Interpreting Avatar

Bloggers who I greatly respect feel Avatar is just another Dances with Wolves — a way of putting a romantic gloss on native authenticity and then appropriating it by having a “white man” out-native the natives. So I want to think a bit about where I agree and disagree with this position.

We could adopt a bunch of interpretations of Avatar — or some combination of them:

  1. Cameron wanted to make a big movie that would advance his career. He picked 3D CGI, the rest was more or less inevitable as “engineering decisions” to optimize his objective function
  2. Cameron had some goals that included endorsing fairly naive political messages (respect for earth, etc.). He hired good people to invent a cool ecology without worrying about the backstory, and then just pasted his agenda on top of that
  3. Cameron had something like the posthuman interpretation in mind, but since he knows what sells, he drenched it in sugar syrup to make it palatable.
  4. The internal logic of the story pulls it into a posthuman shape, and Cameron, however he started, saw he couldn’t fight that and so went with it.

But we don’t have to just guess about which of these is correct. After Titanic, Cameron wrote a “114 page scriptment… known at the time as Project 880″ (apparently a “scriptment” is a preliminary version of a movie script, but in this case much more complete than the movie as shot). Based on an extended description the scriptment was a much more detailed version of Avatar, with pretty much the same focus and a lot more explicit back story. Most of the changes from Project 880 to Avatar as shot are trimming and making the action more obvious.

And Project 880 supports the “naive messages” interpretation, but also is fairly consistent with the “internal logic” interpretation.

There are a few touches in Project 880 that show Cameron had a sense of the the posthuman logic of the story. When the humans are being kicked out they are told that if they come back “Pandora will send them home with a horrible virus that will wipe out humanity” but apparently this is just a threat by the pro-Pandora humans. So Cameron knew this threat fit into the logic of his story but didn’t want to (or didn’t see how to) make it an integral part of the story.

Bottom line, the people who say Avatar is just Dances with Wolves with 3D CGI alien “natives” are right as far as they go. That was the movie Cameron planned to make. But I think we can make a legitimate case that the internal logic of Pandora, the Na’vi, etc. escapes from that formula and has its own very subversive implications. These implications subvert not only the characteristics of the Na’vi — they must be really high tech, only “at one with nature” because they designed it — but also our ideas of posthuman — it doesn’t need to involve metal tech and smart computers.

And regarding the origin of the quadrupedal Na’vi vs. the hexapodal animals (why not hexapeds or quadrupods?) I still like my extreme version. We know from the historical evidence that interpretation (3) — a story about a posthuman high-tech Na’vi + trees symbiosis — wasn’t Cameron’s intention. But we also know (3) is more consistent with what we see in the film than any other backstory. So why not go the whole way and make our backstory fully consistent? The fact that humans identify with and even fall in love with Na’vi is a big tactical advantage to the Pandoran system, so why not say Pandora arranged that? It doesn’t stretch credulity any more than humans being able to grow avatars in the first place — and in writing a back story, we could easily make the avatar tech a covert “gift” from Pandora as well, transferred by subverting early human scientists.

Let’s consider how that would play out in a “prequel”:

Humans first visit Pandora a few decades before Avatar. This is an exploration ship, staffed mainly by scientists, but with some military / naval types as well.

The scientists don’t encounter Na’vi, but they do study the hexapods and the trees, and they find the unobtainum. At some point a scientist dies on the planet and his / her mind is assimilated by the trees. Then the trees start to communicate covertly with some other scientists.

With the help of the trees, scientists figure out some of the biology of Pandora, and figure out how to grow avatars, but initially not human-like ones. Pandora in turn figures out how to grow human-like Na’vi — maybe it even transfers the mind of the initial scientist who dies into one of the first Na’vi. (You could make the scientist Maori for linguistic continuity, since that’s what Cameron’s folks used as a linguistic base. Facial tattoos would be cool.)

After a while, guided by the trees, the scientists “discover” Na’vi living in the jungle. Maybe before the ship leaves, some of the other scientists covertly “jump ship” by dying and getting reborn as Na’vi. Maybe they have to kill or subvert some of the military types to avoid discovery.

(Actually, of course, the smart thing for the trees to do would be to clone some of these minds into multiple bodies. There’s also no reason the original has to die. But we rarely see narratives where the same person is multiply instantiated, except as a joke.)

When the exploration ship gets back to earth, we see some of the floating tree sprites dispersing, putting down roots, and starting to grow as Earth-like trees. Maybe those trees even catch and reprogram some Earth fauna. So we know a “pod people” scenario (or as I prefer to think a “porkchop tree” scenario) is possible, but we don’t know how it will turn out.

Pandora doesn’t need to send a virus to Earth, its minions could just create one here.

One thing that’s missing in this picture: I’d expect the trees would find ways to create moles in the Earth human population as well. Offhand I don’t see how to factor that in.

As written this lacks drama but I that’s why I’m not a fiction writer. I expect Cameron or someone else with the right skills would find it easy to put real people, dramatic tension, etc. into this framework.

Avatar and the posthuman future

Avatar is the best and most elaborate advertisement ever created for posthumanism.

The posthuman message of Avatar is easy to miss because Cameron invents a new form of posthuman — the Na’vi, apparently primitive children of Eywa (the “world spirit”). None the less, the conclusion is unavoidable. Compared to the Na’vi, humans are small, weak, ugly, inept and morally deficient. Avatar’s protagonist is crippled as a human, but athletic as a Na’vi. The whole narrative drive of the film is to transcend the human body, the human condition and human society, to transition to a more perfect world — but a world that is very much material, embodied, and shaped and maintained by its inhabitants. And humans make the transition to the posthuman by “uploading” their minds into Na’vis — a bog standard posthuman trope.

Admittedly, the Na’vi as tribal posthumans don’t fit into typical narratives of posthumanism. Both posthuman critical theory (e.g. Donna Haraway) and typical posthuman science fiction emphasize hard-edged scenarios such as cyborgs (the origin of the Star Trek borg, pretty much the opposite of the Na’vi), uploading our minds into computers and robots, etc. Furthermore, the “noble savage” aspect of the Na’vi seems to be the weakest, most cliched aspect of the movie.

