A History Of Foom

I had occasion recently to review again the causes of the few known historical cases of sudden permanent increases in capacity growth rates in broadly capable systems: humans, farmers, and industry. For each of these transitions, a large number of changes appeared at roughly the same time. The problem is to distinguish the key change that enabled all the other changes.

For humans, it seems that the most proximate cause of faster human than non-human growth was culture – a strong ability to reliably copy the behavior of others allowed useful behaviors to accumulate via a non-genetic path. A strong ritual ability was clearly key. It also helped to have language, to live in large bands friendly with neighboring bands, to cook and travel widely, etc., but these may not have been essential. Chimps are pretty good at culture compared to most animals, just not good enough to support sustained cultural growth.

For farming, it seems to me that the key was the creation of long range trade routes along which domesticated seeds and animals could move. It was the accumulation of domestication innovations that most fundamentally caused the growth in farmers, and it was these long range trade routes that allowed innovations to accumulate so much faster than they had for foragers.

How did farming enable long range trade? Since farmers stay in one place, they are easier to find, and can make more use of heavy physical capital. Higher density living requires less travel distance for trade. But perhaps most important, transferable domesticated seeds and animals embodied innovations directly, without requiring detailed copying of behavior. They were also useful in a rather wide range of environments.

On industry, the first burst of productivity at the start of the industrial revolution was actually in the farming sector, and had little to do with machines. It appears to have come from “amateur scientist” farmers doing lots of little local trials about what worked best, and then communicating them to farmers elsewhere who grew similar crops in similar environments, via “scientific society” like journals and meetings. These specialist networks could spread innovations much faster than could trade in seeds and animals.

Applied to machines, specialist networks could spread innovation even faster, because machine functioning depended even less on local context, and because innovations could be embodied directly in machines without the people who used those machines needing to learn them.

So far, it seems that the main causes of growth rate increases were better ways to share innovations. This suggests that when looking for what might cause future increases in growth rates, we also seek better ways to share innovations.

Whole brain emulations might be seen as allowing mental innovations to be moved more easily, by copying entire minds instead of having one mind train or teach another. Prediction and decision markets might also be seen as better ways to share info about which innovations are likely to be useful where. In what other ways might we dramatically increase our ability to share innovations?

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  • adrianratnapala

    This suggests that when looking for what might cause future increases
    in growth rates, we also look for possible better ways to share
    innovations.

    For some information about new possibilities that people are trying:

    http://creativecommons.org/

    http://opensource.org/

    http://www.arduino.cc/

    http://www.reprap.org/wiki/Main_Page

  • Jef Allbright

    Innovation (and innovation of innovation, …) is facilitated within an environment supporting replication (such that advances tend to be preserved), a small but significant degree of stochastic mutation (such that entrapment within local maxima can be avoided but without excessive disruption), and novel recombination of loosely coupled functional structures, exploiting synergies self-similar over increasing scale.

    For humans, oral and written language greatly facilitated replication. Mutation was a result of noise in all channels of communication, interaction, learning, etc. Recombination was an indirect result of interaction across gradients of geography, resources, etc., and these structures propagated over increasing scale from the domestic group to the tribe to the regional organization.

    For farming, methods were discovered, communicated and replicated. Mutation again played a part due to variations in climate, the indigenous ecology, instability of weather and resources, etc.  Recombination arose out of specialization of crops and related trade, and synergies were available to be discovered at increasing scale of land, manpower, storage, etc.

    For industrialization, again, methods were discovered, communicated, and replicated with increasing efficacy, stochastic variation in quality of replication, quality and availability of resources helped bump the system out of ruts, recombination of loosely linked functional structures abounded in the domains of material, know-how, markets, etc., and methods could be applied with self-similarity over increasing scale.

    Now, as for “whole-brain emulations” as a driver of innovation, questions arise.  Replication is a given.  Mutation, however, would seem to be intentionally limited in order to preserve the integrity of the individual brain. (Emulated human brains, in contrast with anticipated novel forms of artificial intelligence, would lack such intrinsic extensibility.)  Recombination could take place *within a culture* of such integral brains but not between the emulated brains themselves, as opposed to much more scale-friendly “hive-minds”, or even better, a fractally distributed intelligence.  So it seems to me that Robin’s whole-brain emulations would at best play an essentially static, temporary functional role but not be a paradigm changer like the examples of human culture, farming, industry [and information technology.]

    Prediction markets too seem to lack the evolutionary attributes described above, offering intensional but hardly extensional improvements.

  • Margin

    “copying entire minds”

    Useless to us (the individuals reading this).

    It would just mean new competition and potential enemies.

    “In what other ways might we dramatically increase our ability to share innovations?”

    It is already here: The internet.

    I only miss 2 things:

    1) Ownership of ordinary sustainable capital that frees me from work.

    2) Marginal improvements in the quality of digital fun.

