Search Results for: foom

Age of Em Paperback

Today is the official U.S. release date for the paperback version of my first book The Age of Em: Work, Love, and Life when Robots Rule the Earth. (U.K. version came out a month ago.) Here is the new preface:

I picked this book topic so it could draw me in, and I would finish. And that worked: I developed an obsession that lasted for years. But once I delivered the “final” version to my publisher on its assigned date, I found that my obsession continued. So I collected a long file of notes on possible additions. And when the time came that a paperback edition was possible, I grabbed my chance. As with the hardback edition, I had many ideas for changes that might make my dense semi-encyclopedia easier for readers to enjoy. But my core obsession again won out: to show that detailed analysis of future scenarios is possible, by showing just how many reasonable conclusions one can draw about this scenario.

Also, as this book did better than I had a right to expect, I wondered: will this be my best book ever? If so, why not make it the best it can be? The result is the book you now hold. It has over 42% more citations, and 18% more words, but it is only a bit easier to read. And now I must wonder: can my obsession stop now, pretty please?

Many are disappointed that I do not more directly declare if I love or hate the em world. But I fear that such a declaration gives an excuse to dismiss all this; critics could say I bias my analysis in order to get my desired value conclusions. I’ve given over 100 talks on this book, and never once has my audience failed to engage value issues. I remain confident that such issues will not be neglected, even if I remain quiet.

These are the only new sections in the paperback: Anthropomorphize, Motivation, Slavery, Foom, After Ems. (I previewed two of them here & here.)  I’ll make these two claims for my book:

  1. There’s at least a 5% chance that my analysis will usefully inform the real future, i.e., that something like brain emulations are actually the first kind of human-level machine intelligence, and my analysis is mostly right on what happens then. If it is worth having twenty books on the future, it is worth having a book with a good analysis of a 5% scenario.
  2. I know of no other analysis of a substantially-different-from-today future scenario that is remotely as thorough as Age of Em. I like to quip, “Age of Em is like science fiction, except there is no plot, no characters, and it all makes sense.” If you often enjoy science fiction but are frustrated that it rarely makes sense on closer examination, then you want more books like Age of Em. The success or not of Age of Em may influence how many future authors try to write such books.
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How Deviant Recent AI Progress Lumpiness?

I seem to disagree with most people working on artificial intelligence (AI) risk. While with them I expect rapid change once AI is powerful enough to replace most all human workers, I expect this change to be spread across the world, not concentrated in one main localized AI system. The efforts of AI risk folks to design AI systems whose values won’t drift might stop global AI value drift if there is just one main AI system. But doing so in a world of many AI systems at similar abilities levels requires strong global governance of AI systems, which is a tall order anytime soon. Their continued focus on preventing single system drift suggests that they expect a single main AI system.

The main reason that I understand to expect relatively local AI progress is if AI progress is unusually lumpy, i.e., arriving in unusually fewer larger packages rather than in the usual many smaller packages. If one AI team finds a big lump, it might jump way ahead of the other teams.

However, we have a vast literature on the lumpiness of research and innovation more generally, which clearly says that usually most of the value in innovation is found in many small innovations. We have also so far seen this in computer science (CS) and AI. Even if there have been historical examples where much value was found in particular big innovations, such as nuclear weapons or the origin of humans.

Apparently many people associated with AI risk, including the star machine learning (ML) researchers that they often idolize, find it intuitively plausible that AI and ML progress is exceptionally lumpy. Such researchers often say, “My project is ‘huge’, and will soon do it all!” A decade ago my ex-co-blogger Eliezer Yudkowsky and I argued here on this blog about our differing estimates of AI progress lumpiness. He recently offered Alpha Go Zero as evidence of AI lumpiness:

I emphasize how all the mighty human edifice of Go knowledge … was entirely discarded by AlphaGo Zero with a subsequent performance improvement. … Sheer speed of capability gain should also be highlighted here. … you don’t even need self-improvement to get things that look like FOOM. … the situation with AlphaGo Zero looks nothing like the Hansonian hypothesis and a heck of a lot more like the Yudkowskian one.

I replied that, just as seeing an unusually large terror attack like 9-11 shouldn’t much change your estimate of the overall distribution of terror attacks, nor seeing one big earthquake change your estimate of the overall distribution of earthquakes, seeing one big AI research gain like AlphaGo Zero shouldn’t much change your estimate of the overall distribution of AI progress. (Seeing two big lumps in a row, however, would be stronger evidence.) In his recent podcast with Sam Harris, Eliezer said:

Y: I have claimed recently on facebook that now that we have seen Alpha Zero, Alpha Zero seems like strong evidence against Hanson’s thesis for how these things necessarily go very slow because they have to duplicate all the work done by human civilization and that’s hard. …

H: What’s the best version of his argument, and then why is he wrong?

