The other views that I forgot to add is that the knowledge objects
have to components that can be combined in various ways and that there
are no absolute elementary knowledge object. Every kind of knowledge
object including the particles that more coherent objects are made of
have the potential to be opened, explored and related to the greater
world of concepts (knowledge objects.)

Jim Bromer


On Fri, May 1, 2015 at 7:01 AM, Jim Bromer <[email protected]> wrote:
> How can I describe the features and behaviors of a group of
> hypothetical algorithms that would contain the potential to achieve
> advances in AI so that I have some basis for actually designing them?
> The first step is to describe some algorithms from narrow AI and then
> show that my algorithms should, hypothetically, be stronger than them.
>
> You might just say that stronger AI is going to need all kinds of
> algorithms but that does not give you enough to start thinking about
> how you might design an advanced AI program.
>
> First of all, stronger AI has to be more than learning to associate
> particular responses to particular inputs. It also has to be more than
> mere numerical extrapolation or interpolation based on the use of a
> numerical method that represents some particular problem.
>
> So then simplistic reinforcement, for example, is not - in itself -
> enough. Numerical methods - in themselves - are not going to be
> enough.
>
> One thing that I realized while trying to talk to Mike Tintner was
> that true AI needs varying field sizes in order to hold enough
> variation to avoid degenerating into simplistic extrapolations and
> interpolations. (The data type does not have to be variable as long as
> they can be used in strings and fields which are.)
>
> You need some kind of trial and error in stronger AI. You also need to
> recognize that knowledge objects are not typically commensurate. So
> your program needs to be able to fit the pieces of knowledge together
> to see what makes sense and what does not. It needs to discover what
> might be relevant to some situation and what needs to be 'translated'
> from one knowledge object to another. It needs to recognize that even
> though two or more knowledge objects may be relevant, the features may
> not fit against the relevant features of the others. You might use
> simple association or numerical correlation to designate these poorly
> fitting parts but it is often going to take greater insight to
> effectively integrate the different kinds of relevant knowledge
> objects.
>
> So finally I think that Stronger AI is going to need
> reason-based-reasoning. (I still cannot understand how people in these
> AI discussion groups have actually denied that.) In order to learn how
> to use reasons effectively they will need to be integrated with the
> knowledge objects that they are being used with.
>
> Jim Bromer


-------------------------------------------
AGI
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