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Risky and rewarding robots

Robot playpenYesterday I participated in recording a radio program about robotics, and I noted that the participants were approaching the issue from several very different angles:

  • Robots as symbols: what we project things on them, what this says about humanity, how we change ourselves in respect to them, the role of hype and humanity in our thinking about them.
  • Robots as practical problem: how do you make a safe and trustworthy autonomous device that hangs around people? How do we handle responsibility for complex distributed systems that can generate ‘new’ behaviour?
  • Automation and jobs: what kinds of jobs are threatened or changed by automation? How does it change society, and how do we steer it in desirable directions – and what are they?
  • Long-term risks: how do we handle the potential risks from artificial general intelligence, especially given that many people think there are absolutely no problem and others are convinced that this could be existential if we do not figure out enough before it emerges?

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.

Jobs

Robot on break

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.

Scaling

Robin, supply, demand and robots

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.

Mathematical anti-beauty

Browsing Mindfuck Math I came across a humorous Venn diagram originally from Spikedmath.com. It got me to look up the Borwein integral. Wow. A kind of mathematical anti-beauty.

“As we all know”, sinc(x)=sin(x)/x for x\neq 0 and defined to be 1 for x=0. It is not that uncommon as a function. Now look at the following series of integrals:

\int_0^{\infty} sinc(x) dx = \pi/2 ,

\int_0^{\infty} sinc(x) sinc(x/3) dx = \pi/2 ,

\int_0^\infty sinc(x) sinc(x/3) sinc(x/5) dx = \pi/2 .

The pattern continues:

\int_0^\infty sinc(x) sinc(x/3) sinc(x/5) \cdots sinc(x/13) dx = \pi/2 .

And then…

\int_0^\infty sinc(x) sinc(x/3) sinc(x/5) \cdots sinc(x/13) sinc(x/15) dx
=\frac{467807924713440738696537864469}{935615849440640907310521750000}\pi

It is around 0.499999999992646 \pi – nearly a half, but not quite.

What is going on here? Borwein & Borwein give proofs, but they are not entirely transparent. Basically the reason is that 1/3+1/5+…1/13 < 1, while 1/3+1/5+…1/13 + 1/15 > 1, but why this changes things is clear as mud. Thankfully Hanspeter Schmid has a very intuitive explanation that makes what is going on possible to visualize. At least if you like convolutions.

In any case, there is something simultaneously ugly and exciting when neat patterns in math just ends for no apparent reason.

Another good example is the story of the Doomsday conjecture. Gwern tells the story well, based on Klarreich: a certain kind of object is found in dimension 2, 6, 14, 30 and 62… aha! They are conjectured to occur in all  2^n-2  dimensions. A branch of math was built on this conjecture… and then the pattern failed in dimension 254. Oops. 

It is a bit like the opposite case of the number of regular convex polytopes in different dimensions: 1, infinity, 5, 6, 3, 3, 3, 3… Here the series start out crazy, and then becomes very regular.

Another dimensional “imperfection” is the behaviour of the volume of the n-sphere: V_n(r)=\frac{\pi^{n/2}r^n}{\Gamma(1+n/2)}

Volume of unit hyperspheres as a function of dimension

The volume of a unit sphere increases with dimension until n \approx 5.25 , and then decreases. Leaving the non-intuitiveness of why volumes would shrink aside, the real oddness is that the maximum is for a non-integer dimension. We might argue that the formula is needlessly general and only the integer values count, but many derivations naturally bring in the Gamma function and hence the possibility of non-integer values.

Another association is to this integral problem: given a set of integers x_i, is the integral \int_0^\pi \prod_i \sin(x_i \theta) d\theta = 0 ? As shown in Moore and Mertens, this is NP-complete. Here the strangeness is that integrals normally are pretty well behaved. It seems absurd that a particular not very scary trigonometric integral should require exponential work to analyze. But in fact, multivariate integrals are NP-hard to approximate, and calculating the volume of a n-dimensional polytope is actually #P-complete.

We tend to assume that mathematics is smoother and more regular than reality. Everything is regular and exceptionless because it is generated by universal rules… except when it isn’t. The rules often act as constraints, and when they do not mesh exactly odd things happen. Similarly we may assume that we know what problems are hard or not, but this is an intuition built in our own world rather than the world of mathematics. Finally, some mathematical truths maybe just are. As Gregory Chaitin has argued, some things in math are irreducible; there is no real reason (at least in the sense of a comprehensive explanation) for why they are true.

Mathematical anti-beauty can be very deep. Maybe it is like the insects, rot and other memento mori in classical still life paintings: a deviation from pleasantness and harmony that adds poignancy and a bit of drama. Or perhaps more accurately, it is wabi-sabi.

Thunderbolts and lightning, very very frightning

Cloud powerOn the Conversation, I blog about the risks of electromagnetic disruption from solar storms and EMP: Electromagnetic disaster could cost trillions and affect millions. We need to be prepared.

The reports from Lloyds and the National Academies are worrying, but as a disaster it would not kill that many people directly. However, an overall weakening of our societal and global systems is nothing to joke about: when societies have less resources they are less resilient to other threats. In in this case it would both information processing, resources and ability to do stuff. Just the thing to make other risks way worse.

As a public goods problem I think this risk is easier to handle than others; it is more like Y2K than climate since most people have aligned interests. Nobody wants a breakdown, and few actually win from the status quo. But there are going to be costs and inertia nevertheless. Plus, I don’t think we have a good answer yet to local EMP risks.

Cryonics: too rational, hence fair game?

CryotagOn Practical Ethics I blog about cryonics acceptance: Freezing critique: privileged views and cryonics. My argument is that cryonics tries to be a rational scientific approach, which means it is fair game for criticism. Meanwhile many traditional and anti-cryonic views are either directly religious or linked to religious views, which means people refrain from criticising them back. Since views that are criticised are seen as more questionable than non-criticised (if equally strange) views, this makes cryonics look less worth respecting.

Ebola and the dragon

Here is a reason not to worry too much about Ebola… yet. I took the WHO data on Ebola outbreaks and plotted it. The distribution is not power-law distributed (looks bent on a loglog scale) but is decently exponential (straight on a semilog scale). The probability goes down fast with size.

ebolaoutbreaks

However, when we add the final toll from the current outbreak (1603 suspected cases with 887 fatalities at August 1) it might turn out to be a “dragon-king” bucking the line: in that case we should expect that large international outbreaks follow an entirely new dynamic. This is mildly worrying. Still, it is early days. 

Is love double-blind?

On Practical Ethics, I blog about whether there is a difference in the ethics of the dating site OKCupid and Facebook manipulating their users. My tentative conclusion is that there is a form of informal consent in using certain sites – when you use a dating site you assume there will be some magic algorithms matchmaking you, and that your responses will affect these algorithms. This consent might be enough to make many experiment ethically OK.