Y: Nothing can prepare you for Robin Hanson! Ha ha ha. Well, the argument that Robin Hanson has given is that these systems are still immature and narrow, and things will change when they get general. And my reply has been something like, okay, what changes your mind short of the world actually ending. If your theory is wrong do we get to find out about that at all before the world does.

(Sam didn’t raise the subject in his recent podcast with me.)

In this post, let me give another example (beyond two big lumps in a row) of what could change my mind. I offer a clear observable indicator, for which data should have available now: deviant citation lumpiness in recent ML research. One standard measure of research impact is citations; bigger lumpier developments gain more citations that smaller ones. And it turns out that the lumpiness of citations is remarkably constant across research fields! See this March 3 paper in Science:

The citation distributions of papers published in the same discipline and year lie on the same curve for most disciplines, if the raw number of citations c of each paper is divided by the average number of citations c0 over all papers in that discipline and year. The dashed line is a lognormal fit. …

The probability of citing a paper grows with the number of citations that it has already collected. Such a model can be augmented with … decreasing the citation probability with the age of the paper, and a fitness parameter, unique to each paper, capturing the appeal of the work to the scientific community. Only a tiny fraction of papers deviate from the pattern described by such a model.

It seems to me quite reasonable to expect that fields where real research progress is lumpier would also display a lumpier distribution of citations. So if CS, AI, or ML research is much lumpier than in other areas, we should expect to see that in citation data. Even if your hypothesis is that only ML research is lumpier, and only in the last 5 years, we should still have enough citation data to see that. My expectation, of course, is that recent ML citation lumpiness is not much bigger than in most research fields through history.

Added 24Mar: You might save the hypothesis that research areas vary greatly in lumpiness by postulating that the number of citations of each research advance goes as the rank of the “size” of that advance, relative to its research area. The distribution of ranks is always the same, after all. But this would be a surprising outcome, and hence seems unlikely; I’d want to see clear evidence that the distribution of lumpiness of advances varies greatly across fields.

Added 27Mar: More directly relevant might be data on distributions of patent value and citations. Do these distributions vary by topic? Are CS/AI/ML distributed more unequally?

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On Value Drift

The outcomes within any space-time region can be seen as resulting from 1) preferences of various actors able to influence the universe in that region, 2) absolute and relative power and influence of those actors, and 3) constraints imposed by the universe. Changes in outcomes across regions result from changes in these factors.

While you might mostly approve of changes resulting from changing constraints, you might worry more about changes due to changing values and influence. That is, you likely prefer to see more influence by values closer to yours. Unfortunately, the consistent historical trend has been for values to drift over time, increasing the distance between random future and current values. As this trend looks like a random walk, we see no obvious limit to how far values can drift. So if the value you place on the values of others falls rapidly enough with the distance between values, you should expect long term future values to be very wrong.

What influences value change?
Inertia – The more existing values are tied to important entrenched systems, the less they change.
Growth – On average, over time civilization collects more total influence over most everything.
Competition – If some values consistently win key competitive contests, those values become more common.
Influence Drift – Many processes that change the world produce random drift in agent influence.
Internal Drift – Some creatures, e.g., humans, have values that drift internally in complex ways.
Culture Drift – Some creatures, e.g., humans, have values that change together in complex ways.
Context – Many of the above processes depend on other factors, such as technology, wealth, a stable sun, etc.

For many of the above processes, rates of change are roughly proportional to overall social rates of change. As these rates of change have been increased over time, we should expect faster future change. Thus you should expect values to drift faster in the future than then did in the past, leading faster to wrong values. Also, people are living longer now than they did in the past. So even past people didn’t live long enough to see big enough changes to greatly bother them, future people may live to see much more change.

Most increases in the rates of change have been concentrated in a few sudden large jumps (associated with the culture, farmer, and industry transitions). As a result, you should expect that rates of change may soon increase greatly. Value drift may continue at past rates until it suddenly goes much faster.

Perhaps you discount the future rapidly, or perhaps the value you place on other values falls slowly with value distance. In these cases value drift may not disturb you much. Otherwise, the situation described above may seem pretty dire. Even if previous generations had to accept the near inevitability of value drift, you might not accept it now. You may be willing to reach for difficult and dangerous changes that could remake the whole situation. Such as perhaps a world government. Personally I see that move as too hard and dangerous for now, but I could understand if you disagree.

The people today who seem most concerned about value drift also seem to be especially concerned about humans or ems being replaced by other forms of artificial intelligence. Many such people are also concerned about a “foom” scenario of a large and sudden influence drift: one initially small computer system suddenly becomes able to grow far faster than the rest of the world put together, allowing it to quickly take over the world.

