Search Results for: foom

Foom Update

To extend our reach, we humans have built tools, machines, firms, and nations. And as these are powerful, we try to maintain control of them. But as efforts to control them usually depend on their details, we have usually waited to think about how to control them until we had concrete examples in front of us. In the year 1000, for example, there wasn’t much we could do to usefully think about how to control most things that have only appeared in the last two centuries, such as cars or international courts.

Someday we will have far more powerful computer tools, including “advanced artificial general intelligence” (AAGI), i.e., with capabilities even higher and broader than those of individual human brains today. And some people today spend substantial efforts today worrying about how we will control these future tools. Their most common argument for this unusual strategy is “foom”.

That is, they postulate a single future computer system, initially quite weak and fully controlled by its human sponsors, but capable of action in the world and with general values to drive such action. Then over a short time (days to weeks) this system dramatically improves (i.e., “fooms”) to become an AAGI far more capable even than the sum total of all then-current humans and computer systems. This happens via a process of self-reflection and self-modification, and this self-modification also produces large and unpredictable changes to its effective values. They seek to delay this event until they can find a way to prevent such dangerous “value drift”, and to persuade those who might initiate such an event to use that method.

I’ve argued at length (1 2 3 4 5 6 7) against the plausibility of this scenario. Its not that its impossible, or that no one should work on it, but that far too many take it as a default future scenario. But I haven’t written on it for many years now, so perhaps it is time for an update. Recently we have seen noteworthy progress in AI system demos (if not yet commercial application), and some have urged me to update my views as a result.

The recent systems have used relative simple architectures and basic algorithms to produce models with enormous numbers of parameters from very large datasets. Compared to prior systems, these systems have produced impressive performance on an impressively wide range of tasks. Even though they are still quite far from displacing humans in any substantial fraction of their current tasks.

For the purpose of reconsidering foom, however, the key things to notice are: (1) these systems have kept their values quite simple and very separate from the rest of the system, and (2) they have done basically zero self-reflection or self-improvement. As I see AAGI as still a long way off, the features of these recent systems can only offer weak evidence regarding the features of AAGI.

Even so, recent developments offer little support for the hypothesis that AAGI will be created soon via the process of self-reflection and self-improvement, or for the hypothesis that such a process risks large “value drifts”. These current ways that we are now moving toward AAGI just don’t look much like the foom scenario. And I don’t see them as saying much about whether ems or AAGI will appear first.

Again, I’m not saying foom is impossible, just that it looks unlikely, and that recent events haven’t made it seem moreso.

These new systems do suggest a substantial influence of architecture on system performance, though not obviously at a level out of line with that in most prior AI systems. And note that the abilities of the very best systems here are not that much better than that of the 2nd and 3rd best systems, arguing weakly against AAGI scenarios where the best system is vastly better.

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Tegmark’s Book of Foom

Max Tegmark says his new book, Life 3.0, is about what happens when life can design not just its software, as humans have done in Life 2.0, but also its hardware:

Life 1.0 (biological stage) evolves its hardware and software
Life 2.0 (cultural stage) evolves its hardware, designs much of its software
Life 3.0 (technological stage): designs its hardware and software ..
Many AI researchers think that Life 3.0 may arrive during the coming century, perhaps even during our lifetime, spawned by progress in AI. What will happen, and what will this mean for us? That’s the topic of this book. (29-30)

Actually, its not. The book says little about redesigning hardware. While it says interesting things on many topics, its core is on a future “singularity” where AI systems quickly redesign their own software. (A scenario sometimes called “foom”.)

The book starts out with a 19 page fictional “scenario where humans use superintelligence to take over the world.” A small team, apparently seen as unthreatening by the world, somehow knows how to “launch” a “recursive self-improvement” in a system focused on “one particular task: programming AI Systems.” While initially “subhuman”, within five hours it redesigns its software four times and becomes superhuman at its core task, and so “could also teach itself all other humans skills.”

After five more hours and redesigns it can make money by doing half of the tasks at Amazon Mechanical Turk acceptably well. And it does this without having access to vast amounts of hardware or to large datasets of previous performance on such tasks. Within three days it can read and write like humans, and create world class animated movies to make more money. Over the next few months it goes on to take over the news media, education, world opinion, and then the world. It could have taken over much faster, except that its human controllers were careful to maintain control. During this time, no other team on Earth is remotely close to being able to do this.

