I don't see how I am going to make a serous contribution to  XAI at
this time. However, I am interested and I have some insights that
could help. And I would be happy to do some coding if I could be part
of a group.

One of my recent insights is on what I am calling the computational
advantage. There are two types of computational advantages, one is
computational mathematics (arithmetic) and the other is precise memory
of reasonably finite data. It is my conclusion that if you want to
create a competitive product in a challenging field you are going to
need to use these two forms of computational advantage.

But, I do not think that the past efforts in this field are adequate
at this time. For example, the attempt to use semantic vectors looks
really awful to me. (I am not completely dismissive of that approach,
I just do not think that it will work in the near future.) And
although deep learning has made some significant advances I am a
little skeptical of methods which build approximations on
approximations. I do not doubt that we use approximations in our
thinking, but I just have an intuitive sense that a computer program
should use distinct memories to build and act as representatives of
generalizations and abstractions. I am thinking that if an XAI program
is going to build generalizations of some sort it has to be able to
explore the distinct memories of the objects (or of representatives of
the objects) which were foundation to those generalizations. That way,
the use of a speculative generalization or abstraction could be
accompanied by particulars which could be used to examine the use of
the abstraction from other conceptual vantage points. From this basis
I would say that we should develop a mathematical system to index
these kinds of relations so that the effort to find relevant
characteristics of a situation could be done using the computational
advantage of arithmetic thus avoiding many of the deep search
problems.
Jim Bromer


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