To me, foom seems unlikely: it posits an innovation that is extremely lumpy compared to historical experience, and in addition posits an unusually high difficulty of copying or complementing this innovation. Historically, innovation value has been distributed with a long thin tail: most realized value comes from many small innovations, but we sometimes see lumpier innovations. (Alpha Zero seems only weak evidence on the distribution of AI lumpiness.) The past history of growth rates increases suggests that within a few centuries we may see something, perhaps a very lumpy innovation, that causes a growth rate jump comparable in size to the largest jumps we’ve ever seen, such as at the origins of life, culture, farming, and industry. However, as over history the ease of copying and complementing such innovations has been increasing, it seems unlikely that copying and complementing will suddenly get much harder.

While foom seems unlikely, it does seems likely that within a few centuries we will develop machines that can outcompete biological humans for most all jobs. (Such machines might also outcompete ems for jobs, though that outcome is much less clear.) The ability to make such machines seems by itself sufficient to cause a growth rate increase comparable to the other largest historical jumps. Thus the next big jump in growth rates need not be associated with a very lumpy innovation. And in the most natural such scenarios, copying and complementing remain relatively easy.

However, while I expect machines that outcompete humans for jobs, I don’t see how that greatly increases the problem of value drift. Human cultural plasticity already ensures that humans are capable of expressing a very wide range of values. I see no obviously limits there. Genetic engineering will allow more changes to humans. Ems inherit human plasticity, and may add even more via direct brain modifications.

In principle, non-em-based artificial intelligence is capable of expressing the entire space of possible values. But in practice, in the shorter run, such AIs will take on social roles near humans, and roles that humans once occupied. This should force AIs to express pretty human-like values. As Steven Pinker says:

Artificial intelligence is like any other technology. It is developed incrementally, designed to satisfy multiple conditions, tested before it is implemented, and constantly tweaked for efficacy and safety.

If Pinker is right, the main AI risk mediated by AI values comes from AI value drift that happens after humans (or ems) no longer exercise such detailed frequent oversight.

It may be possible to create competitive AIs with protected values, i.e., so that parts where values are coded are small, modular, redundantly stored, and insulated from changes to the rest of the system. If so, such AIs may suffer much less from internal drift and cultural drift. Even so, the values of AIs with protected values should still drift due to influence drift and competition.

Thus I don’t see why people concerned with value drift should be especially focused on AI. Yes, AI may accompany faster change, and faster change can make value drift worse for people with intermediate discount rates. (Though it seems to me that altruistic discount rates should scale with actual rates of change, not with arbitrary external clocks.)

Yes, AI offers more prospects for protected values, and perhaps also for creating a world/universe government capable of preventing influence drift and competition. But in these cases if you are concerned about value drift, your real concerns are about rates of change and world government, not AI per se. Even the foom scenario just temporarily increases the rate of influence drift.

Your real problem is that you want long term stability in a universe that more naturally changes. Someday we may be able to coordinate to overrule the universe on this. But I doubt we are close enough to even consider that today. To quote a famous prayer:

God, grant me the serenity to accept the things I cannot change,
Courage to change the things I can,
And wisdom to know the difference.

For now value drift seems one of those possibly lamentable facts of life that we cannot change.

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Reply to Christiano on AI Risk

Paul Christiano was one of those who encouraged me to respond to non-foom AI risk concerns. Here I respond to two of his posts he directed me to. The first one says we should worry about the following scenario:

Imagine using [reinforcement learning] to implement a decentralized autonomous organization (DAO) which maximizes its profit. .. to outcompete human organizations at a wide range of tasks — producing and selling cheaper widgets, but also influencing government policy, extorting/manipulating other actors, and so on.

The shareholders of such a DAO may be able to capture the value it creates as long as they are able to retain effective control over its computing hardware / reward signal. Similarly, as long as such DAOs are weak enough to be effectively governed by existing laws and institutions, they are likely to benefit humanity even if they reinvest all of their profits.

But as AI improves, these DAOs would become much more powerful than their human owners or law enforcement. And we have no ready way to use a prosaic AGI to actually represent the shareholder’s interests, or to govern a world dominated by superhuman DAOs. In general, we have no way to use RL to actually interpret and implement human wishes, rather than to optimize some concrete and easily-calculated reward signal. I feel pessimistic about human prospects in such a world. (more)

In a typical non-foom world, if one DAO has advanced abilities, then most other organizations, including government and the law, have similar abilities. So such DAOs shouldn’t find it much easier to evade contracts or regulation than do organizations today. Thus humans can be okay if law and government still respect human property rights or political representation. Sure it might be hard to trust such a DAO to manage your charity, if you don’t trust it to judge who is in most need. But you might trust it much to give you financial returns on your financial investments in it.

Paul Christiano’s second post suggests that the arrival of AI arrives will forever lock in the distribution of patient values at that time:

The distribution of wealth in the world 1000 years ago appears to have had a relatively small effect—or more precisely an unpredictable effect, whose expected value was small ex ante—on the world of today. I think there is a good chance that AI will fundamentally change this dynamic, and that the distribution of resources shortly after the arrival of human-level AI may have very long-lasting consequences. ..

