James, It's interesting - there is a general huge block here - and culture-wide - to thinking about intelligence in terms of problems as opposed to the means and media of solution (or if you like the tasks vs the tools).
Your list is all about means - AGI that uses this language or that, and that uses a body or not. Similarly, Pei's and Ben's expositions of their systems are all about how-it-works rather than what-it-does. Everything I listed started at the other end - with types of problems. And that is how you do indeed have to start. What I suggest is an AGI - and almost certainly it will be a robot - that is given a general set of concepts and education about "moving" and "navigating" past "obstacles" towards "goals" - much, I guess, like an infant first learns about navigating round its environment in a very general way, before it gets down to complex, specific activities. Note that infants - and the human brain - do have this central capacity to hold very general concepts - to think in terms of "go there" or "move a bit" - which are supremely general - and understand that "go" can mean "crawl" "run" "hop" "jump" "ride on scooter" "walk" etc - and that "obstacle" or "something in the way" can refer to literally an infinity of differently shaped objects, from a carpet to a human being to a tricycle. - and that "move" can mean "move any part of your body - arms, legs etc". [All this fits, I suspect, if loosely with Hawkins' ideas]. Once you have an AGI that has a brain structured in this way - with a tree of generality/ particularity & abstractness/ concreteness - to understand that there are many ways of moving towards goals - then you can teach it, or it can learn an in principle infinite variety of physical, navigational, goal-seeking activities - from navigating mazes to searching buildings to hunting and chasing other agents/ animals to navigating videogame mazes etc. - for it will know that there are many ways to move its body along many different kinds of paths past many different kinds of obstacles to many different kinds of goals. I thought I made much of that last para. clear already - but obviously it didn't communicate - I'm curious why not. Do try and explain what you found confusing. P.S. These are obviously very rough, somewhat off-the-cuff ideas. But I suspect that they also contain something fundamental. All living General Intelligences, i.e. are indeed "goal-seeking agents" - that's what they are designed to do. The evolution of natural GI began with physical navigation towards physical goals - so in all probability will artificial -AGI. (And indeed the landmark robot of this century is already arguably Thrun's DARPA car). ----- Original Message ----- From: James Ratcliff To: [email protected] Sent: Thursday, May 03, 2007 1:38 AM Subject: Re: [agi] The University of Phoenix Test [was: Why do you think your AGI design will work?] What other list of specific goals though would you posit a first generation AGI accomplishing other than the ones I mentioned? James Ratcliff Mike Tintner <[EMAIL PROTECTED]> wrote: I think you're thinking in too limited ways about the physical tasks, simulated or embodied - although that may be the fault of my definition. You don't, I would suggest, program AGi machines primarily, say, to run mazes. You program them primarily to move towards "goals" by moving around "obstacles" - very generally. Mazes are the particular form of obstacles and whatever prizes they contain are the particular form of goals, that you initially teach the machines. Also you give them general goals as animals have like "food" or "fuel" which can in principle be instantiated in an infinite variety of particular forms - so they can always be open to adding new forms to their motivations. You also I guess (I'm thinking aloud here) give them a drive to move around and explore - possibly for its own sake. If they have a drive to explore new territories beyond those of mazes for new forms of food, there is obviously potential to develop a set of very different activities. You also give them (thinking further aloud) the capacity to form subgoals - (but Gawd knows immediately how to do it) - so that certain subgoals and subactivities, can become primary - as play eventually becomes in animals. Also visual or sensory exploration of the environment ("sightseeing") could become detached as an activity in itself, not just always in service of getting through the maze to the food. Especially if the robot has free time on its hands. I think any AGI machine will have to have a mind structured by a tree of several levels of generality and particularity - and be capable therefore of thinking in very general ways of "goals", "moves", "obstacles," "food" "paths" etc. - and continually adding more and more particular examples of each. (Ben & I seem to agree here) And that tree is key to its adaptivity. ----- Original Message ----- From: James Ratcliff To: [email protected] Sent: Wednesday, April 25, 2007 9:49 PM Subject: Re: [agi] The University of Phoenix Test [was: Why do you think your AGI design will work?] So basically it appears there are 3 areas of possible AGI Test Applciations: 1. Non-Natural Language Required Tasks. - Question Answering (little comprehension) - specific programmed tasks - Most AI work, vision, sound, etc 2. Natural Language Tasks (advanced parsing and comprehension) - conversations, Turing Tests, advanced interactive tasks. 