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Tuesday, October 14, 2014

The Neural Foundations of Complex Symbolic Thought

How can the brain, with its messy mix of neurons and glia spreading activation all over the place, give rise to the precise mathematical structures of symbolic reasoning?

This "symbolic / subsymbolic gap" has been a major puzzle at the center of cognitive science for decades, at least.

In a paper for the IJCNN conference in Beijing in July, I proposed a potential solution that -- while still speculative -- I believe has real potential for solving the issue.

The paper is linked here; and the abstract is as follows:

How Might the Brain Represent Complex Symbolic Knowledge?

Abstract—A novel category of theories is proposed, providing
a potential explanation for the representation of complex
knowledge in the human (and, more generally, mammalian)
brain. Firstly, a ”glocal” representation for concepts is suggested,
involving localized representations in a sparse network
of ”concept neurons” in the Medial Temporal Lobe, coupled
with a complex dynamical attractor representation in other
parts of cortex. Secondly, it is hypothesized that a combinatory
logic like representation is used to encode abstract
relationships without explicit use of variable bindings, perhaps
using systematic asynchronization among concept neurons to
indicate an analogue of the combinatory-logic operation of
function application. While unraveling the specifics of the
brain’s knowledge representation mechanisms will require data
beyond what is currently available, the approach presented
here provides a class of possibilities that is neurally plausible
and bridges the gap between neurophysiological realities and
mathematical and computer science concepts.

Note that this is a hypothesis about brains, and potentially a design principle for closely brain-like AGI systems -- but not a statement about, for example, the OpenCog AGI system, which implements symbolic thought more directly.   However, there are certainly analogies with things that happen inside OpenCog.  OpenCog has explicit symbolic representation (analogous to concept neurons, very roughly) and also subsymbolic representation from which symbolic-like representations may emerge; and the design intention of OpenCog is that these two kinds of representations can work together.   The specific mechanisms of this interaction are quite different in OpenCog from what I hypothesize to take place in the brain, but, on the level of the cognitive processes emerging from these systems at the highest levels, there may not be a large difference.

Wednesday, October 01, 2014

Is Physics Information Geometry on Causal Webs? (Speculations Toward Grand Unification)

I mentioned recently to my dad that I was fiddling around in my (egregiously nonexistent) "spare time" with some ideas regarding quantum gravity and he said "I would question the sanity of someone as busy as you trying to unify physics in his spare time."   Fair enough….

However, I think it's almost a social necessity these days to have one's own eccentric approach to grand unified physics, isn't it?   Admittedly, there are hundreds or maybe thousands of different approaches out there -- but still, if you don't have one of your own, you're not going to be invited into any of the really fancy parties….

And more importantly (I don't have time for those parties anyway) -- sometimes the mind's gotta do what the mind's gotta do.  For months now I've been plagued with some ideas about how to unify physics using information geometric structures defined over spacetime-like discrete structures, and I just HAD to write at least some of them down…. 

Today is a holiday here in Hong Kong (the anniversary of the founding of the People's Republic of China, which the Hongkongese are celebrating with their hugest political protests every), so along with taking a dramatic 5 hour hike with Ruiting (up a beautiful rocky stream rather than along a trail, with some great places to swim & climb en route; but the last 1.5 hours were in the dark which was a bit treacherous), I took some time to slightly polish some of my earlier scribblings on my half-baked but IMO tantalizing thoughts….

Physics as Information Geometry on Causal Webs

Rather than packing a huge load of speculative technical ideas into a blog post, I've put together a concept paper summarizing the direction of my thinking -- click here to read the paper in PDF form!.

For those  too lazy, busy or uncoordinated to point and click on the PDF, the title and abstract are pasted here:

Physics as Information Geometry on Causal Webs: 
Sketch of a Research Direction

A high-level outline is given, suggesting a research direction aimed at unifying the Standard Model with general relativity within a common information-based framework.   Spacetime is modeled spacetime using discrete ``causal webs'', defined as causal sets that have ternary rather than binary directed links, and a dynamic in which each ternary link propagates local values from its sources to its target using multiplication in an appropriate algebra.   One then looks at spaces of (real and complex) probability distributions over the space of ``causal web histories.''   One can then model dynamics as movement along geodesics in this space of probability distributions (under the Fisher-Rao metric).  

The emergence of gravitation in this framework (as a kind of ``entropic force'') is derived from Matsuoka's work founding General Relativity in information geometry; the emergence of quantum theory is largely implicit in work by Goyal and others founding the basic formalism of quantum states, measurements and observables in information geometry.   It is suggested that these various prior works can be unified by viewing quantum theory as consequent from information geometry on complex-valued probability distributions; and general relativity as consequent from the geometry of associated real probability distributions.  Further, quantum dynamics is known to be derivable from the correspondence principle and basic properties of classical mechanics; but the latter is an approximation to general relativity -- which as Matsuoka has shown is information-geometric, thus strongly suggesting that quantum dynamics also can be seen as emergent from information geometry.   It is hypothesized that the Standard Model, beyond basic quantum mechanics, could potentially be obtained from this approach via appropriate choice of the ``local field'' algebra propagated within the underlying causal webs. 

In addition to mathematical elegance, this approach to physics unification has the conceptual advantage of highlighting the parallels between physical dynamics and mental inference (given the close ties between Bayesian inference and information geometry).

Hypergraphs, Hypergraphs Everywhere ...

If you're familiar with the OpenCog AGI architecture (which I've played a leading role in designing) you may note a (maybe not so) peculiar similarity between the OpenCog design and the approach to physics proposed in the above.  In both cases one starts with a hypergraph -- OpenCog's Atomspace, which is a pretty unstructured hypergraph; versus a causal web, that is a hypergraph with a specific structure comprised of ternary directed links.  And in both cases one has nonlinear dynamics flowing across the hypergraph (though again, of fairly different sort).    And then, in both cases, it is posited that {\it emergent structures} from these hypergraph dynamics are key to giving rise to the important structures and dynamics.

Of course, this similarity may reflect nothing more than -- this is the way Ben likes to model things!   But I'd like to think there's something deeper going on, and that by modeling minds and the physical world using similar formalisms, one can more easily think about the relation between mind and matter.

Along these lines -- but even a smidge wackier -- thorough readers of this blog will note that some of these ideas were referenced in my recent blog post on morphic fields, psi and physics.   Of course the concepts in these two posts are somewhat independent: the physics ideas given here could be correct even if psi and morphic fields don't really exist; and my analysis of morphic fields in terms of pattern completion and surprisingness could be correct even if the right solution to unified physics involves utterly different ideas than the ones outlined here.  However, it's fair to say that these various concepts evolved together.  My thoughts about morphic fields and unified physics have definitely shaped each other, for whatever that's worth.

While I think the ideas outlined in the  document I linked here make sense, I'm under no illusion about how much work it would be to fill in the gaps in such a complex line of thinking.  Furthermore, some of the gap-filling might end up requiring the creation of substantial new ideas (though it's not obvious this is the case - it could just be a matter of crunching through relatively straightforward math).   I'm also under no illusion that I have time to pursue a lot of difficult physics calculations right now, nor that I will in the near future.   I'll have time to work on this in spare-time bits and pieces, for whatever that's worth.

However, I'd love to collaborate with someone on working out details of some or all of these ideas.  Any physics grad students out there with a high risk tolerance and a penchant for weird ideas, please consider!

Elsewise, I may get to working out the details (or trying to) in a few years when my task list clears up a bit.  AGI is going to remain the priority of the portion of my existence devoted to research until it's compellingly achieved and takes over its own development -- but to keep my creativity flowing (and not go nuts) I need to spend a certain percentage of time thinking about other areas of science.   That's just how my freaky little human brain works...

Sunday, September 28, 2014

Speculations Toward a Precise Model of Morphic Fields

Gentle Reader Beware: This post presents some fairly out-there ideas about the nature of memory and the relationship between the mind and the universe!  If you're a hard-core psi skeptic or a die-hard materialist you may as well move on and save yourself some annoyance ;-) …

On the other hand, if you're intrigued by new potential ways of connecting known science with the "paranormal", and open to wacky new ways of conceptualizing the universe, please read on !! …

Rupert Sheldrake, Morphic Fields and Psi

In summer 2012, when Ruiting and I were in the UK for the AGI-12 conference at Oxford, we had the pleasure of stopping by the London home of maverick scientist Rupert Sheldrake for a meal and a chat.  (It was a beautiful British-style home with the look of having been host to a lifetime of deep thinking.  The walls were covered floor-to-ceiling with bookshelves containing all manner of interesting books.   We also met Rupert's very personable wife, who is not a scientist but shares her husband's interest in trans-materialist models of the universe.)

I have been fascinated by Sheldrake's idea since reading his book "A New Science of Life" in the 1980s.  His idea of a "morphic field" -- a pattern-field, coupled with yet in some ways distinct from the material world we see around us, shaping and shaped by the patterns observable in matter -- struck me at first sight as intriguing and plausible.  The mathematician in me found Sheldrake's descriptions of the morphic field idea a bit fuzzy, but then I feel that way about an awful lot of biology.  At very least it has always seemed to me an intriguing direction for research.

