I think just skimmed through the outline html -- it seems like a good
start.  I wouldn't start writing any code for quite a while yet.  It
seems to me that you need to fight with those issues first.

On 4/20/13, Logan Streondj <[email protected]> wrote:
> On Sat, Apr 13, 2013 at 6:39 AM, Jim Bromer <[email protected]> wrote:
>
>> Part 1
>>
>> I feel that complexity is a major problem facing contemporary AGI.  It is
>> true, that for most human reasoning we do not need to figure out
>> complicated problems precisely in order to take the first steps toward
>> competency but so far AGI has not been able to get very far beyond the
>> narrow-AI barrier.
>>
>> I am going to start with a text-based AGI program.  I agree that more
>> kinds of IO modalities would make an effective AGI program better.
>> However,
>> I am not aware of any evidence that sensory-based AGI or multi-modal
>> sensory based AGI or robotic based AGI has been able to achieve something
>> greater than other efforts. The core of AGI is not going to be found in
>> the
>> peripherals.  And it is clear that starting with complicated IO
>> accessories would make AGI programming more difficult.  It seems obvious
>> that IO is necessary for AI/AGI and this abstraction is a probably more
>> appropriate basis for the requirements of AGI.
>>
>> My AGI program is going to be based on discreet references. I feel that
>> the argument that only neural networks are able to learn or are able to
>> incorporate different kinds of data objects into an associative field is
>> not accurate. I do, however, feel that more attention needs to be paid to
>> concept integration.  And I think that many of us recognize that a good
>> AGI model is going to create an internal reference model that is a kind
>> of
>> network.  The discreet reference model more easily allows the program to
>> retain the components of an agglomeration in a way in which the
>> traditional
>> neural network does not.  This means that it is more likely that the
>> parts of an associative agglomeration can be detected.  On the other
>> hand, since the program will develop its own internal data objects, these
>> might be formed in such a way so that the original parts might be
>> difficult
>> to detect. With a more conscious effort to better understand concept
>> integration I think that the discreet conceptual network model will prove
>> itself fairly easily.
>>
>
> Yep, so can do concept integration, by representing concepts with
> sentences.
> It works, it's simple, it can show association, it maintains original
> parts.
>
>
>
>> I am going to use weighted reasoning and probability but only to a
>> limited
>> extent.
>>
>
>
>
> On Sat, Apr 13, 2013 at 7:34 AM, Jim Bromer <[email protected]> wrote:
>
> Part 2
>
>> I believe that it takes a great deal of knowledge to 'understand' one
>> thing.  A statement has to be integrated into a greater collection of
>> knowledge in order for the relations of understanding to be formed.
>>
> Just like how a sentence can be integrated into a story.
>
>
>> And the knowledge of a single statement has to be integrated into a
>> greater field of knowledge concerning the central features of the subject
>> for the intelligent entity to truly understand the statement.  While
>> conceptual integration, by some name, has always been a primary subject
>> in
>> AI/AGI, I think it was relegated to a subservient position by those who
>> originally stressed the formal methods of logic, linguistics, psychology,
>> numerics, probability, and neural networks.  Thinking that the details of
>> how ideas work in actual thinking was either part of some
>> predawn-of-science-philosophy or the turn-of-the-crank production of the
>> successful application of formal methods, a focus on the details of how
>> ideas work in actual problems was seen as naïve.
>>
>
> Many people understand things through story. Indeed it is the way in which
> our brains are designed to operate and interpret new information, since the
> cave paintings at the very least.
>
> It's also the most effective way of transmitting information from one
> person to another, as it often bypasses much of the conscious criticality,
> and simply subsumes into the subconscious background.
>
> Even computers understand things through story,  even thought the typical
> programming language may make this hard to see, however the setting or
> variables are declared intially, the rising action is the preperation of
> the variables for interaction, then the conflict or change is the actual
> transmutation of the variables, and resolution is the returning of the
> result.
>
>
>> This problem, where the smartest thinkers would spend lives pursuing the
>> abstract problems without wasting their time carefully examining many
>> real
>> world cases occurs often in science.  It is amplified by ignorance.  If
>> no one knows how to create a practical application then the experts in
>> the
>> field may become overly pre-occupied with the proposed formal methods
>> that
>> had been presented to them.  Formal methods are important - but they are
>> each only one kind of thing.  It takes a great deal of knowledge about
>> many different things to 'understand' one kind of thing.  A reasonable
>> rule of thumb is that formal methods have to be tried and shaped based on
>> exhaustive applications of the methods to real world problems.
>>
> In order to integrate new knowledge the new idea that is being introduced
>> usually has to be verified using many steps to show that it holds.
>>
>
> well there is always parsing, and compiling, if it works it works.  Though
> factual information about the world could be statistically
> cross-referenced.
>
>
>> Since there is no absolute insight into truth for this kind of thing,
>> knowledge has to be integrated in a more thorough trial and error manner.
>>
>
> truth is personal experience, so each perspective has it's own truth.
> knowledge is past-experience, there is true knowledge, and real knowledge.
> real is the set of beliefs that are in common amongst a group.
> exist is anything that can be imagined/compiled.
>
>
>>   The program has to create new theories about statements or reactions it
>> is considering.  This would extend to interpretations of observations for
>> systems where other kinds of sensory systems were used.  A single
>> experiment does not 'prove' a new theory in science.  A large number of
>> experiments are required and most of those experiments have to
>> demonstrate
>> that the application of the theory can lead to better understanding of
>> other related effects.
>>
>
> you know it all depends, are we making little scientists, or AGI's?
> Is there purpose to "prove" stuff, or rather simply to "do" stuff.
> Sure they could use some scientific method, and statistical verification,
> however it's more important to actually get stuff done i.e. result of
> experiment, than have it published in a peer-reviewed journal, or get a
> bunch of peers to re-do the experiments.
> The experiment and results could be shared with other AGI's and people over
> the internet, likely in the form of a story,   if others come across
> similar issues they may wish to try it themselves, and comment if it works
> out for them.
>
>
>> It takes a knowledge of a great many things to verify a statement about
>> one thing.  In order for the knowledge represented by a statement to be
>> verified and comprehended it has to be related to, and integrated with, a
>> great many other statements concerning the primary subject matter.  It is
>> necessary to see how the primary subject matter may be used in many
>> different kinds of thoughts to be able to understand it.
>>
>
> I disagree,   as you don't have to know all the ways to add stuff, to
> simply add some numbers together.
> You can easily learn new things later on, like how to perform addition
> amongst new types of things, like for instance arrays, or ingredients.
> Gotta start somewhere, and arithmetic addition is a sufficient place to do
> so.
>
>
>
>
>
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