Tag Archives: AI

The Betterness Explosion

We all want the things around us to be better. Yet today billions struggle year after year to make just a few things a bit better. But what if our meagre success was because we just didn’t have the right grand unified theory of betterness? What if someone someday discovered the basics of such a theory? Well then this person might use his basic betterness theory to make himself better in health, wealth, sexiness, organization, work ethic, etc. More important, that might help him make his betterness theory even better.

After several iterations this better person might have a much better betterness theory. Then he might quickly make everything around him much better. Not just better looking hair, better jokes, or better sleep. He might start a better business, and get better at getting investors to invest, customers to buy, and employees to work. Or he might focus on making better investments. Or he might run for office and get better at getting elected, and then make his city or nation run better. Or he might create a better weapon, revolution, or army, to conquer any who oppose him.

Via such a “betterness explosion,” one way or another this better person might, if so inclined, soon own, rule, or conquer the world. Which seems to make it very important that the first person who discovers the first good theory of betterness be a very nice generous person who will treat the rest of us well. Right?

OK, this might sound silly. After all, we seem to have little reason to expect there is a useful grand unified theory of betterness to discover, beyond what we already know. “Betterness” seems mostly a concept about us and what we want – why should it correspond to something out there about which we can make powerful discoveries?

But a bunch of smart well-meaning folks actually do worry about a scenario that seems pretty close to this one. Except they talk about “intelligence” instead of “betterness.” They imagine an “intelligence explosion,” by which they don’t just mean that eventually the future world and many of its creatures will be more mentally capable than us in many ways, or even that the rate at which the world makes itself more mentally capable will speed up, similar to how growth rates have sped up over the long sweep of history. No, these smart well-meaning folks instead imagine that once someone has a powerful theory of “intelligence,” that person could create a particular “intelligent” creature which is good at making itself more “intelligent,” which then lets that creature get more “intelligent” about making itself “intelligent.” Within a few days or weeks, the story goes, this one creature could get so “intelligent” that it could do pretty much anything, including taking over the world.

I put the word “intelligence” in quotes to emphasize that the way these folks use this concept, it pretty much just means “betterness.” (Well, mental betterness, but most of the betterness we care about is mental.) And this fits well with common usage of the term “intelligence.” When we talk about machines or people or companies or even nations being “intelligent,” we mainly mean that such things are broadly mentally or computationally capable, in ways that are important for their tasks and goals. That is, an “intelligent” thing has a great many useful capabilities, not some particular specific capability called “intelligence.” To make something broadly smarter, you have to improve a wide range of its capabilities. And there is generally no easy or fast way to do that.

Now if you artificially hobble something so as to simultaneously reduce many of its capacities, then when you take away that limitation you may simultaneously improve a great many of its capabilities. For example, if you drug a person so that they can hardly think, then getting rid of that drug can suddenly improve a great many of their mental abilities. But beyond removing artificial restrictions, it is very hard to simultaneously improve many diverse capacities. Theories that help you improve capabilities are usually focused on a relatively narrow range of abilities – very general and useful theories are quite rare.

All of which is to say that fearing that a new grand unified theory of intelligence will let one machine suddenly take over the world isn’t that different from fearing that a grand unified theory of betterness will let one better person suddenly take over the world. This isn’t to say that such an thing is impossible, but rather that we’d sure want some clearer indications that such a theory even exists before taking such a fear especially seriously.

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Econ of AI on BHTV

Karl Smith of Modeled Behavior and I did a blogging heads tv show on the economics of artificial intelligence:

It was a pleasure to talk Karl, since he is that rare combination: someone who both takes powerful future technologies seriously, and who understands social science. (Watching it now, I suspect that if you counted minutes you’d find I talked too much – sorry Karl.)

I made an analogy between three ways to grow a nation, and to grow a mind. Growing nations:

  1. Play the usual game of trading with other nations, etc.
  2. Develop good internal support for investment & innovation.
  3. Move all your people to become part of a rich nation.

Growing minds:

  1. Play the usual game of writing code to do more things well.
  2. Develop a super learning algorithm to grow from “scratch.”
  3. Copy an existing human brain, via whole brain emulation.

When possible, I favor approach #3.

