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Tuesday, May 21, 2013

RIP Ray Manzarek


What a bummer to read that Ray Manzarek has died.   

I was born in 1966, and the psychedelic rock of the late 1960s and early 1970s was the music I grew up on.   Later I became more interested in jazz fusion, bebop, classical music and so forth -- but the psychedelic 60s/70s music (Hendrix, Doors, Floyd, Zeppelin) was where my love for music started.  This was the music that showed me the power of music to open up the mind to new realities and trans-realities, to bring the mind beyond itself into other worlds....

Hendrix was and probably always will be my greatest musical hero -- but Ray Manzarek was the first keyboardist who amazed me and showed me the power of wild and wacky keyboard improvisation.   I now spend probably 30-45 minutes a day improvising on the keyboard (and more on weekends!).  I don't have Ray's virtuosity but even so, keyboard improv keeps my mind free and agile and my emotions on the right side of the border between sanity and madness.  Each day I sit at my desk working, working working -- and when too much tension builds up in my body or I get stuck on a difficult point, I shift over to the piano or the synth and jam a while.   My frame of mind re-sets, through re-alignment with the other cosmos the music puts my mind in touch with.

The Doors and Ray had a lot of great songs.  But no individual song is really the point to me.  The point is the way Ray's music opens up your mind -- the way, if you close your eyes and let it guide you, you follow it on multiple trans-temporal pathways into other realms, beyond the petty concerns of yourself and society ... and when you return your body feels different and you see your everyday world from a whole new view....

The Singularity, if it comes, will bring us beyond petty human concerns into other realms in a dramatic, definitive way.   Heartfelt, imaginative improvisation like Ray Manzarek's can do something similar, in its own smaller (yet in another sense infinite) way -- opening up a short interval of time into something somehow much broader.

As Ray once said:

“Well, to me, my God, for anybody who was there it means it was a fantastic time, we thought we could actually change the world — to make it a more Christian, Islamic, Judaic Buddhist, Hindu, loving world. We thought we could. The children of the ’50s post-war generation were actually in love with life and had opened the doors of perception. And we were in love with being alive and wanted to spread that love around the planet and make peace, love and harmony prevail upon earth, while getting stoned, dancing madly and having as much sex as you could possibly have.” 


w00t! ... those times are gone, and I was too young in the late 60s early 70s to take part in the "getting stoned and having as much sex as you could possibly have" aspect (that came later for me, including some deep early-80s acid trips to Doors music), but my child self picked up the vibe of that era nonetheless ... all the crazy, creative hippies I saw and watched carefully back then affected more than just my hairstyle....   Somewhat like Steve Jobs, I see the things I'm doing now as embodying much of the spirit of that era.   Ray Manzarek and his kin of that generation wanted to transcend boring, limited legacy society and culture and revolutionize everything and make it all more ecstatic and amazing -- and so do I....


I recall a Simpsons episode where Homer gets to heaven and encounters Jimi Hendrix and Thomas Jefferson playing air hockey.  Maybe my memory has muddled the details, but no matter.   I hope very much that, post-Singularity, one of my uploaded clones will spend a few eons jamming on the keyboard with the uploaded, digi-resurrected Ray Manzarek.

Until then: Rest In Peace, Ray....


Sunday, May 19, 2013

Musing about Mental vs. Physical Energy


Hmmm....

I was talking with my pal Gino Yu at his daughter Oneira's birthday party yesterday … and Gino was sharing some of his interesting ideas about mental energy and force…

Among many other notions that I won't try to summarize here, he pointed out that, e.g. energy (in the sense he meant) is different from arousal as psychologists like to talk about…  You can have a high-energy state without being particular aroused -- i.e. you can be high-energy but still and quiescent.

This started me thinking about the relation between "mental energy" in the subjective sense Gino appeared to be intending, and "energy" in physics.

I have sometimes in the past been frustrated by people -- less precise in their thinking than Gino -- talking about "energy" in metaphorical or subjective ways, and equating their intuitive notion of "energy" with the physics notion of "energy."

Gino was being careful not do to this, and to distinguish his notion of mental energy from the separate notion of physical energy.   However, I couldn't help wondering about the connection.   I kept asking myself, during the conversation: Is there some general notion of energy which the physical and mental conceptions both instantiate?

Of course, this line of thinking is in some respects a familiar one, e.g. Freud is full of ideas about mental energy, mostly modeled on equilibrium thermodynamics (rather than far-from equilibrium thermodynamics which would be more appropriate as an analogical model for the brain/mind)…

Highly General Formulations of Force, Energy, Etc.

Anyway... here is my rough attempt to generalize energy and some other basic physics concepts beyond the domain of physics, while still capturing their essential meaning.

My central focus in this line of thinking is "energy", but I have found it necessary to begin with "force" ...

Force may, I propose, be generally conceived as that which causes some entity to deviate from its pattern of behavior ...

Note that I've used the term "cause" here, which is a thorny one.   I think causation must be understood subjectively: a mind M perceives A as causing B if according to that mind's world-model,

  • A is before B
  • P(B|A) > P(B)
  • there is some meaningful (to M) avenue of influence between A and B, as evidenced e.g. by many shared patterns between A and B

So, moving on ... force quickly gives us energy…

Energy, I suggest (not too originally), may be broadly conceived as a quantity that

  • is conserved in an isolated system (or to say it differently: is added or subtracted from a system only via interactions with other systems)
  • measures (in some sense) the amount of work that a certain force gets done, or (potential energy) the amount of work that a certain force is capable of getting done

Now, in the case of Newtonian mechanics,

  • an entity's default pattern of behavior is to move in a straight line at a constant velocity (conservation of momentum), therefore force takes the form of deviations from constant momentum, i.e. it is proportional to acceleration
  • "mass" is basically an entity's resistance to force…
  • energy = force * distance

However, the basic concepts of force and energy as described above are pertinent beyond the Newtonian context, e.g. to relativistic and quantum physics; and I suppose they may have meaning beyond the physics domain as well.

This leads me to thinking about a couple related concepts...

Entropy maximization: When a mind lacks knowledge about some aspect of the world, its generically best hypothesis is the one that maximizes entropy (this is the hypothesis that will lead to its being right the maximum percentage of the time).   This is Jaynes' MaxEnt principle of Bayesian inference.

Maximum entropy production: When a mind lacks knowledge about the path of development of some system, its generically best hypothesis is that the system will follow the path of maximal entropy production (MEP).   It happens that this path often involves a lot of temporary order production; as Swenson said, "The world, in short, is in the order production business because ordered flow produces entropy faster than disordered flow"

Note that while entropy maximization and MEP are commonly thought of in terms of physics, they can actually be conceived as general inferential principles relevant to any mind confronting a mostly-opaque world.

Sooo... overall, what's the verdict?  Does it make sense to think about "mental energy", qualitatively, as something different from physical energy -- but still deserving the same word "energy?"   Is there a common abstract structure supervening both uses of the "energy" concept?

I suppose that there may well be, if the non-physical use of the term "energy" follows basic principles like I've outlined here.

This is in line with the general idea that subjective experiences can be described using their own language, different from that of physical objects and events -- yet with the possibility of drawing various correlations between the subjective and physical domains.  (Since in the end the subjective and physical can be viewed as different perspectives on the same universe … and as co-creators of each other…)

In What Sense Is Mental Energy Conserved?

But ... hmmm ... I wonder if the notion of "mental energy" -- in folk psychology or in whatever new version we want to create -- really obeys the principles suggested above?

In particular, the notion of "conservation in isolated systems" is a bit hard to grab onto in a psychological context, since there aren't really any isolated systems ... minds are coupled with their environments, and with other minds, by nature.

On the other hand, it seems that whenever physicists run across a situation where energy may seem not to be conserved, they invent a new form of energy to rescue energy conservation!   Which leads to the idea that within the paradigm of modern physics, "being conserved" is essentially part of the definition of "energy."

Also, note that above I used the phrasing that energy "is conserved in an isolated system (or to say it differently: is added or subtracted from a system only via interactions with other systems)."   The alternate parenthetical phrasing may, perhaps, be particularly relevant to the mental-energy case.

(Note for mathematical physicists: Noether's Theorem shows that energy conservation ensues from temporal translation invariance, but it only applies to systems governed by Lagrangians, and I don't want to assume that about the mind, at least not without some rather good reason to....) 

