Luke Maurits

A few months ago I happened to catch an interesting documentary on television.  I don’t remember what it was called, but it was narrated by Neil deGrasse Tyson and the focus of the show as the question: if the universe is so chock-full of intelligent life, as most scientists believe it ought to be, how come we have completely failed to detect any evidence of it, despite 25 years or so (the SETI Institute was started in 1984) of concerted effort to do so? (this situation sometimes referred to as the Fermi Paradox, although I don’t recall the show using that term)

The show was largely structured around the well known Drake equation, which tries to estimate the number of intelligent civilisations within the Milky Way galaxy, other than our own, which we should in principle be able to make contact with.  It does this by multiplying together estimates of a bunch of relevant terms, namely:

• the average rate of star formation per year in our galaxy,
• the fraction of those stars that have planets,
• the average number of planets that can potentially support life per star that has planets,
• the fraction of the above that actually go on to develop life at some point,
• the fraction of the above that actually go on to develop intelligent life,
• the fraction of civilizations that develop a technology that releases detectable signs of their existence into space,
• the length of time such civilizations release detectable signals into space.

The format of the show was basically to explore the most interesting of these concepts and what we know about them – for instance, how we’re starting to get pretty good at discovering exoplanets, planets outside our solar system, which helps give us an idea of how many stars have planets and what those planets are like, at least in terms of very high level features like size and distance from their sun.  What I found most interesting was the discussion of the 5th term in the Drake equation, which deals with the fraction of planets bearing life where that life eventually evolves to be intelligent.

The show’s discussion of this term was mostly centred around the fact that for intelligence to evolve from unintelligent life takes quite a lot of time, and this time may not always be available.  All kinds of events, ranging from asteroid impacts to strong tectonic activity, can very easily completely or almost completely wipe life off a planet (Earth itself has had 5 major extinction events so far, and some would argue, not unconvincingly, that it is currently going through a 6th, in the form of humans wiping out species at an alarming rate), and if these average duration between these events is shorter than the average time it takes unintelligent life to evolve intelligence, then that suggests that the jump from life to intelligent life will be very rare indeed.

There’s nothing wrong with the above analysis, of course: sufficiently frequent extinction events are both a reality (I recently finished reading reading Bill Bryson’s “A Brief History of Nearly Everything“, which was quite an eye-opener on just how inhospitable Earth is to life on long enough timescales) and a real obstacle to the evolution of intelligence.  But underlying all of the discussion on the show seemed to be an implicit assumption that this was all that was standing in the way: that if there was some particularly lucky life-bearing planet out there which was somehow shielded from asteroids and solar flares and supernovas, and had relatively stable, benign weather and tectonic activity, and basically was left completely unmolested by forces of great destruction, then it would be a matter of certainty that, eventually, intelligence would evolve.  To be fair, I don’t know if the producers of the show or Neil himself believe this, but certainly the show did nothing to explicitly dismiss this notion.

The problem with this is that it’s completely wrong, and yet it is surprisingly often overlooked.  I wouldn’t have noticed this oversight myself if I hadn’t previously read either Steven Pinker’s “The Blank Slate” or his “How the Mind Works” (I forget which it was), which talks about this misconception in considerable detail (I can’t remember whether or not it was in the context of the Drake equation).  Although it’s easy to fall into the trap of thinking that it is, evolution is of course absolutely not some kind of driven, inevitable progression from simple to complex organisms.  It’s about adaption to an environment so as to maximise reproductive success, and unless there is an unintelligent organism somewhere in an environment with the following conditions satisfied:

• intelligence is evolutionarily accessible to the organism (i.e. it already has necessary prerequisites, like a sufficiently complex nervous system, for a few mutations to lead to some kind of intelligence),
• evolving intelligence will give it the organism a significant reproductive advantage over its unintelligent companions,
• and there are no other evolutionary pathways open to the organism which will yield a better ratio of reproductive advantage to “cost” (in terms of energy requirements, etc.) than intelligence,

then intelligence isn’t going to just turn up for the sake of carrying life higher and further.  Those organisms will remain unintelligent, possibly extremely successfully, possibly for an extremely long time, until the next extinction event wipes them out.  Intelligence is not inevitable given sufficiently long time.  It needs a good reason to emerge.

