Hi Ivan, I think best if you can spend a bit time on working on a few representative examples that shows what you can do with your embedded language. AI discussions tend to get very abstract, very quickly :-), so to "engineer" ground ourselves its best to talk by way of examples. This helps highlight what one really *means* :-) by what one does.
thank you, Daniel On Friday, 21 April 2017 00:25:19 UTC+3, Ivan Vodišek wrote: > > Mr. Daniel Gross, > > I'm afraid I'm going to leave the juicy AGI details to AGI developers (not > to say it is an easy part, far from that). I decided to be just a technical > guy, if anyone is interested in my low-level solution of programming > language that equally easy (or hard) solves application development and > inferring a knowledge. > > If you are interested in some of my unfinished work, I assembled a paper > for showing off with some of my academic friends > <http://lambda-the-ultimate.org/node/5413>, but it is not yet ready for a > broader public. Public version misses some examples > <https://docs.google.com/document/d/1uPGrUomiffB16osLkIWLROYLl4RYC6z-cx4fFkUDnZA/edit?usp=sharing> > > and their thorough explanations (as a proof of concept), but I decided > first to program the language, build an user community, and then to show up > my face to the real AGI researchers, if they would want to consider another > solution, whoever would be interested. > > The project is conceptually defined, I'm in a process of implementing the > language in Javascript and things look good to me. Wish me luck :) > > ivan > > > > 2017-04-20 22:52 GMT+02:00 Daniel Gross <[email protected] <javascript:>>: > >> Hi Ivan, >> >> thank you for your response. >> >> Pattern matching is a very general purpose mechanism -- in my mind key >> questions are: >> >> what governed the language for pattern description and the semantics of >> how patterns match with inputs >> what governs the language of transformational rules, triggered by patterns >> >> and finally, what mechanism creates patterns and the associated >> transformational rules, so that the inputs and outputs are correlated >> meaningful, relevant (semantically, temporally), and accurate enough in >> relation to the cognitive support they intend (i.e. teleological) provide >> >> >> Daniel >> >> On Thursday, 20 April 2017 23:40:05 UTC+3, Ivan Vodišek wrote: >>> >>> Hey Daniel, great to see someone interested in AGI :) >>> >>> How about us, humans, I mean how do we think? I'm not trying to resemble >>> our neural networks, I took another, top-down approach, in between, but >>> let's observe us as an thinking example. Do we see how our thoughts are >>> formed? I think that we don't see the math behind it (correct me if I'm >>> wrong). All we see in our mind is input sensory data, or memories of it. >>> From what we see in input, we try to adjust our output to reach the input >>> we care about. If we fail, we remember that we failed. If we succeed, we >>> remember the output actions to repeat them at places we find appropriate. >>> In this process, we can see only with our sensory input, yet we don't see >>> the math behind it. Looking from an AGI programming aspect, this math would >>> be that invisible part, the part of notions that programmers would type >>> into the machine. The machine (at run-time) doesn't need to see how it is >>> really functioning behind the curtain, just to perform actions based on its >>> input. Analogy is like the application user doesn't need to know how the >>> application is programmed to actually use the application. She enters some >>> data, observe output and she can do wonderful stuff without even seeing a >>> line of code behind the application. In that sense, it is possible for us >>> to change the world without knowing how we really do it. So, I assume, the >>> machine could do it in the similar fashion. >>> >>> Let's extrapolate this to our imaginary programming language, how would >>> code in this language work?. The code reads some input, do some math >>> invisible to users, and outputs something back to users, but what is this >>> output really? If we say that output is really just a replicated input from >>> the past, then even the programmer doesn't have to know the exact shape of >>> output. All the programmer needs to know is that user entered something >>> back there and that we want to replicate it in our output in given moment, >>> based again on similarities between input data without knowing what the >>> data actually is. And here we come to the essence of the problem: >>> similarity. We need a method to compare the inputs without knowing the >>> actual value of the input: we need to test if input I1 equals input I2. And >>> I believe (with some testing behind) that's all we need to do tasks as >>> complex as solving mathematical equations or concluding new knowledge. My >>> belief comes from existence of a mechanism called pattern matching. We >>> pattern match a set of rules against some input and provide relevant rule >>> output. Remember that all these rule inputs (causes) and outputs >>> (consequences) all came by simply remembering and replicating