On Practical Ethics I blog about Petrov Day: the anniversary of an avoided nuclear cataclysm.
The lovely thing about this incident is that there is a person to focus on, making existential risk dramatically real. The LessWrong community has developed a ritual to commemorate the event and make our individual responsibility for reducing existential risk more vivid.
Averted disasters are hard to see, so we need more and bigger monuments to people who averted things.
Most of the time we encounter probability distributions over the reals, the positive reals, or integers. But one can use the rational numbers as a probability space too.
Recently I found the paper Vladimir Trifonov, Laura Pasqualucci, Riccardo Dalla-Favera & Raul Rabadan. Fractal-like Distributions over the Rational Numbers in High-throughput Biological and Clinical Data. Scientific Reports 1:191 DOI: 10.1038/srep00191. They discuss the distribution of ratios of the number of reads from the same spot of DNA that come from each chromosome in a pair: the number of reads is an integer, so the ratio is rational. They get a peaky, self-similar distribution empirically, and the paper explains why.
If you take positive independent integers from some distribution 
One can of course do the same for non-independent and different distributions of the integers. Oh, and by the way: this whole thing has little to do with ratio distributions (alias slash distributions), which is what happens in the real case.
The authors found closed form solutions for integers distributed as a power-law with an exponential cut-off and for the uniform distribution; unfortunately the really interesting case, the Poisson distribution, doesn’t seem to have a neat closed form solution.
In the case of a uniform distributions on the set 
They note that this is similar to Thomae’s function, a somewhat well-known (and multiply named) counterexample in real analysis. That function is defined as f(p/q)=1/q (where the fraction is in lowest terms). In fact, both graphs have the same fractal dimension of 1.5.
It is easy to generate other rational distributions this way. Using a power law as an input produces a sparser pattern, since the integers going into the ratio tend to be small numbers, putting more probability at simple ratios:
If we use exponential distributions the pattern is fairly similar, but we can of course change the exponent to get something that ranges over a lot of numbers, putting more probability at nonsimple ratios 
![The rational distribution of two convolved Exp[0.1] distributions.](http://aleph.se/andart2/wp-content/uploads/2014/09/trifonovexp01.png)
![Rational distribution of ratio between a Poisson[10] and a Poisson[5] variable.](http://aleph.se/andart2/wp-content/uploads/2014/09/trifonovpoiss105.png)
But multiplying the variates by 10 produces a nice distribution.
This approaches the Chauchy distribution as the discretisation gets finer. But note the fun microstructure (very visible in the Poisson case above too), where each peak at a simple ratio is surrounded by a “moat” of low probability. This is reminiscent of the behaviour of roots of random polynomials with integer coefficients (see also John Baez page on the topic).
The rational numbers do tend to induce a fractal recursive structure on things, since most measures on them will tend to put more mass at simple ratios than at complex ratios, but when plotting the value of the ratio everything gets neatly folded together. The lower approximability of numbers near the simple ratios produce moats. Which also suggests a question to ponder further: what role does the über-unapproximable golden ratio have in distributions like these?
George Dvorsky has a post on io9: 10 Horrifying Technologies That Should Never Be Allowed To Exist. It is a nice clickbaity overview of some very bad technologies:
Some of the these technologies exist, like weaponized pathogens. Others might be impossible, like time travel. Some are embryonic like mind reading (we can decode some brainstates, but it requires spending a while in a big scanner as the input-output mapping is learned).
A commenter on the post asked “Who will have the responsibility of classifying and preventing “objectively evil” technology?” The answer is of course People Who Have Ph.D.s in Philosophy.
Unfortunately I haven’t got one, but that will not stop me.
I wonder what unifies this list. Let’s see: 1, 3, 7, and 8 are all about danger: either the risk of a lot of death, or the risk of extinction. 2, 9 and 10 are all about disvalue: the creation of very negative states of experience. 5 and 6 are threats to autonomy.
4, time travel, is the odd one out: George suggests that it is dangerous, but this is based on fictional examples, and that contact between different civilizations has never ended well (which is arguable: Japan). I can imagine a consistent universe with time travel might be bad for people’s sense of free will, and if you have time loops you can do super-powerful computation (getting superintelligence risk), but I do not think of any kind of plausible physics where time travel itself is dangerous. Fiction just makes up dangers to make the plot move on.
