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Friday, July 10, 2015

Life Is Complexicated


I grew up, intellectually, drinking the Complexity Kool-Aid from a big fat self-organizing firehose.

Back in the 1980s, I ate up the rhetoric, and the fascinating research papers, emanating from the Santa Fe Institute and ilk.  The core idea was extremely compelling — out of very simple rules, large-scale self-organizing dynamics can give rise to extraordinarily complex and subtle phenomena … stuff like stars, ecosystems, people,… the whole physical universe, maybe?

Fast forward a few decades and how does the “complexity” paradigm feel now?

(for simplicity, in the rest of this blog post I will use the word “complexity” to refer to Santa Fe Institute style, self-organizing-systems-ish “complexity”, rather than other meanings of the word)

Artificial life hasn’t panned out all that spectacularly — it has led to lots of cool insights and funky demos, but in the end attempts to get really richly-behaving life-forms or ecosystems to self-organize out of simple rules in the computer haven’t gone that well.

In AI, simplistic “complexity” oriented approaches — e.g. large, recurrent neural networks self-organizing via Hebbian learning or other local rules; or genetic programming systems — haven’t panned out insanely well either.  Again, research results have been obtained and a lot has been learned, but more impressive progress has been made via taking simple elements and connecting them together in highly structured ways to carry out specific kinds of learning tasks (e.g. the currently super-popular deep learning networks).

What about modeling economies, physical or chemical systems, ecosystems, etc.?   “Complex systems” style computer simulation models have provided insightful qualitative models of real systems here and there.  To some extent, the early message of the Santa Fe Institute and the other early complexity pioneers has simply diffused itself throughout science and become part of standard practice.   These days “everybody knows” that one very important way to understand complex real-world phenomena is to set up computer simulation models capturing the key interactions between the underlying elements, and run the simulations with various parameter values and look at the results.

Universal Laws of Complexity?


But back in the 80s the dream of complexity science seemed to go well beyond basic pragmatic lessons about running computer simulations, and high level discussions of properties shared by various systems in various contexts.  Back in the 80s, there was lots of talk about “universal laws of complex systems” — about using simulations of large numbers of very simple elements to understand the rules of complexity and emergence, in ways that would give us concrete lessons about real-world systems.

The great Stephen Wolfram, in 1985, foresaw cellular automaton models as a route to understanding “universal laws of complexity”   … decades later his book “A New Kind of Science” pushed in the same direction, but ultimately not all that compellingly.

I myself, with my occasional (er) grandiose tendencies, was a huge fan of this vision of universal laws of complex systems.   I even tried to lay some out in detail, pages 67-70 of my 2001 book “Creating Internet Intelligence”,

And one still sees mention of this idea here and there, e.g. in 2007

“The properties of a complex system are multiply realisable since they satisfy universal laws—that is, they have universal properties that are independent of the microscopic details of the system.”

But, truth be told, the “laws” of complexity found so far are just not all that law-like.  The grand complexity-science vision has panned out a little, but more complicatedly than anticipated.   Certainly broad self-organization/emergence based phenomena common to a huge variety of real-world complex systems have been identified.  Phase transitions, small world networks, strange attractors, self-organized criticality and so forth are now, simply, part of the language of science.   But these are more like “common phenomena, existing alongside all the other known phenomena characterizing systems, and manifesting themselves in various systems in various subtle and particular ways” — not remotely as law-like as, say, the “laws” of physics or chemistry.

(“Law” of course is a metaphor in all these cases, but the point is that the observational patterns referred to as physical or chemical “laws” are just a lot more solidly demonstrated and broadly applicable than any of the known properties of complex systems…)

Why So Complicated?


So why has the success of complexity science been so, well, complicated?

Some would say it’s because the core ideas of complexity, emergence, self-organization and so forth just aren’t the right ones to be looking at.

But I don’t think it’s that.  These are critical, important ideas.

Rather, I think the correct message is a subtler one: Real-world systems aren’t just complex, in the Santa Fe Institute sense of displaying emergent properties and behaviors that self-organize from the large-scale simple interactions of many simple elements.

Rather, real-world systems are what I’ll -- a bit goofily, I acknowledge -- call “complexicated”.

That is: They are complex (in the Santa Fe Institute sense) AND complicated (in the sense of just having lots of different parts that are architected or evolved to have specific structures and properties, which play specific roles in the whole system).

A modern car or factory is a complicated system - with many specialized parts, each carefully designed to play their own role.

Conway’s Game of Life (a popular, interesting cellular automaton model), or a giant Hebbian recurrent neural net, is a complex system in the SFI sense — it has emergent high-level properties that can ultimately be traced back to zillions of simple interactions between the simple parts.  But doing the tracing-back in detail would be insanely complicated and computationally resource-intensive.

On the other hand, a human body, or a modern economy, or the Internet, combines both of these aspects.   These are complicated systems, with many specialized parts, each carefully created to play their own role — yet key aspects of the roles that these parts play, involve their entrainment in complex emergent dynamics that can ultimately be traced back to zillions of simple interactions between simple parts (but doing the tracing-back in detail would be insanely complicated and computationally resource-intensive).

These kinds of “complexicated” systems lack the elegance of a well-designed car or factory, and they also lack the elegance of Conway’s Game of Life or a Hopfield formal neural network.  They are messy in a lot of different ways.  They have lots of specialized parts all working together, AND they have complex holistic dynamics that are hard to predict from looking at the parts, but that are critical to the practical operation of the parts.

Why does our world consist so much of this sort of perversely complexicated system, instead of nice elegant well-organized systems, or simplistic SFI-style “complex systems” models?   Because when dealing with severe resource constraints, evolutionary processes are going to make use of Any Means Necessary (well, any  means they can find within the searching they have resources to do).  Both self-organizing emergence and well-organized factory-style organization are effective ways of making big systems do difficult things.   If they can be gotten to work together in the same system, sometimes that’s even better.

The simple, uncomplicated self-organizing systems that the SFI-style "complexity science" likes to study, are not generally capable of giving rise to interesting phenomena given realistic amounts of resources.  That's a bit inelegant, but it's a cost of living in a universe that imposes such severe resource constraints on its residents.  To get interesting complex-self-organization-ish phenomena in reality, one generally needs to interweave some complicatedness with one's complexity.  Which means that one obtains systems whose behavior is a mixture of universal complex-systems properties, and highly specific properties resulting from complicated particulars.   Which is either ugly and messy or weirdly beautiful or completely plainly normal and real, depending on one's perspective!

Life and Mind are Complexicated


The all-knowing Urban Dictionary defines “complexicated” as


Something so complex, it's not enough to say it's complicated.

Girl 1: So how are things going with you and that new guy you're seeing?

Girl 2: I don't know, things are really complexicated with us. I'm not sure where things are going.


which isn’t exactly the meaning I’m using here, but I figure it’s aesthetically reasonably enough in synch.

Of course, followers of my AI work will have already anticipated my closing comment here.  The OpenCog AGI design I’ve co-created and am currently working on, combines SFI-style complexity with complicatedness in various subtle ways, some of which can be frustrating to work with at times.

I have spent a fair bit of time trying to figure out how to make a fundamentally simpler AGI design with the smell of “hey, this could work too” — but I haven’t succeeded at that, and instead have kept pressing ahead with OpenCog, along with some great colleagues.   If the line of thinking in this blog post is correct, then the search for a “fundamentally simpler” design may be misguided.   Getting rid of either the complexity or the complicatedness may not be possible.