Whichever values were most influential at one time would remain most influential (in expectation) across all future times. .. The great majority of resources are held by extremely patient values. .. The development of machine intelligence may move the world much closer to this naïve model. .. [Because] the values of machine intelligences can (probably, eventually) be directly determined by their owners or predecessors. .. it may simply be possible to design a machine intelligence who exactly shares their predecessor’s values and who can serve as a manager. .. the arrival of machine intelligence may lead to a substantial crystallization of influence .. an event with long-lasting consequences. (more)

That is, Christiano says future AI won’t have problems preserving its values over time, nor need it pay agency costs to manage subsystems. Relatedly, Christiano elsewhere claims that future AI systems won’t have problems with design entrenchment:

Over the next 100 years greatly exceeds total output over all of history. I agree that coordination is hard, but even spending a small fraction of current effort on exploring novel redesigns would be enough to quickly catch up with stuff designed in the past.

A related claim, that Christiano supports to some degree, is that future AI are smart enough to avoid suffers from coordination failures. They may even use “acasual trade” to coordinate when physical interaction of any sort is impossible!

In our world, more competent social and technical systems tend to be larger and more complex, and such systems tend to suffer more (in % cost terms) from issues of design entrenchment, coordination failures, agency costs, and preserving values over time. In larger complex systems, it becomes harder to isolate small parts that encode “values”; a great many diverse parts end up influencing what such systems do in any given situation.

Yet Christiano expects the opposite for future AI; why? I fear his expectations result more from far view idealizations than from observed trends in real systems. In general, we see things far away in less detail, and draw inferences about them more from top level features and analogies than from internal detail. Yet even though we know less about such things, we are more confident in our inferences! The claims above seem to follow from the simple abstract description that future AI is “very smart”, and thus better in every imaginable way. This is reminiscent of medieval analysis that drew so many conclusions about God (including his existence) from the “fact” that he is “perfect.”

But even if values will lock in when AI arrives, and then stay locked, that still doesn’t justify great efforts to study AI control today, at least relative to the other options of improving our control mechanisms in general, or saving resources now to spend later, either on studying AI control problems when we know more about AI, or just to buy influence over the future when that comes up for sale.

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An Outside View of AI Control

I’ve written much on my skepticism of local AI foom (= intelligence explosion). Recently I said that foom offers the main justification I understand for AI risk efforts now, as well as being the main choice of my Twitter followers in a survey. It was the main argument offered by Eliezer Yudkowsky in our debates here at this blog, by Nick Bostrom in his book Superintelligence, and by Max Tegmark in his recent book Life 3.0 (though he denied so in his reply here).

However, some privately complained to me that I haven’t addressed those with non-foom-based AI concerns. So in this post I’ll consider AI control in the context of a prototypical non-em non-foom mostly-peaceful outside-view AI scenario. In a future post, I’ll try to connect this to specific posts by others on AI risk.

An AI scenario is where software does most all jobs; humans may work for fun, but they add little value. In a non-em scenario, ems are never feasible. As foom scenarios are driven by AI innovations that are very lumpy in time and organization, in non-foom scenarios innovation lumpiness is distributed more like it is in our world. In a mostly-peaceful scenario, peaceful technologies of production matter much more than do technologies of war and theft. And as an outside view guesses that future events are like similar past events, I’ll relate future AI control problems to similar past problems. Continue reading "An Outside View of AI Control" »

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Brains Simpler Than Brain Cells?

Consider two possible routes to generating human level artificial intelligence (AI): brain emulation (ems) versus ordinary AI (wherein I lump together all the other usual approaches to making smart code). Both approaches require that we understand something well enough to create a functional replacement for it. Ordinary AI requires this for entire brains, while ems require this only for brain cells.

That is, to make ordinary AI we need to find algorithms that can substitute for most everything useful that a human brain does. But to make brain emulations, we need only find models that can substitute for what brain cells do for brains: take input signals, change internal states, and then send output signals. (Such brain cell models need not model most of the vast complexity of cells, complexity that lets cells reproduce, defend against predators, etc.)

To make an em, we will also require brain scans at a sufficient spatial and chemical resolution, and enough cheap fast parallel computers. But the difficulty of achieving these other requirements scales with the difficulty of modeling brain cells. The simpler brain cells are, the less detail we’ll need to scan, and the smaller computers we’ll need to emulate them. So the relative difficulty of ems vs ordinary AI mainly comes down to the relative model complexity of brain cells versus brains.

Today we are seeing a burst of excitement about rapid progress in ordinary AI. While we’ve seen such bursts every decade or two for a long time, many people say “this time is different.” Just as they’ve done before; for a long time the median published forecast has said human level AI will appear in thirty years, and the median AI researcher surveyed has said forty years. (Even though such people estimate 5-10x slower progress in their subfield in the past twenty years.)