I also made the point that while people like to justify having fewer kids in terms giving each kid more help, the factors that seem to influence the choice of zero vs. one kid seem pretty similar to the factors that influence some vs. more kids.  This fits better with the choice really being about more for parents vs. more for the kids. Anyone know of hard data on factors that influence zero vs. one kid relative to some vs. more kids?

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Signal Mappers Decouple

Andrew Sullivan notes that Tim Lee argues that ems (whole brain emulations) just won’t work:

There’s no reason to think it will ever be possible to scan the human brain and create a functionally equivalent copy in software. Hanson … fails to grasp that the emulation of one computer by another is only possible because digital computers are the products of human designs, and are therefore inherently easier to emulate than natural systems. … Digital computers … were built by a human being based on a top-down specification that explicitly defines which details of their operation are important. The spec says exactly which aspects of the machine must be emulated and which aspects may be safely ignored. This matters because we don’t have anywhere close to enough hardware to model the physical characteristics of digital machines in detail. Rather, emulation involves re-implementing the mathematical model on which the original hardware was based. Because this model is mathematically precise, the original device can be perfectly replicated.

You can’t emulate a natural system because natural systems don’t have designers, and therefore weren’t built to conform to any particular mathematical model. … Creating a simulation of a natural system inherently means means making judgment calls about which aspects of a physical system are the most important. And because there’s no underlying blueprint, these guesses are never perfect: it will always be necessary to leave out some details that affect the behavior of the overall system, which means that simulations are never more than approximately right. Weather simulations, for example, are never going to be able to predict precisely where each raindrop will fall, they only predict general large-scale trends, and only for a limited period of time. … We may have relatively good models for the operation of nerves, but these models are simplifications, and therefore they will differ in subtle ways from the operation of actual nerves. And these subtle micro-level inaccuracies will snowball into large-scale errors when we try to simulate an entire brain, in precisely the same way that small micro-level imperfections in weather models accumulate to make accurate long-range forecasting inaccurate. … Each neuron is itself a complex biological system. I see no reason to think we’ll ever be able to reduce it to a mathematically tractable model. (more; Eli Dourado agrees; Alex Waller disagrees.)

Human brains were not designed by humans, but they were designed. Evolution has imposed huge selection pressures on brains over millions of years, to perform very particular functions. Yes, humans use more math that does natural selection to assist them. But we should expect brain emulation to be feasible because brains function to process signals, and the decoupling of signal dimensions from other system dimensions is central to achieving the function of a signal processor. The weather is not a designed signal processor, so it does not achieve such decoupling. Let me explain.

A signal processor is designed to mantain some intended relation between particular inputs and outputs. All known signal processors are physical systems with vastly more degrees of freedom than are contained in the relevant inputs they seek to receive, the outputs they seek to send, or the sorts of dependencies between input and outputs they seek to maintain. So in order manage its intended input-output relation, a single processor simply must be designed to minimize the coupling between its designed input, output, and internal channels, and all of its other “extra” physical degrees of freedom. Really, just ask most any signal-process hardware engineer.

Now sometimes random inputs can be useful in certain signal processing strategies, and this can be implemented by coupling certain parts of the system to most any random degrees of freedom. So signal processors don’t always want to minimize extra couplings. But this is a rare exception to the general need to decouple.

The bottom line is that to emulate a biological signal processor, one need only identify its key internal signal dimensions and their internal mappings – how input signals are mapped to output signals for each part of the system. These key dimensions are typically a tiny fraction of its physical degrees of freedom. Reproducing such dimensions and mappings with sufficient accuracy will reproduce the function of the system.

This is proven daily by the 200,000 people with artificial ears, and will be proven soon when artificial eyes are fielded. Artificial ears and eyes do not require a detailed weather-forecasting-like simulation of the vast complex physical systems that are our ears and eyes. Yes, such artificial organs do not exactly reproduce the input-output relations of their biological counterparts. I expect someone with one artificial ear and one real ear could tell the difference. But the reproduction is close enough to allow the artificial versions to perform most of the same practical functions.

We are confident that the number of relevant signal dimensions in a human brain is vastly smaller than its physical degrees of freedom. But we do not know just how many are those dimensions. The more dimensions, the harder it will be to emulate them. But the fact that human brains continue to function with nearly the same effectiveness when they are whacked on the side of the head, or when flooded with various odd chemicals, shows they have been designed to decouple from most other physical brain dimensions.

