Even with the variations you mention, I remain highly confident this is not a difficult problem for narrow-AI machine learning methods
-- Ben G On Sun, Jun 27, 2010 at 6:24 PM, Mike Tintner <tint...@blueyonder.co.uk>wrote: > I think you're thinking of a plodding limited-movement classic Pong line. > > I'm thinking of a line that can like a human player move with varying > speed and pauses to more or less any part of its court to hit the ball, and > then hit it with varying speed to more or less any part of the opposite > court. I think you'll find that bumps up the variables if not > unknowns massively. > > Plus just about every shot exchange presents you with dilemmas of how to > place your shot and then move in anticipation of your opponent's return . > > Remember the object here is to present a would-be AGI with a simple but > *unpredictable* object to deal with, reflecting the realities of there being > a great many such objects in the real world - as distinct from Dave's all > too predictable objects. > > The possible weakness of this pong example is that there might at some > point cease to be unknowns, as there always are in real world situations, > incl tennis. One could always introduce them if necessary - allowing say > creative spins on the ball. > > But I doubt that it will be necessary here for the purposes of anyone like > Dave - and v. offhand and with no doubt extreme license this strikes me as > not a million miles from a hyper version of the TSP problem, where the towns > can move around, and you can't be sure whether they'll be there when you > arrive. Or is there an "obviously true" solution for that problem too? > [Very convenient these obviously true solutions]. > > > *From:* Jim Bromer <jimbro...@gmail.com> > *Sent:* Sunday, June 27, 2010 8:53 PM > *To:* agi <agi@v2.listbox.com> > *Subject:* Re: [agi] Huge Progress on the Core of AGI > > Ben: I'm quite sure a simple narrow AI system could be constructed to beat > humans at Pong ;p > Mike: Well, Ben, I'm glad you're "quite sure" because you haven't given a > single reason why. > > Although Ben would have to give us an actual example (of a pong program > that could beat humans at Pong) just to make sure that it is > not that difficult a task, it seems like such an obviously true statement > that there is almost no incentive for anyone to try it. However, there are > chess programs that can beat the majority of people who play chess without > outside assistance. > Jim Bromer > > On Sun, Jun 27, 2010 at 3:43 PM, Mike Tintner <tint...@blueyonder.co.uk>wrote: > >> Well, Ben, I'm glad you're "quite sure" because you haven't given a >> single reason why. Clearly you should be Number One advisor on every >> Olympic team, because you've cracked the AGI problem of how to deal with >> opponents that can move (whether themselves or balls) in multiple, >> unpredictable directions, that is at the centre of just about every field >> and court sport. >> >> I think if you actually analyse it, you'll find that you can't predict and >> prepare for the presumably at least 50 to 100 spots on a table tennis >> board/ tennis court that your opponent can hit the ball to, let >> alone for how he will play subsequent 10 to 20 shot rallies - and you >> can't devise a deterministic program to play here. These are true, >> multiple-/poly-solution problems rather than the single solution ones you >> are familiar with. >> >> That's why all of these sports have normally hundreds of different >> competing philosophies and strategies, - and people continually can and do >> come up with new approaches and styles of play to the sports overall - there >> are endless possibilities. >> >> I suspect you may not play these sports, because one factor you've >> obviously ignored (although I stressed it) is not just the complexity >> but that in sports players can and do change their strategies - and that >> would have to be a given in our computer game. In real world activities, >> you're normally *supposed* to act unpredictably at least some of the time. >> It's a fundamental subgoal. >> >> In sport, as in investment, "past performance is not a [sure] guide to >> future performance" - companies and markets may not continue to behave as >> they did in the past - so that alone buggers any narrow AI predictive >> approach. >> >> P.S. But the most basic reality of these sports is that you can't cover >> every shot or move your opponent may make, and that gives rise to a >> continuing stream of genuine dilemmas . For example, you have just returned >> a ball from the extreme, far left of your court - do you now start moving >> rapidly towards the centre of the court so that you will be prepared to >> cover a ball to the extreme, near right side - or do you move more slowly? >> If you don't move rapidly, you won't be able to cover that ball if it comes. >> But if you do move rapidly, your opponent can play the ball back to the >> extreme left and catch you out. >> >> It's a genuine dilemma and gamble - just like deciding whether to invest >> in shares. And competitive sports are built on such dilemmas. >> >> Welcome to the real world of AGI problems. You should get to know it. >> >> And as this example (and my rock wall problem) indicate, these problems >> can be as simple and accessible as fairly easy narrow AI problems. >> *From:* Ben Goertzel <b...