In contrast, we see far less excitement now about about rapid progress in brain cell modeling. Few neuroscientists publicly estimate brain emulations soon, and no one has even bothered to survey them. Many take these different levels of hype and excitement as showing that in fact brains are simpler than brain cells – we will more quickly find models and algorithms that substitute for brains than we will those that can substitute for brain cells.

Now while it just isn’t possible for brains to be simpler than brain cells, it is possible for our best models that substitute for brains to be simpler than our best models that substitute for brain cells. This requires only that brains be far more complex than our best models that substitute for them, and that our best models that substitute for brain cells are not far less complex than such cells. That is, humans will soon discover a solution to the basic problem of how to construct a human-level intelligence that is far simpler than the solution evolution found, but evolution’s solution is strongly tied to its choice of very complex brain cells, cells whose complexity cannot be substantially reduced via clever modeling. While evolution searched hard for simpler cheaper variations on the first design it found that could do the job, all of its attempts to simplify brains and brain cells destroyed the overall intelligence that it sought to maintain.

So maybe what the median AI researcher and his or her fans have in mind is that the intelligence of the human brain is essentially simple, while brain cells are essentially complex. This essential simplicity of intelligence view is what I’ve attributed to my ex-co-blogger Eliezer Yudkowsky in our foom debates. And it seems consistent with a view common among fast AI fans that once AI displaces humans, AIs would drop most of the distinctive features of human minds and behavior, such as language, laughter, love, art, etc., and also most features of human societies, such as families, friendship, teams, law, markets, firms, nations, conversation, etc. Such people tend to see such human things as useless wastes.

In contrast, I see the term “intelligence” as mostly used to mean “mental betterness.” And I don’t see a good reason to think that intelligence is intrinsically much simpler than betterness. Human brains sure look complex, and even if big chucks of them by volume may be modeled simply, the other chunks can contain vast complexity. Humans really do a very wide range of tasks, and successful artificial systems have only done a small range of those tasks. So even if each task can be done by a relatively simple system, it may take a complex system to do them all. And most of the distinctive features of human minds and societies seem to me functional – something like them seems useful in most large advanced societies.

In contrast, for the parts of the brain that we’ve been able to emulate, such as parts that process the first inputs of sight and sound, what brain cells there do for the brain really does seem pretty simple. And in most brain organs what most cells do for the body is pretty simple. So the chances look pretty good that what most brain cells do for the brain is pretty simple.

So my bet is that brain cells can be modeled more simply than can entire brains. But some seem to disagree.

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How Different AGI Software?

My ex-co-blogger Eliezer Yudkowsky recently made a Facebook post saying that recent AI Go progress confirmed his predictions from our foom debate. He and I then discussed this there, and I thought I’d summarize my resulting point of view here.

Today an individual firm can often innovate well in one of its products via a small team that keeps its work secret and shares little with other competing teams. Such innovations can be lumpy in the sense that gain relative to effort varies over a wide range, and a single innovation can sometimes make a big difference to product value.

However big lumps are rare; typically most value gained is via many small lumps rather than a few big ones. Most innovation comes from detailed practice, rather than targeted research, and abstract theory contributes only a small fraction. Innovations vary in their generality, and this contributes to the variation in innovation lumpiness. For example, a better washing machine can better wash many kinds of clothes.

If instead of looking at individual firms we look at nations as a whole, the picture changes because a nation is an aggregation of activities across a great many firm teams. While one firm can do well with a secret innovation team that doesn’t share, a big nation would hurt itself a lot by closing its borders to stop sharing with other nations. Single innovations make a much smaller difference to nations as a whole then they do to individual products. So nations grow much more steadily than do firms.

All of these patterns apply not just to products in general, but also to the subcategory of software. While some of our most general innovations may be in software, most software innovation is still made of many small lumps. Software that is broadly capable, such as a tool-filled operating system, is created by much larger teams, and particular innovations make less of a difference to its overall performance. Most software is created via tools that are shared with many other teams of software developers.

From an economic point of view, a near-human-level “artificial general intelligence” (AGI) would be a software system with a near-human level competence across almost the entire range of mental tasks that matter to an economy. This is a wide range, much more like scope of abilities found in a nation than those found in a firm. In contrast, an AI Go program has a far more limited range of abilities, more like those found in typical software products. So even if the recent Go program was made by a small team and embodies lumpy performance gains, it is not obviously a significant outlier relative to the usual pattern in software.

It seems to me that the key claim made by Eliezer Yudkowsky, and others who predict a local foom scenario, is that our experience in both ordinary products in general and software in particular is misleading regarding the type of software that will eventually contribute most to the first human-level AGI. In products and software, we have observed a certain joint distribution over innovation scope, cost, value, team size, and team sharing. And if that were also the distribution behind the first human-level AGI software, then we should predict that it will be made via a great many people in a great many teams, probably across a great many firms, with lots of sharing across this wide scope. No one team or firm would be very far in advance of the others.