The brain still functions reasonably well even flooded with chemicals specifically designed to interfere with neurotransmitters, the key chemicals by which neurons send signals to each other! Yes people on “drugs” don’t function exactly the same, but with moderate drug levels people can still perform most of the functions required for most jobs.

Remember, my main claim is that whole brain emulation will let machines substitue for humans through the vast majority of the world economy. The equivalent of human brains on mild drugs should be plenty sufficient for this purpose – we don’t need exact replicas.

Added 7p: Tim Lee responds:

Hanson seems to be making a different claim here than he made in his EconTalk interview. There his claim seemed to be that we didn’t need to understand how the brain works in any detail because we could simply scan a brain’s neurons and “port” them to a silicon substrate. Here, in contrast, he’s suggesting that we determine the brain’s “key internal signal dimensions and their internal mappings” and then build a digital system that replicates these higher-level functions. Which is to say we do need to understand how the brain works in some detail before we can duplicate it computationally. …

Biologists know a ton about proteins. … Yet despite all our knowledge, … general protein folding is believed to be computationally intractible. … My point is that even detailed micro-level knowledge of a system doesn’t necessarily give us the capacity to efficiently predict its macro-level behavior. … By the same token, even if we had a pristine brain scan and a detailed understanding of the micro-level properties of neurons, there’s no good reason to think that simulating the behavior of 100 billion neurons will ever be computationally tractable.

My claim is that, in order to create economically-sufficient substitutes for human workers, we don’t need to understand how the brain works beyond having decent models of each cell type as a signal processor. Like the weather, protein folding is not designed to process signals and so does not have the decoupling feature I describe above. Brain cells are designed to process signals in the brain, and so should have a much simplified description in signal processing terms. We already have pretty good signal-processing models of some cell types; we just need to do the same for all the other cell types.

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Roberts On Robots

Russ Roberts and I talked for over 90 minutes on the possibility of a future robot-induced “singularity”, i.e., a sudden drastic social change, including a much faster growth rate. As this was his longest podcast in over two years, Russ seems to have been quite engaged by the subject.

Russ said “This may all sound crazy but Robin makes it actually sound plausible.” His two main points of skepticism were:

  1. I say our brains are big piles of brain cells that send signals to each other. We’ve know what parts the brain is made of, and they are the same familiar parts that everything else around us is made of. We know well how these parts interact locally, though figuring out what this implies on large scales is usually beyond our calculation abilities. So a good enough model of the parts and how they are connected must reproduce the same overall input-output behavior. Russ says “there is a reductionist element to this which says–and this is controversial–all there is to our brain is its physicality. Nothing else there. That’s not universally accepted, correct? … Being a religious person I’m capable of imagining something that is not observable.”
  2. I say prices usually fall when a very elastic supply curve rapidly gets cheaper. Russ would probably agree for something like computer memory, but is reluctant to agree for wages – he doesn’t think cheap plentiful immigrants lower wages. I say that if trillions of immigrants willing to work for a dollar an hour were waiting just offshore, letting in as many as wanted in would lower wages to that level. So I say cheap robots getting cheaper fast should rapidly lower wages for tasks they do. Russ objects “You can’t just say your wage will be driven down, because if there are complementary types of labor they’ll increase the wage rate of some people. … There’s all these complicated secondary effects.” I say all things considered, the likely effect is falling wages.

The first point reminds me of my disagreements with Tyler:

The three items on which Tyler most clearly identifies a disagreement [with me] are all in hard science and technology … Tyler doesn’t know that much about hard science and technology. … And yet Tyler feels confident enough in his perception of expert consensus on such topics to base his disagreements with me on them, even though I’ve spend years in such area.

The second point seems easier to settle, as it is just an application of standard econ theory.  Any other economists care to weigh in?

Added 5Jan: Karl SmithNick Rowe , Steven Hsu weigh in.