Stepping away from physics a bit, I'm tempted to consider notion of mental energy in the context of the Vedantic hierarchy, which I wrote about in The Hidden Pattern (here's an excerpt from Page 31 ...)


In a Vedantic context, one could perhaps view the Realm of Bliss as being a source of mental energy that is in effect infinite from the human perspective.   So when a human mind needs more energy, it can potentially open itself to the Bliss domain and fill itself with energy that way (thus perhaps somewhat losing its self, in a different sense!).   This highlights the idea that, in a subjective-mind context, the notion of an "isolated system" may not make much sense.

But one could perhaps instead posit a principle such as

Increase or decreases in a mind-system's fund of mental energy, are causally tied to that mind-system's interactions with the universe outside itself.

This sort of formulation captures the notion of energy conservation without the need to introduce the concept of an "isolated system."    (Of course, we still have to deal with the subjectivity of causality here -- but there's no escaping that, except via stopping to worry about causality altogether!)

But -- well, OK -- that's enough musing and rambling for one Sunday early afternoon; it's time to walk the dogs, eat a bit of lunch, and then launch into removing the many LaTeX errors remaining in the (otherwise complete) Building Better Minds manuscript....

And so it goes...

-- This post was written while listening to Love Machine's version of "One More Cup of Coffee" by Bob Dylan ... and DMT Experience's version of "Red House" by Jimi Hendrix.   I'm not sure why, but it seems a "cover version" sort of afternoon...

Wednesday, May 15, 2013

Quasi-Mathematical Speculations on Contraction Maps and Hypothetical Friendly Super-AIs


While eating ramen soup with Ruiting in the Tai Po MegaMall tonight, I found myself musing about the possible use of the contraction mapping theorem to understand the properties of AGI systems that create other AGI systems that create other AGI systems that … etc. ….

It's a totally speculative line of thinking, that may be opaque to anyone without a certain degree of math background.

But if it pans out, it ultimately could provide an answer to the question: When can an AGI system, creating new AGI systems or modifying itself in pursuit of certain goals, be reasonably confident that its new creations are going to continue respecting the goals for which they were created?

This question is especially interesting when the goals in question are things like "Create amazing new things and don't harm anybody in the process."   If we create an AGI with laudable goals like this, and then it creates a new AGI with the same goals, etc. -- when can we feel reasonably sure the sequence of AGIs won't diverge dramatically from the original goals?

Anyway, here goes…

Suppose that one has two goals, G and H

Given a goal G, let us use the notation " agi(G, C) " to denote the goal of creating an AGI system, operating within resources C, that will adequately figure out how to achieve goal G

Let d(,) denote a distance measure on the space of goals.  One reasonable hypothesis is that, if

d(G,H) = D

then generally speaking,

d( agi(G,c), agi(H,c) ) < k D

for some k ….  That is: because AGI systems are general in capability and good at generalization, if you change the goal of an AGI system by a moderate amount, you have to change the AGI system itself by less than that amount…

If this is true, then we have an interesting consequence….   We have the consequence that

F(X) = agi(X,C)

is a contraction mapping on the space of goals.   This means that, if we are working with a goal space that is a complete metric space, we have a fixed point G* so that

F(G*) = G*

i.e. so that

G* = agi(G*,C)

The fixed point G* is the goal of the following form:

G* = the goal of finding an AGI that can adequately figure out how to achieve G*

Of course, to make goal space a complete metric space one probably needs to admit some uncomputable goals, i.e. goals only computable using infinitely long computer programs.   So a goal like G* can never quite be achieved using ordinary computers, but only approximated.

Anyway, G* probably doesn't seem like a very interesting goal… apart from a certain novelty value….

However, one can vary on the above argument in a way that makes it possibly more useful.

Suppose we look at

agi(G,I,C)

-- i.e., the goal of creating an AGI that can adequately figure out how to achieve goals G and I within resources C.

Then it may also be the case that

d( agi(G,I,C), agi(H, I, C) ) < k d(G,H)

If so, then we can show the existence of a fixed point goal G* so that

G* = agi(G*, I, C)

or in words,

G* = the goal of finding an AGI that can adequately figure out how to achieve both goal G* and goal I

The contraction mapping theorem shows that if we start with a goal G close enough to G*, we can converge toward G* via an iteration such as

G, I
agi(G, I, C)
agi( agi(G,I,C), I, C)
agi( agi( agi(G,I,C), I, C) , I, C)

etc.

At each stage of the iteration, the AGI becomes more and more intelligent, as it's dealing with more and more abstract learning problems.  But according to the contraction mapping theorem, the AGI systems in the series are getting closer and closer to each other -- the process is converging.

So then we have the conclusion: If one starts with a system smart enough to solve the problem agi(G,I, C) reasonably well for the given I and C -- then ongoing goal-directed creation of new AGI systems will lead to new systems that respect the goals for which they were created.

Which may seem a bit tautologous!   But the devil actually lies in the details -- which I have omitted here, because I haven't figured them out!   The devil lies in the little qualifiers "acceptably" and "reasonably well" that I've used above.  Exactly how well does the problem agi(G,I,C) need to be solved for the contraction mapping property to hold?

And of course, it may be that the contraction mapping property doesn't actually hold in the simple form given above -- rather, some more complex property similar in spirit may hold, meaning that one has to use some generalization of the contraction mapping theorem, and everything becomes more of a mess, or at least subtler.

So, all this is not very rigorous -- at this stage, it's more like philosophy/poetry using the language of math, rather than real math.   But I think it points in an interesting direction.  It suggests to me that, if we want to create a useful mathematics of AGIs that try to achieve their goals by self-modifying or creating new AGIs, maybe we should be looking at the properties of mappings like agi() on the metric space of goals.   This is a different sort of direction than standard theoretical computer science -- it's an odd sort of discrete dynamical systems theory dealing with computational iterations that converge to infinite computer programs compactly describable as hypersets.

Anyway this line of thought will give me interesting dreams tonight ... I hope it does the same for you ;-) ...


Wednesday, May 01, 2013

The Dynamics of Attachment and Non-Attachment in Humans and AGIs



A great deal of human unhappiness and ineffectiveness is rooted in what Buddhists call "attachment"… roughly definable as an exaggerated desire not to be separated from someone, something, some idea, some feeling, etc.

Buddhists view attachment as ensuing largely from a lack of recognition of the oneness of all things.  If all things are one, then they can't really be separated anyway, so there's no reason to actively resist separation from some person or thing.

Zen teacher John Daido Loori put it as follows: "[A]ccording to the Buddhist point of view, nonattachment is exactly the opposite of separation. You need two things in order to have attachment: the thing you’re attaching to, and the person who’s attaching. In nonattachment, on the other hand, there’s unity. There’s unity because there’s nothing to attach to. If you have unified with the whole universe, there’s nothing outside of you, so the notion of attachment becomes absurd. Who will attach to what?"

That way of thinking makes plenty of sense to me (in a trans-sensible sort of way!).  However, I think one can also take a more prosaic and less cosmic, but quite compatible, approach to the attachment phenomenon...

In this blog post I will present a simple neural and cognitive model of attachment and its opposite.

I want to clarify that I'm not positing that the subjective experiences of attachment or non-attachment "reduce" to the neural/cognitive mechanisms I'll describe here -- I am not: not in a physics sense nor in a basic ontological sense.  I prefer to think about the ideas presented here as pertaining to the "neural/cognitive correlates of the experiences of attachment and non-attachment."

After presenting my model of attachment and non-attachment, I will dig into AGI theory for a bit, and explain why I think advanced AGI systems would suffer from the attachment phenomenon far less than human beings.   Or in other words:
  • Enlightening human minds is, in practice, a chancy and difficult matter ...
  • Enlightening AGI minds may merely be a matter of reasonable cognitive architecture design...

Hebbian Learning

I will start with some quasi-biological speculation.  What might be the neural roots of attachment?

Let's begin with the concept of Hebbian learning, an idea from neural network theory.  Hebbian learning has to do with a network in which neurons are joined by weighted synapses.  The larger the positive weight on the synapse between neuron N1 and neuron N2, the more of N1's activity will spill over to N2.  The larger the negative weight on the synapse between neuron N1 and neuron N2, the more strongly N1's activity will inhibit activity in N2.