Tegmark later explains: Continue reading "Tegmark’s Book of Foom" »

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Foom Justifies AI Risk Efforts Now

Years ago I was honored to share this blog with Eliezer Yudkowsky. One of his main topics then was AI Risk; he was one of the few people talking about it back then. We debated this topic here, and while we disagreed I felt we made progress in understanding each other and exploring the issues. I assigned a much lower probability than he to his key “foom” scenario.

Recently AI risk has become something of an industry, with far more going on than I can keep track of. Many call working on it one of the most effectively altruistic things one can possibly do. But I’ve searched a bit and as far as I can tell that foom scenario is still the main reason for society to be concerned about AI risk now. Yet there is almost no recent discussion evaluating its likelihood, and certainly nothing that goes into as much depth as did Eliezer and I. Even Bostrom’s book length treatment basically just assumes the scenario. Many seem to think it obvious that if one group lets one AI get out of control, the whole world is at risk. It’s not (obvious).

As I just revisited the topic while revising Age of Em for paperback, let me try to summarize part of my position again here. Continue reading "Foom Justifies AI Risk Efforts Now" »

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I Still Don’t Get Foom

Back in 2008 my ex-co-blogger Eliezer Yudkowsky and I discussed his “AI foom” concept, a discussion that we recently spun off into a book. I’ve heard for a while that Nick Bostrom was working on a book elaborating related ideas, and this week his Superintelligence was finally available to me to read, via Kindle. I’ve read it now, along with a few dozen reviews I’ve found online. Alas, only the two reviews on GoodReads even mention the big problem I have with one of his main premises, the same problem I’ve had with Yudkowsky’s views. Bostrom hardly mentions the issue in his 300 pages (he’s focused on control issues).

All of which makes it look like I’m the one with the problem; everyone else gets it. Even so, I’m gonna try to explain my problem again, in the hope that someone can explain where I’m going wrong. Here goes.

“Intelligence” just means an ability to do mental/calculation tasks, averaged over many tasks. I’ve always found it plausible that machines will continue to do more kinds of mental tasks better, and eventually be better at pretty much all of them. But what I’ve found it hard to accept is a “local explosion.” This is where a single machine, built by a single project using only a tiny fraction of world resources, goes in a short time (e.g., weeks) from being so weak that it is usually beat by a single human with the usual tools, to so powerful that it easily takes over the entire world. Yes, smarter machines may greatly increase overall economic growth rates, and yes such growth may be uneven. But this degree of unevenness seems implausibly extreme. Let me explain. Continue reading "I Still Don’t Get Foom" »

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Foom Debate, Again

My ex-co-blogger Eliezer Yudkowsky last June:

I worry about conversations that go into “But X is like Y, which does Z, so X should do reinterpreted-Z”. Usually, in my experience, that goes into what I call “reference class tennis” or “I’m taking my reference class and going home”. The trouble is that there’s an unlimited number of possible analogies and reference classes, and everyone has a different one. I was just browsing old LW posts today (to find a URL of a quick summary of why group-selection arguments don’t work in mammals) and ran across a quotation from Perry Metzger to the effect that so long as the laws of physics apply, there will always be evolution, hence nature red in tooth and claw will continue into the future – to him, the obvious analogy for the advent of AI was “nature red in tooth and claw”, and people who see things this way tend to want to cling to that analogy even if you delve into some basic evolutionary biology with math to show how much it isn’t like intelligent design. For Robin Hanson, the one true analogy is to the industrial revolution and farming revolutions, meaning that there will be lots of AIs in a highly competitive economic situation with standards of living tending toward the bare minimum, and this is so absolutely inevitable and consonant with The Way Things Should Be as to not be worth fighting at all. That’s his one true analogy and I’ve never been able to persuade him otherwise. For Kurzweil, the fact that many different things proceed at a Moore’s Law rate to the benefit of humanity means that all these things are destined to continue and converge into the future, also to the benefit of humanity. For him, “things that go by Moore’s Law” is his favorite reference class.

