http://goertzel.org/Neural_Foundations_Symbolic_Thought.pdf

On Thu, Jul 27, 2017 at 9:54 PM, EdFromNH . <[email protected]> wrote:

> Ben, could you please send me a free author's copy of the paper at
> http://ieeexplore.ieee.org/document/6889662/ .  Ed Porter
>
> On Thu, Jul 27, 2017 at 12:44 AM, Nanograte Knowledge Technologies <
> [email protected]> wrote:
>
>> Ben
>>
>> Conceptually, I like where you are going with this. Your team's work with
>> human-language-based robotic communication is astounding.
>>
>> I think your idea of a universal attractor has merit. I suppose, in the
>> end, when matter exists, it generates an elcetro-magnetic field. In a
>> genetic sense, the flux of such a field would act as an open and
>> closed-loop communications network. In this sense, the relevant data,
>> information, and a relative perspective of knowledge, would all be packaged
>> within relative, genomic code. In other words, we are imagining a relative
>> system of relative systems from which reality would functionally emerge.
>>
>> Given my systems methodology, what remains to be done in order to
>> visualize a model of human-like machine reasoning, is to be able to link
>> your "attractor" value to the information, from which it should become
>> possible to systematically emerge any informational concept at any level of
>> abstraction within any, dimension of reasoning. The genetics of resultant
>> information would in theory make forward and backchaining possible, and
>> much more.
>>
>> The completeness schema of functional, attractor values seems to be a
>> critical machine-reasoning component to pursue. It would probably also
>> assume the role of a priority systems constraint. I've been doing much
>> thinking about this as a next-step for my own research.
>>
>> I think you've got this. Keep up the great work.
>>
>> Rob
>>
>> ------------------------------
>> *From:* Ben Goertzel <[email protected]>
>> *Sent:* 27 July 2017 04:57 AM
>> *To:* AGI
>> *Subject:* Re: [agi] Neuroscience-Inspired AI
>>
>>
>> Well I would say that none of the work done at Deep Mind and also none of
>> the ideas in Demis etc.'s paper address the questions I raised in this paper
>>
>> http://ieeexplore.ieee.org/document/6889662/
>> How might the brain represent complex symbolic knowledge? - IEEE Xplore
>> Document <http://ieeexplore.ieee.org/document/6889662/>
>> ieeexplore.ieee.org
>> A novel category of theories is proposed, providing a potential
>> explanation for the representation of complex knowledge in the human (and,
>> more generally,
>>
>>
>> (sorry for the paywall ... use sci-hub.cc ...)
>>
>> So there is no real plan for how to achieve abstract symbolic reasoning
>> as needed for human level general intelligence within a purely formal-NN
>> type approach
>>
>>
>> Obviously in opencog we are taking more of a symbolic-neural approach so
>> we don't have issues with abstraction
>>
>> Also if you look at the recent Markram et al paper on algebraic topology
>> and mesoscopic brain structure, there is nothing in the Hassabis etc.
>> universe that seems to address how such structures would be learned or
>> would emerge
>>
>>
>>
>> But sure in a big-picture historical sense the progress happening these
>> days on "narrow AI verging toward AGI" and on "making complex cognitive
>> architectures finally do stuff" is super exciting.   We are on the verge of
>> multiple breakthroughs within the next few years.   Woo hoo !!
>>
>> - -Ben
>>
>>
>> On Thu, Jul 27, 2017 at 5:55 AM, EdFromNH . <[email protected]> wrote:
>>
>>> About the above linked Hassabis paper, Ben said, "It's sort of a high
>>> level inspirational paper... it does lay down pretty clearly what sort of
>>> thinking and approach Deep Mind is likely to be taking in the next years
>>> ... there are no big surprises here though as this has been Demis's
>>> approach, bias and interest all along, right?"
>>>
>>> From my knowledge of several articles and videos by, or about, Hassabis --
>>>  I totally agree.  But I am a little less ho-hum than Ben, perhaps because
>>> I'm not as up on the current state of AGI as Ben.
>>>
>>> Reading Hassabis's paper makes me bullish about how close we are to
>>> powerful, if not fully human-level AGI, within 5 years.
>>>
>>> Why?  Because all of the unsolved challenges Hassabis discusses seem
>>> like they could be easily solved if enough engineering and programming
>>> talent was thrown at them.  I feel like I could relatively easily
>>> -- within a few months -- weave plausible high level architectural
>>> descriptions for solving all of these problems, as, presumably, people
>>> like Demis and Ben could do even better. (Perhaps that is why Ben is so
>>> ho-hum about the paper.)  With the money that's being thrown into AGI, and
>>> the much greater ease of doing cognitive architectural experiments made
>>> possible with Neural Turing Machines -- which allow programmable, modular
>>> plug-and-play with pre-designed and pre-trained neural net modules -- the
>>> world is going to get weird fast.
>>>
>>> Tell me why I am wrong.
>>>
>>> On Sun, Jul 23, 2017 at 8:29 PM, Ed Pell <[email protected]> wrote:
>>>
>>>> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5467749/
>>>>
>>>>
>>>> On 7/23/2017 4:18 PM, Giacomo Spigler wrote:
>>>>
>>>>>
>>>>> An Approximation of the Error Backpropagation
>>>>> Algorithm in a Predictive Coding Network
>>>>> with Local Hebbian Synaptic Plasticity
>>>>>
>>>>
>>>>
>>>>
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>>
>>
>>
>> --
>> Ben Goertzel, PhD
>> http://goertzel.org
>>
>> "I am God! I am nothing, I'm play, I am freedom, I am life. I am the
>> boundary, I am the peak." -- Alexander Scriabin
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-- 
Ben Goertzel, PhD
http://goertzel.org

"I am God! I am nothing, I'm play, I am freedom, I am life. I am the
boundary, I am the peak." -- Alexander Scriabin



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