However, the key local foom claim is that there is some way for small teams that share little to produce innovations with far more generality and lumpiness than these previous distributions suggests, perhaps due to being based more on math and basic theory. This would increase the chances that a small team could create a program that grabs a big fraction of world income, and keeps that advantage for an important length of time.

Presumably the basis for this claim is that some people think they see a different distribution among some subset of AI software, perhaps including machine learning software. I don’t see it yet, but the obvious way for them to convince skeptics like me is to create and analyze a formal dataset of software projects and innovations. Show us a significantly-deviating subset of AI programs with more economic scope, generality, and lumpiness in gains. Statistics from such an analysis could let us numerically estimate the chances of a single small team encompassing a big fraction of AGI software power and value.

That is, we might estimate the chances of local foom. Which I’ve said isn’t zero; I’ve instead just suggested that foom has gained too much attention relative to its importance.

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Youth Movements

Have you heard about the new “effective cars” movement? Passionate young philosophy students from top universities have invented a revolutionary new idea, now sweeping the intellectual world: cars that get you from home to the office or store and back again as reliably, comfortably, and fast as possible. As opposed to using cars used as shrub removers, pots for plants, conversation pits, or paperweights. While effective car activists cannot design, repair, or even operate cars, they are pioneering ways to prioritize car topics.

Not heard of that? How about “effective altruism”?

Effective altruism is about asking, “How can I make the biggest difference I can?” and using evidence and careful reasoning to try to find an answer. Just as science consists of the honest and impartial attempt to work out what’s true, and a commitment to believe the truth whatever that turns out to be, effective altruism consists of the honest and impartial attempt to work out what’s best for the world, and a commitment to do what’s best, whatever that turns out to be. …

I helped to develop the idea of effective altruism while a [philosophy] student at the University of Oxford. … I began to investigate the cost-effectiveness of charities that fight poverty in the developing world. The results were remarkable. We discovered that the best charities are hundreds of times more effective at improving lives than merely “good” charities. .. From there, a community developed. We realized that effective altruism could be applied to all areas of our lives – choosing charity, certainly, but also choosing a career, volunteering, and choosing what ewe buy and don’t buy. (MacAskill, Doing Good Better)

This all sounds rather vacuous; who opposes applying evidence and careful reasoning to figure out how to do better at charity, or anything? But I just gave a talk at Effective Altruism Global, and spent a few days there chatting and listening, and I’ve decided that they do have a core position that is far from vacuous.

Effective altruism is a youth movement. While they collect status by associating with older people like Peter Singer and Elon Musk, those who work and have influence in these groups are strikingly young. And their core position is close to the usual one for young groups throughout history: old codgers have run things badly, and so a new generation deserves to take over.

Some observers see effective altruism as being about using formal statistics or applying consensus scientific theories. But in fact effective altruists embrace contrarian concerns about AI “foom” (discussed often on this blog), concerns based neither on formal statistics nor on applying consensus theories. Instead this community just trusts its own judgment on what reasoning is “careful,” without worrying much if outsiders disagree. This community has a strong overlap with a “rationalist” community wherein people take classes on and much discuss how to be “rational”, and then decide that they have achieved enough rationality to justify embracing many quite contrarian conclusions.

Youth movements naturally emphasis the virtues of youth, relative to those of age. While old people have more power, wealth, grit, experience, task-specific knowledge, and crystalized intelligence, young people have more fluid intelligence, potential, passion, idealism, and a clean slate. So youth movements tend to claim that society has become lazy, corrupt, ossified, stuck in its ways, has tunnel-vision, and forgets its ideals, and so needs smart flexible idealistic people to rethink and rebuild from scratch.

Effective altruists, in particular, emphasize their stronger commitment to altruism ideals, and also the unusual smarts, rationality, and flexibility of their leaders. Instead of working within prior organizations to incrementally change prior programs, they prefer to start whole new organizations that re-evaluate all charity choices themselves from scratch. While most show little knowledge of the specifics of any charity areas, they talk a lot about not getting stuck in particular practices. And they worry about preventing their older selves from reversing the lifetime commitments to altruism that they want to make now.

Effective altruists often claim that big efforts to re-evaluate priorities are justified by large differences in the effectiveness of common options. Concretely, MacAskill, following Ord, suggested in his main conference talk that the distribution looks more like a thick-tailed power law than a Gaussian. He didn’t present actual data, but one of the other talks there did: Eva Vivalt showed the actual distribution of estimated effects to be close to Gaussian.

But youth movements have long motivated members via exaggerated claims. One is reminded of the sixties counter-culture seeing itself as the first generation to discover sex, emotional authenticity, and a concern for community. And saying not to trust anyone over thirty. Or countless young revolutionaries seeing themselves as the first generation to really care about inequality or unwanted dominance.