In basic Hebbian learning the following two rules obtain:
  1. If N1 and N2 are active at the same time, the link (synapse) between N1 and N2 has its weight increased
  2. If N1 is active but N2 is not, or N2 is active but N1 is not, the link between N1 and N2 has its weight decreased
The result is that, over time
  • pairs of neurons that are frequently simultaneously active will be joined by synapses with high positive weights (so when one of them becomes active, the other will tend to be)
  • pairs of neurons that are generally active at different times, will be joined by synapses with very negative weights (so when one of them becomes active, the other will tend not to be active)
This is a very basic form of pattern recognition, but it's been shown to be adequate to learn arbitrarily complex patterns.  In technical terms, Hebbian learning can learn to achieve any computable goal in any computable environment -- though it may be very slow at doing so, and may require a very large network of neurons.

One of the interesting consequences of Hebbian learning is the formation of "cell assemblies" -- groups of neurons that are richly interconnected via high-positive-weight synapses, and hence tend to become activated as a whole.   Donald Hebb, who came up with the idea of Hebbian learning in the late 1940s, suggested that ideas in the mind are represented by neuronal cell assemblies in the brain.  60-odd years later, this still seems a sensible idea, and there is significant evidence in its favor.  The emergence of nonlinear dynamics has deepened the theory somewhat; it now seems likely that the cell assemblies representing ideas, memories and feelings in the human mind are associated with complex dynamical phenomena like strange attractors and strange transients.

Hebbian learning is a conceptual and mathematical model, but the basic idea is reflected in the brain in the form of long-term potentiation of synapses.  It may be found to be reflected in the brain in other ways as well, e.g. as our understanding of the roles of glia in memory increases.

So what does all this have to do with attachment?

Let's explore this via a simple example....

Suppose that Bob's girlfriend has left him.  He misses her.

While his girlfriend was with him, he woke up every morning, found her in the bed next to him, and put his arm around her.  He liked that.  The association between "wake up" and "put arm around girlfriend" become strong.   In Hebbian learning terms, the neurons in the "wake up" cell assembly got strongly positively weighted synapses to the neurons in the "put arm around girlfriend" cell assembly.  A larger assembly of the form " wake up and put arm around girlfriend" formed, linking together the two smaller assemblies.

Now, after the girlfriend left, what happens in Bob's brain?

According to straightforward Hebbian learning, the association between "wake up" and "put arm around girlfriend" should gradually decrease, until eventually there is no longer a positive weight between the two cell assemblies.  The larger assembly should fragment, leaving the "wake up" and "put arm around girlfriend" assemblies separate; and at the same time the "put arm around girlfriend assembly should start to dissipate, as it no longer gets reinforcement via experience.

But this may not actually be what happens.  Suppose, for example, that Bob spends a lot of time thinking about his girlfriend (now his ex-girlfriend).  Suppose he lies awake at night in bed and dwells on the fact that he's the only one there.  In that case, the "wake up" cell assembly and the "put arm around girlfriend" assembly will be activated simultaneously a lot, and will retain their positive association.

What's happening here is that Bob's emotions are causing a cell assembly to remain highly active -- in a case where the external world, in the absence of these emotions, would drive the assembly to dwindle.

This, I suggest, is the key neural correlate of the psychological phenomenon of attachment.  Attachment occurs -- neurally speaking -- when there is a circuit binding a cell assembly to the brain's emotional center, in such a way that emotion keeps the circuit whole and flourishing even though otherwise it would dissipate.

Ideally, a mind with amazing powers of self-control would delete the association between "wake up" and "put arm around girlfriend" as soon as the relationship with the girlfriend ended.   However, a mind without emotional interference in its Hebbian network dynamics would do the next best thing: the association would gradually dwindle over time.   For a typical human mind, on the other hand, the coupling of the "wake up and put arm around girl" network with the mind's emotional centers, will cause this association to persist a long time after simple Hebbian dynamics would have caused it to dwindle.

The example of Bob and his girlfriend is somewhat simplistic of course, and I chose it largely because of its simplicity.  A more pernicious example is when a mind becomes attached to an aspect of its model of itself.  For example, someone who derives pleasure from being correct (say, because someone praises them for being correct), may then become emotionally attached to the idea of themselves as someone who knows the right answer.   They may then have trouble letting go of this idea, even in contexts where the genuinely do not know the answer, and would be better off to admit this to themselves as well as to others.   Becoming attached to inaccurate models of oneself causes all sorts of problems, including the creation of compoundedly, increasingly inaccurate self-models, as well as self-defeating behaviors.

A Semantic Network Perspective

Now let's take a leap from modeling brain to modeling mind.  I've been talking here about neural networks and brains -- but the core idea presented above could actually be relevant to minds with very different biological underpinnings.  It could also be relevant if Hebbian learning turns out to be a terrible model of the brain.

Regardless of how the brain works, one can model the mind as a network of nodes, connected by weighted links.  The nodes represent concepts, actions, and perceptions in the mind; the links represent relationships between these, including associative relationships.  The "semantic networks" often used in AI are a simplistic version of this kind of model, but one can articulate much richer versions, capable of capturing all documented aspects of human cognition.

This sort of model of the mind has been instrumental in my own thinking about AI and cognitive science.  I have articulated a specific network model of minds called SMEPH, Self-Modifying Evolving Probabilistic Hypergraphs.   I won't go into the details of that here, though -- I mention it only to point out that the model of attachment and non-attachment here may be interpreted two ways: as a neural model, and as a cognitive model.   These interpretations are related but far from identical.

COEX Systems

The model of attachment presented here relates closely to Stanislav Grof's notion of a "COEX (Condensed Experience) system."  Roughly, a COEX is a set of related experiences organized around a powerful emotional center.   The emotional center is generally one or a few highly emotionally impactful experiences.  The various experiences in the COEX, all reinforce each other, keeping each other energetic and relevant to the mind.

In a Hebbian perspective, a COEX system would be modeled as a system of cell assemblies, each representing a certain episodic memory, linked together via positive, reinforcing connections.  The memories in the COEX stimulate powerful emotions, and these emotions reinforce the memories -- thus maintaining a powerful, ongoing attachment to the memories.

But Why?

I have said that "Attachment occurs -- neurally speaking -- when there is a circuit binding a cell assembly to the brain's emotional center, in such a way that emotion keeps the circuit whole and flourishing even though otherwise it would dissipate."

But why would the human mind be that way?

Emotions, basically, are system-wide (body and mind inclusive) reactions to events regarding system goals/desires/aspirations.  We are happy when we are achieving goals better and better; especially happy when we're doing so better than expected.  We are sad when we're making progress worse than expected.   We're angry when someone or something stands in the way of our goal fulfillment.   We feel pity when we use our mind's power of analogy to feel someone ELSE's frustration at their inability to fulfill their goals….

So, it's only natural that the emotion-bearing cell assemblies and attractors, wind up getting richly interlinked with other cell assemblies and attractors.

Let's say the "wake up", "put arm around girlfriend" and "happy emotion" assemblies all get richly interlinked.   Then there are multiple reverberating circuits joining all these  assemblies.  So even when the girlfriend goes away, these circuits will keep on cycling.

This won't be such a problem for an animal like a dog -- because in a dog, the associational cortex is not such a big part of its neural processing -- immediate perceptions and actions tend to hold sway.  But a larger and more complex associational cortex brings all sort of new possibilities with it, including the possibilities for more complex and persistent forms of attachment!

The Brains of the Enlightened

In recent years there  has been an increasing amount of work studying the brains of experienced meditators, and of people capable of various "enlightened" states of consciousness.   One of the interesting findings here is that such individuals have unusual patterns activity in a part of the brain called the posterior cingulate cortex (PCC).

The PCC does many different things, so the significance of this finding is not fully clear, and may be multidimensional.  However, it is noteworthy that ONE thing the PCC does is to regulate the interaction between memory and emotion.

The neural/cognitive theory presented above leads directly to the prediction that, if there's a key difference between the brains of attachment-prone versus non-attached people, it should indeed have to do with the interaction between memory and emotion.

I thus submit the hypothesis that ...  ONE of the significant factors the neurodynamics of enlightened states is: A change in the function of the PCC, so that in relatively non-attached people, emotion plays a significantly lesser role in the maintenance and dissolution of cell assemblies and associated attractors representing memories.

Toward Enlightened Digital Minds

This line of thinking, if correct, suggests that it may be relatively straightforward to create digital minds without the persistent phenomenon of attachment that characterizes ordinary human minds.