It also occurred to me, when I first encountered his ideas, that morphic fields could provide some sort of foundation for explaining telepathy. The basic idea of the morphic field is simply that there is a "pattern  memory field" in the universe, which records any pattern that occurs anywhere, and then reinforces the occurrence of that pattern elsewhere.   I reflected on the phenomenon of twin telepathy and it seemed very "morphic field" ish in nature.

More recently, Damien Broderick and I have co-edited a book called "The Evidence for Psi", to appear early next year, published by McFarland Press.   In the book we have gathered together various chapters summarizing  empirical data regarding psi phenomena, attempting to broadly summarize the empirical case that psi is a real phenomenon.  Sheldrake contributed a chapter to our book, summarizing experiments he did on email and telephone telepathy.  I had previously read  Sheldrake's description of his experimental work on dogs anticipating when their owners will get home, and been impressed by his careful and practical methodology.

While "Evidence for Psi" is still awaiting release, I'll point readers interested in the existing corpus of evidence regarding the existence of psi phenomena to my Psi Page, which contains links to a couple prior books I recommend on the topic.   "Evidence for Psi" contains a more up-to-date and systematic overview of the evidence, but it's not out quite yet.

Damien and I are also planning to edit a sequel book on "The Physics of Psi", covering various theories of how psi works.   I've proposed my own sketchy theory in a 2010 essay, which proposed a certain extension to quantum physics that seems to have potential to explain psi phenomena.  I actually have more recent and detailed thoughts these lines, which I'll hint at toward the end of this monster blog post ... but will not enlarge on completely here as it's a long story -- of course I'll lay these ideas out in a chapter of "The Physics of Psi" when the time comes!

While researching possible extensions to quantum theory that might help explain psi, I noticed a paper by famous physicist Lee Smolin presenting an idea called the "Precedence Principle", which struck me as remarkably similar to Sheldrake's morphic field theory.   I discussed this similarity in a previous blog post.

During our visit to Rupert's house, he gave us a gift to take with us -- a copy of his book "Science Set Free".   Being a really nice guy as well as a brilliantly creative thinker, I'm sure Rupert will not be too annoyed at me for repaying his kind gift by writing this blog post, which criticizes some of his ideas while building on others! 

I skimmed the book shortly after receiving it, but only recently started reading through it more carefully.   The overall theme is a call for scientists to look beyond a traditional materialistic approach, and open their minds to the possibility that the universe is richer, more complex, and more holistic than materialist thinking suggests.   Morphic fields are mentioned here and there, as one kind of scientific hypothesis going beyond traditional materialism and potentially explaining certain data.

All this was also the topic of Sheldrake's controversial TEDx talk a couple years back, which was removed from the TEDx archive, apparently due to the controversial nature of Sheldrake's work in general.  For a lengthy online discussion of this incident, see this page….  As I said in my contribution to that discussion, I don't think they were right to remove his video from their archive.  I've heard far more out-there TEDx talks than Rupert's, so it's obviously not the contents of his talk that caused its removal -- it's his general reputation, which someone apparently decided would sully TED's reputation in some circles they values.   Urrrgh.   I generally think TED is great, but I don't like this decision at all.

In general I'm supportive of Rupert's call for science to be more open-minded, and to look beyond traditional materialist approaches.   To me, science is centrally about a process of arriving at agreement among a community of minds regarding which observations should be accepted as collectively valid, and which explanations should be accepted as simpler.  Nothing in this scientific process requires the assumption that matter is more primary than consciousness (for example).  Nor are the notions of a "morphic field", or of precognition or ESP etc., "unscientific" in nature.

The main problem with the morphic field theory as Sheldrake lays it out is, in my view, its imprecision.   From the view of science as a community process of agreeing which observations are collectively valid and which explanations are simple, an issue with Sheldrake's "morphic field" view is that it's not simple at all to figure out how to apply it to a given context.   Different scientists might well come up with very different, and perhaps mutually incompatible, ways of using it to explain a given set of observations, or predict future observations in a given context.  This fuzziness is a kind of complexity, which makes my personal scientific Occam's Razor heuristic uneasy.

For now, what I want to talk about are some of Rupert Sheldrake's comments on memory, in "Science Set Free."   This will segue into some wild-ass quasi-mathematical speculations on how one might go about formalizing the morphic field idea in a more rigorous way than has been done so far.

Memory Traces versus Morphic Fields

In Chapter 7 of "Science Set Free", Sheldrake contrasts two different theories of human memory -- the "trace theory", which holds that memories are embodies as traces in organisms' brains; and the morphic resonance theory, which holds that memories are contained in a morphic field.  Of course the trace theory is the standard understanding in modern neuroscience.   On the other hand, he quotes the neuroscientist Karl Pribram as supporting an alternative understanding,
"Pribram … thought of the brain as a 'waveform analyzer' rather than a storage system, comparing it to a radio receiver that picked up waveforms from the 'implicate order', rendering them explicate.   This aspect of his thinking was influenced by the quantum physicist David Bohm, who suggested that the entire universe is holographic, in the sense that wholeness is enfolded into every part.

According to Bohm, the observable or manifest world is the explicate or unfolded order, which emerges from the implicate or enfolded order.  Bohm thought that the implicate order contains a kind of memory.  What happens in one place is 'introjected' or 'injected' into the implicate order, which is potentially present elsewhere; thereafter when the implicate order unfolds into the explicate order, this memory affects what happens, giving the process very similar properties to morphic resonance.   In Bohm's words, each moment will 'contain a projection of the re-injection of the previous moments, which is a kind of memory; so that would result in a general replication of past forms' "

When I briefly spoke with Karl Pribram on these matters in 2006 (when at my invitation he spoke at the AGI-06 workshop in Bethesda, the initial iteration of the AGI conference series), he seemed a lot less definitive than Sheldrake on the "brain as antenna" versus "brain as storehouse of memories" issue, but on the whole the story he told me was similar to Sheldrake's summary.   Pribram was trying to view the brain as a quantum-mechanical system in a state of macroscopic quantum coherence (perhaps related to coherent states in water megamolecules in the brain, as conjectured by his Japanese collaborators Jibu and Yasue), and then to look at perception as involving some sort of quantum coupling between the brain and environment. 

I actually like the "implicate order" idea; and Bohm's late-life book "Thought as a System" had a huge impact on me.   The first version of my attempt to formalize a theory of psi phenomena --  Morphic Pilot Theory -- was inspired by both morphic fields and Bohm's pilot wave theory of quantum mechanics (though the end part of this blog post presents some ways in which I'm recently trying to go beyond the particulars of that formulation).

However, I really can't buy into Sheldrake's rejection of the massive corpus of neurobiological evidence in favor of what he calls the "trace theory."   There is just a massive amount of evidence that, in a fairly strong sense, an awful lot of memories ARE actually stored "in the brain."

As just one among many examples, I recently looked through the literature on "concept neurons" -- neurons that fire when a person sees a certain face (say, Jennifer Aniston, in the common example).   But there are hundreds of other examples where neuroscientists have figured out which neurons or neuronal subnetworks become active when a given memory is recalled….   The idea that the brain is more like a radio receiver (receiving signals from the morphic field) than a storehouse of information, seems to me deeply flawed.

Sheldrake says
"The brain may be more like a television set than a hard drive recorder.   What you see on TV depends on the resonant tuning of the set to invisible fields.  No one can find out today what programs you watched yesterday by analyzing the wires and transistors in your TV set for traces of yesterday's programs."

While I salute the innovative, maverick thinking underlying this hypothesis, I definitely can't agree.  I very strongly suspect that you COULD tell what TV program a person watched yesterday, by analyzing their brain's connectome.  We can't carry out this exact feat yet,but I bet it will be possible before too long.  We can already tell what a person is looking at via reading out information from their visual cortex, for example.

The main point I want to make here, though, is that one doesn't have to view the trace theory of memory and (some form of) the morphic field theory of memory as contradictory.

The brain, IMO, is plainly not much like a radio receiver or antenna -- it does contain specific neurons, specific subnetworks and specific dynamical patterns that correlate closely with specific memories of various sorts.   Neuroscience data says this and we have to listen.

However, this doesn't rule out the possibility that some sort of "morphic field" could also exist, and could also play a role in memory.

Pattern Completion and Morphic Fields

It seems to me that a better analogy than a radio receiver, would be pattern completion in attractor neural networks.

In a Hopfield neural net, one "trains" the network by exposing it to a bunch of memories (each one of which is a pattern of activity across the network, in which some neurons are active and others are not).   Then, once the network is trained, if one exposes the network to PART of some memory, the nonlinear dynamics of activation flowing through the neural net will cause the whole memory to emerge in the network.   The following figure illustrates this in some simple examples.

Figure illustrating neural net based pattern completion, borrowed from [Ritter, H., Martinetz, Th., Schulten, K. (1992): Neural Computation and Self-organizing Maps. Addison Wesley,].  (a) The Hopfield net consists of 20 x 20 neuroids, which can show two states, illustrated by a dot or a black square, respectively. The weights are chosen to store 20 different patterns; one is represented by the face, the other 19 by different random dot patterns. (b) After providing only a part of the face pattern as input (left), in the next iteration cycle the essential elements of the final pattern can already be recognized (center), and the pattern is completed two cycles later (right). (c) In this example, the complete pattern was used as input, but was disturbed by noise beforehand (left). Again, after one iteration cycle the errors are nearly corrected (center), and the pattern is complete after the second iteration (right)

What does this have to do with morphic fields?  

