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/?member_id=8660244&id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