But arguably if we believe the Na’vi are “noble savages” we are underestimating Cameron, or at least underestimating the potential of the world he has created.

Taking the Na’vi and Pandora at face value implies accepting a lot of incoherence. Many features of Pandora make no sense if we assume the ecosystem “just grew”. There’s no evolutionary reason for “horses” and “dragons” to plug into the nervous systems of “people”. There’s no evolutionary reason for all the trees to wire themselves together into a giant brain. And so forth.

But we can make sense of Pandora if we grant that Na’vi biotech is extremely advanced. Suppose the entire Pandoran ecosystem evolved normally, until the (precursors of the) Na’vi got to the point where they had to (or wanted to) manage their entire planet (the very point we on earth are reaching now). They took the path of adapting themselves and their ecosystem to become fully self-managing, and then could “relax” into a more or less tribal culture.

If the Na’vi engineered a self-managing planet, such techniques as networking the trees into a planetary brain, and providing ways to plug their minds into the nervous systems of plants and animals are sensible engineering solutions. The Na’vi transcend death by having their memories absorbed by the trees. This fits the goal of a sustainable system much better than making each individual biologically immortal (though potentially a society could do both).

The giant Na’vi “mind meld” with the trees to form a powerful system capable of identity transfer between bodies, also makes sense within this account. No mystical trappings are required.

The Na’vi have no need for a process of identity transfer between bodies, much less between human and Na’vi bodies. They must have created the process to deal with the situation, which implies that the Na’vi didn’t simply regress to a tribal culture. They retain the necessary knowledge — presumably stored in the trees — and the ability to integrate with the tree network to a level where they can rework nature, bodies etc. when needed. Again, that’s a good engineering solution to living in a simpler culture and still having high technology available when needed.

Unfortunately there’s one fact about Pandora that still doesn’t fit our account: the Na’vi have bodies that are exactly analogous to humans, except for their tails, while all other animals on Pandora have six legs and breathe through holes in their necks. Obviously the Na’vi similarity to humans is required to engage the audience and to use motion capture, so we could just ignore it. But let’s treat it as a meaningful discrepancy and see where that leads.

The surprising analogy between Na’vi and human suggests a plausible extension of our backstory. Perhaps the Na’vi were reshaped based on genetic samples and observations of the first human explorers, to serve as a “honey pot” that attracts human attention and interaction. In that case the Na’vi similarity to humans is another excellent design choice.

If that is the “real” story, the honeypot strategy has obviously worked well (in both the system security and seductive senses). The corporations and military commanders are totally sucked in to the honeypot and are paying no attention to the real nature of their opponent. Only a few no-account scientists have even noticed that the trees are important.

On the other hand, perhaps we should not think of the Na’vi as the dominant species on Pandora. The trees may in fact run the show (after all they are more or less worshiped by the Na’vi). Perhaps the trees have already snuck stealth versions to Earth, and are now in the process of slowly taking over our planet as well (see James Schmitz’ “The Porkchop Tree”). If the trees are in charge, perhaps we don’t have such a happy ending, but otherwise nothing fundamental changes. This account is more similar to posthuman scenarios in which intelligent machines are in charge but host humans or posthumans for inscrutable reasons. Such stories are often dystopian, but they can be quite positive (as in Ian Banks Culture novels).

All of these accounts involve a very high level of bio-technology and a sophisticated approach to managing the whole planet. In any coherent account the “noble savage” schtick is just a cover story. Cameron’s vision implies a pervasively posthuman world.

I don’t, of course, know if Cameron would endorse any of these accounts. But his vision of Pandora is more feasible and consistent than it at first appears, and adds an important dimension to imagining posthuman futures.

Bubbles of humanity in a post-human world

Austin Henderson had some further points in his comment on Dancing toward the singularity that I wanted to discuss. He was replying to my remarks on a social phase-change toward the end of the post. I’ll quote the relevant bits of my post, substituting my later term “netminds” for the term I was using then, “hybrid systems”:

If we put a pot of water on the stove and turn on the heat, for a while all the water heats up, but not uniformly–we get all sorts of inhomogeneity and interesting dynamics. At some point, local phase transitions occur–little bubbles of water vapor start forming and then collapsing. As the water continues to heat up, the bubbles become more persistent, until we’ve reached a rolling boil. After a while, all the water has turned into vapor, and there’s no more liquid in the pot.

We’re now at the point where bubbles of netminds (such as “gelled” development teams) can form, but they aren’t all that stable or powerful yet, and so they aren’t dramatically different from their social environment. Their phase boundary isn’t very sharp.

As we go forward and these bubbles get easier to form, more powerful and more stable, the overall social environment will be increasingly roiled up by their activities. As the bubbles merge to form a large network of netminds, the contrast between people who are part of netminds and normal people will become starker.

Unlike the pot that boils dry, I’d expect the two phases–normal people and netminds–to come to an approximate equilibrium, in which parts of the population choose to stay normal indefinitely. The Amish today are a good example of how a group can make that choice. Note that members of both populations will cross the phase boundary, just as water molecules are constantly in flux across phase boundaries. Amish children are expected to go out and explore the larger culture, and decide whether to return. I presume that in some cases, members of the outside culture also decide to join the Amish, perhaps through marriage.

After I wrote this I encountered happiness studies that show the Amish are much happier and dramatically less frequently depressed than mainstream US citizens. I think its very likely that the people who reject netminds and stick with GOFH (good old fashioned humanity) may similarly be much happier than people who become part of netminds (on the average).

It isn’t too hard to imagine why this might be. The Amish very deliberately tailor their culture to work for them, selectively adopting modern innovations and tying them into their social practices in specific ways designed to maintain their quality of life. Similarly, GOFH will have the opportunity to tailor its culture and technical environment in the same way, perhaps with the assistance of friendly netminds that can see deeper implications than the members of GOFH.

I’m inclined to believe that I too would be happier in a “tailored” culture. Nonetheless, I’m not planning to become Amish, and I probably will merge into a netmind if a good opportunity arises. I guess my own happiness just isn’t my primary value.