My suggestion is that, potentially, the trace of a memory in an organism's brain, could be considered as a PART of the totality of that memory in the universe.  The nonlinear dynamics of the universe could be such that: When the PART of a memory existing in an organism's brain is activated, then via a pattern-completion type dynamic, the rest of the memory is activated. 

Furthermore, if some memory is activated in the broader universe, then the nonlinear dynamics coupling the rest of the universe with the organism's brain, could cause a portion of that memory to form within the organism's brain.

In the analogy I'm suggesting here, the analogue of the whole Hopfield neural network in which the overall memory would be activated, would be some form of "morphic field." 

In this hypothetical model, the portion of the "universal nonlinear dynamical system" that resides in an organism's brain is not behaving much like an antenna.  It's not just tuning into channels and receiving what is broadcast on them.  Rather, in this model, the brain stores its own memory-fragments and has its own complex dynamics for generating them, modifying them, revising them, and so forth.  But these memory-fragments are nonlinearly coupled with broader memory patterns that exist in a nonlinear-dynamical field that goes beyond the individual organism's rain and body.

In sum, the idea I'm proposing is that
  • a morphic field may be modeled as a nonlinear self-organizing network, including material entities like brains and bodies as a portion
  • memories may be viewed as patterns spread across large portions of a morphic field
  • the portion of a memory that is resident in an organism's brain as a "memory trace" may be viewed as a "memory fragment" from a morphic field perspective; and may   trigger a broader memory to emerge across the morphic field via "pattern completion" type dynamics
  • the emergence of a broader memory across the morphic field, may cause certain memory-fragments to emerge in an organism's brain

This seems a consistent, coherent way to have both morphic fields AND standard neurobiological memory traces.

I'm not claiming to have empirical evidence for this (admittedly out-there and eccentric) perspective on memory.  Nor am I claiming that this constitutes a precise, rigorous, testable hypothesis.   It doesn't.  All I'm trying to do in this post is articulate a conceptual approach that makes the morphic field hypothesis consistent with the almost inarguably strong observation that neural memory traces are real and are powerfully explanatory regarding many aspects of human memory. 

Morphic Fields and Psi, Once Again

Ah -- OK but, what aspects of memory would one need to invoke these broader-memory morphic fields to explain?

It's possible that morphic fields play a small but nontrivial role in a wide variety of memory phenomena, across the board.  This would fit in with Jim Carpenter's theories in his book First Sight, which argues that weak psi phenomena underlie our intuitive understandings of everyday situations.

And it's also possible that one thing distinguishing psi phenomena from ordinary cognition, is a heavy reliance on the morphic-field components of memories.

To turn these vague conceptual notions into really useful scientific theories, would require a more rigorous theory of how morphic fields work.  I have some thoughts along those lines but will save a full, detailed exposition of these for another time.  For now I'll just give a little hint...

How Might One Model Morphic Fields?

OK, now I'm going to go even further "out there", alongside with getting a bit more technical...

A model of morphic fields has to exist within some model of the universe overall.

Existing standard physics models don't seem to leave any natural place for morphic fields.  However, existing standard physics models are also known to be inadequate to explain known physical data in a coherent, self-consistent way (as e.g. general relativity and quantum field theory haven't yet been unified into a single theory).   This certainly gives some justification for looking beyond the standard physics approaches, in searching for a world-model that is conceptually compatible with morphic fields.

The basic ideas I'll outline here could actually be elaborated within many different approaches to theoretical physics.  However, they are easiest and most natural to elaborate in the context of discrete models of the universe -- so that's the route I'll take here.   Discrete models of the universe have been around a while, e.g. the Feynman Checkerboard and its descendants.

One of the more interesting discrete approaches to foundational physics is Causal Sets.  Basically, in causal set theory, "spacetime" is replaced by a network of nodes interconnected by directed edges.   A directed edge indicates an atomic flow of causality.

I suspect it may be interesting to extend the causal set approach into what I call a "causal web" -- in which directed hyperlinks span triples of nodes.  A hyperlink pointing from (A,B) to C indicates a flow of causality from the pair (A,B) to C.   Local field values at A and local field values at B then combine to yield local field values at C.   This combination may be represented as multiplication in some algebra, so one can write F_C(t+1) = F_A(t) * F_B(t), where t refers to a sort of "meta-time" or "implicate time", distinct from the time axis that forms part of the spacetime continuum we see. 

Figuring out the right way to represent electromagnetic and quark fields this way is an interesting line of research, which I've been playing with occasionally in recent weeks.   Gravitation, on the other hand, I would suggest to represent more statistically, as an "entropic force" of a sort arising emergently from dynamics on the causal web.   I'll write another post about that later.

(More broadly, I think one could show that continuous field theories, within fairly broad conditions, can be emulated by causal webs within arbitrarily small errors.    Conceptually, causal webs are a bit like discrete reaction-diffusion equations; and it's known that discrete reaction-diffusion equations can be mapped into discrete quantum field theories.)

The main point I want to explore here is how one might get some sort of morphic field to emerge from this sort of framework.  Namely: One could potentially do so by positing a field, living at the nodes in the causal web, which is acausal in nature, and propagates symmetrically, flowing both directions along directed links.  This would be a "pattern field."   

Imagine running hypergraph pattern mining software - like, say, OpenCog's Pattern Miner -- on a causal web.  This would result in a large index, indicating which patterns occur how often in the web.  Atoms and molecules would emerge as pretty frequent patterns, for example; as would radioactive decay events.  Spatial, temporal and spatiotemporal patterns would be definable in this way.

Each node in the causal web can then be associated with a "pattern set" indicating the frequent patterns that it belongs to, indexed by their frequency (and perhaps by other quantities, such as their surprisingness), and retaining information regarding what slot in the pattern the current node fits into.

One can then view these pattern sets as comprising additional nodes and links, to be added to the web.  Two nodes that are part of the same pattern, even if distant spatiotemporally, would then be linked together by the nodes and links comprising the pattern.  These are non-causal links, representing commonality of pattern, independent of spatiotemporal causality.

Given this framework, we can introduce an additional dynamic: a variant of what philosopher Charles Peirce called "the tendency to take habits."   Namely, we can posit that: Patterns that have a high surprisingness value are more likely to persist in the causal web.  

By "surprisingness value" I mean here that the pattern is more probable than one would infer from looking at its component parts.  As a first hypothesis one can use the I-surprisingness as defined in OpenCog's pattern mining framework.

Among other things, this implies that: When one instance of pattern P is linked with an instance of pattern Q, this increases the odds that another instance of pattern P is linked with some instance of pattern Q. 

Or, a little differently, this "Surprising Multiverse" theory could be viewed as a variation of the Jungian notion of "synchronicity" -- which basically posits that meaningful combinations of events may occur surprisingly often, due to some sort of acausal connecting principle.  (As an aside, I actually first learned about Synchronicity from the Police album way back when -- thanks, Sting!)

Viewed in quantum-theoretic terms, this is a statement about the amplitude (complex probability) distribution over possible causal webs (or more properly, actually: over possible histories of causal webs, where a causal web history is defined as a series of causal-web states so that each one is consistent with the previous and subsequent according to causal web dynamics....  If a causal web is deterministic then each causal web corresponds with just one causal web history, but we don't need to assume this.)   It is a statement that causal web histories with more surprising patterns, should be weighted higher when doing Feynman sums used to determine what happens in the world.

How does a pattern completion type dynamic happen, then, in this perspective?  Suppose that, in a particular part of the causal web, a certain pattern emerges.  The existence of this pattern influences the surprisingness values of other pattern-instances, situated other places in the web.  It thus influences the weightings of Feynman sums occurring all around the web, thus influencing the probabilities of various events.

We thus have a non-local, acausal connecting principle: the surprising-pattern-based weighting of possible causal web histories in Feynman sums.  The "morphic field" is then modeled, not exactly as a "field", but as a multiverse-wide , ongoing dynamic re-weighting of possible universes according to the surprisingness of the patterns they contain (noting that the surprisingness of a universe changes over time as it evolves).   (And note also that nothing is literally represented as a "field" in the causal web approach; fields are replaced in this model by discrete dynamics on hypergraphs representing pre-geometric structures below the level of spacetime.)
For example, suppose one identical twin falls in love with a brown-haired dentist.  There are possible universes (causal web histories) in which both twins fall in love with brown-haired dentists, and others in which only one does, and the other falls in love with a green-haired chiropodist or whatever.  The universes in which both twins fall in love with brown-haired dentists will have an additional surprising pattern as compared to the other universes, and hence will be weighted slightly higher.

Or, suppose a woman's brain remembers what she watched on TV last night.  Again, it will be more surprising, probabilistically, if others know this as well -- so the universes in which others do, will be weighted slightly higher.

Now, there are many different ways to measure surprisingness, so that this approach to more formally specifying the morphic field hypothesis must be considered a research direction rather than a definite theory.  All I'm suggesting here is that it's an interesting direction.

