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" <[email protected]>
> Sent: Monday, June 28, 2010 4:17 AM
> To: "agi" <[email protected]>
> 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
> >> <[email protected]> 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
> >> <[email protected]> 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
> >> <[email protected]> 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
> >> <[email protected]> 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 <[email protected]> wrote:
> >> On Sun, Jun 27, 2010 at 11:56
> >> AM, Mike Tintner
> >> <[email protected]>
> >> 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
> >>
> >>
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> >>
> >> --
> >> 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
> >> [email protected]
> >>
> >> "
> >> “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.”
> >>
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> >>
> >> --
> >> 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
> >> [email protected]
> >>
> >> "
> >> “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
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> >>
> >>
> >>
> >> --
> >> 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
> >> [email protected]
> >>
> >> "
> >> “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
> >>
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