Or in short, OpenCog is complexicated ... human minds and bodies are complexicated ...  the economy is complexicated ... the Global Brain is complexicated ... Life is complexicated.

...

-- And hey, even if the word doesn't have legs (or has complexicated legs -- yeesh, that sounds like an unfortunate disease!!), the underlying concept is important! ;-)

Some Interesting Comments


I posted a link to this post on the Global Brain and AGI email lists and got some interesting responses, e.g.


From Weaver (David Weinbaum):

Yes, I resonate with Ben's observations. It seems that real complexity defies universality as a principle.

Whenever we can describe a phenomenon so that its local particularities are easily decoupled from universal patterns e.g. describing a classic mechanical system in terms of the Hamiltonian and its initial conditions, this is not a complex phenomenon. I would also add to the list of complexicated systems those systems where statistical descriptions do not contribute a lot to their understanding.
Things that seem to be characteristic to complexicated systems are:

  1. Heterogeneity of the elements at various scales.
  2. Diversity of properties and functions.
  3. Degeneracy - every property and function has multiple ways of realization.
  4. 2^3. Very different structures realizing very similar functions while very similar structures may realize radically different functions. (I call it transductive instability, a concept I am working on developing). This seems to be a major key to the evolution of complex systems. 
  5. Variable range correlations - Local interactions may have global effects and vice versa, global patterns may affect local interactions. In other words, it is often hard or entirely impossible to clearly delineate distinct scales within such systems. 
  6. Contingency - certain behaviors are contingent and unpredictable.    

At least some of these are examined in some depth in two papers written by me and Viktoras Veitas that uses the theory of individuation to tackle complexity of the complexicated kind in the context of intelligence and cognition:



From Francis Heylighen:


Ben makes a number of correct observations here, about  truly complex systems (which he calls "complexicated") being more than ordered patterns emerging out of simple, homogeneous agents and rules. In practice, evolution works by developing specialized modules, which are relatively complex systems highly adapted to a particular function of niche, and then fitting these modules together so as to combine them recursively into higher-order systems. This leads to a hierarchical "architecture of complexity", as envisaged by Herbert Simon, where you find complexity at all levels, not only at the top level.

The picture of Simon is still too simple, because the modules are in general not neatly separable, and because sometimes you have distributed patterns of feedback and coordination that exploit the local capabilities of the modules. But I agree with Ben that the old "Santa Fe" vision of deterministic "laws of complexity" that specify how simple rules produce emergent patterns is equally unrealistic. The combination of the two, as Ben seems to propose, is likely to be more fruitful.

My own preferred metaphor for a complex adaptive system is an ecosystem, which consist of an immense variety of complex organisms, from bacteria to bears and trees, and assemblies of such organisms, interacting non-linearly which each other and with the somewhat simpler physical and chemical processes of climate, resource flows, erosion, etc... The components of such a system have co-evolved to be both largely autonomous, and mutually dependent via intricate networks, producing a truly "complexicated" whole.

From Russell Wallace:

Good article! Or, put another way:

  1. The Santa Fe school implicitly optimizes for smallness of source code versus aesthetic interestingness of results.
  2. Biology optimises for ease of creation by evolution versus performance.
  3. Technology optimises for ease of creation by human engineers versus performance.

Looked at that way, it makes sense that 1 isn't a good model for 2 or 3.

Tuesday, June 16, 2015

Why Occam’s Razor Works

(Sketch of a Possible Explanation Why Occam’s Razor Works...)

(Though motivated by deep questions in philosophy, this is a speculative math-y blog post; non-technically oriented readers beware…)

How can, or should, an intelligent mind make sense of the firehose of complex, messy data that its sensors feed it?    Minds recognize patterns, but generally there are many many patterns in the data coming into a mind, and figuring out which data to pay attention to is a significant problem.   Some major aspects of this problem are: Figuring out which of the patterns that have occurred in one context are more likely to occur in other similar contexts, and figuring out which of the patterns that have occurred in the past are more likely to occur in the future. 

One informal principle that seems broadly useful for solving this “pattern selection” problem is “Occam's Razor.”   This principle is commonly taken as a key ingredient in the scientific method – it plays a key role in many philosophies of science, including the “social computational probabilist” philosophy I’ve presented here and in The Hidden Pattern.

Occam’s Razor has been around a while (well before its namesake, William of Ockham) and has been posited in multiple forms, e.g.:

“Nature operates in the shortest way possible” -- Aristotle, BC 384-322

“We consider it a good principle to explain the phenomena by the simplest hypothesis possible.”  -- Ptolemy, c. AD 90 -  168

“Plurality must never be posited without necessity” -- William of Ockham, c. 1287-1347

“Entities must not be multiplied beyond necessity” -- John Punch, 1639

“Everything should be as simple as it can be, but not simpler” --  Albert Einstein (paraphrased by Roger Sessions)

The modern form of the Razor, as used in discussions of scientific methodology and philosophy of science, could be phrased something like:

Given two explanations that explain the same data, the simpler one should be preferred

or as Sklar (1977) phrased it,

In choosing a scientific hypothesis in light of evidential data, we must frequently add to the data some methodological principle of simplicity in order to select out as ''preferred'' one of the many different possible hypotheses, all compatible with the specified data.

This principle is often taken for granted, and has a certain intuitive ring of truth to it -- but why should we actually accept it?   What makes it true? 

Arguments for Occam’s Razor

Perhaps the most compelling argument for Occam's Razor is the theory of Solomonoff Induction, generalized more recently by  Marcus Hutter into a theory of Universal AI.   This theory shows, very roughly speaking, that the assumption that the shortest computer program computing a set of data is the best explanation for that data, where "best" is defined in terms of accurate prediction of missing or future parts of the dataset.  This is very elegant but the catch is that effectively it only applies to fairly large datasets, because it relies heavily on the fact that, in the limit of large datasets, the shortest program explaining the dataset in one programming language is approximately the same length as the shortest program explaining that same dataset in any other programming language.   It assumes one is in a regime where the computational cost of simulating one programming language (or computer) using another is a trivial matter to be brushed aside.

There are other approaches to justifying Occam's Razor as well.   The Akaike Information Criterion (AIC) formalizes the balance between simplicity and goodness-of-fit that is required to achieve extrapolation without overfitting.  However,  the derivations underlying the AIC and its competitor, the Bayesian Information Criterion (BIC), hold only for large datasets.  The AICc version works for small datasets but relies on special distributional assumptions.

There is also Kelly's (2007, 2010) interesting argument that the shortest hypothesis is, under certain assumptions, the one that will require one to change one's mind least often upon exposure to new data.   This is interesting but somewhat begs the question of why it's so bad to change one's mind when exposed to new data.  Kelly's proofs also seem to get bogged down in various technical intricacies and conditions, which  may however be ultimately resolvable in a clean way.

Here I present a new  argument for Occam's Razor, which appears to work for small as well as large datasets, and which is based on the statistical notion of subsampling.   At present the argument is just a sketch, yet to be filled out into a formal proof.  In essence, what is done is to construct a particular computational model, based on the properties of a given dataset, and argue that using Occam's Razor relative to this sort of computational model leads to good explanations for any dataset, large or small.   As the size of the dataset increases, the explanatory advantage gained by choosing a dataset-guided computational model for using Occam's Razor decreases, and the choice of computational model becomes increasingly arbitrary.