When digging into the details of these ideas, an important thing to think about is: Surprising to whom?  Based on whose expectations?  Surprising to the universe?  Or surprising to some particular observer?  In the relational interpretation of quantum theory, all observations occur relative to some observer -- so this is probably the best way to think about it.

The decline effect -- in which psi experiments start to decay in effectiveness after some time has passed -- begins to seem perhaps conceptually explicable in this framework.   Once a psi phenomenon has been demonstrated enough times, to a given class of observers, it fails to be surprising to them, so it fails to be weighted higher in the relevant Feynman sums and doesn't happen anymore.   (Indeed this is extremely hand-wavy, but as I already emphasized, I'm just trying to point in an interesting direction!)

It's also worth noting that one could also extend the sum over causal webs that are inconsistent in terms of temporal direction.  That is, causal webs containing circular arrow structures.  What would likely happen in this case is that, as you add up the amplitudes of all the different causal webs, the causally inconsistent ones tend to cancel each other out, and the overall sum is dominated by the causally consistent ones.  However, this wouldn't be guaranteed to happen, and the surprise bias could in some cases intersect interestingly with this phenomenon, enabling circularly-causal webs to "occasionally" dominate the amplitude sum.

Anyway, I've certainly raised more questions than I've answered here.   But perhaps I've convinced some tiny fraction of readers that there is some hope, by modifying existing (admittedly somewhat radical) physics models, to come up with a coherent formal model of morphic fields.  Getting back to issues of memory, my feeling is that such a formal model is likely to yield a "pattern completion" type theory of morphic memory, rather than a "television receiver" type theory.

In Praise of Wild Wacky Weirdness ... and Data

I've spun out some wacky ideas here, and probably weirded out a lot of readers, but so it goes!   One of the messages of Sheldrake's book "Science Set Free" that I really like is (paraphrasing): Open your mind and rethink every issue from first principles.  Just try to understand, and don't worry so much about agreeing with prevailing points of view; after all, prevailing points of view have been proved wrong many times throughout history.  The ideas given here are presented very much in that spirit.

Another key message of Sheldrake's book, however, is (paraphrasing again): Do pay attention to data.  Look at data very carefully.  Design your own experiments to explore your hypotheses, gather your own data, and study it.   This is one of the next important steps in exploring the ideas presented here.  How could this sort of formalized morphic field explain the various data collected in "The Evidence for Psi", for example?

The journey continues...

Wednesday, September 24, 2014

Semihard Takeoff

Whenever I talk about the future of AGI, someone starts talking about the possibility that AGI will "take over the world."

One question is whether this would be a good or bad thing -- and the answer to that is, of course, "it depends" ... I'll come back to that at the end of this post.

Another relevant question is: If this were going to happen, how would it most likely come about.  How would an "AGI takeover" be likely to unfold, in practice?

One option is what Eliezer Yudkowsky has called AI "FOOM" ...  i.e. a "Hard Takeoff"  (a possibility which I analyzed a bit , some time ago...)

The basic idea of AI Foom or Hard Takeoff is that, sometime in the future, an advanced AGI may go from relatively innocuous subhuman-level intelligence all the way up to superhuman intelligence, in 5 minutes or some other remarkably short period of time.....  By rewriting its code over and over (each time learning better how to rewrite its code), or assimilating additional hardware into its infrastructure, or whatever....

A Hard Takeoff is a special case of the general notion of an Intelligence Explosion -- a process via which AGI gets smarter and smarter via improving itself, and thus getting better and better and faster and faster at making itself smarter and smarter.   A Hard Takeoff is, basically,  a really really fast Intelligence Explosion!

Richard Loosemore and I have argued that an Intelligence Explosion is probable.   But this doesn't mean a Hard Takeoff is probable.

Nick Bostrom's nice illustration of the Hard Takeoff idea

What often seems to happen in discussions of the future of AI (among hardcore futurist geeks, anyway) is something like:

  • Someone presents the Foom / Hard Takeoff idea as a scary, and reasonably likely, option
  • Someone else points out that this is pretty unlikely, since someone watching the subhuman-level AGI system in question would probably notice if the AGI system were ordering a lot of new hardware for itself, or undertaking unusual network activity, or displaying highly novel RAM usage patterns, or whatever...

In spite of being a huge optimist about the power and future of AGI, I actually tend to agree with the anti-Foom arguments.   A hard AGI takeoff in 5 minutes seems pretty unlikely to me.  

What I think is far more likely is an Intelligence Explosion manifested as a "semi-hard takeoff" -- where an AGI takes a few years to get from slightly subhuman level general intelligence to massively superhuman intelligence, and involved various human beings, systems and institutions in the process.

A tasty semihard cheese -- appropriate snack food 
for those living through the semihard takeoff to come
Semihard cheeses are generally good for melting;
and are sometimes said to have the greatest complexity and balance. 

After all, a cunning and power-hungry human-level AGI wouldn't need to suddenly take over the world on its own, all at once, in order to gain power.  Unless it was massively superhuman, it would probably consider this too risky a course of action.   Rather, to take power, a human-level AGI would would simply need to accumulate a lot of money (e.g. on the financial markets, using the superior pattern recognition capability it could achieve via tightly integrating its mind with statistical and machine learning software and financial, economic and news databases) and then deploy this wealth to set up a stronghold in some easily-bought nation, where it could then pay and educate a host of humans to do its bidding, while doing research to improve its intelligence further...

Human society is complex and disorganized enough, and human motivations are complex and confused enough, and human judgment is erratic enough, that there would be plenty of opportunities for an early-stage AGI agent to embed itself in human society in such a way as to foster the simultaneous growth of its power and intelligence over a period of a few years.   In fact an early-stage AGI probably won't even need to TRY for this to happen -- once early-stage AGI systems can do really useful stuff, various governments, companies and other organizations will push pretty hard to use these systems as thoroughly as they can, because of the economic efficiency and scientific and media status this will bring.

Once an AGI is at human level and embedded in human society in judicious ways, it's going to be infeasible for anyone to get rid of it -- and it's going to keep on growing in intelligence and power, aided by the human institutions it's linked with.   Consider, e.g., a future in which

  •  Azerbaijan's leaders get bought off by a wildly successful AGI futures trader, and the nation becomes an AGI stronghold, complete with a nuclear arsenal and what-not (maybe the AGI has helped the country design and build nukes, or maybe it didn't need the AGI for that...).   
  • The nation the AGI has bought is not aggressive, not attacking anyone -- it's just sitting there using tech to raise itself out of poverty ... doing profitable deals on the financial markets, making and selling software products/services, patenting inventions, ... and creating a military apparatus for self-defense, like basically every other country.   

What happens then?  The AGI keeps profiting and self-improving at its own pace, is what happens?  Is the US really gonna nuke a peaceful country just for being smart and getting rich, and risk massive retaliation and World War III?  I doubt it....  In its comfy Azerbaijani stronghold, the AGI can then develop from human-level to massively transhuman intelligence -- and then a lot of things become possible...

I have spun out one scenario here but of course there are lots of others.  Let's not allow the unrealism of the "hard takeoff in 5 minutes and the AGI takes over the world" aka "foom" scenario to blind our minds to the great variety of other possibilities....  Bear in mind that an AGI going from toddler-level to human-level in 5 years, and human-level to superhuman level in 5 more years, is a FOOM on the time-scale of human history, even if not as sudden as a 5 minute hard takeoff on the time-scale of an individual human life...

So how could we stop a semihard takeoff from happening?   We can't really -- not without some sort of 1984++ style fascist anti-AI world dictatorship, or a war destroying modern society projecting us back before the information age.   And anyway, I am not in favor of throttling AGI development personally; I doubt the hypothetical Azerbaijanian AGI would particularly want to annihilate humanity and I suspect transhuman AGIs will do more good than harm, on average over all possible worlds....  I'm not at all sure that "an AGI taking over the world" -- with the fully or partly witting support of some group(s) of humans -- would be a bad thing, compared to other viable alternatives for humanity's future....

In terms of risks to humanity, this more realistic "semihard takeoff" development scenario highlights where the really onerous risks probably are.   SIAI/MIRI and the Future of Humanity Institute seem to spend a lot of energy thinking about the risk of a superhuman AGI annihilating humanity for its own reasons; but it seems to me a much more palpable and probable risk will occur at the stage where an AGI is around human-level but not yet dramatically more powerful and intelligent than humans, so that it still needs cooperation from human beings to get things done.  This stage of development will create a situation in which AGI systems will want to strike bargains with humans, wherein they do some things that certain humans want, in order to get some things that they want...

But obviously, some of the things that some humans want, are highly destructive to OTHER humans...

The point is, there is a clear and known risk of early-stage AGIs being manipulated by humans with nasty or selfish motives, because many humans are known to have nasty or selfish motives.   Whereas the propensity of advanced AGIs to annihilate lesser sentiences, remains a wild speculation (and one that I don't really find all that credible).....

I would personally trust a well-designed, self-improving AGI more than a national government that's in possession of the world's smartest near-human-level AGI; AGIs are somewhat of a wild card but can at least be designed with initially beneficent motivational systems, whereas national governments are known to generally be self-serving and prone to various sorts of faulty judgments....  This leads on to the notion of the AI Nanny, which I've written about before.   But my point here isn't to argue the desirability or otherwise of the AI  Nanny -- just to point out the kind of "semihard takeoff" that I think is actually plausible.