The problem that this situation poses for accurately estimating the 5th term of the Drake equation is that we actually have no idea why humans evolved intelligence.  There are plenty of plausible hypotheses out there, but nothing for certain, and I don’t think there is likely to be anything certain in the near future, given that we know extremely little about the lives of early humans and their ancestors (something else, incidentally, which Bill Bryson’s book gives a good accessible account of) and that we really know extremely little about intelligence (to the extent that there isn’t even a universally agreed upon, objective definition of what intelligence even is).  If we have no idea how we became intelligent, we’re not really in a position to speculate reliably about how likely other organisms are to become intelligent, given the chance.  The 5th term of the Drake equation could, in fact, be arbitrarily close to zero: close enough to zero to completely counteract all the terms in the equation which are very probably quite large.

Of course, it’s by no means a new criticism of the usefulness of the Drake equation to point out that the uncertainty surrounding our best estimates of each of its terms is so great that the final answer can vary by orders of magnitude, and even reach zero.  However, as far as I know, the term relating to the likelihood of the evolution of intelligence is the only one which currently has no reasonable lower bound: you can push it as close to zero as you like and not really reach a point where you can compellingly say “come on, surely it has to be higher than that“.  Which means that no new discovery suggesting that one of the other terms is actually incredibly huge will be sufficient to guarantee a result of more than one.  Which means, somewhat sadly, that perhaps we are much closer to being alone than a lot of people, myself included, have always thought.

On a related note, I recently read this BBC article, which discusses the opinion of one SETI astronomer that we should stop structuring the search for alien intelligence exclusively around the assumption that said intelligence will be biological in nature (which is an implicit assumption – and not the only one – of the Drake equation’s structure) and instead start to consider the possibility that a lot of that intelligence will – in its own version of our own transhumanism movement – have become non-biological in nature; that we should be looking for civilisations of intelligent machines, which are likely to hang out in very different places to intelligent meatbags.  I think this is a fairly persuasive argument.  Eliminating the problem of the mind-blowing slowness of interstellar travel (which is essentially a necessity for a civilisation to be truly long lasting) by figuring out how to transplant our consciousness into machines is probably considerably easier than the alternative of getting around the slowness directly with some sort of sci-fi-esque wormhole stuff.  At the very least, a lot of people who are experts in the relevant field believe that the former may be possible in principle, whereas, as far as I know, the latter is purely speculative.

Another short thought on the nature of the modular mind. When I first started doing a lot of reading on psycholinguistics at the start of my PhD, I made a few brief notes of interesting facts and observations that I thought I might like to be able to easily reference later on. One of those notes is this:

No more than 20,000 – 25,000 genes have to account for the entire human body and brain – the vast majority of these genes are shared with other apes and even other mammals. There are apparently only a few kilobytes of new genetic information in our genome since our last common ancestor with chimps.

This note is annotated as being sourced from Sverker Johansson’s 2005 “Origins of Language: Constraints on hypotheses”, which I recall being excellent. I don’t remember if the note above is a direct quote from Johansson or if I condensed a paragraph down into a few sentences.

Anyway, this is interesting from the point of view of thinking about the extent to which our mental modules rely on a shared toolbox. Considering that the differences in cognitive capacity between humans and chimps are significant, the fact that this difference represents only a few kilobytes of information suggests (although I am admittedly unsure as to how much our intuition about the difference a few KB of DNA can make to an organism’s mind should be allowed to be guided by our intuition as to the difference a few KB of source code can make to a computer program) that our higher cognitive abilities are coded for very efficiently, which argues more in favour of the common toolbox approach than the specialised tools.

In fact, moving into highly speculative territory that I’m not really qualified to talk about, maybe the difference between chimps and humans today corresponds directly to taking these two paths away from our common ancestor: humans spent their few “bonus kilobytes” investing in a rich toolbox of reusable, domain-general processing methods, whereas the other primates coming from the same starting point spent theirs on expanding and refining their kit of specialist, “one-trick pony” algorithms. My concern in my previous entry that general purpose methods were harder to evolve (that q was significantly lower than p) then becomes in fact an explanation as to why humans alone amongst the primates have progressed to the extent we have. Maybe we are the one roll of the evolutionary dice where the unlikely outcome with probability qⁿ actually happened, and our fellow primates all followed the more likely path of getting stuck in local optima. I don’t know enough about the learning abilities displayed by other primates in fields other than language (where, incidentally, they typically perform much more poorly than laypeople and sensationalist media outlets give them credit for) to have a good idea of how plausible this hypothesis is, but it seems plausible enough on the surface. I wish I knew more people knowledgeable about this sort of thing.

I recently started reading Steven Pinker’s “How the Mind Works“. Pinker is a psychologist whose interests broadly overlap with mine, and he writes a lot of popular science books on these issues. I like him, even though our opinions on language acquisition are quite different, because while his books are occasionally fairly biased (a particular problem for his “The Language Instinct“), they also maintain a good sense of intellectual rigour whilst still being fun to read. It’s encouraging to be reminded from time to time that there are smart people thinking about these things sensibly.