Sketch of a New Argument why Occam’s Razor Works

I’ve been thinking for a while about a new, somewhat different argument for why Occam’s Razor makes sense and is a good idea.   I haven’t found time to write up a formal proof , so I’m just going to sketch my “proof idea” here.   Eventually maybe I’ll find time to try to turn this into a real proof, which may well yield to some gotchas, restrictive conditions or new discoveries….  Or maybe some other brave and noble soul will take the bait and try to make a real proof based on my idea…
Ok, so -- the crux of the argument I’m thinking of is as follows.

Consider, for simplicity, a binary classification problem, involving  a data-set D living within a data universe U, and a a "ground truth" mapping F: U --> {0,1}.    This is not the only context my proposed argument applies to, but it’s a simple context for explaining the basic idea.

Then, consider two sets of functions S1 and S2, both learned via application of some learning algorithm L to study D (and not the rest of F).   Suppose:

  • The functions in S1 have accuracy a across D, and have size s1
  • The functions in S2 have accuracy a across D, and have size s2 > s1


Then the proposition I make is: On average, functions in S1 will give a higher accuracy on F than functions in S2 (**).

Why would (**) be true?

I believe it can probably be demonstrated via induction on the size of D  

Suppose (**) holds for D^* that are smaller than D.   Then, suppose we apply crossvalidation to assess the value of functions in S1 and S2; that is, we run a series of experiments in which we partition D into 2 subsets (D1, D2) and then apply L to learn classification functions on D1, and test these functions on D2.   These experiment will yield many functions that don't belong in either S1 or S2, and also some that do belong to these sets.   According to the inductive hypothesis: on average the functions L finds (on the validation folds) belonging to  S1 will have greater accuracy across D as compared to those that L finds (on the validation folds) belonging to S2 (***).  

But then from (***) and the basic theory of crossvalidation (which says that hypotheses doing better on the test portions of validation folds, tend to do better out of sample), we derive (**).

The question then becomes how to start the inductive hypothesis off.   What is the base case?

Well,  one possibility is for the base case to be the situation where D contains two elements d0 and d1, with F(d_0) = 0 and F(d_1)=1.   To understand this case, suppose that the data elements d (in D) each have k internal features.   Based on comparing d0 and d1, there is no way for L to identify dependencies between the different internal features.  Thus, the models most likely to give good accuracy on F are single-feature models.  But these are smaller than any other models.  Thus in this (somewhat degenerate) base case, smaller models are better.

I think this extreme case reflects a more general truth: When D is very small, models that ignore dependencies are more likely to give high accuracy, because it’s generally hard to identify dependencies based on small datasets.


So there you go – Bob’s your uncle!

The crux of the argument is:

  • Simpler models are better for extrapolating from datasets of size N, because simple models are better for extrapolating from size N/k, and crossvalidation theory says that models working better on data subsets are better at working on the whole dataset
  • Simpler models are better for extrapolating from very small datasets because it’s not possible to meaningfully extrapolate dependencies between variables based on very small datasets, and models that treat variables as independent and don’t try to model dependencies, are intrinsically simpler


Dependency on the Expressive Language

The above is admittedly a sketchy argument at this point, and more rigorous analysis may expose some holes.   But, provisionally taking the basic argument for granted, it’s worth asking what the above argument says about the language in which models are expressed.  

The main constraint seems to come from the base case: we need an expressive language in which modeling a dataset in a way that ignores dependencies, is generally more concise than modeling a dataset in a way that takes dependencies into account.   There may also be aspects of the “crossvalidation theory” invoked vaguely above, that depend in some way on the specifics of the expressive language.

While vague and still speculative, this seems promising to me, e.g. as compared to the Solomonoff induction based foundation for Occam’s Razor.   In the Solomonoff approach, the judgment of “what is simple” displays a strong dependence on the underlying Universal Turing Machine, which becomes irrelevant only for large N.  But a lot of everyday-life pattern recognition seems to be best considered in the “small N” context.    A lot of human pattern recognition does seem to depend on what “expressive language” the human mind/brain is using to represent patterns.  On the other hand, in the present approach, the dependency on the expressive language seems much weaker.   “What is simple” seems to be mostly -- What is judged simple by an expressive language in which: models that ignore dependencies are simpler than those that incorporate them….

So What?

What’s the point of this kind of argumentation?  (apart from the copious entertainment value it supplies to those of us with odd senses of aesthetics and humour, that is... ;D )

The point is that Occam’s Razor, valuable as it is, is a vague, hand-wavy sort of principle – but given its very central importance in the philosophy of mind and of science, it would be very nice to have a more precise version!    


Among other goals, a more precise version of the Razor could provide useful guidance to AI systems in analyzing data and AGI systems in thinking about their experiences.

...

A Few Quasi-Random References

@BOOK{Burnham2002,
  title = { Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach},
  publisher = {Springer},
  year = {2002},
  author = {Burnham, K P and Anderson, D R},
}

@book{Hutter2005,
 author = "Hutter, Marcus",
 title = "Universal {Artificial} {Intelligence}: {Sequential} {Decisions} based on {Algorithmic} {Probability}",
 publisher = "Springer",
 year = 2005
}


@ARTICLE{Kelly2010,
  author = {Kevin T Kelly and Conor Mayo-Wilson},
  title = {Ockham Efficiency Theorem for Stochastic Empirical Methods },
  journal = {Journal of Philosophical Logic 39: pp. 679-312. },
 year = {2010},
}


@ARTICLE{Kelly2007,
  author = {Kevin T Kelly},
  title = {Ockham’s Razor, Empirical Complexity, and Truth-finding Efficiency”  },
  journal = {Theoretical Computer Science },
 year = {2007},
}

@BOOK{Sklar1977,
  title = {Space, Time, and Spacetime,},
  publisher = {Berkeley},
  year = {1977},
  author = {L Sklar },
}

Sunday, April 05, 2015

AI-Based Trading is More Likely to Decrease than Increase Problems in the Markets

Futurist Thomas Frey has written an article suggesting that AI-based financial trading is a threat and is likely to cause a series of horrible market crashes....

This is a topic I've thought about a bit due to my being co-founder and Chief Scientist of Aidyia Limited, an AI-based asset management firm that will be launching a series of funds, beginning with a US equity long-short fund in a couple months.   Note that this is not a high-frequency approach -- our average holding period will be weeks to months, not microseconds.

So here are a few quasi-random musings on the topic....

Overall -- It seems clear to me that within a decade or two the financial markets will be entirely dominated by AIs of one form or another. Human minds are simply not well configured for the sorts of problems involved in asset price prediction, in such a complexly interlinked world as we have today.

However, I see no reason why AI-based trading would lead to worse crashes. Generally, when one creates an AI-based trading system, one does so with a certain mandate in mind, including a certain risk/return profile. IMO an AI that is well-done is more likely to operate within its intended risk/return profile, than a human trader.

Many of the trading disasters commonly attributed to quantitative methods are ultimately the result of plain old bad human judgment. For instance the Long Term Capital Management problem in the late 1990s did involve use of advanced quantitative models -- but ultimately the core of that problem was the use of leverage up to 100x, a choice made by the humans running the system not by the equations themselves. Common sense would tell you that trading with 100x leverage is pretty risky no matter what equations you're using. Having AI inside a trading system is not a total protection against the stupidity -- or emotional pathology -- of the humans trading that system.