[A]s the singularity approaches, the “veil” between us and the future will become more opaque for normal people, and at the same time will shift from a “time-like” to a “space-like” boundary. In other words, the singularity currently falls between our present and our future, but will increasingly fall between normal humans and netminds living at the same time. Netminds will be able to “see into” normal human communities–in fact they’ll be able to understand them far more accurately than we can now understand ourselves–but normal humans will find hybrid communities opaque. Of course polite netminds will present a quasi-normal surface to normal humans except in times of great stress.

By analogy with other kinds of phase changes, the distance we can see into the future will shrink as we go through the transition, but once we start to move toward a new equilibrium, our horizons will expand again, and we (that is netminds) may even be able to see much further ahead than we can are today. Even normal people may be able to see further ahead (within their bubbles), as long as the equilibrium is stable. The Amish can see further ahead in their own world than we can in ours, because they have decided that their way of life will change slowly.

Austin raises a number of issues with my description of this phase change. His first question is why we should regard the population of netminds as (more or less) homogeneous:

All water boils the same way, so that when bubbles coalesce they are coherent. Will bubbles of [netmind] attempt to merge, maybe that will take more work than their hybrid excess capability provides, so they will expend all their advantage trying to coalesce so that they can make use of that advantage. Maybe it will be self-limiting: the “coherence factor” — you have to prevent it from riding off at high speed in all directions.

Our current experience with networked systems indicates there’s a messy dynamic balance. Network effects generate a lot of force toward convergence or subsumption, since the bigger nexus tends to outperform the smaller one even if it is not technically as good. (Here I’m talking about nexi of interoperability, so they are conceptual or conventional, not physical — e.g. standards.)

Certainly the complexity of any given standard can get overwhelming. Standards that try to include everything break down or just get too complex to implement. Thus there’s a tendency for standards to fission and modularize. This is a good evolutionary argument for why we see compositionality in any general purpose communication medium, such as human language.

When a standard breaks into pieces, or when competing standards emerge, or when standards originally developed in different areas start interacting, if the pieces don’t work together, that causes a lot of distress and gets fixed one way or another. So the network effects still dominate, through making pieces interact gracefully. Multiple interacting standards ultimately get adjusted so that they are modular parts of a bigger system, if they all continue to be viable.

As for riding off in all directions, I just came across an interesting map of science. In a discussion of the map, a commenter makes just the point I made in another blog post, that real scientific work is all connected, pseudo-science goes off into little encapsulated belief systems.

I think that science stays connected because each piece progresses much faster when it trades across its boundaries. If a piece can’t or won’t connect for some reason it falls behind. The same phenomenon occurs in international trade and cultural exchange. So probably some netminds will encapsulate themselves, and others will ride off in some direction far enough so they can’t easily maintain communication with the mainstream. But those moves will tend to be self-limiting, as the relatively isolated netminds fall behind the mainstream and become too backward to have any power or influence.

None of this actually implies that netminds will be homogeneous, any more than current scientific disciplines are homogeneous. They will have different internal languages, different norms, different cultures, they will think different things are funny or disturbing, etc. But they’ll all be able to communicate effectively and “trade” questions and ideas with each other.

Austin’s next question is closely related to this first one:

Why is there only one phase change? Why wouldn’t the first set of [netminds] be quickly passed by the next, etc. Just like the generation gap…? Maybe, as it appears to me in evolution in language (read McWharter, “The Word on the Street” for the facts), the speed of drift is just matched by our length of life, and the bridging capability of intervening generations; same thing in space, bridging capability across intervening African dialects in a string of tribes matches the ability to travel. Again, maybe mechanisms of drift will limit the capacity for change.

Here I want to think of phase changes as occurring along a spectrum of different scales. For example, in liquid water, structured patterns of water molecules form around polar parts of protein molecules. These patterns have boundaries and change the chemical properties of the water inside them. So perhaps we should regard these patterns as “micro-phases”, much smaller and less robust than the “macro-phases” of solid, liquid and gas.

Given this spectrum, I’m definitely talking about a “macro-phase” transition, one that is so massive that it is extremely rare in history. I’d compare the change we’re going through to the evolution of the genetic mechanisms that support multi-cellular differentiation, and to the evolution of general purpose language supporting culture that could accumulate across generations. The exponential increases in the power of digital systems will have as big an impact as these did. So, yes, there will be more phase changes, but even if they are coming exponentially closer the next one of this magnitude is still quite some time away:

  • Cambrian explosion, 500 Million Years ago
  • General language, 500 Thousand Years ago
  • Human / Digital hybrids (netminds), now
  • next phase change, 500 years from now?

Change vs. coherence is a an interesting issue. We need to distinguish between drift (which is fairly continuous) and phase changes (which are quite discontinuous).

We have a hard time understanding Medieval English, as much because of cultural drift as because of linguistic drift. The result of drift isn’t that we get multiple phases co-existing (with rare exceptions), but that we get opaque history. In our context this means that after a few decades, netminds will have a hard time understanding the records left by earlier netminds. This is already happening as our ability to read old digital media deteriorates, due to loss of physical and format compatibility.

I imagine it would (almost) always be possible to go back and recover an understanding of historical records, if some netmind is motivated to put enough effort into the task — just as we can generally read old computer tapes, if we want to work hard enough. But it would be harder for them than for us, because of the sheer volume of data and computation that holds everything together at any given time. Our coherence is very very thin by comparison.

For example the “thickness” of long term cultural transmission in western civilization can be measured in some sense by the manuscripts that survived from Rome and Greece and Israel at the invention of printing. I’m pretty sure that all of those manuscripts would fit on one (or at most a few) DVDs as high resolution images. To be sure these manuscripts are a much more distilled vehicle of cultural transmission than (say) the latest Tom Cruise DVD, but at some point the sheer magnitude of cultural production overwhelms this issue.

Netminds will up the ante at an exponential rate, as we’re already seeing with digital production technology, blogging, etc. etc. Our increasing powers of communication pretty quickly exceed my ability to understand or imagine the consequences.

A good example of post-capitalist production

This analysis of the Firedoglake coverage of the Libby trial hits essentially all the issues we’ve been discussing.