In the existential risk framework, it is worth noting that extinction is not the only kind of existential risk. We could mess things up so that humanity’s full potential never gets realized (for example by being locked into a perennial totalitarian system that is actually resistant to any change), or that we make the world hellish. These are axiological existential risks. So the unifying aspect of these technologies is that they could cause existential risk, or at least bad enough approximations.
Ethically, existential threats count a lot. They seem to have priority over mere disasters and other moral problems in a wide range of moral systems (not just consequentialism). So technologies that strongly increase existential risk without giving a commensurate benefit (for example by reducing other existential risks more – consider a global surveillance state, which might be a decent defence against people developing bio-, nano- and info-risks at the price of totalitarian risk) are indeed impermissible. In reality technologies have dual uses and the eventual risk impact can be hard to estimate, but the principle is reasonable even if implementation will be a nightmare.
However, extinction risk is an easy category – even if some of the possible causes like superintelligence are weird and controversial, at least extinct means extinct. The value and autonomy risks are far trickier. First, we might be wrong about value: maybe suffering doesn’t actually count morally, we just think it does. So a technology that looks like it harms value badly like hell engineering actually doesn’t. This might seem crazy, but we should recognize that some things might be important but we do not recognize them. Francis Fukuyama thought transhumanist enhancement might harm some mysterious ‘Factor X’ (i.e. a “soul) giving us dignity that is not widely recognized. Nick Bostrom (while rejecting the Factor X argument) has suggested that there might be many “quiet values” important for diginity, taking second seat to the “loud” values like alleviation of suffering but still being important – a world where all quiet values disappear could be a very bad world even if there was no suffering (think Aldous Huxley’s Brave New World, for example). This is one reason why many superintelligence scenarios end badly: transmitting the full nuanced world of human values – many so quiet that we do not even recognize them ourselves before we lose them – is very hard. I suspect that most people find it unlikely that loud values like happiness or autonomy actually are parochial and worthless, but we could be wrong. This means that there will always be a fair bit of moral uncertainty about axiological existential risks, and hence about technologies that may threaten value. Just consider the argument between Fukuyama and us transhumanists.
Second, autonomy threats are also tricky because autonomy might not be all that it is cracked up to be in western philosophy. The atomic free-willed individual is rather divorced from the actual neural and social matrix creature. But even if one doesn’t buy autonomy as having intrinsic value, there are likely good cybernetic arguments for why maintaining individuals as individuals with their own minds is a good thing. I often point to David Brin’s excellent defence of the open society where he points out that societies where criticism and error correction are not possible will tend to become corrupt, inefficient and increasingly run by the preferences of the dominant cadre. In the end they will work badly for nearly everybody and have a fair risk of crashing. Tools like surveillance, thought reading or mind control would potentially break this beneficial feedback by silencing criticism. They might also instil identical preferences, which seems to be a recipe for common mode errors causing big breakdowns: monocultures are more vulnerable than richer ecosystems. Still, it is not obvious that these benefits could not exist in (say) a group-mind where individuality is also part of a bigger collective mind.
These caveats aside, I think the criteria for “objectively evil technology” could be
(1) It predictably increases existential risk substantially without commensurate benefits,
or,
(2) it predictably increases the amount of death, suffering or other forms of disvalue significantly without commensurate benefits.
There are unpredictable bad technologies, but they are not immoral to develop. However, developers do have a responsibility to think carefully about the possible implications or uses of their technology. And if your baby-tickling machine involves black holes you have a good reason to be cautious.
Of course, “commensurate” is going to be the tricky word here. Is a halving of nuclear weapons and biowarfare risk good enough to accept a doubling of superintelligence risk? Is a tiny probability existential risk (say from a physics experiment) worth interesting scientific findings that will be known by humanity through the entire future? The MaxiPOK principle would argue that the benefits do not matter or weigh rather lightly. The current gain-of-function debate show that we can have profound disagreements – but also that we can try to construct institutions and methods that regulate the balance, or inventions that reduce the risk. This also shows the benefit of looking at larger systems than the technology itself: a potentially dangerous technology wielded responsibly can be OK if the responsibility is reliable enough, and if we can bring a safeguard technology into place before the risky technology it might no longer be unacceptable.
