On Mon, 2010-06-28 at 13:21 +0100, Mike Tintner wrote: > MS: I'm solving this by using an algorithm + exceptions routines. > > You're saying there are predictable patterns to human and animal behaviour > in their activities, (like sports and investing) - and in this instance how > humans change tactics? > > What empirical evidence do you have for this, apart from zero, and over 300 > years of scientific failure to produce any such laws or patterns of > behaviour? > > What evidence in the slightest do you have for your algorithm working? Still in the testing phase. It's more complicated than just (algorithm + exceptions), there are multiple levels of accuracy of data and how you combine the multiple levels of data.
> > The evidence to the contrary, that human and animal behaviour, are not > predictable is pretty overwhelming. > > Taking into account the above, how would you mathematically assess the cases > for proceeding on the basis that a) living organisms ARE predictable vs b) > living organisms are NOT predictable? Roughly about the same as a) you WILL > win the lottery vs b) you WON'T win? Actually that is almost certainly being > extremely kind - you do have a chance of winning the lottery. > > -------------------------------------------------- > From: "Michael Swan" <ms...@voyagergaming.com> > Sent: Monday, June 28, 2010 4:17 AM > To: "agi" <agi@v2.listbox.com> > Subject: Re: [agi] Huge Progress on the Core of AGI > > > > > On Sun, 2010-06-27 at 19:38 -0400, Ben Goertzel wrote: > >> > >> Humans may use sophisticated tactics to play Pong, but that doesn't > >> mean it's the only way to win > >> > >> Humans use subtle and sophisticated methods to play chess also, right? > >> But Deep Blue still kicks their ass... > > > > If the rules of chess changed slightly, without being reprogrammed deep > > blue sux. > > And also there is anti deep blue chess. Play chess where you avoid > > losing and taking pieces for as long as possible to maintain high > > combination of possible outcomes, and avoid moving pieces in known > > arrangements. > > > > Playing against another human player like this you would more than > > likely lose. > > > >> > >> The stock market is another situation where narrow-AI algorithms may > >> already outperform humans ... certainly they outperform all except the > >> very best humans... > >> > >> ... ben g > >> > >> On Sun, Jun 27, 2010 at 7:33 PM, Mike Tintner > >> <tint...@blueyonder.co.uk> wrote: > >> Oh well that settles it... > >> > >> How do you know then when the opponent has changed his > >> tactics? > >> > >> How do you know when he's switched from a predominantly > >> baseline game say to a net-rushing game? > >> > >> And how do you know when the market has changed from bull to > >> bear or vice versa, and I can start going short or long? Why > >> is there any difference between the tennis & market > >> situations? > > > > > > I'm solving this by using an algorithm + exceptions routines. > > > > eg Input 100 numbers - write an algorithm that generalises/compresses > > the input. > > > > ans may be > > (input_is_always > 0) // highly general > > > > (if fail try exceptions) > > // exceptions > > // highly accurate exceptions > > (input35 == -4) > > (input75 == -50) > > .. > > more generalised exceptions, etc > > > > I believe such a system is similar to the way we remember things. eg - > > We tend to have highly detailed memory for exceptions - we tend to > > remember things about "white whales" more than "ordinary whales". In > > fact, there was a news story the other night on a returning white whale > > in Brisbane, and there are additional laws to stay way from this whale > > in particular, rather than all whales in general. > > > >> > >> > >> > >> > >> > >> > >> > >> > >> > >> From: Ben Goertzel > >> Sent: Monday, June 28, 2010 12:03 AM > >> > >> To: agi > >> Subject: Re: [agi] Huge Progress on the Core of AGI > >> > >> > >> > >> 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 > >> Sent: Sunday, June 27, 2010 8:53 PM > >> > >> To: agi > >> 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 > >> Sent: Sunday, June 27, 2010 7:33 PM > >> > >> To: agi > >> 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 > >> Sent: Sunday, June 27, 2010 5:57 PM > >> To: agi > >> 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 > >> | Modify Your > >> Subscription > >> > >> > >> agi | Archives | > >> Modify Your > >> Subscription > >> > >> agi | Archives | > >> Modify Your > >> Subscription > >> > >> > >> > >> > >> -- > >> 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 | > >> Modify Your > >> Subscription > >> > >> agi | Archives | > >> Modify Your > >> Subscription > >> > >> > >> agi | Archives | Modify > >> Your Subscription > >> > >> agi | Archives | Modify > >> Your Subscription > >> > >> > >> > >> > >> -- > >> 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 | Modify Your > >> Subscription > >> > >> agi | Archives | Modify Your > >> Subscription > >> > >> > >> > >> > >> -- > >> 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 | Modify Your > >> Subscription > >> > > > > > > > > ------------------------------------------- > > 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/?& > > Powered by Listbox: 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/?& > Powered by Listbox: 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