  • Money was required, but it was generated by small contributions from stakeholders (the audience), targeted to this specific project.
  • A relatively small amount of money was sufficient because the organization was very lightweight and the contributors were doing it for more than just money.
  • The quality was higher than the work done by the conventional organizations (news media) because the FDL group was larger and more dedicated. They had a long prior engagement with this story.
  • FDL could afford to put more feet on the ground than the (much better funded) news media, because they were so cost-effective.
  • The group (both the FDL reporters and their contributors) self-organized around this topic so their structure was very well suited to the task.
  • Entrepreneurship was a big factor — both long-term organization of the site, and short-term organization of the coverage.
  • FDL, with no prior journalistic learning curve, and no professional credentials, beat the professional media on their coverage of a high-profile hard-core news event.

This example suggests that we don’t yet know the inherent limits of this post-capitalist approach to production of (at least) information goods. Most discussions of blogs vs. (traditional) news media have assumed the the costs inherent in “real reporting” meant blogs couldn’t do it effectively. The FDL example shows, among other things, that the majority of those costs (at least in this case) are due to institutional overhead that can simply be left out of the new model.

We’re also discovering that money can easily be raised to cover specific needs, if an audience is very engaged and/or large. Note that even when raising money, the relationship remains voluntary rather than transactional — people contribute dollars without imposing any explicit obligations on their recipient. No one incurs the burden of defining and enforcing terms. In case of fraud or just disappointing performance, the “customers” will quickly withdraw from the relationship, so problems will be self-limiting.

It is interesting to speculate about how far this approach could go. To pick an extreme example, most of the current cost of new drugs is not manufacturing (which will remain capital intensive for the forseeable future), but rather is the information goods — research, design, testing, education of providers, etc. — needed to bring drugs to market. At this point it seems impossible that these processes could be carried out in a post-capitalist way. But perhaps this is a failure of imagination.

The path to a synthesis in statistical modeling

As I discuss in Dancing toward the singularity, progress in statistical modeling is a key step in achieving strongly reflexive netminds. However a very useful post by John Langford makes me think that this is a bigger leap than I hoped. Langford writes:

Attempts to abstract and study machine learning are within some given framework or mathematical model. It turns out that all of these models are significantly flawed ….

Langford lists fourteen frameworks:

  • Bayesian Learning
  • Graphical/generative Models
  • Convex Loss Optimization
  • Gradient Descent
  • Kernel-based learning
  • Boosting
  • Online Learning with Experts
  • Learning Reductions
  • PAC Learning
  • Statistical Learning Theory
  • Decision tree learning
  • Algorithmic complexity
  • RL, MDP learning
  • RL, POMDP learning

Within each framework there are often several significantly different techniques, which further divide statistical modeling practitioners into camps that have trouble sharing results.

In response, Andrew Gelman points out that many of these approaches use Bayesian statistics, which provides a unifying set of ideas and to some extent formal techniques.

I agree that Bayesian methods are helping to unify the field, but statistical modeling still seems quite fragmented.

So in “dancing” I was too optimistic to “doubt that we need any big synthesis or breakthrough” in statistical modeling to create strongly reflexive netminds. Langford’s mini-taxonomy, even with Gelman’s caveats, suggests that we won’t get a unified conceptual framework, applicable to actual engineering practice, across most kinds of statistical models until we have a conceptual breakthrough.

If this is true, of course we’d like to know: How big is the leap to a unified view, and how long before we get there?

Summary of my argument

The current state of statistical modeling seems pretty clearly “pre-synthesis” — somewhat heterogeneous, with different formal systems, computational techniques, and conceptual frameworks being used for different problems.

Looking at the trajectories of other more or less similar domains, we can see pretty clear points where a conceptual synthesis emerged, transforming the field from a welter of techniques to a single coherent domain that is then improved and expanded.

The necessary conditions for a synthesis are probably already in place, so it could occur at any time. Unfortunately, these syntheses seem to depend on (or at least involve) unique individuals who make the conceptual breakthrough. This makes the timing and form of the synthesis hard to predict.

When a synthesis has been achieved, it will probably already be embodied in software, and this will allow it to spread extremely quickly. However it will still need to be locally adapted and integrated, and this will slow down its impact to a more normal human scale.

The big exception to this scenario is that the synthesis could possibly arise through reflexive use of statistical modeling, and this reflexive use could be embodied in the software. In this case the new software could help with its own adoption, and all bets would be off.

Historical parallels

I’m inclined to compare our trajectory to the historical process that led to the differential and integral calculus. First we had a long tradition of paradoxes and special case solutions, from Zeno (about 450 BC) to the many specific methods based on infinitesimals up through the mid 1600s. Then in succession we got Barrow, Newton and Leibnitz. Newton was amazing but it seems pretty clear that the necessary synthesis would have taken place without him.

But at that point we were nowhere near done. Barrow, Newton and Leibnitz had found a general formalism for problems of change, but it still wasn’t on a sound mathematical footing, and we had to figure out how to apply it to specific situations case by case. I think it’s reasonable to say that it wasn’t until Hamilton’s work published in 1835 that we had a full synthesis for classical physics (which proved extensible to quantum mechanics and relativity).

So depending on how you count, the development of the calculus took around 250 years. We now seem to be at the point in our trajectory just prior to Barrow: lots of examples and some decent formal techniques, but no unified conceptual framework. Luckily, we seem to be moving considerably faster.

One problem for this analogy is that I can’t see any deep history for statistical modeling comparable to the deep history of the calculus beginning with Zeno’s paradox.

Perhaps a better historical parallel in some ways is population biology, which seems to have crystallized rather abruptly, with very few if any roots prior to about 1800. Darwin’s ideas were conceptually clear but mathematically informal, and the current formal treatment was established by Fisher in about 1920, and has been developed more or less incrementally since. So in this case, it took about 55 years for a synthesis to emerge after the basic issues were widely appreciated due to Darwin’s work.

Similarly, statistical modeling as a rough conceptual framework crystallized fairly abruptly with the work of the PDP Research Group in the 1980s. There were of course many prior examples of specific statistical learning or computing mechanisms, going back at least to the early 1960s, but as far as I know there was no research program attempting use statistical methods for general learning and cognition. The papers of the PDP Group provided excellent motivation for the new direction, and specific techniques for some interesting problems, but they fell far short of a general characterization of the whole range of statistical modeling problems, much less a comprehensive framework for solving such problems.

