simulation

Ethics of brain emulations, New Scientist edition

Si elegansI have an opinion piece in New Scientist about the ethics of brain emulation. The content is similar to what I was talking about at IJCNN and in my academic paper (and the comic about it). Here are a few things that did not fit the text:

Ethics that got left out

Due to length constraints I had to cut the discussion about why animals might be moral patients. That made the essay look positively Benthamite in its focus on pain. In fact, I am agnostic on whether experience is necessary for being a moral patient. Here is the cut section:

Why should we care about how real animals are treated? Different philosophers have given different answers. Immanuel Kant did not think animals matter in themselves, but our behaviour towards them matters morally: a human who kicks a dog is cruel and should not do it. Jeremy Bentham famously argued that thinking does not matter, but the capacity to suffer: “…the question is not, Can they reason? nor, Can they talk? but, Can they suffer?” . Other philosophers have argued that it matters that animals experience being subjects of their own life, with desires and goals that make sense to them. While there is a fair bit of disagreement of what this means for our responsibilities to animals and what we may use them for, there is a widespread agreement that they are moral patients, something we ought to treat with some kind of care.

This is of course a super-quick condensation of a debate that fills bookshelves. It also leaves out Christine Korsgaard’s interesting Kantian work on animal rights, which as far as I can tell does not need to rely on particular accounts of consciousness and pain but rather interests. Most people would say that without consciousness or experience there is nobody that is harmed, but I am not entirely certain unconscious systems cannot be regarded as moral patients. There are for example people working in environmental ethics that ascribe moral patient-hood and partial rights to species or natural environments.

Big simulations: what are they good for?

Another interesting thing that had to be left out is comparisons of different large scale neural simulations.

(I am a bit uncertain about where the largest model in the Human Brain Project is right now; they are running more realistic models, so they will be smaller in terms of neurons. But they clearly have the ambition to best the others in the long run.)

Of course, one can argue which approach matters. Spaun is a model of cognition using low resolution neurons, while the slightly larger (in neurons) simulation from the Lansner lab was just a generic piece of cortex, showing some non-trivial alpha and gamma rhythms, and the even larger ones showing some interesting emergent behavior despite the lack of biological complexity in the neurons. Conversely, Cotterill’s CyberChild that I worry about in the opinion piece had just 21 neurons in each region but they formed a fairly complex network with many brain regions that in a sense is more meaningful as an organism than the near-disembodied problem-solver Spaun. Meanwhile SpiNNaker is running rings around the others in terms of speed, essentially running in real-time while the others have slowdowns by a factor of a thousand or worse.

The core of the matter is defining what one wants to achieve. Lots of neurons, biological realism, non-trivial emergent behavior, modelling a real neural system, purposeful (even conscious) behavior, useful technology, or scientific understanding? Brain emulation aims at getting purposeful, whole-organism behavior from running a very large, very complete biologically realistic simulation. Many robotics and AI people are happy without the biological realism and would prefer as small simulation as possible. Neuroscientists and cognitive scientists care about what they can learn and understand based on the simulations, rather than their completeness. They are all each pursuing something useful, but it is very different between the fields. As long as they remember that others are not pursuing the same aim they can get along.

What I hope: more honest uncertainty

What I hope happens is that computational neuroscientists think a bit about the issue of suffering (or moral patient-hood) in their simulations rather than slip into the comfortable “It is just a simulation, it cannot feel anything” mode of thinking by default.

It is easy to tell oneself that simulations do not matter because not only do we know how they work when we make them (giving us the illusion that we actually know everything there is to know about the system – obviously not true since we at least need to run them to see what happens), but institutionally it is easier to regard them as non-problems in terms of workload, conflicts and complexity (let’s not rock the boat at the planning meeting, right?) And once something is in the “does not matter morally” category it becomes painful to move it out of it – many will now be motivated to keep it there.