First of all, a digital mind -- if its design is not slavishly tied to that of the human brain -- may be able to explicitly remove associations and other inferences that are no longer rationally judged as relevant.  In other words, when a well-designed robot's girlfriend leaves him, he will just be able to remove any newly irrelevant associations from his brain, so his post-breakup malaise will be brief or nonexistent.

Secondly, even if a digital mind lacks this level of deliberative, rational self-modification, there is no reason it needs to have the same level of coupling of emotion and memory as human beings have.  From an AI software design perspective, it is quite simple to make the coupling of memory and emotion optional, to a much greater degree than the human brain does…

The interaction between memory and emotion is valuable for many purposes.  There is intelligence in emotional response, sometimes.  But there is no need, from a cognitive architecture perspective, for the formation and dissolution of memory attractors to be so inextricably tied to emotion.

Attachment in OpenCog

To explore the notion of attachment in digital minds more concretely, let's take a specific AGI design and muse on it in detail.   This exercise will also help us better understand why human minds get so extremely wrapped up in attachment as they do.

What if Bob's mind were a mature, fully functional OpenCog AGI engine, instead of a human?

(NOTE: to understand this example more thoroughly, take an hour or two and read the overview of the CogPrime cognitive architecture being gradually implemented in the OpenCog open-source AI framework....  Or, if you don't have time for that, just skim through the following instead, and you'll probably grok something!)

Then there would be an explicit link in OpenCog's Atomspace knowledge store, such as

PredictiveImplicationLink
   AND
      PredicateNode: wake_up
      PredicateNode: put_arm_around_girlfriend
   Happy
 
(NOTE: the actual nodes in the OpenCog knowledge base probably wouldn't have such evocative names, as they would be learned via experience --  but the basic structure would be like this.)

There would also be a bunch of HebbianLinks, similar to synapses in a neural network with Hebbian learning, going between various nodes related to wake_up and put_arm_around_girlfriend, and various nodes related to Happy.

When the girlfriend left, human-like attachment dynamics would likely be present, related to the HebbianLinks involved.   But the probabilistic truth value on the PredictiveImplicationLink would decrease.  It would decrease gradually via experience; or might be decreased very rapidly via reasoning (i.e. the AI could rationally infer that since the girlfriend is gone, putting its arm around her is not likely to be associated with happiness anymore).

The question then is: How rapidly and thoroughgoingly would this change in the OpenCog system's explicit knowledge (the PredictiveImplicationLink) cause a corresponding change in the system's implicit knowledge (the HebbianLinks between the assemblies or "maps" of nodes corresponding to "wake-up", "put_arm_around_girlfriend", and "Happy")?

Suppose the OpenCog system has a process that: Whenever the truth value of a link changes dramatically, puts the link in the system's AttentionalFocus (the latter being the set of nodes and links in the system's memory that have the highest Short Term Importance (STI) values, and thus get the most attention from the system's cognitive processes).  Putting the link in the AttentionalFocus, will cause STI to be spread to the nodes that the link connects, and to other nodes related to these.  This will then cause the HebbianLinks among these nodes to have their weights updated.  And this will gradually get rid of assemblies and attractors that are no longer relevant.

So this process that triggers attention based on truth value change, will serve directly to combat attachment.

Why Human Brains Get More Attached than a Smart OpenCog Would

In the human mind/brain, explicit knowledge is purely emergent from implicit knowledge -- different from the situation with OpenCog where the two kinds of knowledge exist in parallel, dynamically coupled together.  Obviously, given this, there must be neural mechanisms for changes in emergent explicit knowledge (derived via reasoning, for example) to cause changes in the corresponding underlying implicit knowledge.   But these mechanisms are apparently more complex and harder to control than the corresponding ones in OpenCog.

Evolutionarily, the reason for the difficulty the human brain has in coordinating explicit and implicit knowledge, seems to be that the brain's mechanisms mostly evolved in the context of brains with a lot less associational cortex than the human brain has.  In the context of a dog or ape brain, a sloppy mechanism for coordinating explicit and implicit knowledge may not be so troublesome.   In the context of a human brain, this sloppy mechanism leads to various problems, such as excessive attachment to ideas, people, feelings, etc.   And these problems can be worked around, to a large extent, via difficult and time-consuming practices like meditation, psychotherapy, etc.  Perhaps future technologies like brain implants will enable the circumvention of excessive attachment and other problematic aspects of the human mind/brain architecture, without the need for as much effort as uncertainty as is involved in current mind-improving disciplines....

...

And
so
it
goes
.
.
.





Sunday, December 02, 2012

What Will Come After Language?


I just gave a talk, via Skype from Hong Kong, at the Humanity+ San Francisco conference….  Here are some notes I wrote before the talk, basically summarizing what I said in the talk (though of course, in the talk I ended up phrasing many things a bit differently...).

I'm going to talk a bit about language, and how it relates to mind and reality … and about what may come AFTER language as we know it, when mind and reality change dramatically due to radical technological advances

Language is, obviously, one of the main things distinguishing humans from other animals.   Dogs and apes and so forth, they do have their own languages, which do have their own kinds of sophistication -- but these animal languages seem to be lacking in some of the subtler aspects of human languages.  They don't have the recursive phrase structure that lets us construct and communicate complex conceptual structures.

Dolphins and whales may have languages as sophisticated as ours -- we really don't know -- but if so their language may be very different.  Their language may have to do with continuous wave-forms rather than discrete entities like words, letters and sentences.  Continuous communication may be better in some ways -- I can imagine it being better for conveying emotion, just as for us humans, tone and gesture can be better at conveying emotion than words are.  Yet, our discrete, chunky human language seems to match naturally with our human cognitive propensity to break things down into parts, and with our practical ability to build stuff out of parts, using tools.

I've often imagined the cavemen who first invented language, sitting around in their cave speculating and worrying about the future changes their invention might cause.  Maybe they wondered whether language would be a good thing after all -- whether it would somehow mess up their wonderful caveman way of life.  Maybe these visionary cavemen foresaw the way language would enable more complex social structures, and better passage of knowledge from generation to generation.  But I doubt these clever cavement foresaw Shakespeare, William Burroughs, Youtube comment spam, differential calculus, mathematical logic or C++ ….   I suppose we are in a similar position to these hypothetical cavemen when we speculate about the future situations our current technologies might lead to.  We can see a small distance into the future, but after that, things are going to happen that we utterly lack the capability to comprehend…

The question I want to pose now is: What comes after language?  What's the next change in communication?

My suggestion is simple but radical: In the future, the distinction between linguistic utterances and minds is going to dissolve.

In the not too distant future, a linguistic utterance is simply going to be a MIND with a particular sort of cognitive focus and bias.

I came up with this idea in the course of my work on the OpenCog AI system.  OpenCog is an open-source software system that a number of us are building, with the goal of  eventually turning it into an artificial general intelligence system with capability at the human level and beyond.  We're using it to control intelligent video game characters, and next year we'll be working with David Hanson to use it to control humanoid robots.

What happens when two OpenCog systems want to communicate with each other?  They don't need to communicate using words and sentences and so forth.  They can just exchange chunks of mind directly.  They can exchange semantic graphs -- networks of nodes and links, whose labels and whose patterns of connectivity represent ideas.

But you can't just take a chunk of one guy's mind, and stick it into another guy's mind.   When you're merging a semantic graph from one mind, into another mind, some translation is required -- because different minds will tend to organize knowledge differently.  There are various ways to handle this.

One way is to create a sort of "standard reference mind" -- so that, when mind A wants to communicate with mind B, it first expresses its idiosyncratic concepts in terms of the concepts of the standard reference mind.   This is a scheme I invented in the late 1990s -- I called it "Psy-nese."   A standard reference mind is sort of like a language, but without so much mess.  It doesn't require thoughts to be linearized into sequences of symbols.  It just standardizes the nodes and links in semantic graphs used for communication.

But Psynese is a fairly blunt instrument.  Wouldn't it be better if a semantic graph created by mind A, had the savvy to figure out how to translate itself into a form comprehensible by mind B?  What if a linguistic utterance contained, not only a set of ideas created by the sender, but the cognitive capability to morph itself into a form comprehensible by the recipient?  This is weird relative to how language currently works, but it's a perfectly sensible design pattern…

That's my best guess at what comes after language.  Impromptu minds, synthesized on the fly, with the goals of translating particular networks of thought into the internal languages of various recipients.