@goertzel.org> >> *Sent:* Sunday, June 27, 2010 7:33 PM >> *To:* agi <agi@v2.listbox.com> >> *Subject:* Re: [agi] Huge Progress on the Core of AGI >> >> >> That's a rather bizarre suggestion Mike ... I'm quite sure a simple narrow >> AI system could be constructed to beat humans at Pong ;p ... without >> teaching us much of anything about intelligence... >> >> Very likely a narrow-AI machine learning system could *learn* by >> experience to beat humans at Pong ... also without teaching us much >> of anything about intelligence... >> >> Pong is almost surely a "toy domain" ... >> >> ben g >> >> On Sun, Jun 27, 2010 at 2:12 PM, Mike Tintner >> <tint...@blueyonder.co.uk>wrote: >> >>> Try ping-pong - as per the computer game. Just a line (/bat) and a >>> square(/ball) representing your opponent - and you have a line(/bat) to play >>> against them >>> >>> Now you've got a relatively simple true AGI visual problem - because if >>> the opponent returns the ball somewhat as a real human AGI does, (without >>> the complexities of spin etc just presumably repeatedly changing the >>> direction (and perhaps the speed) of the returned ball) - then you have a >>> fundamentally *unpredictable* object. >>> >>> How will your program learn to play that opponent - bearing in mind that >>> the opponent is likely to keep changing and even evolving strategy? Your >>> approach will have to be fundamentally different from how a program learns >>> to play a board game, where all the possibilities are predictable. In the >>> real world, "past performance is not a [sure] guide to future performance". >>> Bayes doesn't apply. >>> >>> That's the real issue here - it's not one of simplicity/complexity - >>> it's that your chosen worlds all consist of objects that are predictable, >>> because they behave consistently, are shaped consistently, and come in >>> consistent, closed sets - and can only basically behave in one way at any >>> given point. AGI is about dealing with the real world of objects that are >>> unpredictable because they behave inconsistently,even contradictorily, are >>> shaped inconsistently and come in inconsistent, open sets - and can behave >>> in multi-/poly-ways at any given point. These differences apply at all >>> levels from the most complex to the simplest. >>> >>> Dealing with consistent (and regular) objects is no preparation for >>> dealing with inconsistent, irregular objects.It's a fundamental error >>> >>> Real AGI animals and humans were clearly designed to deal with a world of >>> objects that have some consistencies but overall are inconsistent, irregular >>> and come in open sets. The perfect regularities and consistencies of >>> geometrical figures and mechanical motion (and boxes moving across a screen) >>> were only invented very recently. >>> >>> >>> >>> *From:* David Jones <davidher...@gmail.com> >>> *Sent:* Sunday, June 27, 2010 5:57 PM >>> *To:* agi <agi@v2.listbox.com> >>> *Subject:* Re: [agi] Huge Progress on the Core of AGI >>> >>> Jim, >>> >>> Two things. >>> >>> 1) If the method I have suggested works for the most simple case, it is >>> quite straight forward to add complexity and then ask, how do I solve it >>> now. If you can't solve that case, there is no way in hell you will solve >>> the full AGI problem. This is how I intend to figure out how to solve such a >>> massive problem. You cannot tackle the whole thing all at once. I've tried >>> it and it doesn't work because you can't focus on anything. It is like a >>> Rubik's cube. You turn one piece to get the color orange in place, but at >>> the same time you are screwing up the other colors. Now imagine that times >>> 1000. You simply can't do it. So, you start with a simple demonstration of >>> the difficulties and show how to solve a small puzzle, such as a Rubik's >>> cube with 4 little cubes to a side instead of 6. Then you can show how to >>> solve 2 sides of a rubiks cube, etc. Eventually, it will be clear how to >>> solve the whole problem because by the time you're done, you have a complete >>> understanding of what is going on and how to go about solving it. >>> >>> 2) I haven't mentioned a method for matching expected behavior to >>> observations and bypassing the default algorithms, but I have figured out >>> quite a lot about how to do it. I'll give you an example from my own notes >>> below. What I've realized is that the AI creates *expectations* (again). >>> When those expectations are matched, the AI does not do its default >>> processing and analysis. It doesn't do the default matching that it normally >>> does when it has no other knowledge. It starts with an existing hypothesis. >>> When unexpected observations or inconsistencies occur, then the AI will have >>> a *reason* or *cue* (these words again... very important concepts) to look >>> for a better hypothesis. Only then, should it look for another hypothesis. >>> >>> My notes: >>> How does the ai learn and figure out how to explain complex unforseen >>> behaviors that are not preprogrammable. For example the situation above >>> regarding two windows. How does it learn the following knowledge: the >>> notepad icon opens a new notepad window and that two windows can exist... >>> not just one window that changes. the bar with the notepad icon represenants >>> an instance. the bar at the bottom with numbers on it represents multiple >>> instances of the same window and if you click on it it shows you >>> representative bars for each window. >>> >>> How do we add and combine this complex behavior learning, explanation, >>> recognition and understanding into our system? >>> >>> Answer: The way that such things are learned is by making observations, >>> learning patterns and then connecting the patterns in a way that is >>> consistent, explanatory and likely. >>> >>> Example: Clicking the notepad icon causes a notepad window to appear with >>> no content. If we previously had a notepad window open, it may seem like >>> clicking the icon just clears the content by the instance is the same. But, >>> this cannot be the case because if we click the icon when no notepad window >>> previously existed, it will be blank. based on these two experiences we can >>> construct an explanatory hypothesis such that: clicking the icon simply >>> opens a blank window. We also get evidence for this conclusion when we see >>> the two windows side by side. If we see the old window with the content >>> still intact we will realize that clicking the icon did not seem to have >>> cleared it. >>> >>> Dave >>> >>> >>> On Sun, Jun 27, 2010 at 12:39 PM, Jim Bromer <jimbro...