Hello Michael,Thank you for your email, your paper, and the discussion betweenJim, Steven, and Boris. It'll take me a few days to look at yourpaper, but before there are too many more contributions to the ongoing discussion let me respond to some items:1. Many teachers have recorded a classroom presentation and transcribed the recording, only to be quite surprised at what they actually said...This is very true of spoken language and requires what's calledrobustness. In DBS it is supplied by the lowest level of patternmatching, which correlates the core values in the spoken text to corresponding contents in the database. The amount of contentcoactivated in this way is reduced by inferencing. (see Sect. 11in the paper, FoCL 6.1.2, CLaTR Sect. 5.4)2. For all but AGI (that can't work for decades with any presently known approach because of a lack of processor power) and automatic language translation (that has a large interest in preserving the speaker/writer's frame of mind), there seems to be little real-world application for agent-oriented approaches.It seems to me that a computational theory of any kind should takecare to be of low mathematical complexity (linear or at worst polynomial).As Garey and Johnson showed in 1979 (FoCL 8.2.2), an algorithm may be decidable, but if it is exponential it may take longer than the existence of the universe, currently estimated at 3.77 billion years. So that wouldn't be helped by faster machines.As for applications of DBS, please see Sect. 13 in the paper.3. Summarizing from what I read from the discussion between Jim, Steve, and Boris, it seems an open question whether computers can *understand* natural language and engage in meaningful dialog. DBS takes the view that full understanding by a computer requires an agent with a body in the real world, with interface for recognitionand action. It can use the elementary recognition (e.g., red) and action procedures (e.g., take one step forward) as its basic concepts for building content, and reuse the associated types as the meaning of natural language content words. It is possible to move from such a talking robot to virtual agentswhich are essentially restricted to the keyboard and the screen ofa standard computer. However, as a consequence the virtual agents loose the procedures for autonomous recognition and action, and are thus reduced to core value *place holders* which are understood by the human users, but not by the machine. There are many applications for which virtual agents are sufficient.Also, they may make do with only the hear mode, leaving the thinkand the speak mode aside. A general theory of how natural languageworks is nevertheless useful for such applications because it providesa framework which allows to make different applications compatiblewith each other. Also, the framework may supply applications withoff-the-shelf components like automatic word form recognition,syntactic-semantic parsers, etc., in different languages, resultingin further standardization and interoperability.Happy Easter to you all!Roland
Date: Mon, 1 Apr 2013 02:35:44 -0700 Subject: Re: [agi] Steve's placement/payload theory of language From: [email protected] To: [email protected] Anastasios, On Sun, Mar 31, 2013 at 6:47 PM, Anastasios Tsiolakidis <[email protected]> wrote: On Sun, Mar 31, 2013 at 10:55 PM, Steve Richfield <[email protected]> wrote: Everyone in AGI seems to want to start at the front end (parsing) without knowing where they are going. My point through the discussion you quoted from is that most people expect things from NL "understanding" that are completely unachievable. Sure, you can tease out a LOT of the sort of information you discuss below, but most of it would come with Bayesian probabilities that aren't much better than 50%, and it wasn't at all obvious what to do with such soft data. It is difficult, for me at least, to follow these threads and make up my mind if you agree or disagree with each other, if you made up your own minds at least etc. We have discussed a LOT of details, but I sense general agreement. But Steve seems to include again and again some inaccuracies. Specifically, I am not ready to count even a single failure of NLP or AGI-NLP I have avoided naming names, but the literature is FULL of NL parsing and "understanding" projects, many of which got to the point of demonstrating interesting things, but then they faded away, instead of being populated with rules and turned into products. After talking with some of these people, and then running into my own brick wall in DrEliza.com, I decided to find a better way. since the systems I am familiar with have tried everything except the most obvious (and difficult): to model agents with a mix of intricate biased and unbiased world models and intentions. Language without a minimum of two mental worlds and one "objective" world is nothing but mad ramblings. Perhaps, but does it make sense to parallel this process to tease out this information? The obvious answer is "yes", but there are a LOT of problems doing this in real time. Similarly, several of the AGI builders of the day, myself included, started away from parsing and closer to either the mental worlds and/or the objective one(s), and Ben for example is not in a hurry to focus on the front-end. Shame on us I'd say, since after decades of publications on summarization, disambiguation etc it was a 17 year old who cashed in his summarization service. As Steven mentioned before, the world could be a different place if a few of us here had multimillion dollar liquidity. Yea, either you guys will start converting your IP to cash, or forever remain closet AGI-seekers. AGI is WAY too big for any one person to ever build. It would be a challenge for one person just to build and maintain the parsing and disambiguation rules for everyday English, let alone all of the OTHER things you would have to do to build an AGI. Without cash, you will forever be wage slaves, while others build AGIs or whatever with your efforts. Then again, Yahoo slapped us all in the face by withdrawing Summly, presumably suggesting we are a bunch of losers and can neither improve upon nor match Summly's achievements in reasonable time. Is Summly's algorithm described somewhere? Note a quirk of law: It is conceivable that Summly had adopted my algorithm but kept it proprietary. As such, Yahoo would have NO claim on the technology, and their work would NOT count as prior art. It happens all the time - people validly patent things that it turns out someone else has already developed. These patents are fully enforceable. These questions will soon be answered for my invention, because my application has been "made special" (fast tracked). Or can we? Again, the challenge with AGI is a lack of anything resembling a spec. It is hard to design something to perform an undefined function. However, my invention was NOT what to do, but how to do such things faster. The combinatorial explosion from failed tests hangs over the head of all NL "understanding" efforts. From what I can see, my method is the ONLY presently known way of prospectively running fast enough, once the rules/tables/DB are populated with all the information needed to process everyday English (or other natural language). Steve AGI | Archives | Modify Your Subscription ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