If I really stretch  my brain, I can dimly imagine what such a system of thought and communication would be like.  It would weave together a group of minds into an interesting kind of global brain.  But we can't foresee the particulars of what this kind of communication would lead to, any more than a bunch of cavemen could foresee Henry Miller, reddit or loop quantum gravity.

Finally, I'll pose you one more question, which I'm not going to answer for you.  How can we write about the future NOW, in a way that starts to move toward a future in which linguistic utterances and minds are the same thing?

Sunday, November 25, 2012

Complex-Probability Random Walks and the Emergence of Continuous General-Relativistic Spacetime from Quantum Dynamics


(A post presenting some interesting, but still only half-baked, physics ideas....)

The issue of unifying quantum mechanics and general relativity is perenially bouncing around in the back of my mind.   I don't spend that much time thinking about it, because I decided years ago to focus most of my intellectual energy on AI and understanding the mind, but I can't help now and then revisiting the good old physics problem, and doing occasional relevant background reading....

Of course there are loads of approaches to unified physics out there these days, some of them extremely sophisticated.  Yet I can't help hoping for a conceptually simpler unification.   Here's what I'm thinking today....

I've been enjoying Frank Blume's 2006 paper A Nontemporal Probabilistic Approach to Specialand General Relativity....   It consists of fairly elementary calculations done in pursuit of a philosophical point.  Blume wanted to show that the continuous spacetime assumed in special and general relativity, can be approximated arbitrarily well by discrete random walks.   The subtle point is that these discrete random walks hop around randomly (according to a certain specified probability distribution) not only in space, but also in time.   So Blume's picture has particles hopping back and forth in time, which in his view is in accordance with Julian Barbour's perspective that "physical reality is essentially nontemporal and is best thought of as an ordered sequence of discrete static images" (see Barbour's book  The End of Time).  

I don't feel confident I know how physical reality is "best thought of" ... but I do agree with Barbour and Blume that the view of time as flowing forward from past to future is badly flawed.  This sense of unidirectional time-flow is part of  human psychology, and perhaps part of the dissipative nature of the human mind/body as a macroscopic, thermodynamic system ... but it's not fundamental in the way that people sometimes naively assume.   It's not there in microphysics, either -- at the quantum level the flowing of time from past to future is an alien concept.  If you think this sounds like nonsense, read Barbour's book!

But the philosophy of time is somewhat peripheral to the point I want to make here.   What I've been thinking about is the possibility of replacing Blume's random walk, which is defined in terms of ordinary real-number probabilities, with an analogous random walk defined in terms of complex-number probabilities.   

Saul Youssef, in a series of interesting papers (click here and scroll down to Youssef's name) has shown that if one replaces ordinary real-number probabilities with complex-number probabilities, and adds a few other commonsensical assumptions, then the equations of quantum theory basically pop out.        

This direction of research seems natural once one notes that, according to the basic math of probability theory, there are four options for creating probabilities that obey all the standard probability rules: real-number, complex-number, quaternionic and octonionic probabilities.  Classical physics uses the standard real-number option.  Quantum physics uses the complex-number option.

Ordinary quantum logic uses real-number probabilities, but uses an unusual logic (lattice meet and join on the lattice of subspaces of a complex Hilbert space), which lacks some of the normal rules of Boolean logic, such as distributivity.    Youssef's exotic probability approach retains ordinary Boolean logic rules, but moves to complex number probabilities.   

What I began wondering is: What if you replace Blume's conventional random walk with a random walk in which each movement of a particle is quantified by a certain complex-number probability?

Then a particle may move in various spatiotemporal directions, and there is the possibility for constructive or destructive interference between the different directions.  

And it seems that, in the case where the interference between the different directions cancels out, one would get the same behavior as a real-probability random walk.  

So based on back-of-the-envelope calculations I did the other day, it looks like one can probably get General Relativity to emerge as a statistical approximation to the large-scale behavior of complex-number-probability (quantum) random walks, under conditions of minimal interference.

How far does a perspective like this go, in terms of explaining the particulars of unified physics?  I don't know, and don't seem to have the time to do the rigorous calculations to find out, right now.  But it seems an interesting direction....   If you're a physicist interested in helping work out the details, drop me a line! ...

 

Monday, October 29, 2012

Avoiding the Tyranny of the Majority in Collaborative Filtering



One of the more annoying aspects of the modern Internet is crap comments.  For instance, it's improved in recent years, but for a while the typical comments on Youtube music videos were among the most idiotic examples of human "thought" and behavior I've ever seen…

A common solution to the problem is to have readers rate comments.  Then comments that are highly-rated by readers get ranked near the top of the list, and comments that are panned by readers get ranked near the bottom of the list.  This mechanism is used to good effect on general-purpose sites like Reddit, and specialized-community sites like Less Wrong.

Obviously this mechanism is very similar to the one used on Slashdot and Digg and other such sites, for collaborative rating of news items, web pages, and so forth.

There are many refinements of the methodology.  For instance, if an individual tends to make highly-rated comments, one can have the rating algorithm give extra weight to their ratings of others' comments.

Such algorithms are interesting and effective, but have some shortcomings as well, one of which is a tendency toward "dictatorship of the majority."  For instance, if you have a content that's loved by a certain 20% of readers but hated by the other 80%, it will get badly down-voted.

I started wondering recently whether this problem could be interestingly solved via an appropriate application of basic graph theory and machine learning.

That is, suppose one is given: A pool of texts (e.g. comments on some topic), and a set of ratings for each text, and information on the ratings made by each rater across a variety of texts.

Then, one can analyze this data to discover *clusters of raters* and *networks of raters*.

A cluster of raters is a set of folks who tend to rate things roughly the same way.   Clusters might be defined in a context-specific way -- e.g. one could have a set of raters who form a cluster in the context of music video comments, determined via only looking at music video comments and ignoring all other texts.

A network of raters is a set of folks who tend to rate each others' texts highly, or who tend to write texts that are replies to each others' texts.

Given information on the clusters and networks of raters present in a community, one can then rank texts using this information.  One can rank a text highly if some reasonably definite cluster or network of raters tends to rank it highly.

This method would remove the "dictatorship of the majority" problem, and result in texts being highly rated if any "meaningful subgroup" of people liked it.  

Novel methods of browsing content also pop to mind here.  For instance: instead of just a ranked list of texts, one could show a set of tabs, each giving a ranked list of texts according to some meaningful subgroup.

Similar ideas could also be applied to the results of a search engine.  In this case, the role of "ratings of text X" would be played by links from other websites to site X.   The PageRank formula gives highest rank to sites that are linked to by other sites (with highest weight given to links from other sites with high PageRank, using a recursive algorithm).  Other graph centrality formulas work similarly.  As an alternative to this approach, one could give high rank to a site if there is some meaningful subgroup of other sites that links to it (where a meaningful subgroup is defined as a cluster of sites that link to similar pages, or a cluster of sites with similar content according to natural language analysis, or a network of richly inter-linking sites).   Instead of a single list of search results, one could give a set of tabs of results, each tab listing the results ranked according to a certain (automatically discovered) meaningful subgroup.

There are many ways to tune and extend this kind of methodology.   After writing the above, a moment's Googling found a couple papers on related topics, such as:

http://iswc2004.semanticweb.org/demos/01/paper.pdf

http://www.citeulike.org/user/abellogin/article/2200728

But it doesn't seem that anyone has rolled out these sorts of ideas into the Web at large, which is unfortunate….

But the Web is famously fast-advancing, so there's reason to be optimistic about the future.  Some sort of technology like I've described here, deployed on a mass scale, is going to be important for the development of the Internet and its associated human community into an increasingly powerful "global brain" …

Friday, October 19, 2012

Can Computers Be Creative?


Can Computers Be Creative? -- A Dialogue on Creativity, Radical Novelty, AGI, Physics and the Brain

Over the years, I've repeatedly encountered people making arguments of the form: "Computers can't be creative in the same way that people can." Such arguments always boil down, eventually, to an assertion that human mind/brains have recourse to some sort of "radical novelty" going beyond the mere repermutation of inputs and initial state that computers are capable of.

This argument is there in Roger Penrose's "Emperor's New Mind", in Kampis's "Self-Modifying Systems", and in all manner of other literature. It will surely be around until the first fully human-level AGIs have been created -- and will probably continue even after that, at least to some extent, since no issue verging on philosophy has ever been fully resolved!