I rather have people keep an open mind about these systems. We do not understand experience. We do not understand consciousness. We do not understand brains and organisms as wholes, and there is much we do not understand about the parts either. We do not have agreement on moral patient-hood. Hence the rational thing to do, even when one is pretty committed to a particular view, is to be open to the possibility that it might be wrong. The rational response to this uncertainty is to get more information if possible, to hedge our bets, and try to avoid actions we might regret in the future.

Galactic duck and cover

How much does gamma ray bursts (GRBs) produce a “galactic habitable zone”? Recently the preprint “On the role of GRBs on life extinction in the Universe” by Piran and Jimenez has made the rounds, arguing that we are near (in fact, inside) the inner edge of the zone due to plentiful GRBs causing mass extinctions too often for intelligence to arise.

This is somewhat similar to James Annis and Milan Cirkovic’s phase transition argument, where a declining rate of supernovae and GRBs causes global temporal synchronization of the emergence of intelligence. However, that argument has a problem: energetic explosions are random, and the difference in extinctions between lucky and unlucky parts of the galaxy can be large – intelligence might well erupt in a lucky corner long before the rest of the galaxy is ready.

I suspect the same problem is true for the Piran and Jimenez paper, but spatially. GRBs are believed to be highly directional, with beams typically a few degrees across. If we have random GRBs with narrow beams, how much of the center of the galaxy do they miss?

I made a simple model of the galaxy, with a thin disk, thick disk and bar population. The model used cubical cells 250 parsec long; somewhat crude, but likely good enough. Sampling random points based on star density, I generated GRBs. Based on Frail et al. 2001 I gave them lognormal energies and power-law distributed jet angles, directed randomly. Like Piran and Jimenez I assumed that if the fluence was above 100 kJ/m^2 it would be extinction level. The rate of GRBs in the Milky Way is uncertain, but a high estimate seems to be one every 100,000 years. Running 1000 GRBs would hence correspond to 100 million years.

Galactic model with gamma ray bursts (red) and density isocontours (blue).
Galactic model with gamma ray bursts (red) and density isocontours (blue).

If we look at the galactic plane we find that the variability close to the galactic centre is big: there are plenty of lucky regions with many stars.

Unaffected star density in the galactic plane.
Unaffected star density in the galactic plane.
Affected (red) and unaffected (blue) stars at different radii in the galactic plane.
Affected (red) and unaffected (blue) stars at different radii in the galactic plane.

When integrating around the entire galaxy to get a measure of risk at different radii and altitudes shows a rather messy structure:

Probability that a given volume would be affected by a GRB. Volumes are integrated around axisymmetric circles.
Probability that a given volume would be affected by a GRB. Volumes are integrated around axisymmetric circles.

One interesting finding is that the most dangerous place may be above the galactic plane along the axis: while few GRBs happen there, those in the disk and bar can reach there (the chance of being inside a double cone is independent of distance to the center, but along the axis one is within reach for the maximum number of GRBs).

Density of stars not affected by the GRBs.
Density of stars not affected by the GRBs.

Integrating the density of stars that are not affected as a function of radius and altitude shows that there is a mild galactic habitable zone hole within 4 kpc. That we are close to the peak is neat, but there is a significant number of stars very close to the center.

This is of course not a professional model; it is a slapdash Matlab script done in an evening to respond to some online debate. But I think it shows that directionality may matter a lot by increasing the variance of star fates. Nearby systems may be irradiated very differently, and merely averaging them will miss this.

If I understood Piran and Jimenez right they do not use directionality; instead they employ a scaled rate of observed GRBs, so they do not have to deal with the iffy issue of jet widths. This might be sound, but I suspect one should check the spatial statistics: correlations are tricky things (and were GRB axes even mildly aligned with the galactic axis the risk reduction would be huge). Another way of getting closer to their result is of course to bump up the number of GRBs: with enough, the centre of the galaxy will naturally be inhospitable. I did not do the same careful modelling of the link between metallicity and GRBs, nor the different sizes.

In any case, I suspect that GRBs are weak constraints on where life can persist and too erratic to act as a good answer to the Fermi question – even a mass extinction is forgotten within 10 million years.