The flash crash apparently was mainly due to automated systems, but probably not AI-based systems. Most HFT systems have minimal AI in them -- they're based on reacting super-quickly not super-smartly. The use of HFT shouldn't be conflated with the use of AI. HFT could be pretty much eliminated from any market by imposing a per-transaction tax like we have here in Hong Kong; but this wouldn't get rid of AI. Our AI predictors at Aidyia are currently being used to predict asset price movements 20 days in advance, not microseconds in advance.


But anyway...

As I've written previously in various places, I personally think the whole world financial and economic system is going to transform into something utterly different, once robots and AIs eliminate the need for (and relative value of) human effort in most domains of practical endeavor. So I view these issues with AIs and asset management as "transitional", in a sense. But that doesn't make them unimportant, obviously -- for the period between now and Singularity, they will be relevant.

I worry more about the ongoing increase of income and wealth inequality in nearly every nation, than about the impact of AI on the markets. Computers are already dominant on the markets, AIs will soon be dominant, but as long as the AIs are operating funds owned and controlled by humans, this doesn't really affect the nature of the financial system.  But part of this financial system is increasing wealth concentration -- and I worry that increasing inequality, combined with a situation where robots and AIs ultimately liberate people from their jobs, could eventually lead to a difficult situation.  I believe we ultimately will need some kind of guaranteed minimum income across the planet, the only alternatives being mass warfare or mass dying-off.  But I worry that the worse class divisions get, the harder this guaranteed-income solution will be to put in place, because the folks holding the remnants of human political and economic power will become more and more alienated from the average people.

So I do think there are lots of tricky worries in the medium-term future, regarding the relation between human society and (basically inexorably) advancing AI. But AI-based traders aren't really something to fuss about IMO.   I think that getting messy human emotion out of the mechanics of trading is more likely to decrease the odds of catastrophic crashes than increase it....  If you want to look out for dangers associated with the advent of AI based trading, I'd suggest to keep an eye out for more LTCM like situations where humans make egregiously bad emotional judgments in managing their AI prediction systems.   The AIs themselves are not likely to be the source of irrationality and chaos in the markets.


Friday, April 03, 2015

Easy as 1-2-3: Obsoleting the Hard Problem of Consciousness via Brain-Computer Interfacing and Second-Person Science

NOTE ADDED A FEW DAYS AFTER INITIAL POSTING: The subtitle of this post used to be "Solving the Hard Problem of Consciousness via Brain-Computer Interfacing and Second-Person Science" -- but after reading the comments to the post I changed the first word of the subtitle to "Obsoleting" instead.  I made this change because I realized my initial hope that second-person-experience-enabling technology would "solve" the "hard problem of consciousness" was pretty idealistic.   It might solve the "hard problem" to me, but everyone has their own interpretation of the "hard problem", and in the end, philosophical problems never get solved to everybody's satisfaction.   On the other hand, this seems a great example of my concept of Obsoleting the Dilemma from A Cosmist Manifesto.  A philosophical puzzle like the "hard problem" can't necessarily be finally and wholly resolved -- but it can be made irrelevant and boring to everybody and basically obsolete.  That is what, at minimum, I think second-person-oriented tech will do for the "hard problem."

The so-called “hard problem of consciousness” (thus labeled by philosopher David Chalmers) can be rephrased as the problem of connecting FIRST-PERSON and THIRD-PERSON views of consciousness, where

  • the FIRST-PERSON view of consciousness = what it feels like to be conscious; what it feels like to have a specific form of consciousness
  • the THIRD-PERSON view of consciousness = the physical (e.g. neural or computational) correlates of consciousness … e.g. what is happening inside a person’s brain, or a computer system’s software and hardware, when that person or computer system makes a certain apparently sincere statement about its state of consciousness

The “hard problem”  is the difficulty of bridging the gap between these.  This gap is sufficient that some people, taking only a third-person view, go so far as to deny that the first-person view has any meaning — a perspective that seems very strange to those who are accustomed to the first-person view.  

(To me, from my first-person perspective, for someone to tell me I don’t have any qualia, any conscious experience -- as some mega-materialist philosophers would do -- is much like someone telling me I don’t exist.   In some senses I might not “exist” — say, this universe could be a simulation so that I don’t have concrete physical existence as some theories assert I do.  But even if the universe is a simulation I still exist in some other useful sense within that simulation.  Similarly, no matter what you tell me about my own conscious experience from a third-person perspective, it’s not going to convince me that my own conscious awareness is nonexistent — I know it exists and has certain qualities, even more surely than I am aware of the existence and qualities of the words some materialist philosopher is saying to me…) 

So far, science and philosophy have not made much progress toward filling in this gap  between first and third person views of consciousness— either before or after Chalmers explicitly identified the gap.

What I’m going to suggest here is a somewhat radical approach to bridging the gap: To bridge the gap between 1 and 3, the solution may be 2.  

I.e., I suggest we should be paying more attention to:
  • the SECOND-PERSON view of consciousness = the experience of somebody else’s consciousness

Brain-Machine Interfacing and the Second Person Science of Consciousness

There is a small literature on “second person neuroscience”   which contains some interesting ideas.  Basically it’s about focusing on what peoples brains are doing while they’re socially interacting. 

I also strongly recommend Evan Thompson’s edited book “Between Ourselves: Second-person issues in the study of consciousness”, which spans neurophysiology, phenomenology and neuropsychology and other fields.

What I mean though is something a little more radical than what these folks are describing.   I want to pull brain-computer (or at least brain-wire) interfacing into the picture!

Imagine, as a scientist, you have a brain monitor connected to your subject, Mr. X; and you are able to observe various neural correlates of Mr. X’s consciousness via the brain monitor’s read-out.  And imagine that you also have a wire (it may not actually be a physical wire, but let’s imagine this for concreteness) from your brain to Mr. X’s brain, allowing you to  experience what Mr. X experiences, but on the “fringe” of your own consciousness.  That is, you can feel Mr. X’s mind as something distinct from your own — but nevertheless you can subjectively feel it.  Mr. X’s experiences appear in your mind as a kind of second-person qualia.

Arguably we can have second-person qualia of this nature in ordinary life without need for wires connecting between brains.  This is what Martin Buber referred to as an “I-Thou” rather than “I-It” relationship.   But we don’t need to get into arguments about the genuineness of this kind of distinction or experience.  Though I do think I-Thou relationships in ordinary life have a kind of reality that isn’t captured fully in third-person views, you don’t have to agree with me on this to appreciate the particular second-person science ideas I’m putting forward here.  You just have to entertain the idea that direct wiring between two peoples’ brains can induce a kind of I-Thou experience, where one person can directly experience another’s consciousness.

If one wired two peoples’ brains sufficiently closely together, and setting aside a host of pesky little practical details, then one might end up with a single mind with a unified conscious experience.  But what I’m suggesting is to wire them together more loosely, so that each person’s consciousness appears on the *fringe* of the other’s consciousness.

The point, obviously, is that in this way, comparisons between first and third person aspects of consciousness can be made in a social rather than isolated, solipsistic way.

What is “Science”?

Science is a particular cultural institution that doesn’t necessarily fit into any specific formal definition.  But for sake of discussion here, I’m going to propose a fairly well-defined formal characterization of what science is.