The following dialogue, between two imaginary characters A and B, is my attempt to summarize the crux of the argument, in a way that's admittedly biased by my own peculiar species of pro-AGI perspective, but also attempts to incorporate my understanding of the AGI skeptic's point of view.

The dialogue was inspired in part by a recent dialogue on the AGI email list, in which perpetual AGI gadfly Mike Tintner was playing the role of AGI skeptic "A" in the dialogue, and the role "B" was played by pretty much everyone else on the list. But it's often hard to really get at the crux of an issue in the herky-jerky context of mailing list discussion. I hope I've been able to do better here. I was also heavily inspired by conversations I had years previously, with my friends Margeret Heath and Cliff Joslyn, on the concept of "radical novelty" and what it might mean.

A: It's obvious AIs can never be creative and innovative in the same sense that people are. They're just programs, they just recombine their inputs in ways determined by their programming.

B: How can you say that, though? If you look at the transcript of a computer chess player's game, you'll see plenty of creative moves -- that is, moves you'd call creative if you saw them made by a human player. I wrote some computer music composition software that made up some really cool melodies. If I'd made them up myself, you'd call them creative.

A: OK, but a program is never going to make up a new game, or a new instrument, or a new genre of music.

B: How do you know? Anyway once those things happen, then you'll find some reason to classify *those* achievements as not creative. This is just a variant of the principle that "AI is defined as whatever seems intelligent when people can do it, that computers can't yet do."

A: No, there's a fundamental difference between how computers are doing these things, and how people do these things. A person has to set up the situation for a computer to do these things. A person feeds the computer the input and configures the computer to have a certain goal. Whereas a human's creative activity is autonomous -- the human's not just a tool of some other being.

B: Ah, falling back on mystical notions of free will, are we? But think about it -- if you don't take care to feed a human child proper input, and set up their situation properly, and guide them toward a certain goal -- then they're not going to be playing chess or composing music. They're going to be a "wild child", capable only of hunting and foraging for food like a non-human animal. No one who can read this is independent of their cultural programming.

A: That's not a fair analogy. Computers need much more specialized preparation for each task they're given, than people do.

B: Yes, that's true. Nobody has achieved human-level AGI yet. I believe we're on the path to get there, but we're not there yet. But I never claimed that computer programs are currently creative and innovative on the level of highly creative adult humans. Actually it's hard to compare. Current computer programs can create some things humans can't -- extremely complex circuit designs, music with 10000-voice polyphony, fractal art in 128 dimensions, and so forth -- but they also fall far short of humans in many areas. Your original statement wasn't merely "We don't yet have computers that are as creative as innovative as humans" -- that's obvious. Your statement was that computers intrinsically aren't creative and innovative, in the same manner than humans are. And I don't think you've demonstrated that at all.

A: It's so obvious, it doesn't need demonstration. A computer will never do more than rearrange the elements that have been fed into it. Whereas, a human can come up with something fundamentally new -- a new element that neither it, nor anybody else, has ever heard of.

B: Ah, now I see what you're getting at -- the notion of "radical novelty." I've had this argument before!

A: Yes, radical novelty. The human mind is capable of radical novelty. That's the crux of our general intelligence, our creative innovations. And computers can't do it, because all they can do is rearrange their inputs and their programming -- they can't introduce anything new.

B: You do realize you're not the first one to think of this argument, right? It's been around a rather long time. I myself first encountered it in George Kampis's book "Self-Modifying Systems in Biology and Cognitive Science", which was published in the early 1990s. But of course the argument's been around since long before that. I'm sure someone who knew the history of philosophy better could trace it back far before the advent of computers. There are really two arguments here. One is: Is there more to creativity than combination of pre-existing elements, plus introduction of occasional randomness. The other is: If there some additional, magic ingredient, can computers do it too?

A: What do you mean "Is there more to creativity than combination of pre-existing elements, plus introduction of occasional randomness." Of course there is; that's utterly obvious!

B: Is it? Think about it -- is evolution creative? Evolution created the human body, the human brain, the human eye, the snake's locomotion, the dolphin's sonar, the beautifully patterned wings of the Monarch butterfly. But what does evolution do? It combines previously known elements, it makes use of randomness, and it leverages the intrinsic creativity of the self-organizing processes in the physical world. Or are you going to plead Creationism here?

A: You admit evolution leverages the self-organizing processes of the physical world. The brain is also part of the physical world. A computer is different. The physical world has more creativity built into it. 

B: You admit a computer is part of the physical world, right? It's not some kind of ghostly presence…

A: Yes, but it's a very limited part of the physical world, it doesn't display all the phenomena you can see in the real world.

B: A brain is a very limited part of the physical world too, of course. And so is the Earth. And insofar as we understand the laws of physics, every phenomenon that can occur in the physical world can be simulated in a computer.

A: Ah, but a simulation isn't the real thing! You can't cook your food with a simulation of fire!

B: This brings us rather far afield, I think. I'm sure you're aware of the argument made by Nick Bostrom and many others before him, that it's highly possible we ourselves live in some kind of simulation world. You saw "The Matrix" too, I assume. A simulation isn't always going to look like one to the creatures living inside it.

A: OK OK, I agree, that's a digression -- let's not go there now. Leave that for another day.

B: So do you agree that evolution is creative?

A: Yes, but I'm not sure your version of the evolutionary story is correct. I think there's some fundamental creativity in the dynamics of the physical world, which guides evolution and neural process, but isn't present in digital computers.

B: And what evidence do you have of this? You do realize that there is no support for this in any current theory of physics, right?

A: And you do realize that current fundamental physics is not complete, right? There is no unified theory including gravity and all the other forces. Nor can we, in practice, explain how brains work using the underlying known physics. We can't even, yet, derive the periodic table of the elements from physical principles, without setting a lot of parameters using chemistry-level know-how. Clearly we have a lot more to discover.

B: Sure, no doubt. But none of the concrete proposals out there for unifying physics would introduce this sort of radical creativity and novelty you're looking for. It's ironic to look at physicist Roger Penrose and his twistor theory, for example. Penrose agrees with you that nature includes some kind of radical creativity not encompassable by computers. Yet his own proposal for unifying physics, twistors, is quite discrete and computational in nature -- and his idea of some mystical, trans-computational theory of physics remains a vague speculation.

A: So you really think this whole universe we're in, is nothing but a giant computer, each step determined by the previous one, with maybe some random variations?

B: Like Bill Clinton said in the Monica Lewinsky trial: That depends on what the meaning of is, is.

A: Sorry, you'll have to elaborate a bit. Clintonian metaphysics is outside my range of expertise…

B: I think that, from the perspective of science, there's no reason to choose a non-computational model of the observed data about the universe. This is inevitable, because the totality of all scientific data is just a giant, but finite collection of finite-precision numbers. It's just one big, finite bit-set. So of course we can model this finite bit-set using computational tools. Now, there may be some other way of understanding this data too -- but there is no empirical, scientific way to validate the idea that the best way to model this finite bit-set is using a non-computational model. If you choose to find a non-computational model of a finite set of bits simpler and preferable, I can't stop you from doing or saying that. What I can say though is that: from the perspective of science, there's no reason to choose a non-computational model of the observed data about the universe.

A: That seems like a limitation of science, rather than a limitation of the universe!

B: Maybe so. Maybe some future discipline, descending from our current notion of science, will encompass more possibilities. I've speculated that it may be necessary to expand the notion and practice of science to come to a good understanding of consciousness on the individual and group level. But that's another digression. My strong suspicion is that to build an advanced AGI, with intelligence and creativity at and then beyond the human level, the scientific understanding of the mind is good enough.

A: Hmmm…. You admit that science may not be good enough to fully understand consciousness, or to encompass non-computational models of our observations of intelligent systems. But then why do you think science is good enough to guide the construction of thinking machines?

B: I can't know this for sure. To some extent, in life, one is always guided by one's intuition. Just because I saw the sun rise 1000 mornings in a row, I can't know for sure it's going to rise the next day. As Hume argued long ago, the exercise of induction requires some intuitive judgment as to which hypothesis is simpler. To me, by far the simplest hypothesis about intelligence is that if we engineer mechanisms implementing basically the same sorts of functions that the human brain does then we're going to get a system that's intelligent in basically the same sorts of ways that the brain is. And if there's some aspect of the human mind/brain that goes beyond what mechanism can explain -- well hey, there may well be some aspect of our engineered AGI mind/brain that also goes beyond what mechanism can explain. Both the brain and computer are arrangements of matter in the same universe.

