Tag Archives: Ems

Meaning is Easy to Find, Hard to Justify

One of the strangest questions I get when giving talks on Age of Em is a variation on this:

How can ems find enough meaning in their lives to get up and go to work everyday, instead of committing suicide?

As the vast majority of people in most every society do not commit suicide, and manage to get up for work on most workdays, why would anyone expect this to be a huge problem in a random new society?

Even stranger is that I mostly get this question from smart sincere college students who are doing well at school. And I also hear that such students often complain that they do not know how to motivate themselves to do many things that they “want” to do. I interpret this all as resulting from overly far thinking on meaning. Let me explain.

If we compare happiness to meaning, then happiness tends to be an evaluation of a more local situation, while meaning tends to be an evaluation of a more global situation. You are happy about this moment, but you have meaning regarding your life.

Now you can do either of these evaluations in a near or a far mode. That is, you can just ask yourself for your intuitions on how you feel about your life, within over-thinking it, or you can reason abstractly and idealistically about what sort of meaning you should have or can justify having. In that later more abstract mode, smart sincere people can be stumped. How can they justify having meaning in a world where there is so much randomness and suffering, and that is so far from being a heaven?

Of course in a sense, heaven is an incoherent concept. We have so many random idealistic constraints on what heaven should be like that it isn’t clear that anything can satisfy them all. For example, we may want to be the hero of a dramatic story, even if we know that characters in such stories wish that they could live in more peaceful worlds.

Idealistic young people have such problems in spades, because they haven’t lived long enough to see how unreasonable are their many idealistic demands. And smarter people can think up even more such demands.

But the basic fact is that most everyone in most every society does in fact find meaning in their lives, even if they don’t know how to justify it. Thus I can be pretty confident that ems also find meaning in their lives.

Here are some more random facts about meaning, drawn from my revised Age of Em, out next April.

Today, individuals who earn higher wages tend to have both more happiness and a stronger sense of purpose, and this sense of purpose seems to cause higher wages. People with a stronger sense of purpose also tend to live longer. Nations that are richer tend to have more happiness but less meaning in life, in part because they have less religion. .. Types of meaning that people get from work today include authenticity, agency, self-worth, purpose, belonging, and transcendence.

Happiness and meaning have different implications for behavior, and are sometimes at odds. That is, activities that raise happiness often lower meaning, and vice versa. For example, people with meaning think more about the future, while happy people focus on the here and now. People with meaning tend to be givers who help others, while happy people tend to be takers who are helped by others. Being a parent and spending time with loved ones gives meaning, but spending time with friends makes one happy.

Affirming one’s identity and expressing oneself increase meaning but not happiness. People with more struggles, problems, and stresses have more meaning, but are less happy. Happiness but not meaning predicts a satisfaction of desires, such as for health and money, and more frequent good relative to bad feelings. Older people gain meaning by giving advice to younger people. We gain more meaning when we follow our gut feelings rather than thinking abstractly about our situations.

My weak guess is that productivity tends to predict meaning more strongly than happiness. If this is correct, it suggests that, all else equal, ems will tend to think more about the future, more be givers who help others, spend more time with loved ones and less with friends, more affirm their identity and express themselves, give more advice, and follow gut feelings more. But they will also have more struggles and less often have their desires satisfied.

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Can Human-Like Software Win?

Many, perhaps most, think it obvious that computer-like systems will eventually be more productive than human-like systems in most all jobs. So they focus on how humans might maintain control, even after this transition. But this eventuality is less obvious than it seems, depending on what exactly one means by “human-like” or “computer-like” systems. Let me explain.

Today the software that sits in human brains is stuck in human brain hardware, while the other kinds of software that we write (or train) sit in the artificial hardware that we make. And this artificial hardware has been improving rapidly far more rapidly than has human brain hardware. Partly as a result of this, systems of artificial software and hardware have been improving rapidly compared to human brain systems.

But eventually we will find a way to transfer the software from human brains into artificial hardware. Ems are one way to do this, as a relatively direct port. But other transfer mechanics may be developed.

Once human brain software is in the same sort of artificial computing hardware as all the other software, then the relative productivity of different software categories comes down to a question of quality: which categories of software tend to be more productive on which tasks?

Of course there will many different variations available within each category, to match to different problems. And the overall productivity of each category will depend both on previous efforts to develop and improve software in that category, and also on previous investments in other systems to match and complement that software. For example, familiar artificial software will gain because we have spent longer working to match it to familiar artificial hardware, while human software will gain from being well matched to complex existing social systems, such as language, firms, law, and government.

People give many arguments for why they expect human-like software to mostly lose this future competition, even when it has access to the same hardware. For example, they say that other software could lack human biases and also scale better, have more reliable memory, communicate better over wider scopes, be easier to understand, have easier meta-control and self-modification, and be based more directly on formal abstract theories of learning, decision, computation, and organization.