I can have a back-and-forth conversation with Nick Bostrom, who looks much more favorably on Oracle AI in general than I do, because we’re not playing reference class tennis with “But surely that will be just like all the previous X-in-my-favorite-reference-class”, nor saying, “But surely this is the inevitable trend of technology”; instead we lay out particular, “Suppose we do this?” and try to discuss how it will work, not with any added language about how surely anyone will do it that way, or how it’s got to be like Z because all previous Y were like Z, etcetera. (more)

When we shared this blog, Eliezer and I had a long debate here on his “AI foom” claims. Later, we debated in person once. (See also slides 34,35 of this 3yr-old talk.) I don’t accept the above as characterizing my position well. I’ve written up a summaries before, but let me try again, this time trying to more directly address the above critique.

Eliezer basically claims that the ability of an AI to change its own mental architecture is such a potent advantage as to make it likely that a cheap unnoticed and initially low ability AI (a mere “small project machine in a basement”) could without warning over a short time (e.g., a weekend) become so powerful as to be able to take over the world.

As this would be a sudden big sustainable increase in the overall growth rate in the broad capacity of the world economy, I do find it useful to compare to compare this hypothesized future event to the other pasts events that produce similar outcomes, namely a big sudden sustainable global broad capacity rate increase. The last three were the transitions to humans, farming, and industry.

I don’t claim there is some hidden natural law requiring such events to have the same causal factors or structure, or to appear at particular times. But I do think these events suggest a useful if weak data-driven prior on the kinds of factors likely to induce such events, on the rate at which they occur, and on their accompanying inequality in gains. In particular, they tell us that such events are very rare, that over the last three events gains have been spread increasingly equally, and that these three events seem mainly due to better ways to share innovations.

Eliezer sees the essence of his scenario as being a change in the “basic” architecture of the world’s best optimization process, and he sees the main prior examples of this as the origin of natural selection and the arrival of humans. He also sees his scenario as differing enough from the other studied growth scenarios as to make analogies to them of little use.

However, since most global bio or econ growth processes can be thought of as optimization processes, this comes down to his judgement on what counts as a “basic” structure change, and on how different such scenarios are from other scenarios. And in my judgement the right place to get and hone our intuitions about such things is our academic literature on global growth processes.

Economists have a big literature on processes by which large economies grow, increasing our overall capacities to achieve all the things we value. There are of course many other growth literatures, and some of these deal in growths of capacities, but these usually deal with far more limited systems. Of these many growth literatures it is the economic growth literature that is closest to dealing with the broad capability growth posited in a fast growing AI scenario.

It is this rich literature that seems to me the right place to find and hone our categories for thinking about growing broadly capable systems. One should review many formal theoretical models, and many less formal applications of such models to particular empirical contexts, collecting “data” points of what is thought to increase or decrease growth of what in what contexts, and collecting useful categories for organizing such data points.

With such useful categories in hand one can then go into a new scenario such as AI foom and have a reasonable basis for saying how similar that new scenario seems to old scenarios, which old scenarios it seems most like if any, and which parts of that new scenario are central vs. peripheral. Yes of course if this new area became mature it could also influence how we think about other scenarios.

But until we actually see substantial AI self-growth, most of the conceptual influence should go the other way. Relying instead primarily on newly made up categories and similarity maps between them, concepts and maps which have not been vetted or honed in dealing with real problems, seems to me a mistake. Yes of course a new problem may require one to introduce some new concepts to describe it, but that is hardly the same as largely ignoring old concepts.

So, I fully grant that the ability of AIs to intentionally change mind designs would be a new factor in the world, and it could make a difference for AI ability to self-improve. But while the history of growth over the last few million years has seen many dozens of factors come and go, or increase and decrease in importance, it has only seen three events in which overall growth rates greatly increased suddenly and sustainably. So the mere addition of one more factor seems unlikely to generate foom, unless our relevant categories for growth causing factors suggest that this factor is unusually likely to have such an effect.

This is the sense in which I long ago warned against over-reliance on “unvetted” abstractions. I wasn’t at all trying to claim there is one true analogy and all others are false. Instead, I argue for preferring to rely on abstractions, including categories and similarity maps, that have been found useful by a substantial intellectual community working on related problems. On the subject of an AI growth foom, most of those abstractions should come from the field of economic growth.

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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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Emulations Go Foom

Let me consider the AI-foom issue by painting a (looong) picture of the AI scenario I understand best, whole brain emulations, which I’ll call “bots.”  Here goes.