The second weasel word is “significantly”. Do landmines count? I think one can make the case. According to the UN they kill 15,000 to 20,000 people per year. The number of traffic fatalities per year worldwide is about 1.2 million deaths – but we might think cars are actually so beneficial that it outweighs the many deaths.
The landmines are intended to harm (yes, the ideal use is to make people rationally stay the heck away from mined areas, but the harming is inherent in the purpose) while cars are not. This might lead to an amendment of the second criterion:
(2′) The technology intentionally increases the amount of death, suffering or other forms of disvalue significantly without commensurate benefits.
This gets closer to how many would view things: technologies intended to cause harm are inherently evil. But being a consequentialist I think it let’s designers off the hook. Dr Guillotine believed his invention would reduce suffering (and it might have) but it also led to a lot more death. Dr Gatling invented his gun to “reduce the size of armies and so reduce the number of deaths by combat and disease, and to show how futile war is.” So the intention part is problematic.
Some people are concerned with autonomous weapons because they are non-moral agents making life-and-death decisions over people; they would use deontological principles to argue that making such amoral devices are wrong. But a landmine that has been designed to try to identify civilians and not blow up around them seems to be a better device than an indiscriminate device: the amorality of the decisionmaking is less of problematic than the general harmfulness of the device.
I suspect trying to bake in intentionality or other deontological concepts will be problematic. Just as human dignity (another obvious concept – “Devices intended to degrade human dignity are impermissible”) is likely a non-starter. They are still useful heuristics, though. We do not want too much brainpower spent on inventing better ways of harming or degrading people.
In the end, this exercise can be continued indefinitely. And no doubt it will.
Given the general impotence of ethical arguments to change policy (it usually picks up the pieces and explains what went wrong once it does go wrong) a more relevant question might be how a civilization can avoid developing things it has a good reason to suspect are a bad idea. I suspect the answer to that is going to be not just improvements in coordination and the ability to predict consequences, but some real innovations in governance under empirical and normative uncertainty.
But that is for another day.
Pew Research has posted their Morality Interactive Topline Results for their spring 2013 and winter 2013-2014 survey of moral views around the world. These are national samples, so for each moral issue the survey gives how many thinks it is morally unacceptable, morally acceptable, not a moral issue or whether it depends on the situation.
Plotting countries by whether issues are morally acceptable, morally unacceptable or morally irrelevant gives the following distributions.
Overall, there are many countries that are morally against everything, and a tail pointing towards some balance between acceptable or morally irrelevant.
The situation-dependence scores tended to be low: most people do think there are moral absolutes. The highest situation-dependency scores tended to be in the middle between the morally unacceptable point and the OK side; I suspect there was just a fair bit of confusion going on.
Looking at the correlations between morally unacceptable answers suggested that unmarried sex and homosexuality stands out: views there were firmly correlated but not strongly influenced by views on other things. I regard this as a “sex for fun” factor. However, it should be noted that almost everything is firmly correlated: if a country is against X, it is likely against Y too. Looking at correlations between acceptable or no issue answers did not show any clear picture.
The real sledgehammer is of course principal component analysis. Running it for the whole data produces a firm conclusion: the key factor is something we could call “moral conservatism”, which explains 73% of the variance. Countries that score high find unmarried sex, homosexuality, alcohol, gambling, abortion and divorce unacceptable.
The second factor, explaining 9%, seems to denote whether things are morally acceptable or simply morally not an issue. However, it has some unexpected interaction with whether unmarried sex is unacceptable. This links to the third factor, explaining 7%, which seems to be linked to views on divorce and contraception. Looking at the 3D plot of the data, it becomes clear that for countries scoring low on the moral conservatism scale (“modern countries”) there is a negative correlation between these two factors, while for conservative countries there is a positive correlation.
Plotting the most conservative (red) and least (blue) countries supports this. The lower blue corner is the typical Western countries (France, Canada, US, Australia) while the upper blue corner is more traditionalist (?) countries (Czech republic, Chile, Spain). The lower red corner has Ghana, Uganda, Pakistan and Nigeria, while the upper red is clearly Arab: Egypt, the Palestinian territories, Jordan.