Now consider two informal polls I recently gave my twitter followers:

Surprisingly, at least to me, the main reason that people expect human-like software to lose is that they mostly expect whole new categories of software to appear, categories quite different from both the software in the human brain and also all the many kinds of software with which we are now familiar. If it comes down to a contest between human-like and familiar software categories, only a quarter of them expect human-like to lose big.

The reason I find this surprising is that all of the reasons that I’ve seen given for why human-like software could be at a disadvantage seem to apply just as well to familiar categories of software. In addition, a new category must start with the disadvantages of having less previous investment in that category and in matching other systems to it. That is, none of these are reasons to expect imagined new categories of software to beat familiar artificial software, and yet people offer them as reasons to think whole new much more powerful categories will appear and win.

I conclude that people don’t mostly use specific reasons to conclude that human-like software will lose, once it can be moved to artificial hardware. Instead they just have a general belief that the space of possible software is huge and contains many new categories to discover. This just seems to be the generic belief that competition and innovation will eventually produce a lot of change. Its not that human-like software has any overall competitive disadvantage compared to concrete known competitors; it is at least as likely to have winning descendants as any such competitors. Its just that our descendants are likely to change a lot as they evolve over time. Which seems to me a very different story than the humans-are-sure-to-lose story we usually hear.

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Ems in Walkaway

Some science fiction (sf) fans have taken offense at my claim that non-fiction analysis of future tech scenarios can be more accurate than sf scenarios, whose authors have other priorities. So I may periodically critique recent sf stories with ems for accuracy. Note that I’m not implying that such stories should have been more accurate; sf writing is damn hard work and its authors juggle a many difficult tradeoffs. But many seem unaware of just how often accuracy is sacrificed.

The most recent sf I’ve read that includes ems is Walkaway, by “New York Times bestselling author” Cory Doctorow, published back in April:

Now that anyone can design and print the basic necessities of life—food, clothing, shelter—from a computer, there seems to be little reason to toil within the system. It’s still a dangerous world out there, the empty lands wrecked by climate change, dead cities hollowed out by industrial flight, shadows hiding predators animal and human alike. Still, when the initial pioneer walkaways flourish, more people join them.

The emotional center of Walkaway is elaborating this vision of a decentralized post-scarcity society trying to do without property or hierarchy. Though I’m skeptical, I greatly respect attempts to describe such visions in more detail. Doctorow, however, apparently thinks we economists make up bogus math for the sole purpose of justifying billionaire wealth inequality. Continue reading "Ems in Walkaway" »

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Philosophy Vs. Duck Tests

Philosophers, and intellectuals more broadly, love to point out how things might be more complex than they seem. They identify more and subtler distinctions, suggest more complex dependencies, and warn against relying on “shallow” advisors less “deep” than they. Subtly and complexity is basically what they have to sell.

I’ve often heard people resist such sales pressure by saying things like “if it looks like a duck, walks like a duck, and quacks like a duck, it’s a duck.” Instead of using complex analysis and concepts to infer and apply deep structures, they prefer to such use a “duck test” and judge by adding up many weak surface clues. When a deep analysis disagrees with a shallow appearance, they usually prefer to go shallow.

Interestingly, this whole duck example came from philosophers trying to warn against judging from surface appearances: Continue reading "Philosophy Vs. Duck Tests" »

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“Human” Seems Low Dimensional

Imagine that there is a certain class of “core” mental tasks, where a single “IQ” factor explains most variance in such task ability, and no other factors explained much variance. If one main factor explains most variation, and no other factors do, then variation in this area is basically one dimensional plus local noise. So to estimate performance on any one focus task, usually you’d want to average over abilities on many core tasks to estimate that one dimension of IQ, and then use IQ to estimate ability on that focus task.

Now imagine that you are trying to evaluate someone on a core task A, and you are told that ability on core task B is very diagnostic. That is, even if a person is bad on many other random tasks, if they are good at B you can be pretty sure that they will be good at A. And even if they are good at many other tasks, if they are bad at B, they will be bad at A. In this case, you would know that this claim about B being very diagnostic on A makes the pair A and B unusual among core task pairs. If there were a big clump of tasks strongly diagnostic about each other, that would show up as another factor explaining a noticeable fraction of the total variance. Making this world higher dimensional. So this claim about A and B might be true, but your prior is against it.

