One of the things
that humans are trying to do, currently, is create artificial intelligence (AI).
In humans, we begin with a system with lots of local connection, and then we
have a tipping point; which then turns into a system that has fewer connections
but much stronger, more long-distance connections. So, we start out with a
system that’s very flexible but not very efficient; and that turns into a
system that’s very efficient and not very flexible.
And therein lies
the rub. Instead of trying to produce a program to simulate the modern mind,
why not rather try to produce one which simulates indigenous knowledge? The
explosion of machine learning, as a basis for AI, has made people appreciate
the fact that if you’re interested in systems that are going to learn about the
external world, the system that we know of that does that better than anything
else is indigenous knowledge.
This means taking
a leaf from nature’s playbook. The strategy of producing just a few younger
organisms, giving them a long period where they’re incapable of taking care of
themselves, and then having a lot of resources dedicated to keeping them alive
turns out to be a strategy that - over and over again - is associated with
higher levels of intelligence. And that’s not just true for humans. It’s true for
animals, insects and even plants.
It’s interesting
that that isn’t an architecture that’s typically been used in AI. But it’s an
architecture that life seems to use over and over again to implement
intelligent systems. One of the questions we could ask is, how come? Why would we
see this relationship? Why would we see this characteristic neural
architecture, especially for highly intelligent species?
A good way of
thinking about this may be that it’s a way of resolving the explore-exploit tradeoffs
that we see in AI. One of the problems, characteristic to AI, is a greater
range of solutions that seem to be moving in the direction of a system that’s
more intelligent. A system that understands the world in more different ways, also
produces a big expansion of the search problem.
One way to solve
this problem, that comes out of computer science, is to start out with a very
wide-ranging exploration of the space; and then gradually narrow in on
solutions that are going to be more effective. The problem with such a high
temperature search is that we could be spending a lot of time considering
solutions that aren’t very effective; and if we’re considering solutions that
aren’t effective, we aren’t going to be very good at acting in the world.
By contrast,
indigenous knowledge produces a lot of random variability. Being impulsive and
acting on the world are good ways of getting more feedback, but they’re not
very good ways of planning effectively. This gives a different picture about
the kinds of things we should be looking for in intelligence. It means that
some of the things that have been difficult for AI to do - like creativity,
being able to get to solutions that are genuinely new - are things that indigenous
people are remarkably good at.
For example, one
of the things that we know indigenous people do, is to get into everything.
That's active learning, where they’re determining what will be the exact kind
of information that will cause them to change the current view that they have
of the world. It's a very unusual thing to be able to do, to go out into the
world and spend energy in order to risk being wrong. That’s something that modern
humans very characteristically don’t do.
Another aspect of
what indigenous people do, that would be informative for thinking about
intelligence in general, is that they are cultural learners. One of the effects
is that it gives them this capacity for cultural ratcheting, a way of balancing
innovation and imitation. They produce a constant tension between how much they’re
going to be able to build on the things that the previous generation has done;
and how much they’re producing something that’s new enough, so it would be
worth having the next generation imitate.
The extraordinary
affinity indigenous people have with nature keeps their brains in a state of
plasticity. So, the effect is that it increases the local connections and
breaks the long-distance network connections. What modern humans can learn from
them is how to take a system that’s relatively rigid and inject variability;
which shakes it out of its local optima and lets it settle into something new. Therefore,
having computers that play and explore, might be a model for AI that’s
different from the models of intelligence that we currently have in modern
society.