IMO what we're likely to see is not a FOOM exactly, but still, a lot faster than AI skeptics would want to accept....   A Semihard Takeoff.  Which is still risky in various ways, but in many ways more exciting than a true Hard Takeoff -- because it will happen slowly enough for us to watch and feel it happen....

Monday, June 16, 2014

The Bullshit at the Heart of Humanity

I explained, in a recent blog post, Why Humans Are So Screwy.   But I didn't quite finish the story there.   Here I'll explain an additional aspect of Screwy Human Nature -- the nature of the self-delusion that lies at the heart of our selves.

My previous post identified two major culprits where human screwiness is concerned:

  • The conflict between the results of individual and group (evolutionary) selection, encoded in our genome (as described excellently by E.O. Wilson)
  • The emergence of civilization, to which we are not adapted, which disrupted the delicate balance via which tribal human mind/society quasi-resolved the above-mentioned conflict (as described excellently by Sigmund Freud)

What I want to point out here is the next chapter in the story -- the way these conflicts  impact our “selves” – our “autobiographical self-models”, which play such a large role in our inner lives.   They play a major role in causing our selves to comprise self-damagingly inaccurate models of the thought, feeling and behavior patterns of which we are actually constituted. 

Put relatively simply: in order to avoid the pain that we are conditioned to feel from violating individual or group needs, or violating civilized or tribal standards of individual/group balance, we habitually create false self-models embodying the delusion that such violations are occurring in our minds and actions much less often than they really are.  Emotional attachment to these sorts of inaccurate self-models is perhaps the most directly important cause of human mental and social suffering.

Our Problematic Selves

The primary culprit of human suffering has often been identified as the “self” – meaning the autobiographical, psychosocial self; the self-model that each of us uses to symbolize, define and model our own behavior.   One of the most commonly cited differences between normal human psychology and the psychology of “enlightened” spiritual gurus is that the latter are said to be unattached to their autobiographical selves – indeed they are sometimes said to have “no self at all.”

I think there is some deep truth to this perspective; but one needs to frame the issues with care.   Any mind concerned with controlling a body that persists through time, has got to maintain some sort of model of that body and the behavior patterns that are associated with it.   Without such a model (which may be represented explicitly or implicitly), the mind could not control the body very intelligently.  Any such model can fairly be called a “self-model.”   In this sense any persistently embodied intelligence is going to have a self.

The problem with the human self, however, is that it tends to be a bad model – not a morally bad model, but an inaccurate one.  The self-models we carry around in our minds, are generally not very accurate models of the actual behavior-patterns that our bodies display, nor of the thought-patterns that our minds contain.   And the inaccuracies involved are not just random errors; they are biased in very particular ways.

Our self-models are symbols for clusters of behavior-patterns that are observed to occur among our bodily behaviors and our internal cognitive behaviors.   This is not in itself bad – symbolic reasoning is critical for general intelligence.   However, we are very easily drawn to make incorrect conclusions regarding our symbolic self-models – and to become emotionally attached to these incorrect conclusions.

And this brings us straight back to the two conflicts that I highlighted in my earlier blog post: Self versus Group (Wilson), and Evolved Self/Group Balance versus Civilized Self/Group Balance (Freud).   These layered contradictions yank our self-models around willy-nilly.   Each modern human feels great pressure to be both self-focused and group-focused; and to balance self and group in a tribal way, and in a civilized way.

What’s the simplest way for a person to fulfill all these contradictory requirements?  -- or rather, to feel like they have at least done a halfway-decent job of fulfilling them?

That’s easy: To bullshit themselves! 

Human self-models are typically packed with lies -- lies to the effect that the person is fulfilling all these contradictory requirements much better than is actually the case.  Because when a person clearly sees just how badly they have been fulfilling these contradictory requirements, they will generaly experience a lot of bad emotion – unless that person has somehow managed to let go of the expectations that evolution and society have packed into their brains and minds.

The above analysis of the conflicts in human nature lets us specifically identify four kinds of lies that are typically packed into human selves.  There are two kinds of Wilsonian lies:
  • Lies about how a person has acted against their own goals and desires
  • Lies about how a person has disappointed the others around them

And there are two kinds of Freudian lies:

  • Lies about how a person has repressed their true desires, in order to adhere to general social expectations
  • Lies about how a person has violated general social expectations, in effort to act out their true desires

What if a person could avoid these four kinds of lies, and openly, transparently acknowledge all these kinds of violations to themselves, on an ongoing basis during life?  This would allow the person in question to form an accurate self-model -- not the usual self-delusional self-model biased by the Wilsonian and Freudian contradictions.   But this sort of internal self-honesty is far from the contemporary human norm. 

The problem is that evolution has wired us to become unhappy when we know we have acted against our own goals and desires; OR when we know we have disappointed someone else.   And civilized society has taught us to become unhappy when we violate social expectations; but evolution has taught us to become unhappy when we don’t balance self and group in the way that is “natural” in a tribal context.    So, inside the privacy of our minds, we are constantly tripping over various evolved or learned triggers for unhappiness.  The easiest way to avoid setting off these triggers is to fool ourselves that we haven’t really committed the “sins” required to activate them – i.e. to create a systematically partially-false self-model. 

The harder way to avoid setting off these triggers is to effectively rewire our mind-brains to NOT be reflexively caused unhappiness when we act against our goals/desires, disappoint others, violate social expectations, or balance self and group in tribally inappropriate ways.  Having done this, the need for an inaccurate, self-deluding self-model disappears.  But performing this kind of rewiring is very difficult for human beings, given the current state of technology.   The only reasonably reliable methods for achieving this kind of rewiring today involve years or decades of concentrated effort via meditation or other similar techniques.

And what would a human mind be like without a dishonesty-infused, systematically inaccurate self-model?  Some hints in this direction may be found in the mind-states of spiritually advanced individuals who have in some sense gone beyond the negative reinforcement triggers mentioned above, and also beyond the traditional feeling of self.   My friend Jeffery Martin's recent study of the psychology of the spiritually advanced  (soon to be published) suggests that, without a self in the traditional sense, a person’s mind feels more like an (ever-shifting) set of clusters of personality/behavior patterns.   One of the lies the self tells itself, it seems, is about its own coherence.  Actually human beings are not nearly as coherent and systematic and unified as their typical self-models claim.

Goals Beyond the Legacy Self

So much for the complexly conflicted present.  Let's think a bit about the possibly better future.

In the current state of human nature, self and goals are intimately wrapped up together.   

Substantially, we pursue our goals because we want our self-model to be a certain way – and we do this in a manner that is inextricably tangled up with the various lies the self-model embodies.

But consider, on the other hand, the case of a post-Singularity human or human-like mind that understands itself far better than contemporary humans, thus arriving at far more accurate – and likely less unified and coherent – self-model than a typical pre-Singularity human mind.   What will the goals of such a mind be?  What will a mind without a coherent self --without a self built around lies and confusions regarding self vs. group and repression and status -- actually want to do with itself?

Considering our primary current examples of minds that have discarded their traditional autobiographical selves -- spiritual gurus and the like – provides confusing guidance.   One notes that (with nontrivial exceptions) the majority of such people are mainly absorbed with enjoying the wonder of being, and sometimes with spreading this wonder to others, rather than with attempting to achieve ambitious real-world goals.   The prototypical spiritually advanced human is not generally concerned with pursuing pragmatic goals, because they are in a sense beyond the typical human motives that cause people to become attached to pursuit of such goals.   This makes one wonder if the legacy self – with all its associated self-deception -- is somehow required in order for humans to work hard toward the achievement of wildly ambitious goals, in the manner for instance of the scientists and entrepreneurs who are currently bringing the Singularity within reach.

But it’s not clear that the contemporary or historical spiritual guru is a good model for a post-Singularity, post-legacy-self human mind.   I suspect that in a community of post-delusory-self minds, avid pragmatic goal-pursuit may well emerge for different reasons, mostly unrelated to legacy human motives. 

Why would a community of post-delusory-self minds pursue goals, if not for the usual human reasons of status and ego?   Here we come to grips with deep philosophical issues.   I would argue that, once the conflicts that wrack human nature are mostly removed, other deep human motives will rise to the fore – for instance, the drive to discover new things, and create new things.   That is: the drives for pattern, creation and information.   

One can view the whole long story of the emergence of life and intelligence on Earth as the manifestation of these “drives”, as embedded in the laws of physics and the nature of complex systems dynamics.   From the point of view of the Cosmos rather than humanity in particular, the drives for pattern, creation and information are even deeper than the conflicts that wrack human nature. 

If spiritually advanced humans, having cast aside self and ego and status, tend not to pursue complex goals of discovery and creation, this may be because, given the constraints of the human brain architecture, merely maintaining a peaceful mindstate without self/ego/status-obsession requires a huge amount of the brain’s energy.   The simple, blissful conscious state of these individuals may be bought at the cost of a great deal of ongoing unconscious neural effort. 