Fisher obviously benefited from the advances in mathematical technique, compared with the founders of calculus. We are benefiting from further advances in mathematics, but even more important, statistical modeling depends on computer support, to the point where we can’t study it without computer experiments. Quite likely the rapid crystallization of the basic ideas depended on rapid growth in the availability and power of computers.

So it is reasonable to hope that we can move from problems to synthesis in statistical modeling more quickly than in previous examples. If we take the PDP Group as the beginning of the process, we have already been working on the problems for twenty years.

The good news is that we do seem to be ready for a synthesis. We have a vast array of statistical modeling methods that work more or less well in different domains. Computer power is more than adequate to support huge amounts of experimentation. Sources of almost unlimited amounts of data are available and are growing rapidly.

On the other hand, an unfortunate implication of these historical parallels is that our synthesis may well depend on one or more unique individuals. Newton, Hamilton and Fisher were prodigies. The ability to move from a mass of overlapping problems and partial solutions to a unified conceptual system that meets both formal and practical goals seems to involve much more than incremental improvement.

Adoption of the synthesis

Once a synthesis is created, how quickly will it affect us? Historically it has taken decades for a radical synthesis to percolate into broad use. Dissemination of innovations requires reproducing the innovation, and it is hard to “copy” new ideas from mind to mind. They can easily be reproduced in print, but abstract and unfamiliar ideas are very hard for most readers to absorb from a printed page.

However, the situation for a statistical modeling synthesis is probably very different from our historical examples. Ideas in science and technology are often reproduced by “black boxing” them — building equipment that embodies them and then manufacturing that equipment. Depending on how quickly and cheaply the equipment can be manufactured, the ideas can diffuse quite rapidly.

Development of new ideas in statistical modeling depends on computer experiments. Thus when a synthesis is developed, it will exist at least partly in the form of software tools — already “black boxed” in other words. These tools can be replicated and distributed at almost zero cost and infinite speed.

So there is a good chance that when we do achieve a statistical modeling synthesis, “black boxes” that embody it will become available everywhere almost immediately. Initially these will only be useful to current statistical modeling researchers and software developers in related areas. The rate of adoption of the synthesis will be limited by the rate at which these black boxes can be adapted to local circumstances, integrated with existing software, and extended to new problems. This make adoption of the synthesis comparable to the spread of other innovations through the internet. However the increase in capability of systems will be far more dramatic than with prior innovations, and the size of subsequent innovations will be increased by the synthesis.

There is another, more radical possibility. A statistical modeling synthesis could be developed reflexively — that is, statistical modeling could be an essential tool in developing the synthesis itself. In that case the black boxes would potentially be able to support or guide their own adaptation, integration and extension, and the synthesis would change our world much more abruptly. I think this scenario currently is quite unlikely because none of the existing applications of statistical modeling lends themselves to this sort of reflexive use. It gets more likely the more we use statistical modeling in our development environments.

A reflexive synthesis has such major implications that it deserves careful consideration even if it seems unlikely.

Netminds–present and future

Shel Kaphan commented on “Dancing toward the singularity“:

Personally, I am comfortable with the idea of participating in… groupings that may involve machines and other humans and have their own entity-hood, and I’m comfortable with the idea that my brain probably has already adapted and become dependent on substantial immersion in computing environments and other technology, and I know what it is like to be part of an early generation, fairly tightly coupled, computing-enhanced group with a focus. I’m just saying the name “hybrid system” as such doesn’t sound either desirable or healthy.

And of course he’s right, who wants to be “hybridized” or “part of a hybrid system”? Ugh, terrible marketing.

So from now on, I’ll call them “netminds”: groups of people and machines working together so closely that they form a thinking entity.

People who become part of a netmind don’t lose their own identity, but they adapt. A moderately good analogy is a dance troupe, a repertory theater company or a band. Each individual retains their own identity but they also adapt to the group. At the same time, the troupe or band selects people who fit its identity (maybe unconsciously). And over time the group identity, the set of members and to some extent the individuals (and brains) co-evolve. So the individual and group identities are in a complex interplay.

This interplay will get much more intense as humans and machines get more tightly coupled. The tightest groups could be much closer than any today, with individuals interacting through machine interpretation of details of muscle tension, micro-gestures, brain state, etc. etc. In such a group people would be “inside each others heads” and would need to give up most personal boundaries between group members. The boundary would fall between the netmind and the outside world.

The fullest exploration of such a merger (without machines) is Vernor Vinge’s Tines in A Fire Upon the Deep. But even Vinge can only sustain the dual point of view (individual and netmind) in places, and elsewhere falls back into treating the netmind as a monolithic entity. This may be necessary in a narrative that appeals to normal humans. Joan Vinge explores the emotional side of netminds in Catspaw.

Netminds today

Does it make sense to talk about netminds as existing today? I think it does, although today’s netminds are relatively weakly coupled.

Gelled development teams, working closely together in a shared online environment, are netminds. The level of coupling we can attain through a keyboard is pathetically low, but as anyone who has been part of such a team can attest, the experience is intense and the sense that one is part of a larger entity is strong.

Quite likely a guild in an online game is a netmind, especially when they are engaged in a raid. I don’t personally have any experience with this, but since it is a more or less real-time experience, it probably has some interesting attributes that are mostly lacking in the software development case.

At the other end of the spectrum, we might want to call some very large, diffuse systems netminds. An appealing example is the Wikipedia editors plus the Wikipedia servers (note that I’m not including readers who don’t contribute). Here the coupling is fairly weak, but arguably the resulting system is still a thinking entity. It forms opinions, makes decisions (albeit with internal conflicts), gets distracted, etc. We can also see the dynamics that I describe above: individuals adapt, some individuals are expelled, the netmind develops new processes to maintain its integrity, and so forth. Human groups without network support do the same kinds of things, but a non-networked group the size of Wikipedia would “think” hundreds or thousands of times more slowly, and probably couldn’t even remain a coherent entity.