When investors anticipate that a bot may be feasible soon, they will estimate their chances of creating bots of different levels of quality and cost, as a function of the date, funding, and strategy of their project.  A bot more expensive than any (speedup-adjusted) human wage is of little direct value, but exclusive rights to make a bot costing below most human wages would be worth many trillions of dollars.

It may well be socially cost-effective to start a bot-building project with a 1% chance of success when its cost falls to the trillion dollar level.  But not only would successful investors probably only gain a small fraction of this net social value, is unlikely any investor group able to direct a trillion could be convinced the project was feasible – there are just too many smart-looking idiots making crazy claims around.

But when the cost to try a 1% project fell below a billion dollars, dozens of groups would no doubt take a shot.  Even if they expected the first feasible bots to be very expensive, they might hope to bring that cost down quickly.  Even if copycats would likely profit more than they, such an enormous prize would still be very tempting.

The first priority for a bot project would be to create as much emulation fidelity as affordable, to achieve a functioning emulation, i.e., one you could talk to and so on.  Few investments today are allowed a decade of red ink, and so most bot projects would fail within a decade, their corpses warning others about what not to try.  Eventually, however, a project would succeed in making an emulation that is clearly sane and cooperative.

Continue reading "Emulations Go Foom" »

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AI Go Foom

It seems to me that it is up to [Eliezer] to show us how his analysis, using his abstractions, convinces him that, more likely than it might otherwise seem, hand-coded AI will come soon and in the form of a single suddenly super-powerful AI.

As this didn’t prod a response, I guess it is up to me to summarize Eliezer’s argument as best I can, so I can then respond.  Here goes:

A machine intelligence can directly rewrite its entire source code, and redesign its entire physical hardware.  While human brains can in principle modify themselves arbitrarily, in practice our limited understanding of ourselves means we mainly only change ourselves by thinking new thoughts.   All else equal this means that machine brains have an advantage in improving themselves. 

A mind without arbitrary capacity limits, that focuses on improving itself, can probably do so indefinitely.  The growth rate of its "intelligence" may be slow when it is dumb, but gets faster as it gets smarter.  This growth rate also depends on how many parts of itself it can usefully change.  So all else equal, the growth rate of a machine intelligence must be greater than the growth rate of a human brain. 

No matter what its initial disadvantage, a system with a faster growth rate eventually wins.  So if the growth rate advantage is large enough then yes a single computer could well go in a few days from less than human intelligence to so smart it could take over the world.  QED.

So Eliezer, is this close enough to be worth my response?  If not, could you suggest something closer?

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Why Not Wait On AI Risk?

Years ago when the AI risk conversation was just starting, I was a relative skeptic, but I was part of the conversation. Since then, the conversation has become much larger, but I seem no longer part of it; it seems years since others in this convo engaged me on it.

Clearly most who write on this do not sit close to my views, though I may sit closer to most who’ve considered getting into this topic, but instead found better things to do. (Far more resources are available to support advocates than skeptics.) So yes, I may be missing something that they all get. Furthermore, I’ve admittedly only read a small fraction of the huge amount since written in this area. Even so, I feel I should periodically try again to explain my reasoning, and ask others to please help show me what I’m missing.

The future AI scenario that treats “AI” most like prior wide tech categories (e.g., “energy” or “transport”) goes as follows. AI systems are available from many competing suppliers at similar prices, and their similar abilities increase gradually over time. Abilities don’t increase faster than customers can usefully apply them. Problems are mostly dealt with as they appear, instead of anticipated far in advance. Such systems slowly displace humans on specific tasks, and are on average roughly as task specialized as humans are now. AI firms distinguish themselves via the different tasks their systems do.

The places and groups who adopt such systems first are those flexible and rich enough to afford them, and having other complementary capital. Those who invest in AI capital on average gain from their investments. Those who invested in displaced capital may lose, though over the last two decades workers at more automated jobs have not seen any average effect on their wages or number of workers. AI today is only a rather minor contribution to our economy (<5%), and it has quite a long way to go before it can make a large contribution. We today have only vague ideas of what AIs that made a much larger contribution would look like.