Now consider the question of how “human-like” something is. Many indicators may be relevant to judging this, and one may draw many implications from such a judgment. In principle this concept of “human-like” could be high dimensional, so that there are many separate packages of indicators relevant for judging matching packages of implications. But anecdotally, humans seem to have a tendency to “anthropomorphize,” that is, to treat non-humans as if they were somewhat human in a simple low-dimensional way that doesn’t recognize many dimensions of difference. That is, things just seem more or less human. So the more ways in which something is human-like, the more you can reasonably guess that it will be human like in other ways. This tendency appears in a wide range of ordinary environments, and its targets include plants, animals, weather, planets, luck, sculptures, machines, and software. Continue reading "“Human” Seems Low Dimensional" »

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Boost For Being Best

The fraction of a normal distribution that is six or more standard deviations above the mean is one in ten billion. But the world has almost eight billion people in it. So in principle we should be able to get six standard deviations in performance gain by selecting the world’s best person at something, compared to using an average person.

I’m revising Age of Em for a paperback edition, expected in April. The rest of this post is from a draft of new text elaborating that point, and its implication for em leisure:

Em workers also earn wage premiums when they are the very best in the world at what they do. Even under the most severe wage competition, a best em can earn an extra wage equal to the difference between their productivity and the productivity of the second best em. When clans coordinate internally on wage negotiations, this is the difference in productivity between clans. (Clans who can’t coordinate internally are selected out of the em world, as they don’t cover their fixed costs, such as for training and marketing.)

Out of 10 billion independently and normally distributed (IID) samples, the maximum is on average about 6.4 standard deviations above the mean. Average spacings between the second, third, fourth highest samples are roughly 0.147, 0.075, and 0.05 standard deviations respectively (Branwen 2017). So when ems are selected out of 10 billion humans, the best em clan may be this much better than other em clans on normally distributed parameters. Using the log-normal wage distribution observed in our world (Provenzano 2015), this predicts that the best human in the world at any particular task is four to five times more productive than the median person, is over three percent more productive than the second most productive person, and is five percent more productive than the third most productive person.

If em clan relative productivity is drawn from this same distribution, if maximum em productivity comes at a 70 hour workweek, and if the best and second best em clans do not coordinate on wages they accept, then even under the strongest wage competition between clans, the best clan could take an extra 20 minutes a day more leisure, or two minutes per work hour, in addition to the six minutes per hour and other work breaks they take to be maximally productive.

This 20 minute figure is an underestimate for four reasons. First, the effective sample size of ems is smaller due to age limits on desirable ems. Second, most parameters are distributed so that the tails are thicker than in the normal distribution (Reed and Jorgensen 2004).

Third, differing wealth effects may add to differing productivity effects. On average over the last 11 years, the five richest people on Earth have each been about 10 percent richer than the next richest person. If future em income ratios were like this current wealth ratio, then the best em worker could afford roughly an extra hour per day of leisure, or an additional six minutes per hour.

Fourth, competition probably does not take the strongest possible form, and the best few ems can probably coordinate to some extent. For example, if the best two em clans coordinate completely on wages, but compete strongly with the third best clan, then instead of the best and second best taking 20 and zero minutes of extra leisure per day, they could take 30 and 10 extra minutes, respectively.

Plausibly then, the best em workers can afford to take an additional two to six minutes of leisure per hour of work in a ten hour work day, in addition to the over six minutes per hour of break needed for maximum productivity.

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A Post-Em-Era Hint

A few months ago I noticed a pattern across the past eras of forager, farmer industry: each era has a major cycle (ice ages, empires rise & fall, business cycle) with a period of about one third of that era’s doubling time. So I tentatively suggested that a em future might also have a major cycle of roughly one third of its doubling time. If that economic doubling time is about a month, the em major cycle period might be about a week.

Now I report another pattern, to be treated similarly. In roughly the middle of each past era, a pair of major innovations in calculating and communicating appeared, and gradually went from barely existing to having big social impacts.

  • Forager: At unknown periods during the roughly two million year forager era, humanoids evolved reasoning and language. That is, we became able to think about and say many complex things to each other, including our reasons for and against claims.
  • Farmer: While the farming era lasted roughly 7 to 10 millennia, the first known writing was 5 millennia ago, and the first known math textbooks 4 millennia ago. About 2.5 millennia ago writing became widespread enough to induce major religious changes worldwide.
  • Industry: While the industry era has lasted roughly 16 to 24 decades, depending on how you count, the telegraph was developed 18 decades ago, and the wholesale switch from mechanical to digital electronic communication happened 4 to 6 decades ago. The idea of the computer was described 20 decades ago, the first digital computer was made 7 decades ago, and computers became widespread roughly 3 decades ago.

Note that innovations in calculation and communication were not independent, but instead intertwined with and enabled each other. Note also that these innovations did not change the growth rate of the world economy at the time; each era continued doubling at the same rate as before. But these innovations still seem essential to enabling the following era. It is hard to imagine farming before language and reasoning, nor industry before math and writing, nor ems before digital computers and communication.

