Tag Archives: Future

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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A Tangled Task Future

Imagine that you want to untangle a pile of cables. It wasn’t tangled on purpose; tangling just resulted naturally from how these cables were used. You’d probably look for the least tangled cable in the least tangled part of the pile, and start to work there. In this post I will argue that, in a nutshell, this is how we are slowly automating our world of work: we are un- and re-tangling it.

This has many implications, including for the long-term future of human-like creatures in a competitive world. But first we have a bit of explaining to do. Continue reading "A Tangled Task Future" »

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Mormon Transhumanists

A standard trope of science fiction has religious groups using violence to stop a new technology. Perhaps because of this, many are surprised by the existence of religious transhumanists. Saturday I gave a keynote talk on Age of Em at the Mormon Transhumanist Association (MTA) annual conference, and had a chance to study such folks in more detail. And I should say right off the top that this MTA audience, compared to other audiences, had notably fewer morality or religious related objections to my em scenario.

I’m not surprised by the existence of religious tech futurists. Overall, the major world religions have been quite successful in adapting to the many social changes since most of them first appeared many millennia ago. Also, the main predictor of interest in tech futurism and science fiction is an interest in science and technology, and religious folks are not underrepresented there. Even so, you might ask what your favorite theories of religion predict about how MTA folk would differ from other transhumanists.

The most obvious difference I saw is that MTA does community very well, with good organization, little shirking, and lots of polite, respectful, and friendly interaction. This makes sense. Mormons in general have strong community norms, and one of the main functions of religion is to build strong communities. Mormonism is a relatively high commitment religion, and those tend to promote stronger bonds.

Though I did not anticipate it, a predictable consequence of this is that MTA is more of a transhuman take on Mormonism than a Mormon take on transhumanism. On reflection, this reveals an interesting way that long-lived groups with dogmas retain and co-op smart intellectuals. Let me explain.

One standard sales technique is to try to get your mark to spend lots of time considering your product. This is a reason why salespeople often seem so slow and chatty. The more time you spend considering their product, the longer that you will estimate it will take to consider other products, and the more likely you are to quit searching and take their product.

Similarly, religions often expose children to a mass of details, as in religious stories. Smart children can be especially engaged by these details because they like to show off their ability to remember and understand detail. Later on, such people can show off their ability to interpret these details in many ways, and to identify awkward and conflicting elements.

Even if the conflicts they find are so severe as to reasonably call into question the entire thing, by that time such people have invested so much in learning details of their religion that they’d lose a lot of ability to show off if they just left and never talked about it again. Some become vocally against their old religion, which lets them keep talking and showing off about it. But even in opposition, they are still then mostly defined by that religion.

I didn’t meet any MTA who took Mormon claims on miraculous historical events literally. They seemed well informed on science and tech and willing to apply typical engineering and science standards to such things. Even so, MTA folks are so focused on their own Mormon world that they tend to be less interested in asking how Mormons could anticipate and prepare for future changes, and more interested in how future/sci/tech themes could reframe and interpret key Mormon theological debates and claims. In practice their strong desire to remain Mormons in good standing means that they mostly accept practical church authority, including the many ways that the church hides the awkward and conflicting elements of its religions stories and dogma.

For example, MTA folks exploring a “new god argument” seek scenarios wherein we might live in a simulation that resonate with Mormon claims of a universe full of life and gods. While these folks aren’t indifferent to the relative plausibility of hypotheses, this sort of exercise is quite different from just asking what sort of simulations would be most likely if we in fact did live in a simulation.

I’ve said that we today live in an unprecedented dreamtime of unadaptive behavior, a dream from which some will eventually awake. Religious folks in general tend to be better positioned to awake sooner, as they have stronger communities, more self-control, and higher fertility. But even if the trope applies far more in fiction than in reality, it remains possible that Mormon religious orthodoxy could interfere with Mormons adapting to the future.