On the other hand, once the legacy human brain architecture becomes flexibly mutable, most of the old constraints no longer apply.   It may become possible to maintain a peaceful, blissful conscious state – relatively free of Freudian repression and individual/group conflicts – while still avidly pursuing the deeper goals of gaining more and more information, and creating more and more structures and patterns in the universe.   Here we are far beyond the domain of the currently scientifically testable – but this is indeed my strong suspicion.

Current human nature got where it is largely via the advent of certain technologies – the technologies of agriculture and construction that enabled civilization, for example.   The folks who invented the plow and the brick weren’t thinking about the consequences their creations would have for the emergence and dynamics of the superego  -- but these consequences were real enough anyway.
Similarly, the next steps in human nature may well emerge as a consequence of technological advancements like brain-computer interfacing and mind uploading – even though the scientists and engineers building these technologies will mostly have other goals in mind, rather than explicitly focusing their work toward reducing conflict in the human psyche and bringing about an era where self is less critical and discovery and creation are the main motivations.

Growth, joy and creation beyond the constrictions of the self-delusory self -- I'm more than ready!

Conceptor Networks

I read today about a new variant of recurrent neural nets called Conceptor Networks, which look pretty. interesting,

In fact this looks kinda like a better-realized variant of the idea of "glocal neural nets" that my colleagues and I experimented with a few years ago.

The basic idea, philosophically (abstracting away loads of important details) is to

  • create a recurrent NN
  • use PCA to classify the states of the NN
  • create explicit nodes or neurons corresponding to these state-categories, and then to imprint these states directly on the dynamics

So there is a loop of "recognizing patterns in the NN and then incorporating these patterns explicitly in the NN dynamics", which is a special case of the process of "a mind identifying patterns in
itself and then embodying those patterns explicitly in itself", which I long ago conjectured to be critical to cognition in general (and which underlies the OpenCog design on a philosophical level...)

There is some hacky Matlab code here implementing the idea; but as code, it's pretty specialized to the exact experiments described in the above technical report...

My intuition is that, for creating a powerful approach to machine perception, a Conceptor Network would fit very well inside a DeSTIN node, for a couple reasons
  1. It has demonstrated ability to infer complex dynamical patterns in time series
  2. It explicitly creates "concept nodes" representing the patterns recognized, which could then be cleanly exported into a symbolic system like OpenCog

Of course, Conceptor Networks are still at the research stage, so getting them to really work inside DeSTIN nodes would require a significant amount of fiddling...

But anyhow it's cool stuff ;)

Monday, June 09, 2014

Review of "More Than Nature Needs" by Derek Bickerton

I've been a fan of Derek Bickerton's writing and thinking on linguistics since happening upon Language and Species in a Philadelphia bookstore, disturbingly many decades ago.   More Than Nature Needs, the latest addition to Bickerton's canon, is an intriguing and worthy one, and IMO is considerably deeper than its predecessor Adam's Tongue.

Adam's Tongue argues that the elements of human symbolic language likely emerged via scavenging behavior, as this was an early case in which early humans would have needed to systematically refer to situations not within the common physical enviroment of the speaker and hearer.  This is an interesting speculation, showcasing Bickerton's inventiveness as a lateral thinker.   MTNN continues in this vein, exploring the ways in which language may have emerged from simplistic proto-language.  However, MTNN draws more extensively on Bickerton's expertise as a linguist, and hence ends up being more profoundly thought-provoking and incisive.

As I see it, the core point of MTNN -- rephrased into my own terminology somewhat -- is that the developmental trajectory from proto-language to fully grammatical, proper language should be viewed as a combination of natural-selection and cultural/psychological self-organization.   To simplify a bit: Natural selection gave humans the core of language, the abstract "universal grammar" (UG) which underlies all human languages and is in some way wired into the brain; whereas cultural/psychological self-organization took us the rest of the way from universal grammar to actual specific languages.

The early stages of the book spend a bunch of time arguing against a purely learning-oriented view of language organization, stressing the case that some sort of innate, evolved universal grammar capability does exist.   But the UG Bickerton favors is a long way from classic-Chomskian Principles and Parameters -- it is more of an abstract set of word-organization patterns, which requires lots of individual and cultural creativity to get turned into a language.

I suspect the view he presents is basically correct.   I am not sure it's quite as novel as the author proposes; a review in Biolinguistics cites some literature where others present similar perspectives.  In a broader sense, the mix of selection-based and self-organization-based ideas reminded me of the good old cognitive science book Rethinking Innateness (and lots of other stuff written in that same vein since).   However, Bickerton presents his ideas far more accessibly and entertainingly than the typical academic paper, and provides interesting stories and specifics going along with the abstractions.

He also bolsters his perspective via relating it to the study of creoles and pidgins, an area in which he has done extensive linguistics research over many decades.  He presents an intriguing argument that children can create a creole (a true language) in a single generation, building on the pidgins used by their parents and the other adults around them.   I can't assess this aspect of his argument carefully, as I'm not much of a creologist (creologian??), but it's fascinating to read.  There is ingenuity in the general approach of investigating creole language formation as a set of examples of recent-past language creation.

The specific linguistics examples in the book are given in a variant of Chomskian linguistics (i.e. generative grammar), in which a deep and surface structure are distinguished, and it's assumed that grammar involves "moving" of words from their positions in the deep structure to their new positions in the surface structure.  Here I tend to differ from Bickerton.  Ray Jackendoff and others have made heroic efforts to modernize generative grammar and connect it with cognitive science and neuroscience, but in the end, I'm still not convinced it's a great paradigm for linguistic analysis.  I much more favor Dick Hudson's Word Grammar approach to grammatical formalization (which will not be surprising to anyone familiar with my work, as Word Grammar's theory of cognitive linguistics is similar to aspects of the OpenCog AGI architecture that I am now helping develop; and Word Grammar is fairly similar to the link grammar that is currently used within OpenCog).

Word Grammar also has a deep vs. surface structure dichotomy - but the deep structure is a sort of semantic graph.  In a Word Grammar version of the core hypothesis of MTNN, the evolved UG would be a semantic graph framework for organizing words and concepts, plus a few basic constraints for linearizing graphs into series of words (e.g. landmark transitivity, for the 3 Word Grammar geeks reading this).   But the lexicon, along with various other particular linearization constraints dealing with odd cases, would emerge culturally and be learned by individuals.

(If I were rich and had more free time, I'd organize some sort of linguistics pow-wow on one of my private islands, and invite Bickerton and Hudson to brainstorm together with me for a few weeks; as I really think Word Grammar would suit Bickerton's psycholinguistic perspective much better than the quasi-Chomskian approach he now favors.)

But anyhow, stepping back from deep-dive scientific quibbles: I think MTNN is very well worth reading for anyone interested in language and its evolution.   Some of the technical bits will be slow going for readers unfamiliar with technical linguistics -- but this is only a small percentage of the book, and most of it reads very smoothly and entertainingly in the classic Derek Bickerton style.   Soo ... highly recommended!

Saturday, March 22, 2014

Lessons from Deep Mind & Vicarious

Recently we've seen a bunch of Silicon Valley money going into "deep learning" oriented AI startups -- an exciting trend for everyone in the AI field.  Even for those of us who don't particularly aspire to join the Silicon Valley crowd, the symbolic value of these developments is dramatic.   Clearly AI is getting some love once again.

The most recent news is a US$40M investment from Mark Zuckerberg, Elon Musk, Ashton Kutcher and others into Vicarious Systems, a "deep learning computer vision" firm led by Dileep George, who was previously Jeff Hawkins' lead researcher at Numenta.

A couple months ago, the big story was Google acquiring London deep reinforcement learning firm Deep Mind for something like US$500M.   Many have rumored this was largely an "acqui-hire", but with 60 employees or so, that would set the price per employee at close to US$10M, way above the $1M-$2M value assigned to a Silicon Valley engineer in a typical acqui-hire transaction.   Clearly a tightly-knit team of machine learning theory and implementation experts is worth a lot to Google these days, dramatically more than a comparable team of application programmers.

Both of these are good companies led by great researchers, whom I've admired in the past.   I've met Deep Mind's primary founder, Demis Hassabis, at a few conferences, and found him to have an excellent vision of AGI, plus a deep knowledge of neuroscience and computing.   One of Deep Mind's other founders, Shane Legg, worked for me at Webmind Inc. during 1999-2001.   I know Dileep George less well; but we had some interesting conversations last summer in Beijing, when at my invitation he came to speak at the AGI-13 conference in Beijing.

Vicarious's focus so far has been on visual object recognition --- identifying what are the objects in a picture.  As Dileep described his progress at AGI-13: Once they crack object recognition, they will move onto recognizing events in videos. Once they crack that, they will move on to other aspects of intelligence.   Dileep, like his mentor Jeff Hawkins, believes that perceptual data processing is the key to general intelligence... and that vision is the paradigm case of human perceptual data processing...

Zuckerberg's investment in Vicarious makes a lot of sense to me.  Given Facebook's large trove of pictures and the nature of their business, it seems they would have great value for software that can effectively identify objects in pictures.

Note that Facebook just made a big announcement about the amazing success of their face recognition software, which they saddled with the probably suboptimal name "Deep Face" (a bit Linda Lovelace, no?).  If you dig into the research paper behind the press release, you'll see that DeepFace actually uses a standard, well known "textbook" AI algorithm (convolutional neural nets) -- but they deployed it across a huge amount of data, hence their unprecedented success...