In the end, I guess the data doesn’t tell us that much truly new. A large part of the world hold traditional conservative moral views. Perhaps the most interesting part is that the things people regard as morally salient or not interacts in a complicated manner with local culture. There are also noticeable differences even within the same cultural sphere: Tunisia has very different views from Egypt on divorce.
For those interested, here is my somewhat messy Matlab code and data to generate these pictures.
Slate Star Codex has another great post: If the media reported on other dangers like it does AI risk.
The new airborne superplague is said to be 100% fatal, totally untreatable, and able to spread across an entire continent in a matter of days. It is certainly fascinating to think about if your interests tend toward microbiology, and we look forward to continuing academic (and perhaps popular) discussion and debate on the subject.
I have earlier discussed how AI risk suffers from the silliness heuristic.
Of course, one can argue that AI risk is less recognized as a serious issue than superplagues, meteors or economic depressions (although, given what news media have been writing recently about Ebola and 1950 DA, their level of understanding can be debated). There is disagreement on AI risk among people involved in the subject, with some rather bold claims of certainty among some, rational reasons to be distrustful of predictions, and plenty of vested interests and motivated thinking. But this internal debate is not the reason media makes a hash of things: it is not like there is an AI safety denialist movement pushing the message that worrying about AI risk is silly, or planting stupid arguments to discredit safety concerns. Rather, the whole issue is so out there that not only the presumed reader but the journalist too will not know what to make of it. It is hard to judge credibility, how good arguments are and the size of risks. So logic does not apply very strongly – anyway, it does not sell.
This is true for climate change and pandemics too. But here there is more of an infrastructure of concern, there are some standards (despite vehement disagreements) and the risks are not entirely unprecedented. There are more ways of dealing with the issue than referring to fiction or abstract arguments that tend to fly over the heads of most. The discussion has moved further from the frontiers of the thinkable not just among experts but also among journalists and the public.
How do discussions move from silly to mainstream? Part of it is mere exposure: if the issue comes up again and again, and other factors do not reinforce it as being beyond the pale, it will become more thinkable. This is how other issues creep up on the agenda too: small stakeholder groups drive their arguments, and if they are compelling they will eventually leak into the mainstream. High status groups have an advantage (uncorrelated to the correctness of arguments, except for the very rare groups that gain status from being documented as being right about a lot of things).
Another is demonstrations. They do not have to be real instances of the issue, but close enough to create an association: a small disease outbreak, an impressive AI demo, claims that the Elbonian education policy really works. They make things concrete, acting as a seed crystal for a conversation. Unfortunately these demonstrations do not have to be truthful either: they focus attention and update people’s probabilities, but they might be deeply flawed. Software passing a Turing test does not tell us much about AI. The safety of existing AI software or biohacking does not tell us much about their future safety. 43% of all impressive-sounding statistics quoted anywhere is wrong.
Truth likely makes argumentation easier (reality is biased in your favour, opponents may have more freedom to make up stuff but it is more vulnerable to disproof) and can produce demonstrations. Truth-seeking people are more likely to want to listen to correct argumentation and evidence, and even if they are a minority they might be more stable in their beliefs than people who just view beliefs as clothing to wear (of course, zealots are also very stable in their beliefs since they isolate themselves from inconvenient ideas and facts).
Truth alone can not efficiently win the battle of bringing an idea in from the silliness of the thinkability frontier to the practical mainstream. But I think humour can act as a lubricant: by showing the actual silliness of mainstream argumentation, we move them outwards towards the frontier, making a bit more space for other things to move inward. When demonstrations are wrong, joke about their flaws. When ideas are pushed merely because of status, poke fun at the hot air cushions holding them up.
Brian Tomasik has a fascinating essay: Is there suffering in fundamental physics?
He admits from the start that “Any sufficiently advanced consequentialism is indistinguishable from its own parody.” And it would be easy to dismiss this as taking compassion way too far: not just caring about plants or rocks, but the possible suffering of electrons and positrons.