MTA could help to deal with such problems by becoming trusted guides to the future for other Mormons. To fill that role, they would of course need to show enough interest in Mormon theology to convince the others that they are good Mormons. But they would also need to pay more attention to just studying the future regardless of its relevance to Mormon theology. Look at what is possible, what is likely, and the consequences of various actions. For their sakes, I hope that they can make this adjustment.

By the way, we can talk similarly about libertarians who focus on criticizing government regulation and redistribution. The more one studies the details of government actions, showing off via knowing more such detail, then even if one mostly criticizes such actions, still one’s thinking becomes mostly defined by government. To avoid this outcome, focus more on thinking about what non-government organizations should do and how. It isn’t enough to say “without government, the market will do it.” Become part of a market that does things.

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Better Babblers

You can think of knowing how to write as knowing how to correlate words. Given no words, what first word should you write. Then given one word, what second word best correlates with that. Then given two words, what third word best fits with those two. And so on. Thus your knowledge of how to write can be broken into what you know at these different correlation orders: one word, two words, three words, and so on. Each time you pick a new word you can combine knowledge at these different orders, by weighing all their different recommendations for your next word.

This correlation order approach can also be applied at different scales. For example, given some classification of your first sentence, what kind of second sentence should follow? Given a classification of your first chapter, what kind of second chapter should follow? Many other kinds of knowledge can be similarly broken down into correlation orders, at different scales. We can do this for music, paintings, interior decoration, computer programs, math theorems, and so on.

Given a huge database, such as of writings, it is easy to get good at very low orders; you can just use the correlation frequencies found in your dataset. After that, simple statistical models applied to this database can give you good estimates for correlations to use at somewhat higher orders. And if you have enough data (roughly ten million examples per category I’m told) then recently popular machine learning techniques can improve your estimates at a next set of higher orders.

There are some cases where this is enough; either you can get enormous amounts of data, or learning low order correlations well is enough to solve your problem. These cases include many games with well defined rules, many physical tasks where exact simulations are feasible, and some kinds of language translation. But there are still many other cases where this is far from enough to achieve human level proficiency. In these cases an important part of what we know can be described as very high order correlations produced by “deep” knowledge structures that aren’t well reduced to low order correlations.

After eighteen years of being a professor, I’ve graded many student essays. And while I usually try to teach a deep structure of concepts, what the median student actually learns seems to mostly be a set of low order correlations. They know what words to use, which words tend to go together, which combinations tend to have positive associations, and so on. But if you ask an exam question where the deep structure answer differs from answer you’d guess looking at low order correlations, most students usually give the wrong answer.

Simple correlations also seem sufficient to capture most polite conversation talk, such as the weather is nice, how is your mother’s illness, and damn that other political party. Simple correlations are also most of what I see in inspirational TED talks, and when public intellectuals and talk show guests pontificate on topics they really don’t understand, such as quantum mechanics, consciousness, postmodernism, or the need always for more regulation everywhere. After all, media entertainers don’t need to understand deep structures any better than do their audiences.

Let me call styles of talking (or music, etc.) that rely mostly on low order correlations “babbling”. Babbling isn’t meaningless, but to ignorant audiences it often appears to be based on a deeper understanding than is actually the case. When done well, babbling can be entertaining, comforting, titillating, or exciting. It just isn’t usually a good place to learn deep insight.

As we slowly get better at statistics and machine learning, our machines will slowly get better at babbling. The famous Eliza chatbot went surprisingly far using very low order correlations, and today chatbots best fool us into thinking they are human when they stick to babbling style conversations. So what does a world of better babblers look like?

First, machines will better mimic low quality student essays, so schools will have to try harder to keep such students from using artificial babblers.

Second, the better machines get at babbling, the more humans will try to distinguish themselves from machines via non-babbling conversational styles. So expect less use of simple easy-to-understand-and-predict speech in casual polite conversation, inspirational speeches, and public intellectual talk.