A: Heh…. I guess we digressed again, didn't we.

B: It seems that's how these arguments usually go. We started out with creativity and ended up with life, the universe and everything. So let's get back to radical novelty for a bit. I want to run through my thinking about that for you a little more carefully, OK?

A: Sure, go for it!

B: Ok…. Consider, in the fashion of second-order cybernetics, that it's often most sensible to consider a system S in the context of some observer O of the system.

A: Sure. Quantum mechanics would also support that sort of perspective.

B: Indeed -- but that's another digression! So let's go on...

My first point is: It's obvious that, in many cases, a system S can display radical novelty relative to an observer O. O may have devised some language L for describing the behaviors and internal states of S, and then S may do something which O finds is more easily describable using a new language L1, that has some additional words in it, and/or additional rules for interaction of the words in the language.

A: That's a bit abstract, can you give me an example or something?

B: Sure. Consider a pot of water on the stove, gradually heating up but not yet boiling. An observer of that pot may come up with a set of descriptions of the water, with a theory of the water's behavior based on his observations of the water. But then when the temperature gets high enough and the water starts to boil -- all of a sudden he sees new stuff, and he has to add new words to his language for describing the water. Words for bubbles, for example.

A: Yes. The pot of water has introduced radical novelty. It's come up with a new element - bubbles -- that didn't exist there before.

B: Yeah -- but now we get to the tricky point, which is the crux of the matter. In general, for a given system S, a certain behavior or internal state change may appear as radical novelty to observer O but not to observer O1.

In the case of the pot of water, suppose that in addition to our original observer O, we had another observer O1 who was watching every elementary particle in the pot of water, to the extent physics allows; and who was smart enough to infer from these observations the laws of physics as currently understood. This observer O1 would not be surprised when the water started to boil, because he would have predicted it using his knowledge of the laws of physics. O1 would be describing the water's structures and dynamics using the language of particle physics, whereas O would be describing it using "naive physics" language regarding the macroscopic appearance of the water. The boiling of the water would introduce radical novelty from the perspective of O, but not O1.

For a slightly broader example, think about any deterministic system S, and an observer O1 who has complete knowledge of S's states and behaviors as they unfold over time. From the view of O1, S will never do anything radically novel, because O1 can describe S using the language of S's exact individual states and behaviors; and each new thing that emerges in S is by assumption determined by the previous states of S and S's environment. But from the view of another observer O, one which has a coarser-grained model of S's states or behaviors, S may well display radical novelty at some points in time.

The question regarding radical novelty then becomes: given a system S and an observer O who perceives S as displaying radical novelty at some point in time, how do we know that there isn't some other observer O1 who would not see any radical novelty where O does? Can we ever say, for sure, that S is in a condition such that any possible observer would perceive S to display radical novelty?

It seems we could never say this for sure, because any observer O, ultimately, only sees that data that it sees.

A: That's quite interesting, indeed, and I'll probably need some time to digest it fully.

But I still wonder if you're fudging the distinction between digital systems like computers and real physical systems like brains.

I mean: in the case of a computer, we can easily see that it's doing nothing new, just repermuting what comes in through its sensors, and what it was given in its initial programming. In the case of a human, you could try to argue that a human brain is just doing the same thing -- repermuting its sensations and its initial brain-state. But then, the rules of quantum mechanics forbid us from knowing the initial brain state or the sensations in full detail. So doesn't this leave the brain more room to be creative?

B: Not if you really look at what quantum mechanics says. Quantum mechanics portrays the dynamics of the world as a sort of deterministic unfolding in an abstract mathematical space, which isn't fully observable. But it's proved quite clearly that a quantum system can't actually do anything beyond what an ordinary computer system can do, though in some cases it can do things much faster. So any quantum system you build, can be imitated precisely by some ordinary computer system, but the ordinary computer system may run much slower.

The arguments get subtle and threaten to turn into yet another digression -- but the bottom line is, quantum theory isn't going to save your position. That's why Penrose, who understands very well the limits of quantum theory, needs to posit some as yet unspecified future unified physics to support his intuitions about radical novelty.

A: Hmm, OK, let's set that aside for now. Let's focus back on the computer, since we both agree that physicists don't yet really understand the brain.

How can you say a computer can be fundamentally creative, when we both know it just repermutes its program and its inputs?

B: This becomes a quite funny and subtle question. It's very obvious to me that, to an observer with a coarse-grained perspective, a computer can appear to have radical novelty -- can appear quite creative.

A: Yes, but that's only because the observer doesn't really know what's going on inside the computer!

B: So then the real question becomes: For a given computer, is there hypothetically some observer who could understand the computer's inputs and program well enough to predict everything the computer does, even as it explores complex environments in the real world. So for this observer, the computer would display no radical novelty.

A: Yes. For any computer, there is an observer like that, at least hypothetically. And for a brain, I really doubt it, no matter what our current physics theories might suggest.

B: But why do you doubt it so much? Because of your own subjective feeling of radical novelty, right? But consider: The deliberative, reflective part of your mind, which is explicitly aware of this conversation, is just one small part of your whole mental story. Your reflective conscious mind has only a coarse-grained view of your massive, teeming "unconscious" mind (I hesitate to really call the rest of your mind "unconscious" because I tend toward panpsychism -- but that would be yet another digression!). This is because the "conscious" mind has numerous information-processing limitations relative to the "unconscious" -- for instance the working memory limitation of 7 +/-2 items. Given this coarse-grained view, your "conscious" mind is going to view your "unconscious" mind as possessing radical novelty. But to another observer with fuller visibility into your "unconscious" mind, this radical novelty might not be there.

We all know the conscious mind is good at fooling itself. The radical novelty that you feel so acutely, may be no more real than the phantom limbs that some peoples' brains tell them so vividly are really there. In one sense, they are there. In another, they are not.

Let me go back to the Matrix scenario for a moment…

A: You're kind of obsessed with that movie, aren't you? I wonder what that tells us about YOUR unconscious?

B: Heh… actually, I thought the first Matrix movie was pretty good, but it's not really a personal favorite film. And let's not even get started on the sequels… All in all, Cronenberg's "Existenz" had a similar theme and agreed with my aesthetics better…. But anyway…

A: … you digress

B: Indeed! I'm not trying to do "Roger Ebert Goes to the AI Lab" here, I just want to use the Matrix as a prop for another point.

Imagine we live in a Matrix type simulation world, but the overlords of the world -- who live outside the simulation -- are subtly guiding us by introducing new ideas into our minds now and then. And also by introducing new ideas into the minds of our computer programs. They are tweaking the states of our brains, and the RAM-states of the computers running our AI systems, in tricky ways that don't disrupt our basic functioning, but that introduce new ideas. Suppose these ideas are radically new -- i.e. they're things that we would never be able to think of, on our own.

A: So this scenario is like divine inspiration, but with the overlords of the Matrix instead of God?

B: Yeah, basically. But I wanted to leave the religious dimension out of it.

A: Sure… understandably.  We've made a big enough mess already!

B: So my point is: if this were the case, how would we ever know? We could never know.

A: That's true I suppose, but so what?

B: Now think about all the strange, ill-understood phenomena in the Cosmos -- psi phenomena, apparent reincarnation, and so forth. I know many of my colleagues think these things are just a load of BS, but I've looked at the data fairly carefully, and I'm convinced there's something strange going on there.

A: OK, sure, actually I tend to agree with you on that point. I've had some strange experiences myself. But aren't you just digressing again?

B: Partly. But my point is, if psi processes exist, they could potentially be responsible for acting sort of like the Matrix overlords in my example -- introducing radical novelty into our minds, and potentially the minds of our computers. Introducing inputs that are radically novel from some perspectives, anyway. If some kind of morphogenetic field input new stuff into your brain, it would be radically novel from your brain's view, but not from the view of the universe.

A: You're trying to out-weird me, is that it?

B: Aha, you caught me!!… Well, maybe. But if so that's a secondary, unconscious motive!

No, seriously…. My point with that digression was: We don't really understand the universe that well.

In actual reality, nobody can predict what a complex computer program is going to do, when it's interacting with a complex world.

You want to tell a story that some hypothetical super-observer could predict exactly what any complex computer program will do -- and hence, for any computer program, there is some perspective from which it has no radical novelty.