I think he has enough arguments to show that the idea is not entirely crazy: we do not understand the ontology of phenomenal experience well enough that we can easily rule out small systems having states, panpsychism is a view held by some rational people, it seems a priori unlikely that there is some mid-sized systems that have all the value in the universe rather than the largest or the smallest scale, we have strong biases towards our kind of system, and information physics might actually link consciousness with physics.
None of these are great arguments, but there are many of them. And the total number of atoms or particles is huge: even assigning a tiny fraction of human moral consideration to them or a tiny probability of them mattering morally will create a large expected moral value. The smallness of moral consideration or the probability needs to be far outside our normal reasoning comfort zone: if you assign a probability lower than 
I suspect most readers will regard this outside their “ultraviolett cutoff” for strange theories: just as physicists successfully invented/discovered a quantum cutoff to solve the ultraviolet catastrophe, most people have a limit where things are too silly or strange to count. Exactly how to draw it rationally (rather than just base it on conformism or surface characteristics) is a hard problem when choosing between the near infinity of odd but barely possible theories.
One useful heuristic is to check whether the opposite theory is equally likely or important: in that case they balance each other (yes, the world could be destroyed by me dropping a pen – but it could also be destroyed by not dropping it). In this case giving greater weight to suffering than neutral states breaks the symmetry: we ought to investigate this possibility since the theory that there is no moral considerability in elementary physics implies no particular value is gained from discovering this fact, while the suffering theory implies it may matter a lot if we found out (and could do something about it). The heuristic is limited but at least a start.
Another way of getting a cutoff for theories of suffering is of course to argue that there must be a lower limit of the system that can have suffering (this is after all how physics very successfully solved the classical UV catastrophe). This gets tricky when we try to apply it to insects, small brains, or other information processing systems. But in physics there might be a better argument: if suffering happens on the elementary particle level, it is going to be quantum suffering. There would be literal superpositions of suffering/non-suffering of the same system. Normal suffering is classical: either it exists or not to some experiencing system, and hence there either is or isn’t a moral obligation to do something. It is not obvious how to evaluate quantum suffering. Maybe we ought to perform a quantum-action that moves the wavefunction to a pure non-suffering state (a bit like quantum game theory: just as game theory might have ties to morality, quantum game theory might link to quantum morality), but this is constrained by the tough limits in quantum mechanics on what can be sensed and done. Quantum suffering might simply be something different from suffering, just as quantum states do not have classical counterparts. Hence our classical moral obligations do not relate to it.
But who knows how molecules feel?
I was just watching “Humans Need Not apply” by CGPGrey,
when I noticed a tweet from Wendy Grossman, who I participated with in a radio panel about robotics (earlier notes on the discussion). She has some good points inspired by our conversation in her post, robots without software.
I think she has a key observation: much of the problem lies in the interaction between the automation and humans. On the human side, that means getting the right information and feedback into the machine side. From the machine side, it means figuring out what humans – those opaque and messy entities who change behaviour for internal reasons – want. At the point where the second demand is somehow resolved we will not only have really useful automation, but also essentially a way of resolving AI safety/ethics. But before that, we will have a situation of only partial understanding , and plenty of areas where either side will not be able to mesh well. Which either forces humans to adapt to machines, or machines to get humans to think that what they really wanted was what they got served. That is risky.
Incidentally, I have noted that many people hearing the current version of the machines will take our jobs story bring up the idea of a guaranteed minimum income as a remedy. If nobody has a job but there is a GMI we can still live a good life (especially since automation would make most things rather cheap). This idea has a long history, and Hans Moravec suggested it in his book Robot (1998) in regard to a future where AI-run corporations would be running the economy. It can be appealing even from a libertarian standpoint since it does away with a lot of welfare and tax bureaucracy (even Hayek might have been a fan).
I’m not enough of an economist to analyse it properly, but I suspect the real problem is stability when countries compete on tax: if Foobonia has a lower corporate tax rate than Baristan and the Democratic Republic of Baaz, then companies will move there – still making money by selling stuff to people in Baristan and Baaz. The more companies there are in Foobonia, the less taxes are needed to keep the citizens wealthy. In fact, as I mentioned in my earlier post, having fewer citizens might make the remaining more well off (things like this have happened on a smaller scale). The ideal situation would be to have the lowest taxes in the world and just one citizen. Or none, so the AI parliament can use the entire budget to improve the future prosperity and safety of Foobonia.