One option is to put a higher premium on talk that actually makes deep sense, in terms of deep concepts that experts understand. That would be nice for those of us who have always emphasized such things. But alas there are other options.

A second option is to put a higher premium on developing very distinctive styles of talking. This would be like how typical popular songs from two centuries ago could be sung and enjoyed by most anyone, compared to how popular music today is matched in great detail to the particular features of particular artists. Imagine most all future speakers having as distinct a personal talking style.

A third option is more indirect, ironic, and insider style talk, such as we tend to see on Twitter today. People using words and phrases and cultural references in ways that only folks very near in cultural space can clearly accept as within recent local fashion. Artificial babblers might not have enough data to track changing fashions in such narrow groups.

Bottom line: the more kinds of conversation styles that simple machines can manage, the more humans will try to avoid talking in those styles, a least when not talking to machines.

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The Robot Protocol

Talking with a professor of robotics, I noticed a nice approachable question at the intersection of social science, computer science, and futurism.

Someday robots will mix with humans in public, walking our streets, parks, hospitals, and stores, driving our streets, swimming our waterways, and perhaps flying our skies. Such public robots may vary enormously in their mental and physical capacities, but if they are to mix smoothly with humans in public they then we will probably expect them to maintain a minimal set of common social capacities. Such as responding sensibly to “Who are you?” and “Get out of my way.” And the rest of us would have a new modified set of social norms for dealing with public robots via these capacities.

Together these common robot capacities and matching human social norms would become a “robot protocol.” Once ordinary people and robots makers have adapted to it, this protocol would be a standard persisting across space and time, and relatively hard to change. A standard that diverse robots could also use when interacting with each other in public.

Because it would be a wide and persistent standard, the robot protocol can’t be matched in much detail to the specific local costs of implementing various robot capacities. Instead, it could at best be matched to broad overall trends in such costs. To allow robots to walk among us, we’d try to be forgiving and only expect robots to have capacities that we especially value, and that are relatively cheap to implement in a wide range of contexts.

(Of course this general robot protocol isn’t the only thing that would coordinate robot and human interactions. There’d also be many other more context-dependent protocols.)

One simple option would be to expect each public robot to be “tethered” via fast robust communication to a person on call who can rapidly respond to all queries that the robot can’t handle itself. But it isn’t clear how sufficient this approach will be for many possible queries.

Robots would probably be expected to find and comply with any publicly posted rules for interacting in particular spaces, such as the rules we often post for humans on signs. Perhaps we will simplify such rules for robots. In addition, here are some things that people sometimes say to each other in public where we might perhaps want robots to have analogous capacities:

Who are you? What are you doing here? Why are you following me? Please don’t record me. I’m serving you with this legal warrant. Stop, this is the police! You are not allowed to be here; leave. Non-authorized personnel must evacuate this area immediately. Get out of my way. You are hurting me. Why are you calling attention to me? Can you help me? Can you take our picture? Where is the nearest bathroom? Where is a nearby recharging station? (I may add more here.)

It seems feasible to start now to think about the design of such a robot protocol. Of course in the end a robot protocol might be just a social convention without the force of law, and it may result more from decentralized evolution than centralized design. Even so, we may now know enough about human social preferences and the broad outlines of the costs of robot capacities to start to usefully think about this problem.

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Big Software Firm Bleg

I haven’t yet posted much on AI as Software. But now I’ll say more, as I want to ask a question.

Someday ems may replace humans in most jobs, and my first book talks about how that might change many things. But whether or not ems are the first kind of software to replace humans wholesale in jobs, eventually non-em software may plausibly do this. Such software would replace ems if ems came first, but if not then such software would directly replace humans.

Many people suggest, implicitly or explicitly, that non-em software that takes over most jobs will differ in big ways from the software that we’ve seen over the last seventy years. But they are rarely clear on what exact differences they foresee. So the plan of my project is to just assume our past software experience is a good guide to future software. That is, to predict the future, one may 1) assume current distributions of software features will continue, or 2) project past feature trends into future changes, or 3) combine past software feature correlations with other ways we expect the future to differ.