And then you want to tell a story that, for a human brain, some mysterious future physics will prove current physics wrong in its assertion that there is an analogous hypothetical super-observer for human brains.

But I don't particularly believe either of these stories. I think we have a lot to learn about the universe, and probably from the view of the understanding we'll have 100 years from now, both of these stories will seem a bit immature and silly.

A: Immature and silly, huh?? I know you are but what am I !!!

But if there are such big holes in our understanding of the universe, how come you think you know enough to build a thinking machine? Isn't *that* a bit immature and silly?

B: We started out with your argument that no computer can ever be creative and innovative, because the human mind/brain has some capability for radical novelty that computers lack -- remember?

A: Vaguely. My brain's a bit dizzied by all the quantum mechanics and psychic powers. Maybe I'd be less confused if my brain were hybridized with a computer.

B: But that's another digression…

A: Exactly…

B: But really -- after more careful consideration, what's left of your argument? What evidence is there for radical novelty in the human mind/brain, that's not there in computers? Basically the hypothesis of this special additional radical novelty in humans, comes down to your intuitive sense of what creativity feels like to you, plus some observations about the limits of current computer programs, plus some loosely connected, wild speculations about possible future physics. It's far from a compelling argument.

A: And where is your compelling argument that computers CAN display the same kinds of creativity and innovation that humans can? You haven't proved that to me at all. All you have is your faith that you can somehow make future Ai programs way more creative and innovative than any program has ever been so far.

B: I have that scientific intuition -- and I also have the current laws of physics, which imply that a digital computer can do everything the brain does. It's possible, according to physics, that we'll need a quantum computer rather than a conventional digital computer to make AGI systems run acceptably fast -- but so far there's no real evidence of that.

And then there's current neuroscience, psychology and so forth -- all of which appear quite compatible with current physics.

I'm willing to believe current science has profound limitations. But given the choice between 1) current science, versus 2) your own subjective intuition about your thought process and your utterly scientifically ungrounded speculations about possible future physics -- which am I going to choose as a guide for engineering physical systems? Sorry my friend, but I'm gonna go with current science, as a provisional guide at any rate.

In everything we've learned so far about human cognition and neuroscience (and physics and chemistry and biology, etc. etc.), there's no specific thing that seems to go beyond what we can do on digital computers. What it seems to me is that human-level intelligence using available computational resources is just a complex thing to do. It requires lots of different computational processes, all linked together in complex ways. And these processes are different from the serial calculations that current computer architectures are best at performing, which means that implementing them involves a lot of programming and software design trickery. Furthermore we don't understand the brain that well yet, due to limitations of current brain scanning equipment -- so we have to piece together models of intelligence based on integrating scattered information from various disciplines. So implementing human-level AGI is a difficult task right now. 50 or 100 years from now it will probably be an exercise for schoolchildren!!

A: That's not a compelling argument, hombre. That's just a statement of your views.

B: Urrghh…. Look, what happens inside the mind is a lot like evolution! Not exactly the same, but closely analogous. The mind -- be it a human or computer mind -- generates a lot of different ideas, internally. It generates them by randomly tweaking its prior ideas, and by combining its previous ideas. It also has a good trick that evolution doesn't have: it can explicitly generalize its previous ideas to form new, more abstract ones, that then get thrown back into the ever-evolving idea pool. Most of the ideas emerging from this pool are rubbish. Eventually it finds some interesting ones and refines them.

What's complicated is going all this re-combination, mutation and generalization of ideas judiciously given limited computing resources and current computer architectures, which were built for other sorts of things. This requires somewhat complex cognitive architectures, which take a while to implement and refine... which leads us back to linking together appropriate complex computational processes to as to carry out these processes effectively on current hardware, as we're trying to do with OpenCog...

A: Blah, blah, blah...

B: OK, OK…. I guess this argument has gone on long enough. I admit neither of us has a 100% solid argument in favor our our position -- but that's because anything regarding the mind eventually impinges on philosophy, and there are no 100% solid arguments in philosophy of any kind. Philosophical issues can get obsoleted (not many folks bother arguing about how many angels can dance on the head of a pin anymore), but they never get convincingly resolved….

But do you admit, at least, the matter isn't as clear as you thought initially? That the inability of computers to be truly creative isn't so entirely obvious as you were saying at first?

A: Yes, I understand now, that it's possible that computers can achieve creativity via the influx of radical novelty into their RAM-states via psychic projection from the Matrix Overlords.

B: Ah, good, good, glad we cleared that up.

Uh, you do understand that was just a rhetorical illustration, right?

A: A rhetorical illustration, eh? I know you are but what am I !!!


Tuesday, October 16, 2012

Reports of Reincarnation: What's Really Going On?


For most of my life I considered belief in reincarnation completely ridiculous, and an obvious example of wishful thinking.   These people just don't want to face the reality of their impending doom, I figured, so they latch onto crazy stories about life after death and their souls moving on to occupy other bodies.  Yeah, right.

But the more I read about the topic, the more this attitude of facile dismissal started to grate on me.   I encourage anyone interested in understanding this aspect of universe to read Ian Stephenson's book Children Who Remember Previous Lives: A Question of Reincarnation ... and the Wikipedia page on Reincarnation Research gives some other useful references too. 


This research definitely doesn't prove that reincarnation exists, in any of the classic definitions of reincarnation.   Yet, it's also very difficult to dismiss as simple fraud or self-deception.If you really read the evidence carefully, you come to the inescapable conclusion -- Something Strange Is Going On Here.
A similar situation is noted in Robert McLuhan's book Randi's Prize, which recounts many examples of strange phenomena occurring during seances in previous centuries.   When one really studies the historical record regarding these phenomena, explanations in terms of hoaxes and self-deception become difficult to maintain.  One comes to the conclusion: something strange was happening there, though due to its peculiar nature it's hard to study with repeatable experiments.  Yeah, there was lots of fraud and self-deception; yet when you finally consider all the evidence carefully, it's not really plausible that these account for all of it.   It seems hard to account for the evidence without positing some kind of psi phenomena -- though exactly what kind remains unclear.

My general thoughts on psi are given on this page. I'm not going to try to convince skeptics to believe in psi, reincarnation or anything, in the space of this short blog post.  If you're truly skeptical about psi, but open-minded, I encourage you to read the references given on that page, and Stephenson's book on reincarnation as well.

What I'm musing about is: Supposing some of the evidence gathered by Anderson and others about reincarnation is real (as seems likely to me) -- then, what the heck is going on in this universe?

The classic religious stories of reincarnation don't make much sense.   Many folks have poked many holes in them; it's extremely easy to do.

Yet, there does seem to be evidence that knowledge about one person's life, often a recently dead one, can somehow leak into the mind of some other human, often a very young one.   

One can imagine a lot of different psi phenomena that could lead to this.  My own speculation is that the phenomenon has something to do with the primacy of pattern over time.  

I realize that, with this sort of speculation, I'll utterly lose anyone who takes a reductionist, naive-realist type view of the world -- a view in which ordinary physical reality is primary and mental, subjective reality is an epiphenomenon.  But, so it goes...

The linear flow of time, as we perceive it subjectively in ordinary human states of mind and as we study it in physics, is not necessarily a fundamental property of the universe.  It may be "just one way that information self-organizes".   If one views the universe as a sort of pool of forms, patterns, feelings etc., with the conventional linear time axis being just one form of organization among many that emerges in this pool, then reincarnation-type phenomena seem a lot less strange.

Suppose, for instance, that the two brains involved in a reincarnation-type phenomenon have some sort of similarity to them, in their pattern of organization or dynamics.  Could this similarity set up some kind of "resonance" between these brain/minds, acting in the broader pattern-space of the universe but outside the particular pattern that is the linear order of time?   This possibility fits in well with Sheldrake's ideas about morphogenetic fields, which I've tried to tie in with some proposed modifications to quantum mechanics.

Sorry to disappoint, but I have no grand conclusion on these matters.  My current way of thinking is:
  • There is some valid, strange (to our normal world-view) phenomenon going on, underlying some of the known examples of reincarnation-like phenomena
  • The classic religious stories about reincarnation are almost surely not correct
  • Maybe, maybe, maybe some morphogenetic field type explanation could help explain what's really going on

I look forward to the science --- or trans-science --- of the future, which I suspect will be able to explain reincarnation-type phenomena and other paranormalities to us, using entirely new concepts, or concepts like morphogenetic fields that currently exist only in very blurry form....