Today most of the ways that humans help and harm each other are via our relations. Such as: customer-supplier, employer-employee, citizen-politician, defendant-plaintiff, friend-friend, parent-child, lover-lover, victim-criminal-police-prosecutor-judge, army-army, slave-owner, and competitors. So as AIs replace humans in these roles, the main ways that AIs help and hurt humans are likely to also be via these roles.

Our usual story is that such hurt is limited by competition. For example, each army is limited by all the other armies that might oppose it. And your employer and landlord are limited in exploiting you by your option to switch to other employers and landlords. So unless AI makes such competition much less effective at limiting harms, it is hard to see how AI makes role-mediated harms worse. Sure smart AIs might be smarter than humans, but they will have other AI competitors and humans will have AI advisors. Humans don’t seem much worse off in the last few centuries due to firms and governments who are far more intelligent than individual humans taking over many roles.

AI risk folks are especially concerned with losing control over AIs. But consider, for example, an AI hired by a taxi firm to do its scheduling. If such an AI stopped scheduling passengers to be picked up where they waited and delivered to where they wanted to go, the firm would notice quickly, and could then fire and replace this AI. But what if an AI who ran such a firm became unresponsive to its investors. Or if an AI who ran an army becoming unresponsive to its oversight government? In both cases, while such investors or governments might be able to cut off some outside supplies of resources, the AI might do substantial damage before such cutoffs bled it dry.

However, our world today is well acquainted with the prospect of “coups” wherein firm or army management becomes unresponsive to its relevant owners. Not only do our usual methods usually seem sufficient to the task, we don’t see much of an externality re these problems. You try to keep your firm under control, and I try to keep mine, but I’m not especially threatened by your losing control of yours. We care a bit more about others losing control of their cars, planes, or nuclear power plants, as those might hurt bystanders. But we care much less once such others show us sufficient liability, and liability insurance, to cover our losses in these cases.

I don’t see why I should be much more worried about your losing control of your firm, or army, to an AI than to a human or group of humans. And liability insurance also seems a sufficient answer to your possibly losing control of an AI driving your car or plane. Furthermore, I don’t see why its worth putting much effort into planning how to control AIs far in advance of seeing much detail about how AIs actually do concrete tasks where loss of control matters. Knowing such detail has usually been the key to controlling past systems, and money invested now, instead of spent on analysis now, gives us far more money to spend on analysis later.

All of the above has been based on assuming that AI will be similar to past techs in how it diffuses and advances. Some say that AI might be different, just because, hey, anything might be different. Others, like my ex-co-blogger Eliezer Yudkowsky, and Nick Bostrom in his book Superintelligence, say more about why they expect advances at the scope of AGI to be far more lumpy than we’ve seen for most techs.

Yudkowsky paints a “foom” picture of a world full of familiar weak stupid slowly improving computers, until suddenly and unexpectedly a single super-smart un-controlled AGI with very powerful general abilities appears and is able to decisively overwhelm all other powers on Earth. Alternatively, he claims (quite implausibly I think) that all AGIs naturally coordinate to merge into a single system to defeat competition-based checks and balances.

These folks seem to envision a few key discrete breakthrough insights that allow the first team that finds them to suddenly catapult their AI into abilities far beyond all other then-current systems. These would be big breakthroughs relative to the broad category of “mental tasks”, and thus even bigger than if we found big breakthroughs relative to the less broad tech categories of “energy”, “transport”, or “shelter”. Yes of course change is often lumpy if we look at small tech scopes, but lumpy local changes aggregate into smoother change over wider scopes.

As I’ve previously explained at length, that seems to me to postulate a quite unusual lumpiness relative to the history we’ve seen for innovation in general, and more particularly for tools, computers, AI, and even machine learning. And this seems to postulate much more of a lumpy conceptual essence to “betterness” than I find plausible. Recent machine learning systems today seem relatively close to each other in their abilities, are gradually improving, and none seem remotely inclined to mount a coup.

I don’t mind groups with small relative budgets exploring scenarios with proportionally small chances, but I lament such a large fraction of those willing to take the long term future seriously using this as their default AI scenario. And while I get why people like Yudkowsky focus on scenarios in which they fervently believe, I am honestly puzzled why so many AI risk experts seem to repudiate his extreme scenarios, and yet still see AI risk as a terribly important project to pursue right now. If AI isn’t unusually lumpy, then why are early efforts at AI control design especially valuable?