Lessons to Learn?

So what can other AGI entrepreneurs learn from these recent big-$$ infusions to Deep Mind (via acquisition) and Vicarious (by investment)?

The main lesson I take from this is the obvious one, that a great really working demo (not a quasi faked up demo like one often sees) goes a long way...

Not long ago Vicarious beat CAPTCHA -- an accomplishment very easy for any Internet user to understand

On the other hand, the Deep Mind demo that impressed Larry Page was the ability to beat simple video games via reinforcement learning

Note that (analogously to IBM Watson), both of these demos involve making the AI meet a challenge that was not defined by the AI makers themselves, but was rather judiciously plucked from the space of challenges posed by the human world....

I.e.: doing something easily visually appreciable, that previously only humans could do...

Clearly Deep Mind and Vicarious did not excel particularly in terms of business model, as compared to many other firms out there...

Other, also fairly obvious points from these acquisitions are:
  1. For an acquihire-flavored acquisition at a high price, you want a team of engineers in a First World country, who look like the profile of people the acquiring company would want to hire.
  2. Having well-connected, appropriately respected early investors goes a long way.  Vicarious and Deep Mind both had Founders Fund investment.   Of course FF investment didn't save Halcyon Molecular, so it's no guarantee, but having the right early-stage investors is certainly valuable..


Bubble or Start of a Surge?

And so it goes.  These are interesting times for AI, indeed.    

A cynic could say it's the start of a new AI bubble -- that this wave of hype and money will be followed by disappointment in the meager results obtained by all the effort and expense, and then another "AI winter" will set in.

But I personally don't think so.   Whether or not the Vicarious and Deep Mind teams and technologies pay off big-time for their corporate investors (and I think they do have a decent chance to, given the brilliant people and effective organizations involved), I think the time is now ripe for AI technology to have a big impact on the world. 
DeepFace is going to be valuable for Facebook; just as machine learning and NLP are already being valuable for Google in their core search and ads businesses, and will doubtless deliver even more value with the infusion of the Deep Mind team, not to mention Ray Kurzweil's efforts as a Google Director of Engineering.

The love that Silicon Valley tech firms are giving AI is going to help spur many others all around the world to put energy into AI -- including, increasingly, AI projects verging on AGI -- and the results are going to be amazing.


Are Current Deep Learning Methods Enough for AGI?

Another lesson we have learned recently is that contemporary "deep learning" based machine learning algorithms, scaled up on current-day big data and big hardware, can solve a lot of hard problems.

Facebook has now pretty convincingly solved face recognition, via a simple convolutional neural net, dramatically scaled.   Self-driving cars are not here yet -- but a self-driving car can, I suspect, be achieved via a narrow-AI integration of various components, without any general intelligence underlying.   IBM Watson beat Jeopardy, and a similar approach can likely succeed in other specialized domains like medical diagnosis (which was actually addressed fairly well by simpler expert systems decades ago, even without Watson's capability to extract information from large bodies of text).  Vicarious, or others, can probably solve the object recognition problem pretty well, even with a system that doesn't understand much about the objects it's recognizing -- "just" by recognizing patterns in massive image databases.

Machine translation is harder than the above two areas, but if one is after translation of newspaper text or similar, I suppose it may ultimately be achievable via statistical ML methods.  Although, the rate of improvement of Google Translate has not been that amazing in recent years -- it may have hit a limit in terms of what can be done by these methods.  The MT community is looking more at hybrid methods these days.

It would be understandable to conclude from these recent achievements, that these statistical machine learning / deep learning algorithms basically have the AI problem solved, and focus on different sorts of Artificial General Intelligence architectures is unnecessary.

But such a conclusion would not be correct.   It's important to note that all these problems I've just mentioned are ones that have been focused on lately, precisely because they  can be addressed fairly effectively by narrow-AI statistical machine learning methods on today's big data/hardware...

If you picked other problems like 
  • being a bicycle messenger on a crowded New York Street
  • writing a newspaper article on a newly developing situation
  • learning a new language based on real-world experience
  • identifying the most meaningful human events, among all the interactions between people in a large crowded room
then you would find that today's statistical / ML methods aren't so useful...

In terms of my own work with OpenCog, my goal is not to outdo CNNs or statistical MT on the particular problems for which they were developed.  The goal is to address general intelligence...

The recent successes  of deep learning technology and other machine learning / statistical learning approaches are exciting, in some cases amazing.  Yet these technologies address only certain aspects of the broader AI problem.

One hopes that the enthusiasm and resource allocation that the successes of these algorithms are bringing, will cause more attention, excitement and funding to flow into the AI and AGI worlds as a whole, enabling more rapid progress on all the different aspects of the AGI problem.

Thursday, February 06, 2014

Why Humans Are So Screwy

Aha!!! ... Last night I had the amusing and satisfying feeling that I was finally grokking the crux of the reason why we humans are so screwy -- I never saw it quite so clearly before!

Here's the upshot: A big factor making human beings so innerly complicated is that in our psyches two different sources of screwiness are layered on top of each other:

  1. The conflict between the results of individual and group (evolutionary) selection, encoded in our genome
  2. The emergence of civilization, to which we are not adapted, which disrupted the delicate balance via which tribal human mind/society quasi-resolved the above-mentioned conflict

I.e.: the transition to civilized society disrupted the delicate balance between self--oriented and group-oriented motivations that existed in the tribal person's mind.   In place of the delicate balance we got a bunch of self vs. group conflict and chaos -- which  makes us internally a bit twisted and tormented, but also stimulates our creativity and progress.

Screwiness Source 1: Individual versus Group Selection

The first key source of human screwiness was best articulated by E.O. Wilson; the second was best articulated by Freud.  Putting the two together, we get a reasonably good explanation for why and how we humans are so complexly self-contradictory and, well "screwy."

E.O. Wilson, in his recent book The Social Conquest of Earth, argues that human nature derives its complex, conflicted nature from the competitive interplay of two kinds of evolution during our history: individual and group selection.  Put simply:

  • Our genome has been shaped by individual selection, which has tweaked our genes in such a way as to maximize our reproductive success as individuals
  • Our genome has also been shaped by group selection, which has tweaked our genes in such a way as to maximize the success of the tribes we belonged to

What makes a reproductively successful individual is, by and large, being selfish and looking out for one's own genes above those of others.  What makes a successful *tribe* is, by and large, individual tribe members who are willing to "take one for the team" and put the tribe first.

Purely individual selection will lead to animals like tigers that are solitary and selfish.  Purely group selection will lead to borg-like animals like ants, in which individuality takes a back seat to collective success.  The mix of individual and group selection will lead to animals with a complex balance between individual-oriented and group-oriented motivations.

As Wilson points out, many of the traits we call Evil are honed by individual selection; and many of the trains we call Good are honed by group selection.

That's Screwy Human Nature, Part 1.

Good vs. Evil vs. Hierarchy-Induced Constraints 

These points of Wilson's tie in with general aspects of constraint in hierarchical systems.   This observation provides a different way of phrasing things than Wilson's language of  Good vs. Evil.   As opposed to adopting traditional moral labels, wonder if a better way to think about the situation might be in terms of the tension and interplay between
  • adapting to constraints


  • pushing against constraints and trying to get beyond them
In the context of social constraints, it seems that individual selection (in evolution) would lead us to push against social constraints to seek individual well-being; whereas group selection would lead us to adapt to the social constraints regardless of our individual goals...

Much great (and mediocre) art comes from pushing against the constraints of the times -- but it's critical to have constraints there to push against; that's where a lot of the creativity comes from. You could think about yoga and most sports similarly ... you're both adapting to to the particularities of the human body; and trying to push the body beyond its normal everyday-life limits...

From the point of view of the tribe/society, those who push against the constraints too much can get branded as Evil and those who conform can get branded as Good..... But it all depends on what level you're looking at.... From the point of view of the human body, the cell that doesn't conform to the system will branded as Evil (non-self) and eliminated by the immune system!!

In any hierarchical system, from the perspective of entities on level N, the entities on level N+1 impose constraints -- constraints that restrict the freedom of the level N entities in order to enable functionality on level N+1; but also have potential to guide the creativity of level N entities.  Stan Salthe's book Evolving Hierarchical Systems makes this point wonderfully.   In some cases, like the human body vs. its cells, the higher level is dominant and the creativity of the lower level entities is therefore quite limited.  In thhe case of human society vs. its members, the question of whether the upper or lower level dominates the dynamics is trickier, leaving more room for creativity on the part of the lower level entities (humans), but also making the lives of the lower level entities more diversely complex.

Screwiness Source 2:The Discontents of Civilization

Moving on -- Screwy Human Nature, Part 2 was described with beautiful clarity by Sigmund Freud in his classic book Civilization and its Discontents.

What Freud pointed out there is that neurosis, internal mental stress and unhappiness and repression and worry, is a result of the move from nomadic tribal society to sedentary civilized society.  In tribal societies, he pointed out, by and large people were allowed to express their desires fairly freely, and get their feelings out of their system relatively quickly and openly, rather than represssing them and developing complex psychological problems as a result.