I suppose we could even call the whole web plus the Google servers a netmind, in the weakest possible sense. (Probably it is only fair to include all the other search and ranking systems as well.) Because the coupling is so weak, the effect on individual identity is minimal, but people (and certainly websites) do adapt to Google, and Google does exclude websites that participate in (what it considers) inappropriate ways. Furthermore Google works fairly hard to retain its integrity in the face of challenges from link farms, click fraud, etc. But this case is so large and diffuse that it stretches my intuition about netminds past its limits.

Netminds tomorrow

Let’s return to the more tightly coupled cases. Humans seem to naturally get caught up in intense group activities. Usually immersion in the group identity is fleeting — think of rock concerts, sports events, and riots. But intense creative group activity can generate a prolonged emotional high. Many physicists who worked on development of the atomic bomb at Los Alamos remembered it as the peak experience of their life. Engineers can get almost addicted to intense team development.

We already have the technology to make gaming environments fairly addictive, even without intense human interaction; there’s a reason Everquest is called Evercrack.

It’s easy to imagine that tightly coupled netminds could exert a very powerful emotional hold over their participants. Netminds will tend to move in the direction of tighter, more intense bonds on their own, since they feel so good. As our technology for coupling individuals into netminds gets better, we’ll have to be careful to manage this tendency, and there are certain to be some major, highly publicized failures.

A related problem is exemplified by cults. Cults don’t provide the emotional high of intense creative effort; they seem to retain people by increasing their dependency and fear of the outside world. Probably technology for tight coupling could be exploited to produce cult-like bonds. Net cults are likely to to be created by exploitative people, rather than arising spontaneously, and such phenomena as cult-like info-sweatshops are disturbingly likely — in fact they arguably already exist in some online games.

Whether creative or cult-like, tightly coupled netminds are also likely to shape their participants brains quite strongly. The persistent personality changes in cult members, long term hostages, etc. are probably due to corresponding changes in their brains — typically reversible, but only with difficulty. Participants in tightly coupled creative groups probably undergo brain changes just as large, but these changes they tend to enhance the individual rather than disabling them, so they produce less concern. Nobody tries to deprogram graduate students who are too involved with their lab.

We already know enough to build netminds that would deliberately induce changes in participants’ brains. We’re already building systems that produce outcomes similar to very disciplined practice. But tightly coupled systems could probably go far beyond this, reshaping brains in ways that ordinary practice could never achieve. As with most of these scenarios, such reshaping could have major beneficial or even therapeutic effects or could go horribly wrong.

Dancing toward the singularity

Vernor Vinge gave a talk in the Long Now Foundation seminar series last week (which is great, by the way, you should go if you can). Stewart Brand sent out an email summary but it isn’t on the web site yet.

As Brand says, “Vinge began by declaring that he still believes that a Singularity event in the next few decades is the most likely outcome — meaning that self-accelerating technologies will speed up to the point of so profound a transformation that the other side of it is unknowable. And this transformation will be driven by Artificial Intelligences (AIs) that, once they become self-educating and self-empowering, soar beyond human capacity with shocking suddeness.”

At Stewart’s request, Vinge’s talk was about knowable futures – which by definition mean that the singularity doesn’t happen. But the follow up questions and discussion after the talk were mostly about the singularity.

All of this has crystallized my view of the singularity. The path isn’t all that strange, but I now have a much better sense of the details, and see aspects that haven’t been covered in any essays or science fiction stories I know.

One point that came out at Vinge’s talk is important for context. Vinge (and I) aren’t imagining a singularity as an absolute point in time, and in that sense the term “singularity” is quite misleading. The sense of singularity arises because looking further up the curve from a given point, we see “so profound a transformation that the other side of it is unknowable.” However as we’re moving along the curve we won’t perceive a discontinuity; the future will remain comprehensible to some extent, though our horizon may get closer. However, we also won’t be the same people we are now. Among other things we’ll only be able to understand what’s going on because we are gradually integrated into hybrid human / machine systems. I’ll discuss how that will happen as we go along.

What’s missing from current discussions of the singularity?

Above I claim that the path I imagine has “some features that haven’t been a part of any essays or science fiction stories about approaching the singularity”. So I’ll elaborate on that first.

A major enabler of progress for systems (computer or human / computer hybrid) is their ability to quickly and accurately learn effective models of parts of their environment.

Examples of current model learning technology:

  • model road conditions by tracking roads (Thrun);
  • model safe driving by watching a driver (Thrun);
  • model the immediate visual indicators of a “drivable road” by watching the road right in front of the vehicle (Thrun);
  • model the syntactic and semantic mapping between two languages by analyzing parallel texts (many);
  • model the motion style of dancers (Brand);
  • model the painting style of various painters (Lyu)
  • model users’ choices of topics, tags etc. for text (lots of people);
  • etc. etc. etc.

A few observations about this list:

  • Often model learning delivers performance comparable to humans. Thrun’s models performed at near human levels in adverse real-world conditions (130 miles of driving unpaved, unmarked roads with lots of obstacles). The best parallel text language learning is near mediocre human translation ability. Model learning performs far better for spam filtering than hand built filters. Statistical modeling of painting style can identify artists as well as human experts. Etc.
  • Model learning is quickly getting easier. Thrun created three quite robust modeling capabilities in less than a year. A basic parallel text translation system can be created with off the shelf components. Fairly good text classification can be done with simple open source software. Etc.
  • None of these models is part of a reflexive loop, except in the extremely weak sense that e.g. spam filtering contributes to the productivity of developers who work on spam filtering.

Generally, discussions of and stories about AI, the singularity etc. don’t emphasize this sort of modeling. For example, when have we seen a story in which robots could quickly and accurately imitate the body language, intonations, verbal style etc. of the people they are dealing with? But this is a basic human ability, and it probably is essential to human level interaction.

The larger story

The modeling I discuss above is just one of a set of trends that taken together will lead to the singularity (unless something stops us, like nuclear war, pandemic virus, or some as yet unexplained metaphysical impossibility). If all the following trends continue, I think we’ll get to the singularity within, as Vinge says, “a few decades”.

Increasing processing power, storage, and bandwidth per $

It seems like Moore’s law and its various relatives will continue to smile upon us for a while yet, so I don’t think we need to worry about this.