This effort may encourage others to better clarify how they think future software will differ, and help us to estimate the consequences of such assumptions. It may also help us to more directly understand a software-dominated future, if there are many ways that future software won’t greatly change.

Today, each industry makes a kind of stuff (product or service) we want, or a kind of stuff that helps other industries to make stuff. But while such industries are often dominated by a small number of firms, the economy as a whole is not so dominated. This is mainly because there are so many different industries, and firms suffer when they try to participate in too many industries. Will this lack of concentration continue into a software dominated future?

Today each industry gets a lot of help from humans, and each industry helps to train its humans to better help that industry. In addition, a few special industries, such as schooling and parenting, change humans in more general ways, to help better in a wide range of industries. In a software dominated future, humans are replaced by software, and the schooling and parenting industries are replaced by a general software industry. Industry-independent development of software would happen in the general software industry, while specific adaptations for particular industries would happen within those industries.

If so, the new degree of producer concentration depends on two key factors: what fraction of software development is general as opposed to industry-specific, and how concentrated is this general software industry. Regarding this second factor, it is noteworthy that we now see some pretty big players in the software industry, such as Google, Apple, and Microsoft. And so a key question is the source of this concentration. That is, what exactly are the key advantages of big firms in today’s software market?

There are many possibilities, including patent pools and network effects among customers of key products. Another possibility, however, is one where I expect many of my readers to have relevant personal experience: scale economies in software production. Hence this bleg – a blog post asking a question.

If you are an experienced software professional who has worked both at a big software firm and also in other places, my key question for you is: by how much was your productive efficiency as a software developer increased (or decreased) due to working at a big software firm?  That is, how much more could you get done there that wasn’t attributable to having a bigger budget to do more, or to paying more for better people, tools, or resources. Instead, I’m looking for the net increase (or decrease) in your output due to software tools, resources, security, oversight, rules, or collaborators that are more feasible and hence more common at larger firms. Ideally you answer will be in the form of a percentage, such as “I seem to be 10% more productive working at a big software firm.”

Added 3:45p: I meant “productivity” in the economic sense of the inputs required to produce a given output, holding constant the specific kind of output produced. So this kind of productivity should ignore the number of users of the software, and the revenue gained per user. But if big vs small firms tend to make different kinds of software, which have different costs to make, those differences should be taken into account. For example, one should correct for needing more man-hours to add a line of code in a larger system, or in a more secure or reliable system.

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On Homo Deus

Historian Yuval Harari’s best-selling book Sapiens mostly talked about history. His new book, Homo Deus, won’t be released in the US until February 21, but I managed to find a copy at the Istanbul airport – it came out in Europe last fall. This post is about the book, and it is long and full of quotes; you are warned. Continue reading "On Homo Deus" »

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Avoid “Posthuman” Label

Philosophy is mainly useful in inoculating you against other philosophy. Else you’ll be vulnerable to the first coherent philosophy you hear. (source)

Long ago (’81-83 at U Chicago) I studied Conceptual Foundations of Science (mainly philosophy of science) because I wanted to really understand this “science” thing, and the main thing I learned was to avoid the word “science”. If necessary, the word can refer to obvious social groups and how they maintain boundaries, but beyond that other words and concepts are more useful.

I’ve always felt similarly wary of “transhuman” and “posthuman”, because it isn’t clear what they can or do mean. In the latest Bioethics, David Lawrence elaborates an argument for such wariness:

Human is itself a greatly abused term, especially in the context of the enhancement/posthuman debate, and the myriad of meanings ascribed to it could give posthuman a very different slant depending on ones understanding. .. There are, perhaps, three main senses in which the term human is frequently employed- the biological, the moral, and the self- (or other-) idealizing. In the first of these, human .. refer[s] to our taxonomic species, In the second sense, human generally refers to a community of beings which qualify as having a certain moral value or status; and the third .. denoting .. what matters about those who matter. ..