In our current world tax competition is only one factor determining where companies go. Not every company moves to Bahamas, Chile, Estonia or the UAE. One factor is other legal issues and logistics, but a big part is that you need to have people actually working in your company. Human capital is distributed very unevenly, and it is rarely where you want it (and the humans often do not want to move, for social reasons). But in an automated world machine capital will exist wherever you buy it so it can be placed where the taxes are cheaper. There will be a need to perform some services and transport goods in other areas, but unless they are taxed (hence driving up the price for your citizens) this is going to be a weaker constraint than now. How much weaker, I do not know – it would be interesting to see it investigated properly.
The core problem remains that if humans are largely living off the rents from a burgeoning economy there better exist stabilizing safeguards so these rents remain, and stabilizers that keep the safeguards stable. This is a non-trivial legal/economical problem, especially since one failure mode might be that some countries become zero citizen countries with huge economic growth and gradually accumulating investments everywhere (a kind of robotic Piketty situation, where everything in the end ends up owned by the AI consortium/sovereign wealth fund with the strongest growth). In short, it seems to require something just as tricky to develop as the friendly superintelligence program.
In any case, I suspect much of the reason people suggest GMI is that it is an already existing idea and not too strange. Hence it is thinkable and proposable. But there might be far better ideas out there for how to handle a world with powerful automation. One should not just stick with a local optimum idea when there might be way more stable and useful ideas further out.
Recently encountered the paper The last sunset on mainland Europe by Jorge Mira. No, despite the ominous title and my other interests it is not an estimate of when the ultimate sunset would happen (presumably either when Europe is subducted or when it gets vaporized together with Earth a few billion years hence by the ultimate sunrise). It is more along the lines of XKCD What-If’s “When will the sun finally set on the British Empire?”
Mira points out that the terminator is a great circle that changes direction throughout the year, so at different times different parts of Europe will be last. He found that these parts are Cabo de São Vicente (Portugal, Oct 19-Feb 21), Cabo da Roca (Portugal, Feb 21-Mar 24, Sep 20-Oct 19), Cabo Tourinan (Spain, Mar 24-Apr 23, Aug 18-Sep 19), a site near Aglapsvik (Norway, Apri 24-May 1, Aug 11-Aug 18), and a place in Masoy south of Havoysund(Norway, May 1-May 10, Aug 2-Aug 10). From May 11-Aug 1 the point skips along the coast to the Arctic circle and back. Which technically might mean it moves instantly through Sweden, Finland and Russia too at the summer solstice.
I happened to be taking a sunset photo at Cabo de São Vicente when I was there Dec 27: this was the last mainland sunset for that day.
My scientific Kardashian index is 3.34 right now.
This weeks talkie in the scientific blogosphere is a tongue-in-cheek paper by Neil Hall, The Kardashian index: a measure of discrepant social media profile for scientists (Genome Biology 2014, 15:424). He suggests it as the ratio 
Of course, not everybody took it well, and various debates erupted. Since I am not in the danger zone (just as my blood pressure, cholesterol and weight are all just barely in the normal range and hence entirely acceptable) I can laugh at it, while recognizing that some people may have huge K scores while actually being good scientists – in fact, part of being a good scientific citizen is to engage with the outside world. As Micah Allen at UCL said: “Wear your Kardashian index with pride.”
Incidentally, the paper gives further basis for my thinking about merit vs. fame. There has been debate over whether fame depends linearly on merit (measured by papers published) (Bagrow et al.) or increases exponentially (M.V. Simkin and V.P. Roychowdhury, subsequent paper). The above paper suggests a cube-root law, more dampened than Bagrow’s linear claim. However, Hall left out people on super-cited papers and may have used a small biased sample: I suspect, given other results, that there will be a heavy tail of super-followed scientists (Neil deGrasse Tyson, anyone?)