3. Embodied Tasks Either virtual or robot. movement and smart interaction with the environment, to complete tasks. Can an AGI be built at all that first into area 1? It seems too limited to me (rough delineated definitions above granted) I think any system in one is written for a specific task, chess, maze solving, etc. So does it require 2 and/or 3 and which is better. #2 I think would qualify as an AGI, if it could talk and pass the Turing test and converse. How useful is it though, what kind of system does it create? Only expert systems for a domain, or chatbots? #3 Video game characters or robots are an AGI as well, but how limiting are they if they dont have complex language skills to be given and learn ever increasingly complex tasks to do. Mike Tintner <[EMAIL PROTECTED]> wrote: Nobody came back on my suggestion for a much simpler AGI test. Let's call it the Neo-Maze Test. You program a robot rover or simulation robot with flexible rules to run fairly basic-type mazes, including mazes with multiple solutions. The test is then whether it can run very different kinds of neo-mazes (to be set by other programming teams) - let's say, mazes with holes-in-the-wall, large open squares, roundabouts, spaghetti junctions, underpasses, ramps, traffic lights and traffic problems etc. etc. - for which its rules have NOT equipped it, but which it does have the basic raw capacity to run. This will be a test of its ability to fundamentally adapt its approach to an activity to fundamentally altered environments - to adapt its rules, steps/ moves and recognized paths to its goals. That, I reckon, is the primary requirement of AGI. (Animals can do it). From there you can move to ever more complex, and higher-level activity tests. But setting your first goal as passing a language test is to my mind absurd. P.S. Another comparable test would a Video-Game Test, where some gameplaying agent that is programmed to play something like Pac-Man, will have to adapt to fundamental variations on that game, with again radical alterations to the game's maze structure and the type of predators it must deal with, and treasures it must find. Or, thinking of the link screen on the Novamente site, you could have simply a Building Navigation Test - a robot or agent programmed to negotiate fairly simple rooms with simple furniture arrangements, and fairly simple corridors - will then have to megotiate ever more complex rooms and building corridors, ideally the full range of modern architecture. That surely would be a highly practical test with highly commercial applications. ----- Original Message ----- From: "Derek Zahn" To: Sent: Wednesday, April 25, 2007 2:40 PM Subject: Re: [agi] The University of Phoenix Test [was: Why do you think your AGI design will work?] > Ben Goertzel writes: > >> I don't think there are any good, general incremental tests for progress >> toward >> AGI. There are just too many different potentially viable approaches, >> with >> qualitatively different development arcs. > > Nevertheless, I wish somebody would try to specify some that are perhaps > not completely general. Without that, the only way to determine whether > any > progress at all is being made is by an analysis of internal structures -- > pointing to > a data structure and making claims about its meaning. And the developers > of > the system are notoriously bad at doing this as they are too emotionally > and > intellectually tied to the work. > > I wonder at what point our ancesters became "generally intelligent"? Were > humans > of 10,000 years ago generally intelligent? If so, why did it take them so > many billions > of person-years to develop the most rudimentary capabilities that we seem > to expect > our artifical general intelligences to breeze through effortlessly? I > suppose the > real test is at what point an individual from the past would be able to > pass the Turing > test (or some similar thing) if born into our present world and educated > like we were > and I doubt any scientists could make any confident guesses about that. > > I think that figuring out a good working definition of general > intelligence and > demonstratable intermediate steps is the single most important missing > piece > of the endeavor. > > ----- > This list is sponsored by AGIRI: http://www.agiri.org/email > To unsubscribe or change your options, please go to: > http://v2.listbox.com/member/?&; > > > > -- > Internal Virus Database is out-of-date. > Checked by AVG Free Edition. 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