The essence of science, in my view, is a community of people agreeing on
  • a set of observations as valid 
  • a certain set of languages for expressing hypotheses about observations
  • some intuitive measures of the simplicity of observation-sets and hypotheses

Given such a community, science can then proceed via the search for hypotheses that the community will agree are simple ways of explaining certain sets of agreed-on observations.   The validity of hypotheses can then be explored statistically by the community.  

No Clear Route to “First Person Science”

The problem with first-person views of consciousness is that they can’t directly enter into science, because a first-person experience can’t be agreed-upon by a community as valid.  

Now, you might argue its not entirely IMPOSSIBLE for first-person aspects of consciousness to enter into science.   It’s possible because a certain community may decide, for example, to fully trust each other’s verbal reports of their subjective experiences.   This situation is approximated within various small groups of individuals who work together in various wisdom traditions, aimed at collectively improving their state of consciousness according to certain metrics.   Consider a group of close friends meditating together, and sharing their states of consciousness and discussing their experiences and trying to collectively find reliable ways to achieve certain states.   Arguably the mystical strains of various religions have at various times contained groups of people operating in this sort of way.

A counter-argument against this kind of first-person science might be that there are loads of fake gurus around, claiming to have certain “enlightened” states of consciousness that they seem not to really have.   But of course, fraud occurs in third-person science too…

A stronger counter-argument, I think, is that even a group of close friends meditating together is not really operating in terms of a shared group of first-person observations.  They are operating in terms of third-person verbal descriptions and physical observations of each other’s states of consciousness — and maybe in terms of second-person I-Thou sensations of each other’s states of consciousness.

But There Likely Will Soon Come Robust Second-Person Science

On the other hand, second-person observations clearly do lie within the realm of science as I’ve characterized it above.   As long as any sane observer within the scientific community who wires their brain into Mr. X’s brain , receives roughly the same impression of Mr. X’s state of mind, then we can say that the second-person observation of Mr. X is part of science within that community. 

You might argue this isn’t so, because how do we know what Ms. Y perceived in Mr. X’s brain, except by asking Ms. Y?  But if we’re relying on Ms. Y’s verbal reports, then aren’t we ultimately relying on third-person data?  But this objection doesn’t really hold water — because if we wanted to understand what Ms. Y was experiencing when second-person-experiencing Mr. X’s brain, we could always stick a wire into her brain at the same time as she’s wired into X, and experience her own experience of Mr. X vicariously.   Or we could stick  a wire into her brain later, and directly experience her memory of what she co-experience with Mr. X.  Etc. 

Granted, if we follow a few levels of indirection things are going to get blurry — but still, the point is that, in the scenario I’m describing, members of a scientific community can fairly consider second-person observations achieved via brain-computer interfacing as part of the “observation-set collectively verifiable by the community.”   Note that scientific observations don’t need to be easily validated by every member of a community - it’s a lot of work to wire into Ms. Y’s brain, but it’s also a lot of work to set up a particle accelerator and replicate someone’s high-energy physics experiment, or to climb up on a mountain and peer through a telescope.   What matters in the context of science as I understand it is that the observations involved can in principle, and in some practical even if difficult way, be validated by any member of the scientific community.

Solving (Or at least Obsoleting) the Hard Problem of Consciousness

Supposing this kind of set-up is created, how does it relate to first and third person views of consciousness?

I presume that what would happen in this kind of scenario is that, most of the time, what X reports his state of consciousness to be, will closely resemble what Y perceives X’s state of consciousness to be, when the two are neurally wired together.   Assuming this is the case, then we have a direct correlation between first-person observations about consciousness and second-person observations — where the latter are scientifically verifiable, even though the first or not.  And of course we can connect the second person observations to  third-person observations as well.

Thus it appears likely to me the hard problem of consciousness can be "solved" in a meaningful and scientific way, via interpolating 2 between 1 and 3.   At very least it can be obsoleted, and made as uninteresting as the problem of solipsism currently is (are other people really conscious like me?), or, say, the philosophical problem of whether time exists or not (we can't solve that one intellectually, but we don't spend much time arguing about it)....

Can Computers or Robots be Conscious in the Same Sense as Humans Are?

Of course, solving or obsoleting the hard problem of consciousness is not the only useful theoretical outcome that would ensure from this kind of BCI-enabled second-person science.

For instance, it's not hard to see how this sort of approach could be used to explore the question of whether digital computers, robots, quantum computers or whatever other artifact you like can be "genuinely conscious" in the same sense that people are.

Just wire your brain into the robot's brain, in a similar way to how you'd wire your brain into a human subject's brain.   What do you feel?  Anything?  Do you feel the robot's thoughts, on the fringe of your consciousness?   Or does it feel more similar to wiring your brain into a toaster?

Is Panpsychism Valid?

And what does it feel like, actually, to wire your brain into that toaster?   What is it like to be a toaster?  If you could wire some of your neurons into the toaster's sensors and actuators, could you get some sense of this?  Does it feel like nothing at all?  Or does it feel, on the fringe of your awareness, like some sort of simpler and less sophisticated consciousness?

When your friend hits the toaster with a sledgehammer, what is it you feel on the fringe of your awareness, where you (hypothetically) sense the toaster's being?   Do you just feel the toaster breaking?   Or do you feel some kind of painful sensation, at one remove?   Is the toaster crying out, even though (if not for your wiring your brain into it) nobody would normally hear...?

The second-person knowledge about the toaster's putative awareness would be verifiable across multiple observers, thus it would be valid scientific content.   Panpsychism, in a certain sense, could become a part of science....

Toward a Real Science of Consciousness

In sum -- to me the hard problem is about building a connection between the first person and third person accounts of consciousness, and I think the second person account can provide a rich connection.... 

 That is, I think a detailed theory of consciousness and its various states and aspects is going to come about much more nicely as a mix of first, second and third person perspectives, than if we just focus on first and third...


Tuesday, March 31, 2015

Kermit the Frog, Maximum Entropy Production, Etc.




While in Shanghai on a business trip recently, in a restaurant eating some terrifyingly spicy fish hot-pot with a couple of my Aidyia colleagues, I noticed the radio was playing a cover version of a song from the original Muppet Movie, “The Rainbow Connection”…. 

As often happens, this got me thinking….