When they work well, youth movements can create a strong bond within a generation than can help them to work together as a coalition as they grow in ability and influence. As with the sixties counter-culture, or the libertarians a bit later, while at first their concrete practice actions are not very competent, eventually they gain skills, moderate their positions, become willing to compromise, and have substantial influence on the world. Effective altruists can reasonably hope to mature into such a strong coalition.

Added 1a: The last slide of my talk presented this youth movement account. The talk was well attended and many people mentioned talked to me about it afterward, but not one told me they disagreed with my youth movement description.

Added 10a: Most industrials and areas of life have a useful niche to be filled by independent quality evaluators, and I’ve been encouraged by the recent increase in such evaluators within charity, such as GiveWell. The effective altruism movement consists of far more, however, than independent quality evaluators.

Added 8Aug: OK, for now I accept Brienne Yudkowsky’s summary of Vivalt, namely that she finds very little ability to distinguish the effectiveness of different ways to achieve any given effect, but that she doesn’t speak to the variation across different kinds of things one might try to do.

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Regulating Infinity

As a professor of economics in the GMU Center for the Study of Public Choice, I and my colleagues are well aware of the many long detailed disputes on the proper scope of regulation.

One the one hand, the last few centuries has seen increasing demands for and expectations of government regulation. A wider range of things that might happen without regulation are seen as intolerable, and our increasing ability to manage large organizations and systems of surveillance is seen as making us increasingly capable of discerning relevant problems and managing regulatory solutions.

On the other hand, some don’t see many of the “problems” regulations are set up to address as legitimate ones for governments to tackle. And others see and fear regulatory overreach, wherein perhaps well-intentioned regulatory systems actually make most of us worse off, via capture, corruption, added costs, and slowed innovation.

The poster-children of regulatory overreach are 20th century totalitarian nations. Around 1900, many were told that the efficient scale of organization, coordination, and control was rapidly increasing, and nations who did not follow suit would be left behind. Many were also told that regulatory solutions were finally available for key problems of inequality and inefficient resource allocation. So many accepted and even encouraged their nations to create vast intrusive organizations and regulatory systems. These are now largely seen to have gone too far.

Or course there have no doubt been other cases of regulatory under-reach; I don’t presume to settle this debate here. In this post I instead want to introduce jaded students of regulatory debates to something a bit new under the sun, namely a newly-prominent rationale and goal for regulation that has recently arisen in a part of the futurist community: stopping preference change.

In history we have seen change not only in technology and environments, but also in habits, cultures, attitudes, and preferences. New generations often act not just like the same people thrust into new situations, but like new kinds of people with new attitudes and preferences. This has often intensified intergenerational conflicts; generations have argued not only about who should consume and control what, but also about which generational values should dominate.

So far, this sort of intergenerational value conflict has been limited due to the relatively mild value changes that have so far appeared within individual lifetimes. But at least two robust trends suggest the future will have more value change, and thus more conflict:

  1. Longer lifespans – Holding other things constant, the longer people live the more generations will overlap at any one time, and the more different will be their values.
  2. Faster change – Holding other things constant, a faster rate of economic and social change will likely induce values to change faster as people adapt to these social changes.
  3. Value plasticity – It may become easier for our descendants to change their values, all else equal. This might be via stronger ads and schools, or direct brain rewiring. (This trend seems less robust.)

These trends robustly suggest that toward the end of their lives future folk will more often look with disapproval at the attitudes and behaviors of younger generations, even as these older generations have a smaller proportional influence on the world. There will be more “Get off my lawn! Damn kids got no respect.”

The futurists who most worry about this problem tend to assume a worst possible case. (Supporting quotes below.) That is, without a regulatory solution we face the prospect of quickly sharing the world with daemon spawn of titanic power who share almost none of our values. Not only might they not like our kind of music, they might not like music. They might not even be conscious. One standard example is that they might want only to fill the universe with paperclips, and rip us apart to make more paperclip materials. Futurists’ key argument: the space of possible values is vast, with most points far from us.

This increased intergenerational conflict is the new problem that tempts some futurists today to consider a new regulatory solution. And their preferred solution: a complete totalitarian takeover of the world, and maybe the universe, by a new super-intelligent computer.

You heard that right. Now to most of my social scientist colleagues, this will sound bonkers. But like totalitarian advocates of a century ago, these new futurists have a two-pronged argument. In addition to suggesting we’d be better off ruled by a super-intelligence, they say that a sudden takeover by such a computer will probably happen no matter what. So as long as we have to figure out how to control it, we might as well use it to solve the intergenerational conflict problem.

Now I’ve already discussed at some length why I don’t think a sudden (“foom”) takeover by a super intelligent computer is likely (see here, here, here). Nor do I think it obvious that value change will generically put us face-to-face with worst case daemon spawn. But I do grant that increasing lifespans and faster change are likely to result in more intergenerational conflict. And I can also believe that as we continue to learn just how strange the future could be, many will be disturbed enough to seek regulation to prevent value change.