It is a mistake to envisage the posthuman as a different species. It is a mistake to imagine traits such as immortality or godlike powers as being changes that indicate a significant discontinuity. .. The mere act of assigning terminology is inherently one of division. .. The use of these terms is designed to classify and separate. As I hope to have shown, this is precisely the problem with the notional posthuman. ..

The commentators on both sides of the debate concerning the meaning of posthuman do so as if it had currency. .. To use the term to imply species or value change, or a radical transition (the meaning of which is unclear in any case), there needs to be justification in a way which does not seem to have been delivered within the existing dialogue. Here, I have argued that this is not a plausible understanding, and furthermore that it is based in error. The analogous changes we have undergone throughout our history have not been thought to signal a qualitative change, or at least, not to any significant degree. We are, today, post-internet age humans; we are post-neolithic, post-bronze age, post-iron age. These transitions have not changed our value or the nature of our being: machine-age man, Homo augmentus, is still man. The touted posthuman is, in general, overhyped and unwarranted by the evidence – either factual, or conceptual – and does not seem to have been subject to a close analysis until now.

Here’s what Lawrence suggests we say instead:

Enhancement technologies exist, are used, and will continue to develop; and it is idle to claim that we ought avoid them wholesale. .. It is important that we find a way to reconcile ourselves with the beings we may become, since they and we are products of the same process. .. To be posthuman is in truth to be more human than human – more successful at embodying these traits than we, who consider ourselves the model of humanity, do. It is not, as critics may claim, to be beyond, to be something to fear, something fundamentally different.

A habit of talking as if there will be a natural progression from “human” to “transhuman” to “posthuman” makes our descendants by default into “others” less worthy of our help and allegiance, without specifying the key traits on which they will be deficient. Yes, it is possible that our descendants will in fact have traits we dislike so much as to make us reject them as no longer part of the “us” that matters. But this is hardly inevitable, and those who argue that it will happen should have to specify the particular key traits they expect will cause such a divergence.

Only half those who imagine entering a star trek transporter see the person who exits as themselves, but all those who imagine exiting see the person entering as themselves. Similarly, we tend to see all our ancestors for the last million years as part of the “us” that matters, even though many of them might reject us as being part of the “us” that matters to them. And so our descendants are more likely to see us today as part of the “us” that matters to them, compared to our seeing them in that way.

So let us talk first of the various kinds of descendants we may have, the traits by which they may differ from us, and which of those traits matter most to us in deciding who matters. After that, perhaps, we might argue about which descendants will become a “them” who matter much less to us. We could perhaps call such folks “posthuman,” but know that they will probably reject such a label.

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Beware Futurism As Political Allegory

Imagine that you are junior in high school who expects to attend college. At that point in your life you have opinions related to frequent personal choices if blue jeans feel comfortable or if you prefer vanilla to chocolate ice cream. And you have opinions on social norms in your social world, like how much money it is okay to borrow from a friend, how late one should stay at a party, or what are acceptable excuses for breaking up with boy/girlfriend. And you know you will soon need opinions on imminent major life choices, such as what college to attend, what major to have, and whether to live on campus.

But at that point in life you will have less need of opinions on what classes to take as college senior, and where to live then. You know you can wait and learn more before making such decisions. And you have even less need of opinions on borrowing money, staying at parties, or breaking up as a college senior. Social norms on those choices will come from future communities, who may not yet have even decided on such things.

In general, you should expect to have more sensible and stable opinions related to choices you actually make often, and less coherent and useful opinions regarding choices you will make in the future, after you learn many new things. You should have less coherent opinions on how your future communities will evaluate the morality and social acceptability of your future choices. And your opinions on collective choices, such as via government, should be even less reliable, as your incentives to get those right are even weaker.

