That was a great conference. I'm still kind of buzzed.  My ideas are
partially identical to everybody else's with a few exceptions.

On Mon, Oct 18, 2021 at 12:18 PM Jim Bromer <[email protected]> wrote:
>
> I am really enjoying listening to AGI-21 presentations. I liked Ben's 
> presentation and I learned something from it although I can't remember most 
> of it off hand. I also got something about the discussion about databases 
> even though that is more of a user-group thing. But I find myself heartily 
> approving much of what Linas is saying.  First of all I agree about his 2013 
> ideas about hypergraphs (which Ben mentioned) but I am concerned because 
> there isn't even a hint of a fantasy of fail-safe in that. (DBT: Dragons Be 
> There.)  But if you are going to rely on graph theory than hypergraphs seem 
> like a necessity for general intelligence that is capable of reflection and 
> understanding.
> But Linas's presentation on INLP is really interesting to me because I have 
> been thinking about something I call Artificial Artificial Neural Networks 
> and Linas had some ideas that could be relevant to what I would like to do. 
> (I have only thought about it and I do not see myself getting much of 
> anything done because my life of luxury is quickly coming to an end.) For 
> instance his normalization technique to find Similarity Scores is 
> interesting. I have thought about things like that but never tried anything.  
> I probably do not understand some of it but I am in the ballpark. I did 
> notice that he hasn't demonstrated that his Explainable Patterns INLP really 
> would work in more complicated situations but it is an important step to try 
> something like he did try.
>
> I am so glad that someone is capable of thinking outside the box.
>
> As I have been watching the presentations I have wondered if highly developed 
> abstractions are really necessary for advancing toward AGI. (I think 
> abstractions of 'expression' are necessary but I have wondered about 
> developed graph theory or logic or developed probability theory and so on).
> There was another reason I wondered about highly developed abstraction 
> theories. They are amazing in mathematics but, as I have repeatedly 
> mentioned, computational arithmetic is effective because the n-ary or base n 
> representations of numbers where n>1 is an effective compression of the 1-ary 
> representation (ie making a mark for each item that is being counted.) And 
> then, the higher n-ary representations can be used in computational 
> arithmetic without being decompressed. It's amazing and it is the reason that 
> math is so important in science and things like computers.
> But when you use abstractions for AI you do not have the spectacular 
> compression of (a general) representation of objects and of compression of 
> functions on those objects. So are highly developed abstractions for AGI 
> really going to be that useful? One thing I did not like about old-fashioned 
> probability reasoning was that the probability resultants lose the 
> association with their relevant sources of input. For a simple example, if we 
> want to try various what-if cases then the sources have to be found and 
> reimplemented. Yes they can be implemented but then the efficiency of the 
> system would be lost.
> But I found an answer to my question: abstract representations might be 
> useful when the abstractions refer to reasoning itself (rather than just the 
> 'factoid' 'objects' of reasoning.)  If an abstract form refers to some 
> reasoning, then not only could it be used to represent the generalization of 
> the reasoning, but it could also be used to implement a tool to rapidly try 
> various what-if scenarios without needing to go through endless "what was I 
> thinking" unraveling's.
>
> Artificial General Intelligence List / AGI / see discussions + participants + 
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