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 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