Thus I accept that our literatures on regulation should be expanded to add one more entry, on the problem of intergenerational value conflict and related regulatory solutions. Some will want to regulate infinity, to prevent the values of our descendants from eventually drifting away from our values to parts unknown.

I’m much more interested here in identifying this issue than in solving it. But if you want my current opinion it is that today we are just not up to the level of coordination required to usefully control value changes across generations. And even if we were up to the task I’m not at all sure gains would be worth the quite substantial costs.

Added 8a: Some think I’m unfair to the fear-AI position to call AIs our descendants and to describe them in terms of lifespan, growth rates and value plasticity. But surely AIs being made of metal or made in factories aren’t directly what causes concern. I’ve tried to identify the relevant factors but if you think I’ve missed the key factors do tell me what I’ve missed.

Added 4p: To try to be even clearer, the standard worrisome foom scenario has a single AI that grows in power very rapidly and whose effective values drift rapidly away from ones that initially seemed friendly to humans. I see this as a combination of such AI descendants having faster growth rates and more value plasticity, which are two of the three key features I listed.

Added 15Sep: A version of this post appeared as:

Robin Hanson, Regulating Infinity, Global Government Venturing, pp.30-31, September 2014.

Those promised supporting quotes: Continue reading "Regulating Infinity" »

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Irreducible Detail

Our best theories vary in generality. Some theories are very general, but most are more context specific. Putting all of our best theories together usually doesn’t let us make exact predictions on most variables of interest. We often express this fact formally in our models via “noise,” which represents other factors that we can’t yet predict.

For each of our theories there was a point in time when we didn’t have it yet. Thus we expect to continue to learn more theories, which will let us make more precise predictions. And so it might seem like we can’t constrain our eventual power of prediction; maybe we will have powerful enough theories to predict everything exactly.

But that doesn’t seem right either. Our best theories in many areas tell us about fundamental limits on our prediction abilities, and thus limits on how powerful future simple general theories could be. For example:

  • Thermodynamics – We can predict some gross features of future physical states, but the entropy of a system sets a very high (negentropy) cost to learn precise info about the state of that system. If thermodynamics is right, there will never be a general theory to let one predict future states more cheaply than this.
  • Finance – Finance theory has identified many relevant parameters to predict the overall distribution of future assets returns. However, finance theory strongly suggests that it is usually very hard to predict details of the specific future returns of specific assets. The ability to do so would be worth such a huge amount that there just can’t be many who often have such an ability. The cost to gain such an ability must usually be more than the gains from trading it.
  • Cryptography – A well devised code looks random to an untrained eye. As there are a great many possible codes, and a great many ways to find weaknesses in them, it doesn’t seem like there could be any general way to break all codes. Instead code breaking is a matter of knowing lots of specific things about codes and ways they might be broken. People use codes when they expect the cost of breaking them to be prohibitive, and such expectations are usually right.
  • Innovation – Economic theory can predict many features of economies, and of how economies change and grow. And innovation contributes greatly to growth. But economists also strongly expect that the details of particular future innovations cannot be predicted except at a prohibitive cost. Since knowing of innovations ahead of time can often be used for great private profit, and would speed up the introduction of those innovations, it seems that no cheap-to-apply simple general theories can exist which predict the details of most innovations well ahead of time.
  • Ecosystems – We understand some ways in which parameters of ecosystems correlate with their environments. Most of these make sense in terms of general theories of natural selection and genetics. However, most ecologists strongly suspect that the vast majority of the details of particular ecosystems and the species that inhabit them are not easily predictable by simple general theories. Evolution says that many details will be well matched to other details, but to predict them you must know much about the other details to which they match.

In thermodynamics, finance, cryptography, innovations, and ecosystems, we have learned that while there are many useful generalities, the universe is also chock full of important irreducible incompressible detail. As this is true at many levels of abstraction, I would add this entry to the above list:

  • Intelligence – General theories tell us what intelligence means, and how it can generalize across tasks and contexts. But most everything we’ve learned about intelligence suggests that the key to smarts is having many not-fully-general tools. Human brains are smart mainly by containing many powerful not-fully-general modules, and using many modules to do each task. These modules would not work well in all possible universes, but they often do in ours. Ordinary software also gets smart by containing many powerful modules. While the architecture that organizes those modules can make some difference, that difference is mostly small compared to having more better modules. In a world of competing software firms, most ways to improve modules or find new ones cost more than the profits they’d induce.

If most value in intelligence comes from the accumulation of many expensive parts, there may well be no powerful general theories to be discovered to revolutionize future AI, and give an overwhelming advantage to the first project to discover them. Which is the main reason that I’m skeptical about AI foom, the scenario where an initially small project quickly grows to take over the world.

Added 7p: Peter McCluskey has thoughtful commentary here.

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