Steve Richfield asks: > 1. THEORY: In broad computer science terms, how does your system work? > From what I can tell, it is an ad hoc text manipulation program capable > of gathering information and answering simple questions within the limited > subject domains that have been programmed. Right?
Aside: "ad hoc" (Latin "for this") means "special purpose" or "non-general". No, Steve, my AI system is a very general emulation of the human brain -- which I spent the first thirteen years (from Anno Ben Goertzel 0-13) of my efforts deciphering into a http://mind.sourceforge.net/theory5.html Theory of Mind. So it is not simply a "text manipulation program" but rather a "concept manipulation program." As such -- dealing with concepts -- it is not restricted to "limited subject domains" but rather it may deal with any subject imaginable. It does not deal with "domains that have been programmed" but rather with "structures of thought that have been programmed", such as Subject-Verb-Object (SVO) and query-formats such as "WHAT DO [subject]s DO?" and "WHO [verb]s [object]?" Steve Richfield asks: 2. APPLICATION: What will your approach be able to do that the machine learning approaches discussed here can never ever be extended to do, and why? As I understand ML, machine learning massages enormous data-sets to discover patterns and to make predictions (such as Matt Mahoney et al. talk about). My three AI Minds -- all basically the same program in Perl, JavaScript and Forth -- deal with brief (small) inputs and not with the statistics of large data-sets. The most significant achievement of each AI Mind is Natural Language Understanding (NLU) as posed as an AI-hard problem at the http://en.wikipedia.org/wiki/Natural-language_understanding webpage. That is, my AI Minds understand natural language inputs insofar as the minds assign the correct associative tags among the concepts mentioned in each input. At first each AI could only understand single-sentence inputs in the Subject-Verb-Object format. Then in 2016 the ghost.pl AI became able to understand the input of indirect objects ("John gives the BOY a book") and prepositional phrases ("John works IN the school"). Now in 2019 the AI Minds are beginning to understand the extremely complex use of conjunctions. Please see http://ai.neocities.org/EnVerbPhrase.html for how the AI Mind can shorten multiple ideas AND-ed together into a run-on sentence. MP says: > in one of his "earlier" journels, he references a "boulematic accumulator" - > in normal lingo, it means neuron, like a neural network neuron. That document was my private journal of AI theorizing. "Bouleuma" is the Greek word for "will" or "volition". I could have written "volitional accumulator". In the http://ai.neocities.org/Volition.html webpage on 2019-02-08 I wrote: "3.B. A chief characteristic of AI volition is the integrative nature of the will as it contemplates a candidate for action. Feelings or ideas in favor of a proposed initiative gradually move the Volition module towards the threshold of launching the motor execution of the proposed behavior, while contrary feelings and countervailing ideas delay or even prevent the launching of the motor initiative." Joshua Maurice wrote: > Probably most people here haven't had time to look at 15k lines > of code and form an evaluation of it. As I gradually do more and more debugging of each AI Mind, people will not need to inspect the code so much as simply to interact with the AI. Cheers, Arthur On Sat, Feb 16, 2019 at 10:50 AM Steve Richfield <[email protected]> wrote: > Arthur, > > I have been one of your few supporters, but if you are going to usefully > engage with the present audience, you REALLY need to answer two questions, > that if done well will lead to other questions, that will lead to a useful > conversation... > > 1. THEORY: In broad computer science terms, how does your system work? > From what I can tell, it is an ad hoc text manipulation program capable of > gathering information and answering simple questions within the limited > subject domains that have been programmed. Right? > > 2. APPLICATION: What will your approach be able to do that the machine > learning approaches discussed here can never ever be extended to do, and > why? For example, my system works to diagnose chronic illnesses in a way > that can NEVER EVER be equalled with ML approaches. From what I can tell, > your system might be extended to make a really good military inventory > program. > > As with all AI programs, their authors have dreams for them that exceed > everyone else's expectations, and you and I are no exceptions. I understand > that ONLY ad hoc logic will EVER be capable of incorporating human > understanding of our world into a computer, a simple fact that is > universally rejected by others on this forum for NO good non-religious > reason. So, until others here wake up, at minimum, I should be able to > relate to your postings. If you can't carry me along, then you truly are > COMPLETELY wasting your time and your life by continuing to post. > > Steve Richfield > > > *Artificial General Intelligence List <https://agi.topicbox.com/latest>* > / AGI / see discussions <https://agi.topicbox.com/groups/agi> + > participants <https://agi.topicbox.com/groups/agi/members> + delivery > options <https://agi.topicbox.com/groups/agi/subscription> Permalink > <https://agi.topicbox.com/groups/agi/Tc360a468d4050822-M52937d89df6c0330608929d7> > > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tc360a468d4050822-Mb0b69c64698c1ee9a2a63d37 Delivery options: https://agi.topicbox.com/groups/agi/subscription