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Goertzel on Friendly AI

Ben Goertzel isn’t big on friendly AI:

SIAI’s “Scary Idea”:  … Progressing toward advanced AGI without a design for “provably non-dangerous AGI” is highly likely to lead to an involuntary end for the human race. …

Reasons for believing the Scary Idea: …

  1. If one pulled a random mind from the space of all possible minds, the odds of it being friendly to humans are very low.
  2. … If you create an AGI with a roughly-human-like value system, then this … is likely to rapidly diverge into something with little or no respect for human values.
  3. “Hard takeoffs” (in which AGIs recursively self-improve and massively increase their intelligence) are fairly likely once AGI reaches a certain level of intelligence; and humans will have little hope of stopping these events.
  4. A hard takeoff, unless it starts from an AGI designed in a “provably Friendly” way, is highly likely to lead to an AGI system that doesn’t respect the rights of humans to exist.

… I think the first of the above points is reasonably plausible, though I’m not by any means convinced. … I agree much less with the final three points listed above. …

I doubt human value is particularly fragile. Human value has evolved and … already takes multiple different forms. … I think it’s fairly robust.  … I think a hard takeoff is possible, though … I think it’s very unlikely to occur until we have an AGI system… at the level of a highly intelligent human. And I think the path to this … somewhat gradual, not extremely sudden. …

Pointing out that something scary is possible, is a very different thing from having an argument that it’s likely. The Scary Idea is certainly something to keep in mind, but there are also many other risks to keep in mind, some much more definite and palpable. …

I’m also quite unconvinced that “provably safe” AGI is even feasible. … The goal of “Friendliness to humans” or “safety” or whatever you want to call it, is rather nebulous and difficult to pin down. … One is going to need to build systems with a nontrivial degree of fundamental unpredictability. …

I think the way to come to a useful real-world understanding of AGI ethics is going to be to … study these early-stage AGI systems empirically, with a focus on their ethics as well as their cognition in the usual manner of science. … So what’s wrong with this approach?  Nothing, really — if you hold the views of most AI researchers or futurists.

I’m also not big on friendly AI, but my position differs somewhat. I’m pretty skeptical about a very local hard takeoff scenario, where within a month one unnoticed machine in a basement takes over a world like ours. And even given on such a scenario the chance that its creators could constrain it greatly via a provably friendly design seems remote. And the chance such constraint comes from a small friendliness-design team that is secretive for fear of assisting reckless others seems even more remote.

On the other hand, I think it pretty likely that growth in the world economy will speed up greatly and suddenly, that increasing intelligence in creatures will contribute to that growth, and that most future intelligence will be machine-based.  I also think it inevitable that uncontrolled evolution in a competitive world leads to future creatures with values different from ours, inducing behavior we dislike. So in this sense I see a fast takeoff to unfriendly AI as likely.

I just see little point anytime soon in trying to coordinate to prevent such an outcome. Like Ben, I think it is ok (if not ideal) if our descendants’ values deviate from ours, as ours have from our ancestors. The risks of attempting a world government anytime soon to prevent this outcome seem worse overall.

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Yawn, World Remade

What dramatic new events are in store for humanity? Here we contemplate 12 possibilities and rate their likelihood of happening by 2050. … They all have the power to forever reshape how we think about ourselves and how we live our lives.

That is the June Scientific American, which doesn’t seem to realize that one of their 12 possibilities matters far more than the rest. They assign a greater than 50% chance to advanced AI by 2050!

LIKELY: machine-selfawareness
What happens when robots start calling the shots?

Artificial-intelligence (AI) researchers have no doubt that the development of highly intelligent computers and robots that can self-replicate, teach themselves and adapt to different conditions will change the world. … Computers with adaptable and advanced hardware and software might someday become self-aware. … When machine self-awareness first occurs, it will be followed by self-improvement. … Improvements would be made in subsequent generations, which, for machines can pass in only a few hours. In other words, Wright notes, self-awareness leads to self-replication leads to better machines made without humans involved. “Personally, i’ve always been more scared of this scenario than a lot of others” in regard to the fate of humanity, he says. … Not everyone is so pessimistic. … This emergence of more intelligent AI won’t come on “like an alien invasion of machines to replace us,” agrees futurist and prominent author Ray Kurzweil. Machines, he says, will follow a path that mirrors the evolution of humans. Ultimately, however, self-aware, self-improving machines will evolve beyond humans’ ability to control or even understand them, he adds.