This is not remotely an original observation, but it’s one of these cliches that has struck me over and over again during my life: There’s a certain beauty to the process of seeking, which often exceeds the satisfaction of finding what one is looking for.  This is related to why so many people enjoy the process of entrepreneurship — of starting new things and chasing success.  The feeling of increasing success is exciting, the ups and downs as one moves toward the goal and then toward and then away etc. in complex patterns are exciting….  Actually achieving the goal may give an oomph of satisfaction but then the oomph goes away and one craves the joy of the process of seeking again.  Of course there are always new things to seek though — one can seek to grow one’s company bigger and bigger, etc.  Many people enjoy seducing new men or women more than actually having an ongoing relationship with one whom they’ve captured, but I’ve never quite felt that way; I guess seeking a really good ongoing relationship is enough of a challenging quest for me, given my  peculiar and in some ways difficult personality…

This point struck me hard as a kid when I was watching the Muppet Movie and saw Kermit the Frog singing the “The Rainbow Connection”


Why are there so many songs about rainbows
And what's on the other side
Rainbows are visions
But only illusions
And rainbows have nothing to hide

So we've been told
And some choose to believe it
I know they're wrong, wait and see
Some day we'll find it
The rainbow connection
The lovers, the dreamers, and me
 
Who said that every wish
Would be heard and answered
When wished on the morning star
Somebody thought of that
And someone believed it
And look what it's done so far

What's so amazing
That keeps us stargazing
And what do we think we might see
Some day we'll find it
The rainbow connection
The lovers, the dreamers, and me

All of us under its spell, we know that it's probably magic

Have you been half asleep?
And have you heard voices?
I've heard them calling my name;
Is this the sweet sound
That called the young sailors?
The voice might be one and the same

I've heard it too many times to ignore it
It's something that I'm supposed to be
Some day we'll find it
The rainbow connection
The lovers, the dreamers, and me

Kermit’s plaintive froggy voice moved my emotions and I have to say it still does, way more than the typical ballad sung by a human pop star…   What occurred to me as a child as I watched him sing (maybe not the first time I saw the movie — we had it on video-tape when I was a kid and I heard the song more than once!), was that he had found his Rainbow Connection right there, inside the song — He was seeking something else, something beyond himself and his life, but actually inside the beauty of the song, and the feeling of singing the song, and the connection between him and the singer — and the songwriters and puppeteers behind the Kermit persona — and the various listeners of the song such as myself, and the people singing and humming the song around the world at various times and places … this whole melange of feeling and creation and expression and interaction obviously WAS the “Rainbow Connection” — a connection between different minds and voices, sounds waving through the air and colored pictures flashing on screens decoded from electromagnetic waves cast through the air via antennas … a diversity of colors beyond the humanly perceived rainbow and including all sorts of other frequencies ….  When I listened to the song I was basking in the reality of the Rainbow Connection and so was the imaginary and real Kermit.  Of course as a child I didn’t articulate it exactly this way but less-crystallized versions of these thoughts verged through my mind (as probably has happened with many other listeners to this same song, in another aspect of the Good Old Rainbow Connection).   And I could only suspect that somewhere in the back of his good-natured though not that bright little froggy mind, Kermit realized that the beauty was really in the process of seeking and not in the goal — that the beauty and connection and joy he was after, were already there in the the song he was singing about the quest for these things, and in the life and love he expressed that constituted and animated this quest itself….

So, well, all hail Kermit !!! ... what else?

Similar ideas have occurred to me recently in an utterly different context…

A different twist on the aesthetic primacy of process over goal is provided by the Maximum Entropy Production Principle, which hypothesizes that, in many circumstances, complex systems evolve along paths of *maximum entropy production*.   The fine print is complex, but there's a lot of evidence mathematical, conceptual and physical in favor of this idea, e.g.:


This is rather fascinating — it suggests we can think about the wonderful complexity of life, nature, humanity and so forth as, in some measure, resulting from a rush to achieve the goal of the Second Law of Thermodynamics — heat death — as rapidly as possible!!   Of course this isn’t really the total story of complexity and life and all that, but it seems to be an important ingredient — and it’s certainly a poignant one.   The goal in this case is a humanly repellent and disturbing one: the loss of complex form and the advent of stultifyingly uniform random movement in the universe.  The path followed in working toward this goal is a deep, rich, tremendously beautiful one.  

Whether you’re seeking the Rainbow Connection or Ultimate Heat Death, it seems that the process of optimization, in many cases, has a great deal of power to create beauty and structure and feeling.   The process of seeking a goal in the face of limitations and constraints forces a tradeoff between the degree of goal fulfillment and the constraints — and it’s this dance that leads to so much structure and beauty.  

In the case of a song like the Rainbow Connection, the constraints are about time (people get bored if a song is too long) and human comprehension (it’s hard to express a universal human feeling in a way that humans can universally appreciate, given the diversity of our mind-sets and cultures) and the physics of sound waves and the limitations of the human ear and so on.  In the case of Jimi Hendrix, whose music I prefer to even that of Kermit, it was about Hendrix’s musical creativity and the sounds he heard in his head interacting with the constraints of what could be done with the electric guitar and the amplification and production equipment at the time.

In the case of thermodynamics, the core constraints are the “laws” of mechanics and temporal continuity.   The end goal is Ultimate Heat Death, perhaps, but a physical system can only change so much at each point in time.   The physical system is trying to maximize entropy production, yeah, but it can only do so in a manner consistent with the laws of physics, which — among many other constraints — only allow a certain pace of change over time.  Figuring out how to maximize entropy production in the context of obeying the laws of physics and what they say about the relation between matter and spacetime — this is the interplay that helps yield the wonderful complexity we see all around us. 

If the constraints were too relaxed, the goal might get approached too quickly and surely, and there would be no beauty on the path along the way.  If the goal and the constrants were both weak, things might just drift around quasi-randomly in less than interesting ways.  If the constraints were too strong there might just be no interesting ways for the overall objective function to get pursued (be it heat death or writing a great song or whatever).   Constraints that are strong but not too strong, imposed on a suitable objective function, are what yield wonderful complexity.  Lots of analogies arise here, from raising kids to the evolution of species.

To view it in terms of optimization theory: Constraints take a simple objective function and turn it into a complex objective function with multiple extrema and subtle dependencies all across the fitness landscape.  These subtleties in the objective function lead to subtle, intricate partial solutions — and when there is a continuity constraint, so that newly posed solutions must constitute slight variations on previously posed solutions, the only way to dramatically maximize the core objective function is to pass through a variety of these partial solutions.

The ultimate bliss and glorious spectral togetherness Kermit was seeking — or that my childhood self thought he was seeking — or whatever — is an amazing, thrilling vision for sure.  But the process of gradually moving toward this ultimate cosmic vision, in a manner consistent with the constraints of human and froggy life, and the continuity constraint in moving through possible solutions, is what yields such subtle, interesting and moving forms as we see, hear and are in this world right now…



OK OK, that’s all pretty fast and loose, I know.  Hey, I’m just musing while listening to a song, not proving a bloody theorem.  My petty human mind, not yet achieved ultimate superintelligence, has got to churn through stuff like this day by day to gradually muck toward a fuller understanding of the world.  It’s a process ;-) ….

As Captain Beefheart said, “a squid eating dough in a polyethylene bag is fast and bulbous — fast and bulbous, got me?”

Sunday, March 08, 2015

Paranormal Phenomena, Nonlocal Mind and Reincarnation Machines

How I Came to Accept the Paranormal

While I’m generally an extremely stubborn person,  my opinion has radically changed on some topics over the years.   I don't view this as a bad thing.   I don't aspire to be one of those people whose ideas are set in stone, impervious to growth or adaptation.

Some of my changes of opinion have been purely "changes of heart" -- e.g. in my early 20s I transitioned from a teenage solipsism to a more compassion-oriented attitude, due more to internal growth than any external data or stimuli.   

Other times, the cause of my change of opinion has been encountering some body of evidence that I simply hadn’t been aware of earlier.  

The change of mind I'm going to write about here has been of the latter kind -- data-driven.

What I’m going to write about here is a certain class of paranormal phenomena that seem related to religious notions of “survival after death.”   In my teens and 20s I was pretty confident these phenomena were obviously nothing more than wishful thinking.   People didn't want to admit they were doomed to die one day, so they made up all sorts of fanciful stories about heavens and hells after death, and reincarnation, and ghosts and what-not.  