All of this suggests that you be wary of simply asking your intuition for opinions about what you or anyone else should do in strange distant futures. Especially regarding moral and collective choices. Your intuition may dutifully generate such opinions, but they’ll probably depend a lot on how the questions were framed, and the context in which questions were asked. For more reliable opinions, try instead to chip away at such topics.

However, this context-dependence is gold to those who seek to influence others’ opinions. Warriors attack where an enemy is weak. When seeking to convert others to a point of view, you can have only limited influence on topics where they have accepted a particular framing, and have incentives to be careful. But you can more influence how a new topic is framed, and when there are many new topics you can emphasize the few where your preferred framing helps more.

So legal advocates want to control how courts pick cases to review and the new precedents they set. Political advocates want to influence which news stories get popular and how those stories are framed. Political advocates also seek to influence the choices and interpretations of cultural icons like songs and movies, because being less constrained by facts such things are more open to framing.

As with the example above of future college choices, distant future choices are less thoughtful or stable, and thus more subject to selection and framing effects. Future moral choices are even less stable, and more related to political positions that advocates want to push. And future moral choices expressed via culture like movies are even more flexible, and thus more useful. So newly-discussed culturally-expressed distant future collective moral choices create a perfect storm of random context-dependent unreliable opinions, and thus are ideal for advocacy influence, at least when you can get people to pay attention to them.

Of course most people are usually reluctant to think much about distant future choices, including moral and collective ones. Which greatly limits the value of such topics to advocates. But a few choices related to distant futures have engaged wider audiences, such as climate change and, recently, AI risk. And political advocates do seem quite eager to influence such topics, due to their potency. They seem select such topics from a far larger set of similarly important issues, in part for their potency at pushing common political positions. The science-fiction truism really does seem to apply: most talk on the distant future is really indirect talk on our world today.

Of course the future really will happen eventually, and we should want to consider choices today that importantly influence that future, some of those choices will have moral and collective aspects, some of these issues can be expressed via culture like movies, and at some point such issue discussion will be new. But as with big hard problems in general, it is probably better to chip away at such problems.

That is: Anchor your thoughts to reality rather than to fiction. Make sure you have a grip on current and past behavior before looking at related future behavior. Try to stick with analyzing facts for longer before being forced to make value choices. Think about amoral and decentralized choices carefully before considering moral and collective ones. Avoid feeling pressured to jump to strong conclusions on recently popular topics. Prefer robust and reliable methods even when they are less easy and direct. Mostly the distant future doesn’t need action today – decisions will wait a bit for us to think more carefully.

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This AI Boom Will Also Bust

Imagine an innovation in pipes. If this innovation were general, something that made all kinds of pipes cheaper to build and maintain, the total benefits could be large, perhaps even comparable to the total amount we spend on pipes today. (Or even much larger.) And if most of the value of pipe use were in many small uses, then that is where most of these economic gains would be found.

In contrast, consider an innovation that only improved the very largest pipes. This innovation might, for example, cost a lot to use per meter of pipe, and so only make sense for the largest pipes. Such an innovation might make for very dramatic demonstrations, with huge vivid pipes, and so get media coverage. But the total economic gains here will probably be smaller; as most of pipe value is found in small pipes, gains to the few biggest pipes can only do so much.

Now consider my most viral tweet so far:

This got almost universal agreement from those who see such issues play out behind the scenes. And by analogy with the pipe innovation case, this fact tells us something about the potential near-term economic impact of recent innovations in Machine Learning. Let me explain.

Most firms have piles of data they aren’t doing much with, and far more data that they could collect at a modest cost. Sometimes they use some of this data to predict a few things of interest. Sometimes this creates substantial business value. Most of this value is achieved, as usual, in the simplest applications, where simple prediction methods are applied to simple small datasets. And the total value achieved is only a small fraction of the world economy, at least as measured by income received by workers and firms who specialize in predicting from data.