The other eleven possibilities:

cloning of a human (likely), extra dimensions (50-50), extraterrestrial intelligence (unlikely), nuclear exchange (unlikely), creation of life (almost certain), room-temperature superconductors (50-50), polar meltdown (likely), pacific earthquake (almost certain), fusion energy (very unlikely), asteroid collision (unlikely), deadly pandemic (50-50).

Scientific American seems unaware that the AI possibility’s expected effects far outweigh all the rest.  If accurate, this one forecast deserves vastly more attention than a 700 word comment.  If they really took it seriously, they might devote an entire issue to the subject, or perhaps even their entire future magazine.  Either they don’t really believe their >50% number, they don’t understand its enormous civilization-remaking consequences, or they (and their readers) don’t find such vast consequences several decades hence of much interest. Which is it?

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Robots vs. Aliens vs. …

There are a many kinds of potentially powerful creatures one might consider.  These include: robots, aliens, spirits, gods, alters, revived hominids (e.g., neanderthals, hobbits), time-travelers (e.g., ancestors, descendants), and extreme human personality types (e.g., aspergers, psychopaths).

For each creature type, consider the degree to which you might:

  1. accept/want to live intermingled with them?
  2. seek/expect to gain via deals & trade with them?
  3. worry if they have similar enough values?
  4. exterminate them if you could?
  5. enslave them if you could?
  6. hide us from them if you could?
  7. fear them killing us all?
  8. fear them enslaving us?
  9. fear them out competing us?
  10. mind them marrying your child?
  11. take their advice?
  12. mind killing a single one of them?
  13. help them lots if that were cheap for you?
  14. mind becoming one of them?
  15. mind if they dominate the universe?

OK, now here is the interesting meta question: what patterns are there in how different sorts of people answer these questions differently for the different possibly-powerful creature types?  Once we have some patterns, we can seek explanations for them.

For example, compared to other types of creatures, we seem to less fear alters having differing values or our-competing us, seem more willing to take their advice and kill them, but seem less willing to enslave them.

Added 7Apr: For spirits or time-travelers, stories about dominance or gift-exchange relations sometimes go well, but stories about trade relations usually go very badly.

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Seek Peace, Not Values

David Chalmers has a new paper on future artificial minds:

If humans survive, the rapid replacement of existing human traditions and practices would be regarded as subjectively bad by some but not by others. … The very fact of an ongoing intelligence explosion all around one could be subjectively bad, perhaps due to constant competition and instability, or because certain intellectual endeavours would come to seem pointless. On the other hand, if superintelligent systems share our values, they will presumably have the capacity to ensure that the resulting situation accords with those values. …

If at any point there is a powerful AI+ or AI++ with the wrong value system, we can expect disaster (relative to our values) to ensue. The wrong value system need not be anything as obviously bad as, say, valuing the destruction of humans. If the AI+ value system is merely neutral with respect to some of our values, then in the long run we cannot expect the world to conform to those values. For example, if the system values scientific progress but is neutral on human existence, we cannot expect humans to survive in the long run. And even if the AI+ system values human existence, but only insofar as it values all conscious or intelligent life, then the chances of human survival are at best unclear.

Chalmers is an excellent philosopher, but to me the above reflects an unhealthy obsession with foreigner values, one common among the economically-illiterate.  So let me try to educate him (and you).

Why fear future robots with differing values? Here is one possible cause:

Fear Of Strangers:  Our distant ancestors evolved a deep fear of strangers.  They knew that their complex ways to keep peace only worked for folks they knew, who looked, talked, and acted like them.  Unexpected strangers were probably best killed on sight.

This is a good explanation, but much less a good reason, to fear robots.  Over recent millennia humans have developed many ways, e.g., trade, contract, law, and treaties, to keep peace with folks who look, talk, and act differently.  We only need others to be similar enough to us to use these methods; they need to know what equilibrium behavior to expect, and to speak in languages we can translate. They don’t otherwise need to share our values.

But even if peace is preserved, other reasons for fear remain:

Outbid By Rich:  In some situations you can reasonably expect declining relative future wealth for yourself and those you care about.  For example, a century ago folks who foresaw cars replacing horses, and who had a very strong heritable preference for working with horses, could reasonably expect falling demand, and lower relative wages, for their preferred job skills. (The horses themselves did far worse; most could not afford subsistence wages.)  Now for many things you want it is absolute, not relative, wages that matter.  But some things, like prime sea-view property, can be commonly valued and in limited supply.  So you might fear others’ richer descendants outbidding yours for sea views.