Also my intuition (for which I have very little evidence) is that we can do the vast majority of what we need with the processing power, storage and bandwidth we already have. Of course, moving up the curve will make things easier, and perhaps most importantly, economically accessible to many more people.

Huge amounts of real world data easily accessible

This is the sine qua non of effective model learning; any area where we don’t have a lot of raw data can’t be modeled effectively. Conversely, if we do have lots of data, even simple approaches are likely to produce fairly good models.

This is taking care of itself very nicely, with immense amounts of online text, click streams, video chat, surveillance cameras, etc. etc. As the model acquisition ability is built, the data will be available for it.

General purpose, high performance population-based computing

All model learning is population-based – that is, it works with distributions, not crisp values. Continued progress in modeling will lead to more and more general grasp of population-based computing. Conversely, general population-based computing will make writing new modeling tools much easier and faster. Also, population-based computing makes it immensely easier to scale up using massively parallel, distributed systems.

Right now we have piecemeal approaches to population-based computing, but we don’t have a family of general mechanisms, equivalent to the “instruction stream + RAM” model of computing that we know so well. I think a better conceptual synthesis is required here. The synthesis may or may not be a breakthrough in the sense of requiring big conceptual changes, and/or providing big new insights.

Tightly coupled hybrid human / machine systems

To make hybrid systems more powerful, we need high bandwidth interaction mechanisms, and UI support to maximize coupling. Multi-touch displays, for example, allow much higher bandwidth human input. Good speech understanding would also help. Etc. In the opposite direction, visual and auditory output needs to make good use of human ecological perception (e.g. our ability to notice potential predators or prey during a jungle walk without consciously looking for them, our ability to unconsciously read subtle shades of feeling on people’s faces, our ability to unconsciously draw on context to interpret what people say, etc.).

Lots of people are working on the underlying technology for this. I don’t know of any project that is explicitly working on “ecological” machine – human communication, but with better modeling of humans by machines, it will probably come about fairly naturally. I don’t see a need for any big synthesis or breakthrough to make this all work, just a lot of incremental improvement.

As an aside, I bet that the best ideas on full engagement of people’s ecological abilities for perception and action will come from gaming. The Wii is already a good example. Imagine a gaming system that could use all joint angles in a player’s arms and hands as input without requiring physical contact. This would certainly require modeling the player at several levels (physical, motor control, gesture interpretation). Such a UI will only be highly usable and natural if it supports rapid evolution of conventions between the machine and the human, largely without conscious human choice.

General, powerful model learning mechanisms

This is a huge topic and is getting lots of attention from lots of different disciplines: statistics, computer science, computational neuroscience, biological neuroscience, differential topology, etc.

Again here I doubt that we need any big synthesis or breakthrough (beyond the underlying general model of population computing above). However this is such a huge area that I have trouble guessing how it will evolve. There is a significant chance that along the way, we’ll get insights into model learning that will cause a lot of current complexity to collapse down into a much simpler and more powerful approach. This isn’t essential, but obviously it would accelerate progress.

Reflexive improvement of hybrid systems

This is where the big speedup comes. Existing hybrid systems, such as development teams using intensive computer support, are already reflexively improving themselves by writing better tools, integrating their social processes more deeply into their networks, etc. Once the machines in a hybrid system can model their users, this reflexive improvement should accelerate.

I don’t see any serious obstacles to user modeling in development systems, but I also don’t see any significant examples of it, which is somewhat puzzling. Right now, as far as I’m aware, there are no systems that apply user modeling to accelerating their own development.

Such systems are technically possible today; in fact, I can imagine feasible examples fairly easily. Here’s one: A machine could “watch” a developer identifying interesting events in trouble logs or performance logs, learn a model that can predict the person’s choices, and use the model to filter a much larger collection of log data. Even if the filter wasn’t all that good, it would probably help the human to see patterns they’d otherwise miss, or would take much longer to find. The human and the machine could continue to refine and extend the filter together. This kind of model learning isn’t terribly challenging any more.

We may be stuck in a conceptual blind spot here, as we were with hypertext prior to Berners-Lee. If so, the Christiansen disruptive innovation scenario will probably apply: A new “adaptive” development environment will be created by someone outside the mainstream. It will be much less functional than existing development environments, and will be denounced as a total pile of crap by existing development gurus. However it will be adopted rapidly because it fills a huge unmet need for much easier, more tightly coupled human / machine development.

At the risk of dangerous hubris, I think these ingredients are all we need to produce the singularity. Specifically, I believe that no conceptual breakthroughs are required beyond those listed above. (Of course unexpected breakthroughs might occur and accelerate things.) None of the ingredients seems particularly risky, although most of us (probably including me) would have been astounded by current model learning if it was demonstrated five years ago. (Bernie Widrow and a few other people knew we could do this even thirty years ago.)

How long will it be before things get really weird?

Systems will be hybrid human / machine up to the singularity, and maybe far beyond (allowing for some equivocation about what counts as “human”). I don’t expect the banner of progress to be carried by systems with no humans in them any time soon.

The rate of improvement of reflexive hybrid human / machine systems determines the rate of progress toward the singularity. These are systems that can monitor their own performance and needs, and adapt their own structure, using the same kinds of processes they use for anything else. This kind of hybrid system exists today – online software development communities are examples, since they can build their own tools, reconfigure their own processes, etc.

The power of these systems is a function of the depth of integration of the humans and machines, and the breadth of the tasks they can work on (flexibility). Since they can apply their power to improving themselves, increasing their power will generally accelerate their improvement.

We already learn models of our machines. Good UIs and programming environments are good because they help us build effective models. To really accelerate progress, our machines will have to model us, and use those models to improve the coupling between us and them. The depth of integration, and thus the power, and thus the rate of improvement of human / machine hybrids will depend on how accurately, deeply, and quickly machines can model humans.

The big unknowns in determing how fast we’ll move are

  • how quickly model learning will evolve,
  • how effectively we’ll apply model learning to enhancing reflexive hybrid human / machine development environments and
  • how quickly we’ll transition to population-based computing.

Probably I can do a worst case / best case / expected case trend projection for these three unknowns, but this will take some further research and thought.

