrote:
> > How exactly do you suggest to bridge the functionality gap between
> > visual pattern recognition and all the other things human beings do?
>
> =)
>
> Setting aside problems noted as still being unsolved, here's a crude
sketch
> of how the system can be organized. For the sake of brevity, only the
> cortical-thalamic-cortical system will be considered.
>
> The first thing to note is that this is an unsupervised pattern learner.
> That should be pretty amazing all by itself. The second thing to note is
> that all it deals with are vectors of numbers. There is no reason on earth
> that it can't be made to work with any conceivable stimulus that can be
> encoded as a vector of numbers. There are some serious channel dependence
> problems, previously noted, but the basic process is present.
>
> The third thing to note is that they could run their matrix stack in
reverse
> and "imagine" what a face looks like. This is critical, especially for
motor
> control! =P
>
> This is your basic algorithm. The next challenge is that you need to break
> channel dependence and introduce associations between patterns ie with
faces
> and the various representations of the word "face". I suspect that once
> channel dependence is fixed, then, at some high level in the network,
these
> associations will emerge on their own.
>
> The next issue is topology. You could organize the topology like the human
> brain and, in theory, it should be human equivalent. Motor control is
> implemented just like perception. It builds up complex sequences of
actions
> from simple sequences of actions exactly as complex perceptions are built
up
> from simple perceptions. To do something, you just run the stack in
reverse,
> as mentioned above. Combined with channel dependence and free association,
> you obtain arbitrary sequences of planned actions.
> Actions that are fully learned become habitual (simply initiate the top
> level abstraction). Other actions require an iterative system-wide process
> for planning, but most of the mechanisms are already present.
>
> You obtain episodic memory by having a pipeline that associates concurrent
> perceptions, which appears to be what the hypocampus does.
>
> To obtain super-human intelligence, you need to make the topology of the
> system adaptive, or even accessible to the system itself. Ideally, you
want
> a highly redundant, highly distributed, highly parallel and highly
efficient
> architecture. This architecture does have a second class of scalability
> issues, each matrix, at each level of abstraction is of fixed size, There
> needs to be a process that simplifies and consolidates knowledge to a more
> ideal representation. At that point you're off the edge of the
> (metaphorical) napkin I sketched this all out on. =P
>
> About 80% of everything else you need is already available off the shelf,
> the other 20% might have some important, perhaps even difficult,
challenges
> but then we're talking about emotions and motivation instead of
> intelligence.
>
> --
> E T F
> N H E
> D E D
>
> Powers are not rights.
>
>
>
>
>
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