I don't think the HTM can be described as discreet-based-AGI. However,
discrete network models can take on so many properties of the network
models that some of the distinctions may be more theoretical or based
on some appreciation of model confluences.

I think that discrete-based AI is a stronger base to work on just
because concepts can be described so concisely and relative properties
(like the abstraction of varieties of relations that may have an
implicit or correlated impact on some concept) can be described
soundly. The problem is complexity and what I am suggesting in this
thread is that perhaps the combinatorics of discrete systems should be
considered fundamental to systems that have a strong logic-like basis
instead of being seen as an auxiliary that is used in analysis or
specialization of these systems.

However, I am very interested in the SDR paper and I will read it carefully.
Jim Bromer


On Mon, Jun 8, 2015 at 9:35 PM, Matt Lind <[email protected]> wrote:
> Jim:
>
> Agree. An interesting concept in this regard is SDR (sparse distributed
> representation) which heavily relies on those characteristics of
> combinatorics. Numenta's HTM (hierarchical temporary memory) framework uses
> SDRs to model the brain's internal communication language. Interesting read:
> http://arxiv.org/abs/1503.07469
>
> Matt
>
>
>
> 发自我的iPhone
>
>
> ------------------ 原始邮件 ------------------
> 发件人: Jim Bromer <[email protected]>
> 发送时间: 2015年06月09日 02:50
> 收件人: AGI <[email protected]>
> 主题: [agi] Combinatorics Is Important to Use With Discrete-Based AI
>
> I have not been that interested in combinatorics, graph theory,
> weighted reasoning, Bayesian methods and so on because I felt that
> these were all logic based and that the simplest way to develop an AGI
> application would be to stick to basics and only use simple forms of
> these methods if I needed them. I believed that there are certain
> discreet obstacles to advancing AGI at this time and solutions to
> these problems really needed to be found before any substantial
> advances could be made. However, now that I am starting to appreciate
> how combinatorics can help with discreet based systems and may even be
> essential to using logic effectively, I am, unsurprisingly, changing
> my view about this.
>
> So I recommend that anyone in this group who feels as I felt should
> start brushing up on combinatorics, graph theory and so on.
>
> Many of us know that computers are good at working with narrow results
> and this has produced major advances in ‘narrow AI’. Developers who
> worked with probability reasoning thought that their methods should be
> sufficient to overcome the narrow AI dilemma, but I haven’t seen that.
> (I do appreciate the many advances that AI has made and I do not
> totally accept the narrow AI dismissal, but on the other hand I am
> still amazed that AI programs do not seem to exhibit the ability to
> learn even in the simple ways that we would expect from toddlers.)
>
> So why are combinatorics so important for discrete symbolic AGI? Using
> logic as an example of a tightly defined discrete system, you can see
> that multiple solutions can be held in a tightly compressed formula
> string.  What is more, subclasses of these different solutions can
> often be derived from a portion of the formula or from a simple
> analysis and rearrangement using portions of the formula.
> Combinatorics is one way we can get to this logical multiplicity as it
> may relate to logical sub-relations.  Right now these methods are
> limited but there is no reason to expect that this method will be
> stuck with its current limitations.
> Jim Bromer
>
>
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