Many obstacles limit such applications. For example, the value of better predictions for related decisions may be low, data may be in a form poorly suited to informing predictions, making good use of predictions might require larger reorganizations, and organizations that hold parts of the data may not want to lose control of that data. Available personnel may lack sufficient skills to apply the most effective approaches for data cleaning, merging, analysis, and application.

No doubt many errors are made in choices of when to analyze what data how much and by whom. Sometimes they will do too much prediction, and sometimes too little. When tech changes, orgs will sometimes wait too long to try new tech, and sometimes will not wait long enough for tech to mature. But in ordinary times, when the relevant technologies improve at steady known rates, we have no strong reason to expect these choices to be greatly wrong on average.

In the last few years, new “deep machine learning” prediction methods are “hot.” In some widely publicized demonstrations, they seem to allow substantially more accurate predictions from data. Since they shine more when data is plentiful, and they need more skilled personnel, these methods are most promising for the largest prediction problems. Because of this new fashion, at many firms those who don’t understand these issues well are pushing subordinates to seek local applications of these new methods. Those subordinates comply, at least in appearance, in part to help they and their organization appear more skilled.

One result of this new fashion is that a few big new applications are being explored, in places with enough data and potential prediction value to make them decent candidates. But another result is the one described in my tweet above: fashion-induced overuse of more expensive new methods on smaller problems to which they are poorly matched. We should expect this second result to produce a net loss on average. The size of this loss could be enough to outweigh all the gains from the few big new applications; after all, most value is usually achieved in many small problems.

But I don’t want to draw a conclusion here about the net gain or loss. I instead want to consider the potential for this new prediction tech to have an overwhelming impact on the world economy. Some see this new fashion as just first swell of a tsunami that will soon swallow the world. For example, in 2013 Frey and Osborne famously estimated:

About 47 percent of total US employment is at risk .. to computerisation .. perhaps over the next decade or two.

If new prediction techs induced a change that big, they would be creating a value that is a substantial fraction of the world economy, and so consume a similar fraction of world income. If so, the prediction industry would in a short time become vastly larger than it is today. If today’s fashion were the start of that vast growth, we should not only see an increase in prediction activity, we should also see an awe-inspiring rate of success within that activity. The application of these new methods should be enabling huge new revenue streams, across a very wide range of possible application areas. (Added: And the prospect of that should be increasing stock values in this area far more than we’ve seen.)

But I instead hear that within the areas where most prediction value lies, most attempts to apply this new tech actually produce less net value than would be achieved with old tech. I hear that prediction analysis tech is usually not the most important part the process, and that recently obsession with showing proficiency in this new analysis tech has led to neglect of the more important and basic issues of thinking carefully about what you might want to predict with what data, and then carefully cleaning and merging your data into a more useful form.

Yes, there must be exceptions, and some of those may be big. So a few big applications may enable big value. And self-driving cars seem a plausible candidate, a case where prediction is ready to give large value, high enough to justify using the most advanced prediction tech, and where lots of the right sort of data is available. But even if self-driving vehicles displace most drivers within a few decades, that rate of job automation wouldn’t be out of the range of our historical record of job automation. So it wouldn’t show that “this time is different.” To be clearly out of that range, we’d need another ten jobs that big also displaced in the same period. And even that isn’t enough to automate half of all jobs in two decades.

The bottom line here is that while some see this new prediction tech as like a new pipe tech that could improve all pipes, no matter their size, it is actually more like a tech only useful on very large pipes. Just as it would be a waste to force a pipe tech only useful for big pipes onto all pipes, it can be a waste to push advanced prediction tech onto typical prediction tasks. And the fact that this new tech is mainly only useful on rare big problems suggests that its total impact will be limited. It just isn’t the sort of thing that can remake the world economy in two decades. To the extend that the current boom is based on such grand homes, this boom must soon bust.

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