I’m only a little way into the book as of yet but already it’s been quite rewarding because it has dispelled a misconception I had about evolutionary psychology, a school of thought of which Pinker is a strong advocate. The stance of this school is essentially summarised by this excerpt:

The mind is a system of organs of computation, designed by natural selection to solve the kinds of problems our ancestors faced in their foraging way of life…The mind is organized into modules or mental organs, each with a specialized design that makes it an expert in one arena of interaction with the world. The modules’ basic logic is specified by our genetic program. Their operation was shaped by natural selection to solve the problems of the hunting and gathering life led by our ancestors in most of our evolutionary history.

I have never taken issue with the essential issues of this school of thought. I have embraced the computational theory of mind for as long as I can remember, and I have no doubt that the structure of the brain – and hence the mind – has been shaped by evolutionary pressures that acted in the distant past. It’s the “module” thing that has always kind of bugged me. The reason for this is that I have always interpreted the modular view of the mind espoused by evolutionary psychology as implying a mind made up of separate and autonomous parts bolted together. This is apparently not uncommon, as Pinker goes on to say:

The word “module” brings to mind detachable, snap-in components, and that is misleading…mental modules need not be tightly sealed off from one another, communicating only through a few narrow pipelines.

It’s a relief to hear that evolutionary psychology does not consider this position to be required. Of course, it’s one thing to not claim that the modules of the mind are necessarily distinct, and another thing to actually make a claim about the extent to which they are. This question really interests me. Do the modules of the mind look like this:

i.e. a collection of highly domain-specific modules with minimal overlap, most of the work being done by specialised faculties with little sharing of data or tools between modules? Or do they look like this:

i.e. a collection of highly overlapping modules with minimal domain-specific components, most of the work being done by a large, shared toolbox of general purpose algorithms?

As an aside, these charts were produced using Google’s free Charts API, a pretty nifty tool.

It’s easy to frame this question in terms of object oriented programming, too. If each module of the mind is a class and each class is a subclass a common BaseMindModule class, then is the interface of BaseMindModule just a few simple attributes and methods dealing with common stuff like I/O, with each subclass adding a lot of domain specific behaviour, or is BaseMindModule a large class with a rich API of general purpose methods, with each subclass being a thin wrapper around this API?

There seem to be two questions to consider here: firstly, just how large can the centre of the Venn diagram be, i.e. how much of human cognition can, in principle be explained by general purpose tools; secondly, even if a large common toolbox is possible in principle, is evolution likely to favour it over a a dispirit pile of specialist tools?

On the first question, I’m actually fairly confident that domain general tools can get a tremendous amount of work done. A background in mathematics makes this seem almost obvious. Mathematics is full of “tools” which are defined at such an abstract level that they can be applied to just about anything, while still being sufficiently meaningful that are practical. The student who has learned basic differential and integral calculus, for example, can construct simple models of phenomena from domains as diverse as biology, chemistry, economics, epidemiology, physics, sociology and more. Markov chains are an example of a tool with a rather different flavour that still manages to be very broadly applicable – Markov chains can play a role in models of things from all of the above fields as well. And, of course, statistics is taught to a wide range of students as little but a tool box of techniques and tests that work on any kind of data whatsoever – linear regression, hypothesis tests and confidence intervals are probably the closest thing that exists in academia to a universally common component of education across departments and disciplines, from engineering to psychology. If the mind contained circuits that processed data by approximating the logic of coupled linear ordinary differential equations, or of building hidden Markov chain models, then couldn’t those circuits be the workhorses of a whole host of mental modules for a wide range of problems? I can’t think of a compelling reason why not.

The second question feels less straightforward. The large, common toolbox approach offers a certain economy of design that on the one hand should be preferable to reinventing essentially identical wheels again and again for each problem that is encountered. But simultaneously, it feels like general tools are in some sense harder things to come up with. Inventing m general tools instead of n specific ones is of course less work overall if m is significantly less than n, but as so many people fail to grasp, evolution is an emphatically blind watchmaker which cannot look ahead like this and is thus extremely susceptible to getting stuck in local optima. Sticking with our counts of m and n, if the probability of a sequence of mutations leading to a domain specific tool is p and the probability of it leading to a domain general tool is q, the question becomes one of which is greater: p^m or qⁿ? As the ratio of n to m tends to infinity, chance favours a general toolbox, but as the ratio of q to p tends to infinity, chance favours a collection of specialist tools. We can’t answer the question without sensible estimates for these ratios, but how could we even begin to make such estimates? It’s not a trivial task.