Note that this fear requires an expectation that, relative to others, your nature or preferences conflicts more with your productivity.  Note also that in some ways this problem gets worse as others get more similar.  For example, if others prefer mountain views while you prefer sea views, their wealth would less reduce your access to sea views.  If this is the problem, you should prefer others to have different values from you.

What if you worry that rich others threaten your descendants’ existence, and not just their sea view access?  Well since interest rates have long been high, and since typical wages are now far above subsistence, then modest savings today, and secure property rights tomorrow, could ensure many surviving descendants tomorrow.  But you might still fear:

War & Theft:  Over the last few centuries we have vastly improved our ability to coordinate on larger scales, greatly reducing the rate of war, theft, and other property violations. Nevertheless, war and theft still happen, and we cannot guarantee recent trends will continue.  So many fear foreign nations, e.g., China or India, getting rich and militarily powerful, then seeking world conquest.  One may also fear theft of one’s innovations if intellectual property rights remain weak.

Note that these new ways to coordinate on large scales to prevent war and theft rely little on our empathy for, or similarity with, distant others.  They depend far more on our ways to make commitments and to monitor key acts.  And the mere possibility of future theft would hardly be a good reason for genocide today; we now seem to benefit greatly on net when distant foreigners get rich.  This doesn’t mean we should ignore the risks of future war and theft, but it does suggest that our efforts should focus more on improving our ways to coordinate on large scales, and less on preparing to exterminate them before they exterminate us.

Chalmers does not say why exactly we should expect robots with the “wrong” values to give “disaster,” so much so that he is sympathetic to preventing their autonomy if only that were possible:

We might try to constrain their cognitive capacities in certain respects, so that they are good at certain tasks with which we need help, but so that they lack certain key features such as autonomy. … On the face of it, such an AI might pose fewer risks than an autonomous AI, at least if it is in the hands of a responsible controller.  Now, it is far from clear that AI or AI+ systems of this sort will be feasible. … Such an approach is likely to be unstable in the long run.

Chalmers offers no reasons to fear robots beyond the three standard reasons to fear foreigners I’ve listed above: fear of strangers, outbid by rich, and war & theft.  Nor does he offer reasons why it is robots’ differing values that are the problem, even though differing values are mainly only important for the fear of strangers motive, which has little relevance in the modern world.  Until we have particular credible reasons to fear robots more than other foreigners, we should treat robots like generic foreigners, with caution but also an expectation of mutual gains from trade.

Finally, let me note that Chalmers’ discussion could benefit from economists’ habit of noting that our ability to make most anything depends on the price of inputs, and therefore on the entire world economy, and not just on internal features of particular systems. Chalmers:

All we need for the purpose of the argument is (i) a self-amplifying cognitive capacity G: a capacity such that increases in that capacity go along with proportionate (or greater) increases in the ability to create systems with that capacity, (ii) the thesis that we can create systems whose capacity G is greater than our own, and (iii) a correlated cognitive capacity H that we care about, such that certain small increases in H can always be produced by large enough increases in G.

Unless the “system” here is our total economy, this description falsely suggests that a smaller system’s capacity to create other systems depends only on its internal features.

Added 6Apr: From the comments it seems my main point isn’t getting through, so let me rephrase: I’m not saying we have nothing to fear from robots, nor that their values make no difference.  I’m saying the natural and common human obsession with how much their values differ overall from ours distracts us from worrying effectively.  Here are better priorities for living in peace with strange potentially-powerful creatures, be they robots, aliens, time-travelers, or just diverse human races:

  1. Reduce the salience of the them-us distinction relative to other distinctions.  Try to have them and us live intermingled, and not segregated, so that many natural alliances of shared interests include both us and them.
  2. Have them and us use the same (or at least similar) institutions to keep peace among themselves and ourselves as we use to keep peace between them and us.  Minimize any ways those institutions formally treat us and them differently.

Added 7Apr: See also two posts from October.