I didn’t want to die either, but I was interested in achieving physical immortality via fixing the problems that make the human body age, or  by uploading my  mind into a robot or computer or whatever – by methods that made good solid rational sense according to science, even if they seemed outlandish according to most peoples’ everyday world-views.   

(I did, in my youth, acquire from somewhere some sort of intuitive spiritual sense that my mind would continue to exist after my death, fused with the rest of the universe somehow.  But I didn’t imagine I’d continue to have any individuality or will after my body died – I intuitively, non-rationally felt I’d continue to exist in some sort of inert form, always on the verge of having a thought or taking an action but never quite doing so….)

My current view of these "survival-ish" paranormal phenomena is quite different.   I definitely haven’t had any sort of religious conversion, and I don’t believe any of the traditional stories about an afterlife are anywhere near accurate.    But I now am fairly confident there is SOMETHING mysterious and paranormal going on, related to reincarnation, channeling and related phenomena.

My new perspective doesn’t fit that well into our standard contemporary verbiage, but a reasonable summary might be:
  • Individual human minds have an aspect that is "nonlocal", in the sense of not being restricted to exist within the flow of our time-axis, in the same sense that our bodies are generally restricted.  
  • Due to this non-localized aspect, it’s possible for human minds that are evidently grounded in bodies in a certain spacetime region, to manifest themselves in various ways outside this spacetime region – thus sometimes generating phenomena we now think of as “paranormal”
  • This non-localized aspect of human minds probably results from the same fundamental aspects of the universe that enable psi phenomena like ESP, precognition, and psychokinesis
  • The path from understanding which core aspects of physics enable these phenomena, to  understanding why we see the precise paranormal phenomena we do, may be a long one – just as the path from currently known physics to currently recognized biology and psychology is a long one


How did I come to this new view?

The first step was to accept, based on an extensive review of the available evidence, that psi phenomena are almost surely real.   My perspective on this is summarized in the introductory and concluding chapters of  Evidence for Psi, a book I co-edited with Damien Broderick last year.   See also the links on this page.   I don’t want to take space and time here summarizing the evidence for psi phenomena, which includes lots of carefully-analyzed laboratory data, alongside loads of striking, well-substantiated anecdotal evidence.   It was the laboratory data that first convinced me psi was very likely real.  After getting largely convinced by the laboratory data, I started reading through the literature on anecdotal psi phenomena, and it started to seem less and less feasible that it was all just fabricated.

I’ve also speculated a bit about how one might tweak currently understood physics to obtain a physics in which psi could be possible.   See my blog post on Surprising Multiverse Theory.  Basically, I think it might be enough to posit that the various histories summed over in quantum-mechanical sums-over-histories are weighted in a manner that depends on their information content, rather than just on their energy.   This relates closely to a proposal made by the famous physicist Lee Smolin in a totally different context, as well as to Sheldrake’s morphic field hypothesis.


I recall reading (a few years ago) the excellent book Varieties of Anomalous Experience, with its run-down of various case studies of apparent reincarnation, and then digging into that literature a bit further afterwards.   I became almost-convinced there was SOMETHING paranormal going on there, though not terribly convinced that this something was really “reincarnation” as typically conceived.

Now I’ve just read the equally excellent book Immortal Remains by the philosopher Stephen Braude.   In the careful, rationalist manner of the analytical philosopher, he summarizes and dissects the evidence for various paranormal phenomena that others have taken as indicative of an afterlife for humans – reincarnation, mediumistic channeling, possession, out-of-body experiences, and so forth.   (But the book is a lot more fun to read than most academic philosophy works, with lots of entertaining tidbits alongside the meticulous deductions and comparisons – it’s highly recommended to anyone with a bit of patience who wants to better understand this confusing universe we live in!).

Survival versus SuperPsi versus ??

One of Braude’s themes in the book is the comparison of what he (following generally accepted terminology in this area) calls “survival” based versus “SuperPsi” based explanations of these phenomena. SuperPsi in this context means any combination of recognized psi phenomena like ESP, precognition, psychokinesis and so forth – even very powerful combinations of very powerful instances of these phenomena.

One thing that Braude points out in the book is that, for nearly all the phenomena he considers, there seems to be a thinkable SuperPsi-based explanation, as well as a thinkable survival-based explanation.   This is not surprising since neither the SuperPsi hypothesis nor the survival hypothesis can be very clearly formulated at this stage of our knowledge.  So, he considers the choice between the two classes of hypothesis to come down mainly to considerations of simplicity.  In his view, the SuperPsi explanations often tend to get way too complex and convoluted, leading him to the conclusion that there is most probably some survival-esque phenomenon going on along with probably lots of psi phenomena....  (For a discussion of why I agree with Braude that simplicity is key to a good scientific explanation, see this essay, which was reprinted with slight changes as part of my book The Hidden Pattern.)

The contrast of survival vs. SuperPsi makes a compelling theme for a book, but I suspect it may not be the best way to think about the issues.   

As far as my attitudes have drifted, I still strongly doubt that “survival” in any traditional sense is the real situation.   I really doubt that, after people have died, they keep on living in some “other world” – whether a heaven or hell or just another planet or whatever.   I also really doubt that, after someone dies, their soul or essence enters another person so that this other person is “a new version of them” (the traditional reincarnation story in its most common form).    One thing Braude’s careful review makes clear is how scantily the evidence supports these traditional conclusions.  

The evidence DOES support the conclusion that the paranormal phenomena Braude considers actually happen in the world, and don’t have explanations in terms of science as we now know it.  But the evidence does NOT especially strongly support any of the classical religious analyses of these paranormal phenomena.  My own view is that these religious attempts at explanation have largely served to cloud the matter.   Personally, the main reason I previously rejected these sorts of phenomena entirely, was my reaction to the various conceptual inconsistencies and obvious appeals to human emotion that I saw in these traditional religious explanations.

What we see in the data Braude considers is that:
  • After a human being dies, it is sometimes possible for “self and other mind patterns” associated with that human being’s mind to manifest themselves in the world at a later time. 
  • While a human being is alive, it is sometimes possible for  “self and other mind patterns” associated with that human being’s mind to manifest themselves in the world at some distant physical location, without any good conventional explanation for how this could happen
  • Sometimes these “self and other mind patterns” manifest themselves in a manner that is mixed up with other things, e.g. with someone else’s mind
  • Sometimes these “self and other mind patterns” provide evidence of their association with the mind of a spatially or temporally distant human, which is very difficult to “explain away” in terms of known science


Exactly what specific forms the above phenomena take is a long story, which Braude tells in his book, which I don’t feel like taking time to summarize here right now.  Read the book!

Anyway, it should be pretty clear that the above does not imply “survival / afterlife” in any traditional sense.   Yet Braude makes a good case that hypothesizing these phenomena to be caused by some combination of ESP, psychokinesis, precognition and so forth becomes inordinately complicated.

From Carbon to Ecosystems


One thing that strikes me is what a long distance exists between potential “physics of psi” explanations like my Surprising Multiverse Theory, and the complex, messy particulars of phenomena like mediumistic channeling.   Channeling, for instance, apparently involves subtle intersections between individual and social psychology and culture, and appears to mix up instances of ESP and psychokinesis with other “nonlocal mind” phenomena that are more distinct from traditional psi.