Yesterday I participated in recording a radio program about robotics, and I noted that the participants were approaching the issue from several very different angles:
In many cases the discussion got absurd because we talked past each other due to our different perspectives, but there were also some nice synergies. Trying to design automation without taking the anthropological and cultural aspects into account will lead to something that either does not work well with people or forces people to behave more machinelike. Not taking past hype cycles into account when trying to estimate future impact leads to overconfidence. Assuming that just because there has been hype in the past nothing will change is equally overconfident. The problems of trustworthiness and responsibility distribution become truly important when automating many jobs: when the automation is an essential part of the organisation, there needs to be mechanisms to trust it and to avoid dissolution of responsibility. Currently robot ethics is more about how humans are impacted by robots rather than ethics for robots, but the latter will become quite essential if we get closer to AGI.
I focused on jobs, starting from the Future of Employment paper. Maarten Goos and Alan Manning pointed out that automation seems to lead to a polarisation into “lovely and lousy jobs“: more non-routine manual jobs (lousy), more non-routine cognitive jobs (lovely). The paper strongly supports this, showing that a large chunk of occupations that rely on routine tasks might be possible to automate but things requiring hand-eye coordination, human dexterity, social ability, creativity and intelligence – especially applied flexibly – are pretty safe.
Overall, the economist’s view is relatively clear: automation that embodies skills and ability to do labour can only affect the distribution of jobs and how much certain skills are valued and paid compared with others. There is no rule that if task X can be done by a machine it will be done by a machine: handmade can still pay premium, and the law of comparative advantage might mean it is not worth using the machine to do X when it can do the even more profitable task Y. Still, being entirely dependent on doing X for your living is likely a bad situation.
Also, we often underestimate the impact of “small” parts of tasks that in formal analysis don’t seem to matter. Underwriters are on paper eminently replaceable… except that the ability to notice “Hey! Those numbers don’t make sense” or judge the reliability of risk models is quite hard to implement, and actually may constitute most of their value. We care about hard to automate things like social interaction and style. And priests, politicians, prosecutors and prostitutes are all fairly secure because their jobs might inherently require being a human or representing a human.
However, the development of AI ability is not a continuous predictable curve. We get sudden surprises like the autonomous cars (just a few years ago most people believed autonomous cars were a very hard, nearly impossible problem) or statistical translation. Confluences of technology conspire to change things radically (consider the digital revolution of printing, both big and small, in the 80s that upended the world for human printers). And since we know we are simultaneously overhyping and missing trends, this should not give us a sense of complacency at all. Just because we have always failed to automate X in the past doesn’t mean X might not suddenly turn out to be automateable tomorrow: relying on X being stably in the human domain is a risky assumption, especially when thinking about career choices.
Robots also have another important property: we can make a lot of them if we have a reason. If there is a huge demand for humans doing X we need to retrain or have children who grow up to be Xers. That makes the price go up a lot. Robots can be manufactured relatively easily, and scaling up the manufacturing is cheaper: even if X-robots are fairly expensive, making a lot more X-robots might be cheaper than trying to get humans if X suddenly matters.
This scaling is a bit worrisome, since robots implement somebody’s action plan (maybe badly, maybe dangerously creatively): they are essentially an extension of somebody or something’s preferences. So if we could make robot soldiers, the group or side that could make the most would have a potential huge strategic advantage. Making innovations in fast manufacture becomes important, in turn leading to a situation where there is an incentive for an arms race in being able to get an army by a press of a button. This is where I think atomically precise manufacturing is potentially risky: it might enable very quick builds, and that is potentially destabilizing. But even just automatic production (remember, this is a scenario where some robotics is good enough to implement useful military action, so manufacturing robotics will be advanced too). Also, countries running mostly on export on raw materials, if they automate the production there might not be much of a need of most of the population… An economist would say the population might be used for other profitable activities, but many nasty resource-driven governments do not invest in their human capital very much. In fact, they tend to see it as a security problem.
Of course, if we ever get to the level where intellectual tasks and services close to the human scale can be done, the same might apply to more developed economies too. But at that point we are so close to automating the task of making robots and AI better that I expect an intelligence explosion to occur before any social explosions. A society where nobody needs to work might sound nice and might be very worth striving for, but in order to get there we need at the very least get close to general AI and solve its safety problems.
See also this essay: commercializing the robot ecosystem in the anthropocene.