Some scenarios

Vinge talked a bit about different trajectories approaching the singularity – in particular, he distinguished between “hard takeoffs” in which the necessary technology is developed in one place and then spreads explosively, and “soft takeoffs” in which the technology development is more open and a large fraction of the population participates in the takeoff.

My analysis has some strong implications for those scenarios, and introduces a new, intermediate one:

A hard takeoff

For example due to a large secret military project. This is possible, but unlikely. Such a takeoff would have to create human / machine hybrids far more powerful than any generally available, and at the same time, prevent the techniques involved from diffusing out into the world.

Over and over, we’ve seen that most closed research projects fail, because they lack the variety and external correctives that come from being embedded in a larger network of inquiry. Also, the very institutional mechanisms that can create a big project and keep it secret tend to prevent creative exploration of the marginal ideas that are probably needed for success.

Note: This point is about research projects, not engineering projects that operate within good existing theories. Thus e.g. the Manhattan project is not a counter-example.

A soft takeoff

This is very likely as long as progress depends on combining innovations across a wide range of groups, and I think this is likely to be essential throughout the process. As things speed up, the dynamics are increasingly sensitive to the ratio between the rate of local change and the rate of diffusion of innovations. Aside from institutional barriers, software innovations in a networked world are likely to diffuse extremely quickly, so the network is likely to approach takeoff as a whole.

Local disruptions

As we get closer to the singularity, a third possibillity emerges. Powerful reflexive systems may enable local, disruptive innovation, for example by relatively small groups that keep their activities secret. These groups would not be able to “outrun” the network as a whole, so they would not have a sustainable lead, but they could temporarily be very disruptive.

In Rainbow’s End Vinge describes a group like this, who are the villains, as it happens. Another example would be a horribly lethal disease created in a future rogue high school biology lab.

These localized disruptive innovations could make the world a very uncomfortable place during the transition. However, a much larger population of networked hybrid systems with local computing power and high bandwidth connections will be able to move faster and farther than such a small group. Furthermore, as we’ve increasingly been seeing, in a highly networked world, it gets very hard to hide anything substantial, and our emergency response keeps getting faster.

How will normal humans experience the singularity?

Humans who are not part of a hybrid system (and probably there will be lots of them) will gradually lose their ability to understand what’s going on. It will be as though their horizon shrinks, or perhaps as though their conceptual blind spots expand to cover more of their world. They will find larger and larger parts of the world just incomprehensible. This could produce a lot of social stress. Maybe it already does – for example, this could be a contributing factor to the anxiety that gets exploited by “stop the world, I want to get off” movements.

We don’t experience our blind spot as a hole in our visual field because our brain fills it in. I think this is a good analogy for how we deal with conceptual blind spots – we fill them in with myths, and we can’t tell (unless we do careful analysis) where our understanding ends and the myths begin. So non-hybridized humans mostly won’t be aware that a lot of the world is disappearing into their blind spots, they will spontaneously generate myths to “wrap” the incomprehensible parts of their world. Some will defend the validity of those myths very aggressively, others will accept them as “just stories”. Again this is pretty consistent with the rise of retrograde movements like intelligent design and modern geocentrism.

This phenomenon is closely related to Clark’s law: “Any sufficiently advanced technology is indistinguishable from magic.” “Sufficiently advanced” just means “beyond my understanding”.

A phase transition

Putting this all together, I now think that we actually could end up with a fairly sharp discontinuity that plays the role of the singularity, but is spread out in time and space.

This could happen if the transition through the singularity is effectively a phase transition. A metaphor may help. If we put a pot of water on the stove and turn on the heat, for a while all the water heats up, but not uniformly – we get all sorts of inhomogeneity and interesting dynamics. At some point, local phase transitions occur – little bubbles of water vapor start forming and then collapsing. As the water continues to heat up, the bubbles become more persistent, until we’ve reached a rolling boil. After a while, all the water has turned into vapor, and there’s no more liquid in the pot.

We’re now at the point where bubbles of hybrid systems (such as “gelled” development teams) can form, but they aren’t all that stable or powerful yet, and so they aren’t dramatically different from their social environment. Their phase boundary isn’t very sharp.

As we go forward and these bubbles get easier to form, more powerful and more stable, the overall social environment will be increasingly roiled up by their activities. As the bubbles merge to form a large hybrid network, the contrast between people who are part of hybrid systems and normal people will become starker.

Unlike the pot that boils dry, I’d expect the two phases—normal people and hybrid systems—to come to an approximate equilibrium, in which parts of the population choose to stay normal indefinitely. The Amish today are a good example of how a group can make that choice. Note that members of both populations will cross the phase boundary, just as water molecules are constantly in flux across phase boundaries. Amish children are expected to go out and explore the larger culture, and decide whether to return. I presume that in some cases, members of the outside culture also decide to join the Amish, perhaps through marriage.

If this is correct, as the singularity approaches, the “veil” between us and the future will become more opaque for normal people, and at the same time will shift from a “time-like” to a “space-like” boundary. In other words, the singularity currently falls between our present and our future, but will increasingly fall between normal humans and hybrid systems living at the same time. Hybrid systems will be able to “see into” normal human communities – in fact they’ll be able to understand them far more accurately than we can now understand ourselves – but normal humans will find hybrid communities opaque. Of course polite hybrids will present a quasi-normal surface to normal humans except in times of great stress.

By analogy with other kinds of phase changes, the distance we can see into the future will shrink as we go through the transition, but once we start to move toward a new equilibrium, our horizons will expand again, and we (that is hybrid systems) may even be able to see much further ahead than we can are today. Even normal people may be able to see further ahead (within their bubbles), as long as the equilibrium is stable. The Amish can see further ahead in their own world than we can in ours, because they have decided that their way of life will change slowly.

I’d love to read stories set in this kind of future world. I think the view of a normal human, watching their child go off into the hybrid world, and wondering if they will return, would be a great topic. The few stories that touch on this kind of transition are very poignant: for example Tunç Blumenthal’s brief story in Vinge’s Across Realtime, and the story of Glenn Tropile’s merger into the Snowflake, and then severance from it, in Pohl and Kornbluth’s Wolfbane.