@gmail.com>wrote: >>> >>>> On Sun, Jun 27, 2010 at 11:56 AM, Mike Tintner < >>>> tint...@blueyonder.co.uk> wrote: >>>> >>>>> Jim :This illustrates one of the things wrong with the >>>>> dreary instantiations of the prevailing mind set of a group. It is only a >>>>> matter of time until you discover (through experiment) how absurd it is to >>>>> celebrate the triumph of an overly simplistic solution to a problem that >>>>> is, >>>>> by its very potential, full of possibilities] >>>>> >>>>> To put it more succinctly, Dave & Ben & Hutter are doing the wrong >>>>> subject - narrow AI. Looking for the one right prediction/ explanation is >>>>> narrow AI. Being able to generate more and more possible explanations, wh. >>>>> could all be valid, is AGI. The former is rational, uniform thinking. >>>>> The >>>>> latter is creative, polyform thinking. Or, if you prefer, it's convergent >>>>> vs >>>>> divergent thinking, the difference between wh. still seems to escape Dave >>>>> & >>>>> Ben & most AGI-ers. >>>>> >>>> >>>> Well, I agree with what (I think) Mike was trying to get at, except that >>>> I understood that Ben, Hutter and especially David were not only talking >>>> about prediction as a specification of a single prediction when many >>>> possible predictions (ie expectations) were appropriate for consideration. >>>> >>>> For some reason none of you seem to ever talk about methods that could >>>> be used to react to a situation with the flexibility to integrate the >>>> recognition of different combinations of familiar events and to classify >>>> unusual events so they could be interpreted as more familiar *kinds* of >>>> events or as novel forms of events which might be then be integrated. For >>>> me, that seems to be one of the unsolved problems. Being able to say that >>>> the squares move to the right in unison is a better description than saying >>>> the squares are dancing the irish jig is not really cutting edge. >>>> >>>> As far as David's comment that he was only dealing with the "core >>>> issues," I am sorry but you were not dealing with the core issues of >>>> contemporary AGI programming. You were dealing with a primitive problem >>>> that has been considered for many years, but it is not a core research >>>> issue. Yes we have to work with simple examples to explain what we are >>>> talking about, but there is a difference between an abstract problem that >>>> may be central to your recent work and a core research issue that hasn't >>>> really been solved. >>>> >>>> The entire problem of dealing with complicated situations is that these >>>> narrow AI methods haven't really worked. That is the core issue. >>>> >>>> Jim Bromer >>>> >>>> >>>> *agi* | Archives <https://www.listbox.com/member/archive/303/=now> >>>> <https://www.listbox.com/member/archive/rss/303/> | >>>> Modify<https://www.listbox.com/member/?&;>Your Subscription >>>> <http://www.listbox.com/> >>>> >>> >>> *agi* | Archives <https://www.listbox.com/member/archive/303/=now> >>> <https://www.listbox.com/member/archive/rss/303/> | >>> Modify<https://www.listbox.com/member/?&;>Your Subscription >>> <http://www.listbox.com/> >>> *agi* | Archives <https://www.listbox.com/member/archive/303/=now> >>> <https://www.listbox.com/member/archive/rss/303/> | >>> Modify<https://www.listbox.com/member/?&;>Your Subscription >>> <http://www.listbox.com/> >>> >> >> >> >> -- >> Ben Goertzel, PhD >> CEO, Novamente LLC and Biomind LLC >> CTO, Genescient Corp >> Vice Chairman, Humanity+ >> Advisor, Singularity University and Singularity Institute >> External Research Professor, Xiamen University, China >> b...@goertzel.org >> >> " >> “When nothing seems to help, I go look at a stonecutter hammering away at >> his rock, perhaps a hundred times without as much as a crack showing in it. >> Yet at the hundred and first blow it will split in two, and I know it was >> not that blow that did it, but all that had gone before.” >> >> *agi* | Archives <https://www.listbox.com/member/archive/303/=now> >> <https://www.listbox.com/member/archive/rss/303/> | >> Modify<https://www.listbox.com/member/?&;>Your Subscription >> <http://www.listbox.com/> >> *agi* | Archives <https://www.listbox.com/member/archive/303/=now> >> <https://www.listbox.com/member/archive/rss/303/> | >> Modify<https://www.listbox.com/member/?&;>Your Subscription >> <http://www.listbox.com/> >> > > *agi* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > *agi* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > -- Ben Goertzel, PhD CEO, Novamente LLC and Biomind LLC CTO, Genescient Corp Vice Chairman, Humanity+ Advisor, Singularity University and Singularity Institute External Research Professor, Xiamen University, China b...@goertzel.org " “When nothing seems to help, I go look at a stonecutter hammering away at his rock, perhaps a hundred times without as much as a crack showing in it. Yet at the hundred and first blow it will split in two, and I know it was not that blow that did it, but all that had gone before.” ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