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Consciousness as Middleman

First thing we do, let’s kill all the lawyers. King Henry VI

People have long been suspicious of “middlemen,” e.g., traders, lawyers, bankers, salesman, marketers, managers, and politicians.  For millennia, most people have suspected such middlemen of being mostly social parasites, and many “Utopian” reforms have planned to eliminate them.  Economists have faced an uphill battle arguing that middlemen usually serve important functions.  Among intellectuals, engineers and physical scientists find it especially hard to appreciate roles other than designing, building, maintaining, fueling, and distributing physical goods.

A similar scenario plays out today for the “middlemen” of our minds.  Engineers and physical scientists can see the value of big human brains for solving puzzles or making and using tools.  But such folks find it harder to see functions of play, laughter, friendship, love, music, art, stories, and consciousness. They sort of see that our best theories suggest these have important social functions, but they presume this is a temporary glitch, due to our stupidity or hostility; they can’t imagine really advanced efficient societies retaining such things.  They don’t get that coordination is hard.  So when they consider how our descendants minds may evolve in the future, they feel confident that puzzle-solving must remain, but fear that the rest will disappear.

For example, I just finished the (good) hard science fiction novel Blindsight by Peter Watts (free here), whose main theme is that consciousness is a parasite, which efficient aliens avoid.  Spoiler quotes below the fold: Continue reading "Consciousness as Middleman" »

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Is The City-ularity Near?

The land around New York City is worth a lot.  A 2008 analysis estimated prices for land, not counting buildings etc., for most (~80%?) of the nearby area (2750 square miles, = a 52 mile square).  The total New York area land value (total land times ave price) was 5.5T$ (trillion) in 2002 and 28T$ in 2006.

The Economist said that in 2002 all developed nation real estate was worth 62T$.  Since raw land value is on average about a third of total real estate value, that puts New York area real estate at over 30% of all developed nation real estate in 2002!  Whatever the exact number, clearly this agglomeration contains vast value.

New York land is valuable mainly because of how it is organized.  People want to be there because they want to interact with other people they expect to be there, and they expect those interactions to be quite mutually beneficial.  If you could take any other 50 mile square (of which Earth has 72,000), and create that same expectation of mutual value from interactions, you could get people to come there, make buildings, etc., and sell that land for many trillions of dollars of profit.

Yet the organization of New York was mostly set long ago based on old tech (e.g., horses, cars, typewriters).  Worse, no one really understands at a deep level how it is organized or why that works so well.  Different people understand different parts, in mostly crude empirical ways.

So what will happen when super-duper smarties wrinkle their brows so hard that out pops a deep math theory of cities, explaining clearly how city value is produced?  What if they apply their theory to designing a city structure that takes best advantage of our most advanced techs, of 7gen phones, twitter-pedias, flying Segways, solar panels, gene-mod pigeons, and super-fluffy cupcakes?  Making each city aspect more efficient makes the city more attractive, increasing the gains from making other aspects more efficient, in a grand spiral of bigger gains.

Once they convince the world of the vast value in their super-stupendous city design, won’t everyone flock there and pay mucho trillions for the privilege? Couldn’t they leverage this lead into better theories enabling better designs giving far more trillions, and then spend all that on a super-designed war machine based on those same super insights, and turn us all into down dour super-slaves?  So isn’t the very mostest importantest cause ever to make sure that we, the friendly freedom fighters, find this super deep city theory first?

Well, no, it isn’t.  We don’t believe in a city-ularity because we don’t believe in a super-city theory found in a big brain flash of insight.  What makes cities work well is mostly getting lots of details right.  Sure new-tech-based cities designs can work better, but gradual tech gains mean no city is suddenly vastly better than others.  Each change has costs to be weighed against hoped-for gains.  Sure costs of change might be lower when making a whole new city from scratch, but for that to work you have to be damn sure you know which changes are actually good ideas.

For similar reasons, I’m skeptical of a blank-slate AI mind-design singularity.  Sure if there were a super mind theory that allowed vast mental efficiency gains all at once, but there isn’t.  Minds are vast complex structures full of parts that depend intricately on each other, much like the citizens of a city.  Minds, like cities, best improve gradually, because you just never know enough to manage a vast redesign of something with such complex inter-dependent adaptations.

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