other inputs >>> from the past of running the same process. From what I've seen in my work, >>> with this pattern matching we can do pretty mean stuff, even comparing >>> numbers regarding to their positive or negative distance from zero, or >>> branching through different decisions, and all we need is testing if two >>> inputs are equal. We don't even have to know what these inputs represent, >>> numbers, letters, colors, cats or mice, to do something nice with them, >>> making the world a better place to live in. >>> >>> I hope I didn't scare you with this philosophy massage, things are a lot >>> simpler when it comes to burning in the rules by which the machine do this >>> or that, being changing lights on semaphore, or deciding the moment in >>> which it has to stop lip motors and speaker, not to offend a person in a >>> morning that asked "how do I look?" :) It could be all about input, >>> equality match and output. I am pretty sure about it by now. >>> >>> Tx for asking interesting questions :) >>> >>> ivan >>> >>> >>> 2017-04-20 21:37 GMT+02:00 Daniel Gross <[email protected]>: >>> >>>> Hi Ivan, >>>> >>>> Your work sounds very exciting ... would be great to hear more about >>>> it. >>>> >>>> I think one issue with the approach you are describing is that you have >>>> to assume the knowledge of a second language and a mapping, in principle, >>>> from the first to the second. >>>> >>>> I think systems that aim to self-learn (unsupervised) try to omit such >>>> an a-priori mapping because it would (presumably) make the knowledge >>>> capture process non-scalable. >>>> >>>> So, you end up with a system that tries to self learn meaning of system >>>> A on its own terms (and via "meta-cognitive" strategies derived from the >>>> machine learning approach at hand- which are by definition meaning >>>> agnostic) ... so i wonder where is the meaning in this kind of machine . >>>> -- if the semantic graph is actually constructed out of the machine >>>> learned >>>> parse of natural language text without a predefined mapping to a semantic >>>> graph (which is what ones want to build in the first place). >>>> >>>> I think this is essentially what confuses me -- if i managed to explain >>>> it correctly ... . >>>> >>>> Daniel >>>> >>>> >>>> On Friday, 14 April 2017 14:07:08 UTC+3, Alex wrote: >>>>> >>>>> Hi! >>>>> >>>>> What is the best texbook (most relevant to Opencog Node and Link >>>>> Types) in Knowledge representation? I am aware about books about PLN and >>>>> egineering AGI (and I am reading them and they are relevant to >>>>> probabilisti >>>>> reasoning side of knowledge represenatation), but I feel that e.g. >>>>> concepts >>>>> of inheritance (extensional and intensional) as adopted by OpenCog >>>>> Atomsapce is coming from earlier work - so from what work? I would like >>>>> to >>>>> see this work, to include it into broader context. I have adapted to UML, >>>>> ER, OO design and I am still struggling to model knowledge using OpenCog >>>>> nodes and links. That is why I am seeking more books to dive into this >>>>> line >>>>> of thinkin. >>>>> >>>>> I am reading now: >>>>> Knowledge Representation and Reasoning (The Morgan Kaufmann Series in >>>>> Artificial Intelligence) >>>>> <https://www.amazon.co.uk/Knowledge-Representation-Reasoning-Artificial-Intelligence/dp/1558609326/ref=sr_1_1?s=books&ie=UTF8&qid=1492167755&sr=1-1&keywords=knowledge+representation>17 >>>>> >>>>> Jun 2004 >>>>> by Ronald Brachman and Hector Levesque Dr. >>>>> >>>> -- >>>> You received this message because you are subscribed to the Google >>>> Groups "opencog" group. >>>> To unsubscribe from this group and stop receiving emails from it, send >>>> an email to [email protected]. >>>> To post to this group, send email to [email protected]. >>>> Visit this group at https://groups.google.com/group/opencog. >>>> To view this discussion on the web visit >>>> https://groups.google.com/d/msgid/opencog/54b5f383-e6a3-41bb-b2ee-64f7f7cc3c8f%40googlegroups.com >>>> >>>> <https://groups.google.com/d/msgid/opencog/54b5f383-e6a3-41bb-b2ee-64f7f7cc3c8f%40googlegroups.com?utm_medium=email&utm_source=footer> >>>> . >>>> >>>> For more options, visit https://groups.google.com/d/optout. >>>> >>> >>> -- >> You received this message because you are subscribed to the Google Groups >> "opencog" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to [email protected] <javascript:>. >> To post to this group, send email to [email protected] >> <javascript:>. >> Visit this group at https://groups.google.com/group/opencog. >> To view this discussion on the web visit >> https://groups.google.com/d/msgid/opencog/d608c012-cfd9-450a-804f-c612dc4228ef%40googlegroups.com >> >> <https://groups.google.com/d/msgid/opencog/d608c012-cfd9-450a-804f-c612dc4228ef%40googlegroups.com?utm_medium=email&utm_source=footer> >> . >> >> For more options, visit https://groups.google.com/d/optout. >> > > -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/opencog. To view this discussion on the web visit https://groups.google.com/d/msgid/opencog/7b5f0cdd-ddd8-454c-8485-9b55cb244ec9%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
