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 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