A fascinating recent book encountering one modern linguist/missionary's contact with a modern Stone Age society in the Amazon, the Piraha, is Daniel Everett's Don't Sleep There Are Snakes.   A book I read in the 1980s, recounting an average guy from Jersey dropping his life and migrating to Africa to live with a modern Stone Age pygmy tribe in central Africa, is Song From the Forest.  (The phoos below show Louis and some of his Bayaka friends.  Some recent news from Louis Sarno is here, including an intriguing recent video, a trailer for a forthcoming movie.) These accounts and others like them seem to validate Freud's analysis.  The tribal, Stone Age lifestyle tends not to lead to neurosis, because it matches the human emotional makeup in a basic way that civilization does not.

Wilson + Freud = Why We Are So Screwy

I full well realize the "noble savage" myth is just that -- obviously, the psychology of tribal humans was not as idyllic and conflict-free as some have imagined.   Tribal humans still have the basic conflict between individual and group selection embedded into their personalities.  BUT it seems to me that, in tribal human sociopsychology, evolution has worked out a subtle balance between these forces.  The opposing, conflicting forces of Self and Group are intricately intermeshed.

What civilization does is to throw this balance off -- and put the self-focused and group-focused aspects of human nature out of whack in complex ways.  In tribal society  Self and Group balance against each other elegantly and symmetrically -- there is conflict, but it's balanced like yin and yang.  In civilized society, Self and Group are perpetually at war, because the way our self-motivation and our group-motivation have evolved was right for making them just barely balance against each other in a tribal context; so it's natural that they're out of balance in complex ways in a civilization context.

For example, in a tribal situation, it is a much better approximation to say that: What's good for the individual is good for the group, and vice versa.   The individual and group depend a lot on each other. Making the group stronger helps the individual in very palpable ways (if a fellow hunter in the tribe is stronger for instance, he's more likely to kill game to share with you).  And if you become happier or stronger or whatever, it's likely to significantly benefit the rest of the group, who all directly interact with you and are materially influenced by you.   The harmony between individual interest and group interest is not perfect, but it's at least reasonably present ... the effects of individual and group selection have been tuned to work decently together.

On the other hand, in a larger civilized society the connection between individual and group benefit is far more erratic   What's good for me, as a Hong Kong resident, is not particularly the same as what's good for Hong Kong.   Of course there's a correlation, but it's a relatively weak one.   It's reasonably likely that what's good for Hong Kong as a unit could actually make my life worse (e.g. raising taxes, as my income level is above average for HK).  Similarly, most things that are likely to improve my life in the near future are basically irrelevant to the good of Hong Kong; in fact, my AGI research work is arguably bad for all political units in the long term, as advanced AGI is likely to lead to the transcendent of nation-states.   There is definitely some correlation between my benefit and Hong Kong's benefit -- if I create a successful company here in HK, that benefits the HK economy.   But the link is fairly weak, meaning that my society is often going to push me to do stuff that goes against my personal interest; and vice versa.  This seems almost inevitable in a complex society containing people playing many different roles.

Another interesting case is lying.   Lying of course occurs in tribal societies just like in advanced civilizations -- humans are dishonest by nature, to some extent.   Yet, only in complex civilizations do we have a habit of systematically putting on "false fronts" before others.  This doesn't work so well if you're around the same 50 people all the time.   Yet it's second nature to all of us in modern civilization -- we learn in childhood to act one way at home, one way at school, one way around grandma, etc.

As we mature, the habit of putting on false fronts -- or as Nietzsche called them, "masks" -- becomes so integrated into our personalities that the fronts aren't even "false" anymore.   Rather, our personalities become melanges of subselves, with somewhat different tastes and interests and values, in a complex coopetition for control of our thoughts and memories.  This is complex and stressful, but stimulates  various sorts of creativity.

Sarno reports how the interaction of the Bayaka pygmies with civilization caused them to develop multiple subpersonalities.  A pygmy's personality while living the traditional nomadic lifestyle in the bush, may be very different from that same pygmy's personality while living in a village with Africans from other tribes, drinking alcohol and doing odd jobs for low wages.

Individually, we have a motive to lie and make others think we are different in various ways than we actually are.   Tribally, group-wise, there is a reason for group members to tell the truth -- a group with direct and honest communication and understanding is likely to do better on average, in many important contexts, because deception often brings with it lots of complexity and inefficiency.   The balance between truth and lying is wired into our physiology -- typical people can lie only a little bit without it showing in their faces.   But modern society has bypassed these physiological adaptations, which embody tribal society's subtle balance between self and group motivations, via the creation of new media like telephones, writing and the Internet, which bypass telltale facial expressions and open up amazing new vistas for systematic self-over-group dishonesty.   Then society, and the minds of individuals within it, must set up all sorts of defense mechanisms to cope with the rampant dishonesty.   The balance of self versus group is fractured, and complexity emerges in an attempt to cope, but never quite copes effectively, and thus keeps ramifying and developing.

In Freudian terms, civilization brought with it the split between the Ego and Super-ego -- between what we are (at a given point in time); and what we think we should be.  It also brought with it a much mor complex and fragmented Ego that was present in tribal peoples.

What Wilson makes clear is: the pre-civilized human mind already had within it the split between the Self-motivation and Group-motivation.  Freud somewhat saw this as well, with his Id as a stylized version of the pure Self-motivation and his Ego going beyond this to balance Self versus Group.

The Freudian Ego and Super-ego are different ways of balancing Self versus Group.  The perversity and complexity of civilized society is that each of us is internally pushed to balance the conflict of Self vs. Group in one way (via our Ego, which is largely shaped for tribal society), while feeling we "should" be carrying out this balance in a different way (via our Super-Ego, which comes from civilized culture).  Of course these Freudian terms are not scientific or precisely defined, and shouldn't be taken too seriously.   But they do paint an evocative picture.

How much of this kind of inner conflict is a necessary aspect of being an intelligent individual mind living in a civilization?  Some, to be sure -- there is always going to be some degree of conflict between what's good for the individual and what's good for the group.  But having genomes optimized for tribal society, while living in civilized society, foists an additional layer of complexity on top of the intrinsic conflict.  The fact that our culture changes so much faster than our genomes, means that we are not free to seek the optimal balance between our current real-life Self and Group motivations, consistent with the actual society we are living in.  Instead we must live with methods of balancing these different motivations, that were honed in radically different circumstances than the ones we actually reside in and care about.

A Transhumanist Punchline

This is Benjamin Nathaniel Robot Goertzel's blog, so you knew there would be a transhumanist angle coming eventually, right? -- Once we achieve the ability to modify our brains and bodies according to our wishes, we will be able to adapt the way we balance Self versus Group in a much more finely-tuned and contextually appropriate way.

To the extent that layers of conflict within conflict are what characterize humanity, this will make us less human.  But it will also make us less perverse, less confused, and more fulfilled.

Our Screwiness Spurs Our Creativity and Progress

The punchier punchline, though, is that what is driving us toward the reality of amazing possibilities like flexible brain and body modification is -- precisely the screwiness I've analyzed above.

It's the creative tension between Self and Group that drove us to create sophisticated language in the first place.   One of the earliest uses of language, that helped it to grow into the powerful tool it now is, was surely gossip -- which is mainly about Self/Group tensions.

And our Self and Group aspects conspired to enable us to develop sophisticated tools.  Invention of new tools generally occurs via some wacky mind off in the corner fiddling with stuff and ignoring everybody else.  But, we do much better than other species at passing our ideas about new tools on from generation to generation, leveraging language and our rich social networking capability -- which is what allows our tool-sets to progressively improve over time.

The birth of civilization clearly grew from the same tension.   Tribal groups that set up farms and domesticated animals, in certain ecological situations, ended up with greater survival value -- and thus flourished in the group selection competition.  But individuals, seeking the best for themselves, then exploited this new situation in a variety of complex ways, leading to developments like markets, arts, schools and the whole gamut.  Not all of these new developments were actually best for the tribe -- some of the ways individuals grew to exploit the new, civilized group dynamics actually were bad for the group.  But then the group adapted, and got more complex to compensate.  Eventually this led to twisted sociodynamics like we have now ... with (post)modern societies that reject and psychologically torment their individualistic nonconformist rebels, yet openly rely on these same rebels for the ongoing innovation needed to compensate the widespread dissatisfaction modernity fosters.

And the creativity spurred by burgeoning self/group tensions continues and blossoms multifariously.  Privacy issues with Facebook and the NSA ... the rise and growth and fluctuation of social networks in general ... the roles of anonymity and openness on the Net ... websites devoted to marital infidelity ... issues regarding sharing of scientific data on the Net or keeping it private in labs ... patents ... agile software development ... open source software licenses and processes ... Bill Gates spending the first part of his adult life making money and the second part giving it away.   The harmonization of individual and group motivations remains a huge theme of our world explicitly, and is even more important implicity.

I imagine that, long after humans have transcended their legacy bodies and psychologies, the tension between Self and Group will remain in some form.  Even if we all turn into mindplexes, the basic tension that exists between different levels in any hierarchical system will still be there.   But at least, if it's screwy, it will be screwy in more diverse and fascinating ways!  Or beyond screwy and non-screwy, perhaps ;-)