An analogy that springs to mind, however, is the relation between the carbon atom and the complexities of the Earth’s ecosystem.   The carbon atom enables the development of life, and this can be seen, in a general sense, via looking at the atom at the micro level, and the nature of the bonds it permits.   On the other hand, predicting the specifics of macroscopic life based on the microscopic properties of the carbon atom is something we still can’t come close to doing.   We can’t, yet, even solve the protein folding problem (a very particular subcase of this more general problem).  

Similarly, it’s “easy” to see that hypotheses like the Surprising Multiverse Theory have some potential to explain how the universe could contain phenomena like mediumistic channeling, apparent reincarnation, and so forth.   But getting from something like a low-level information-theoretic tweak to quantum physics, up to specific predictions about paranormal phenomena among human beings, is bound to involve a lot of complexity, just like any explanation bridging multiple hierarchical levels of reality.  

Toward a Paranormal-Friendly (Patternist) Philosophy of the Cosmos


I don’t have anywhere near a scientific explanation of these paranormal phenomena I’m talking about, at present.  I would like to find one, perhaps by building up from Surprising Multiverse Theory or something similar, perhaps by some other means.  Of course, I don’t think it makes sense to reject evidence simply because we don’t have a good theory for it yet.

I do have a philosophical perspective on these phenomena, which helps me think about them in what I hope is a coherent way.   My basic philosophy of the universe is summarized in The Hidden Pattern (free pdf) and A Cosmist Manifesto (free pdf).  But thinking about paranormal phenomena leads me to enrich and extend that basic philosophy in certain ways.

As I’ve said in my previous writings, my preferred way of thinking about these things involves positing a Pattern Space, which exists outside our spacetime continuum.   The whole spacetime universe that defines our everyday being, is just one particular pattern of organization, which in some sense exists within a much larger space of patterns.   When a pattern like a human mind emerges within our spacetime continuum, it also exists in the broader pattern space.   

But what is meant by a pattern being “within our spacetime continuum"?  I haven’t thought about this deeply before.  Basically, I suggest, what it means that this pattern is heavily interlinked with other patterns that are “within our spacetime continuum”, and not so heavily interlinked with other patterns that are not “within our spacetime continuum.”   That is: the spacetime continuum may be thought of as a special sort of cluster of interlinked patterns.

Since the spacetime continuum is just one powerful, but not omnipotent, pattern of organization, it’s not so bizarre that sometimes a pattern that is heavily interlinked with other patterns in the “spacetime continuum pattern cluster”, could sometimes interlink with other patterns that are outside this cluster.   Extra-cluster pattern interactions are then perceived, by patterns inside the cluster, as “paranormal.”

This way of thinking ties in with philosopher Charles Peirce’s “one law of mind” – which he formulated as “the tendency to take habits.”   Peirce observed that, in our universe (but NOT in a hypothetical random universe), once a pattern has been observed to exist, the probability of it being observed again is surprisingly high.  This is the basic idea underlying the Surprising Multiverse Theory.   This seems conceptually related to the statement that the patterns we observe mainly live inside a cluster in pattern space.   Inside a cluster, the odds of various entities being connected via a strong pattern should be atypically high – that’s closely related to what makes the cluster a cluster.

Mind Uploading via Reincarnation Machines?

If indeed the paranormal phenomena Braude surveys are real, and have some sort of scientific explanation that we just haven’t found yet, then this has fascinating potential implications for mind uploading.   It suggests that, when someone dies, their mind is still in some sense somewhere – and can potentially be brought back by appropriate dynamics in certain biophysical systems (e.g. the mind of a medium, or a child born as an apparent reincarnation, etc.).

This raises the possibility that, by engineering the right kind of physical system, it might be possible to specifically induce “paranormal” phenomena that cause a dead person’s mind to manifest itself in physical reality, long after that person’s death.

Of course, this is utterly wild speculation.   But what makes it fun is that it’s also fairly logical extrapolation from empirical observations.   If the data about the paranormal is real, but the data ultimately has some scientific explanation rather than a religious one, then most likely the underlying phenomena can be tapped into and manipulated via engineered systems, like all other scientifically understood phenomena.

Of course, a scientific understanding of these phenomena will likely include an understanding of their limitations.  Maybe these limitations will prevent us from building reincarnation machines.   But maybe they won’t.

If we buy the “morphic field” type idea, then what would attract the reincarnation of a deceased person’s mind, would likely be a set of mind-patterns very similar to that person’s mind.  This would be in the spirit of the well-demonstrated phenomenon of ESP among identical twins.   

In this case, it would follow that one very good way to engineer reincarnation might be to create an intelligent robot (perhaps with a quantum-computer infrastructure?) with
  • Lots of the mind-patterns of the deceased person one wishes to reincarnate 
  •  Lots of degrees of freedom capable of being adjusted and adapted


This would be achieved, for instance, if one created a robot intended as a simulacrum of a deceased person based on information they had left behind – videos, emails and what-not.  There are existing organizations focused specifically on accumulating information about people so as to facilitate this kind of post-mortem simulation.

The strange and exciting hypothesis one is led to, is that such a simulacrum might actually attract some of the mind-patterns of the person simulated, seducing these patterns out of the overarching pattern space – and thus animating the simulacrum with the “feel” of the person being simulated, and perhaps specific memories and capabilities of that person, beyond what was programmed into the simulacrum.

Oh Really?


If you’re a typical tech geek who’s a fan of my AGI work, you may think I’ve gone totally nuts by this point.   That doesn’t bother me particularly though.  

I mean, AGI is almost trendy now, but when I started out with it 30 years ago everyone thought I was nuts to be thinking about it or trying to work on it.    Peer pressure doesn’t really work on me.

I don’t have any real interest in arguing these points with people who haven’t taken the time to inform themselves about the relevant data.   If you want to discuss the points I’ve raised here, do us all a favor and read at least


If you’ve absorbed all this data and are still highly skeptical, then I’m quite willing to discuss and debate with you.   On the other hand, if you feel like you don’t want to take the time to read so many pages on this sort of topic, that’s understandable – but yet, IMO, by  making this choice you are forfeiting your right to debate these points with people who HAVE familiarized themselves with the data.

This is weird stuff, for sure.   But don’t blame the messenger.   It’s a weird world we live in.   We understand very little of it, at present.   If we want to increase our understanding as rapidly as we can, the best strategy is to keep an open mind – to look at what reality is showing us and really think about it, rather than shutting out troublesome data because it doesn’t match our preconceptions, and rather than accepting emotionally satisfying simplifications (be they scientific or religious in nature).

Immortality and Immortality


Does this line of thinking I’ve presented here reassure me that my possible forthcoming physical death (I’m 48 years old now, certainly old enough to be thinkig about such things!) may not be so bad after all?    Hmmm – kind of, I guess.  But I’m not going to cancel my Alcor membership, nor stop devoting a portion of my time to longevity research.   I want to keep this body going, or port my mind to a different physical substrate in a direct way.   

The apparent fact that my mind exists outside of spacetime, and can potentially be brought back into spacetime – at least in some partial way – after my death, doesn’t really diminish my urge to keep on continually existing within THIS spacetime continuum, going forward from right now.   Why would it?  

The overarching pattern space is no doubt wonderful, but ongoing existence in this limiting time-axis is pretty cool too – and keeping on living here is very unlikely to stop my mind-patterns from flourishing and romping trans-temporally in the cosmic pattern space, sooo....