So far I’ve mentioned two widely expressed AI concerns. First, AIs may hurt human workers by displacing them, and second, AIs may start coups wherein they wrest control of some resources from their owners. A third widely expressed concern is that the world today may be stable, and contain value, only due to somewhat random and fragile configurations of culture, habits, beliefs, attitudes, institutions, values, etc. If so, our world may break if this stuff drifts out of a safe and stable range for such configurations. AI might be or facilitate such a change, and by helping to accelerate change, AI might accelerate the rate of configuration drift.

Similar concerns have often been expressed about allowing too many foreigners to immigrate into a society, or allowing the next youthful generation too much freedom to question and change inherited traditions. Or allowing many other specific transformative techs, like genetic engineering, fusion energy, social media, or space. Or other big social changes, like gay marriage.

Many have deep and reasonable fears regarding big long-term changes. And some seek to design AI so that it won’t allow excessive change. But this issue seems to me much more about change in general than about AI in particular. People focused on these concerns should be looking to stop or greatly limit and slow change in general, and not focus so much on AI. Big change can also happen without AI.

So what am I missing? Why would AI advances be so vastly more lumpy than prior tech advances as to justify very early control efforts? Or if not, why are AI risk efforts a priority now?

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Will Design Escape Selection?

In the past, many people and orgs have had plans and designs, many which made noticeable differences to the details of history. But regarding most of history, our best explanations of overall trends has been in terms of competition and selection, including between organisms, species, cultures, nations, empires, towns, firms, and political factions.

However, when it comes to the future, especially hopeful futures, people tend to think more in terms of design than selection. For example, H.G. Wells was willing to rely on selection to predict a future dystopia in The Time Machine, but his utopia in Things to Come was the result of conscious planning replacing prior destructive competition. Hopeful futurists have long painted pictures of shiny designed techs, planned cities, and wise cooperative institutions of charity and governance.

Today, competition and selection continue on in many forms, including political competition for the control of governance institutions. But instead of seeing governance, law, and regulation as driven largely by competition between units of governance (e.g., parties, cities, or nations), many now prefer to see them in design terms: good people coordinating to choose how we want to live together, and to limit competition in many ways. They see competition between units of governance as largely passé, and getting more so as we establish stronger global communities and governance.

My future analysis efforts have relied mostly on competition and selection. Such as in Age of Em, post-em AI, Burning the Cosmic Commons, and Grabby Aliens. And in my predictions of long views and abstract values. Their competitive elements, and what that competition produces, are often described by others as dystopian. And the most common long-term futurist vision I come across these days is of a “singleton” artificial general intelligence (A.G.I.) for whom competition and selection become irrelevant. In that vision (of which I am skeptical), there is only one A.G.I., which has no internal conflicts, grows in power and wisdom via internal reflection and redesign, and then becomes all powerful and immortal, changing the universe to match its value vision.

Many recent historical trends (e.g., slavery, democracy, religion, fertility, leisure, war, travel, art, promiscuity) can be explained in terms of rising wealth inducing a reversion to forager values and attitudes. And I see these design-oriented attitudes toward governance and the future as part of this pro-forager trend. Foragers didn’t overtly compete with each other, but instead made important decisions by consensus, and largely by appeal to community-wide altruistic goals. The farming world forced humans to more embrace competition, and become more like our pre-human ancestors, but we were never that comfortable with it.

The designs that foragers created, however, were too small to reveal the key obstacle to this vision of civilization-wide collective design to over-rule competition: rot (see 1 2 3 4). Not only is it quite hard in practice to coordinate to overturn the natural outcomes of competition and selection, the sorts of complex structures that we are tempted to use to achieve that purpose consistently rot, and decay with time. If humanity succeeds in creating world governance strong enough to manage competition, those governance structures are likely to prevent interstellar colonization, as that strongly threatens their ability to prevent competition. And such structures would slowly rot over time, eventually dragging civilization down with them.

If competition and selection manages to continue, our descendants may become grabby aliens, and join the other gods at the end of time. In that case one of the biggest unanswered question is: what will be the key units of future selection? How will those units manage to coordinate, to the extent that they do, while still avoiding the rotting of their coordination mechanisms? And how can we now best promote the rise of the best versions of such competing units?

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