This pattern weakly suggests that another pair of key innovations in calculation and communication may appear and then grow in importance across a wide middle of the em era. This era may only last a year or two in objective time, though typical ems may experience millennia during this time.

This innovation pair would be interdependent, not change the growth rate, and perhaps enable a new era to follow. I can think of two plausible candidates:

  1. Ems might discover a better language for expressing and manipulating something like brain states. This could help ems to share their thoughts and use auxiliary hardware to help calculate useful thoughts.
  2. Ems might develop analogues to combinatorial prediction markets, and thus better share beliefs and aggregate information on a wide range of topics.

(Or maybe the innovation produces some combination of these.) Again, these are crude speculations based on a weak inference from a rough pattern in only three data points. But even so, they give us a vague hint about what an age after ems might look like. And such hints are actually pretty hard to find.

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Fuller on Age of Em

I’d heard that an academic review of Age of Em was forthcoming from the new Journal of Posthuman Studies. And after hearing about Baum’s review, the author Steve Fuller of this second academic review (which won’t be published for a few months) gave me permission to quote from it here. First some praise: Continue reading "Fuller on Age of Em" »

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Baum on Age of Em

In the Journal Futures, Seth Baum gives the first academic review of Age of Em. First, some words of praise: Continue reading "Baum on Age of Em" »

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Future Gender Is Far

What’s the worst systematic bias in thinking on the future? My guess: too much abstraction. The far vs. near mode distinction was first noticed in future thinking, because the effect is so big there.

I posted a few weeks ago that the problem with the word “posthuman” is that it assumes our descendants will differ somehow in a way to make them “other,” without specifying any a particular change to do that. It abstracts from particular changes to just embody the abstract idea of othering-change. And I’ve previously noted there are taboos against assuming that something we see as a problem won’t be solved, and even against presenting such a problem without proposing a solution.

In this post let me point out that a related problem plagues future gender relation thoughts. While many hope that future gender relations will be “better”, most aren’t at all clear on what specifically that entails. For some, all differing behaviors and expectations about genders should disappear, while for others only “legitimate” differences remain, with little agreement on which are legitimate. This makes it hard to describe any concrete future of gender relations without violating our taboo against failing to solve problems.

For example, at The Good Men Project, Joseph Gelfer discusses the Age of Em. He seems to like or respect the book overall:

Fascinating exploration of what the world may look like once large numbers of computer-based brain emulations are a reality.

But he less likes what he reads on gender:

Hanson sees a future where an em workforce mirrors the most useful and productive forms of workforce that we experience today. .. likely choose [to scan] workaholic competitive types. Because such types tend to be male, Hanson imagines an em workforce that is disproportionately male (these workers also tend to rise early, work alone and use stimulants).

This disproportionately male workforce has implications for how sexuality manifests in em society. First, because the reproductive impetus of sex is erased in the world of ems, sexual desire will be seen as less compelling. In turn, this could lead to “mind tweaks” that have the effect of castration, .. [or] greater cultural acceptance of non-hetero forms of sexual orientation, or software that make ems of the same sex appear as the opposite sex. .. [or] paying professional em sex workers.

It is important to note that Hanson does not argue that this is the way em society should look, rather how he imagines it will look by extrapolating what he identifies in society both today and through the arc of human history. So, if we can identify certain male traits that stretch back to the beginning of the agricultural era, we should also be able to locate those same traits in the em era. What might be missing in this methodology is a full application of exponential change. In other words, Hanson rightly notes how population, technology and so forth have evolved with increasing speed throughout history, yet does not apply that same speed of evolution to attitudes towards gender. Given how much perceptions around gender have changed in the past 50 years, if we accept a pattern of exponential development in such perceptions, the minds that are scanned for first generation ems will likely have a very different attitude toward gender than today, let alone thousands of years past. (more)

Obviously Gelfer doesn’t like something about the scenario I describe, but he doesn’t identify anything particular he disagrees with, nor offer any particular arguments. His only contrary argument is a maximally abstract “exponential” trend, whereby everything gets better. Therefore gender relations must get better, therefore any future gender relations feature that he or anyone doesn’t like is doubtful.

For the record, I didn’t say the em world selects for “competitive types”, that people would work alone, or that there’d be more men. Instead I have a whole section on a likely “Gender Imbalance”:

Although it is hard to predict which gender will be more in demand in the em world, one gender might end up supplying proportionally more workers than the other.

Though I doubt Gelfer is any happier with a future with may more women than men; any big imbalance probably sounds worse to most people, and thus can’t happen according to the better future gender relations principle.

I suspect Gelfer’s errors about my book are consistently in the direction of incorrectly attributing features to the scenario that he likes less. People usually paint the future as a heaven or a hell, and so if my scenario isn’t Gelfer’s heaven, it must be his hell.

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