  • mjgeddes

    I think
    the answer is to be found in the earlier thread ‘Tower Of Babel Still’.  By far the most effective way to share
    innovations is improved language coordination. 
    Let me reiterate the key points I made:

    “An
    entirely new general-purpose ‘language of thought’ needs to be built up from
    scratch, starting from a finite set of ontoogical ‘prims’, which constitute the
    basic ‘building blocks’ of thought. Only logical pinpointing of the most
    primative categories of thought (the base categorizations used by minds) can
    finally collapse the tower of babel.”

    I also
    clarified that I was using the word ‘language’ in the most general sense:

    “My
    usage of the word ‘language’ is in the most general sense to mean a
    representational system of manipulating concepts and logical relations. So this
    would include mathematics, data modelling and ontology, ‘languages’ far more
    powerful than ordinary natural languages used for speaking.”

    I made
    clear the aforementioned ‘language of thought’ was not intended to be a fixed
    ontology that attempts to start from omniscience, but an open-ended one that
    allows continuous modifications as ever more knowledge comes to light:

    “Not
    one based on a fixed ontology with neccessary definitions, but an open-ended
    one based on prototypes.”

    Finally,
    I pulled my greatest trick.  Rather than attempting
    some grand ontology of everything (that ends up with minimal information
    content or meaningless concepts), I suggested instead that the ultimate ‘language
    of thought’ does not need to deal with all of reality, but only has to describe
    the domain ‘intelligent minds’:

    “We are
    looking for a language sufficiently general to fully describe the domain
    ‘intelligent minds’. This is all we need, because this would include all
    thoughts that minds could ever think about reality.”

    #winning

    • dEMOCRATIC_cENTRALIST

       What are those basic building blocks? Is “existence” among the basic concepts? (See my “The meaning of existence':Lessons from infinity.” — http://tinyurl.com/bx2ujj2 [existence as primitive; sense-data incoherent].)

      Would you agree that actual infinities would not be part of such a language? (See “Can infinite quantities exist?” — http://tinyurl.com/aqcy99w )

      • mjgeddes

        No ‘existence’ is too vague a concept to carry much information content.  Read what I said in my last paragraph, I am not attempting a grand ontology of everything, I’m looking for an ontology of ‘intelligent minds’.

        Infinities are a perfectly valid mathematical concept and I have no reason to doubt their existence.   The history of math shows that notions such as ‘infinity’ had a lot of skeptics in the early days (eg Kronecker) , but eventually most math experts came to accept the concepts (probably for good reasons). But I leave questions of ‘infinity’ to the logicians.

        To reiterate, we don’t need to start with a grand ontology of everything.

        “We are looking for a language sufficiently general to fully describe the domain ‘intelligent minds’. This is all we need, because this would include all thoughts that minds could ever think about reality.”

    • http://bur.sk/en Viliam Búr

      Any experiment to support this hypothesis? (As far as I know, Loglan started with similar goals, but I don’t know about any increases in rationality that would result from using it.)

      • mjgeddes

        The language tells you how to build an AGI.  The empirical test is simply writing the program  (I have determined that 27 is the minimum number of independent concepts needed to make the language AGI-complete) then it should  FOOM.  (ie., initiate Singularity).  Obviously a highly dangerous ‘test’ of logical correctness of course.

          
         
         

      • http://www.facebook.com/peterdjones63 Peter David Jones

         You seem to have confused a necessity condition with a sufficiency condition.

      • http://www.facebook.com/peterdjones63 Peter David Jones

         Languages don’t tell  you things, they just express thoughts you already have.

  • Lorem Ipsum

    It seems like a more pessimistic outlook might be that the easy ways of improving growth rates have been largely tapped out, as at this point communication between any two persons and trade are quite easy. Any improvements in this might be more marginal rather than revolutionary as in the past.

    On the other hand, prediction markets or whole brain emulation might increase the rate of innovation (rather than its sharing), which I think should be seen as a separate avenue of growth, rather than a continuation of the older model.

  • Johnicholas Hines

     Academia generates a lot of valuable innovations – for example, I work in industry, and spend a lot of time reading academic papers describing algorithms and systems.

    An alternative system for credentialing and funding academics, producing peer-reviewed texts, perhaps using hyperlinks instead of citations might be valuable.

  • Rationalist

    “In what other ways might we dramatically increase our ability to share innovations?”

    – perhaps better ways to incentivize the right kind of innovation, and better metrics of success for innovators than the current profit motive.

    Having toyed around with startup ideas, I found that a lot of them are unworkable despite having clear benefits to a lot of people. All sorts of annoying, random things can get in the way. The same goes for charity; a lot of the best ideas don’t get much money.

    Better ways of rewarding people who do good today which benefits the long-term future would be nice too.

    E.g. Jimmy Wales is very poorly remunerated given his services to the current generation of children around the world who can all learn from Wikipedia, and all those who benefit from that learning.

    • http://www.facebook.com/peterdjones63 Peter David Jones

      There’s more to reward than remuneration. People like Tim Berners-Lee and Linus Torvalds are hugely feted, if moderately paid.Bill Gates has to give away truck loads of $$$ to get that kind of kudos.

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