Understanding
Natural Intelligence
Natural intelligence (NI) is the opposite of artificial intelligence:
it is all
the systems of control present in biology. Normally when we think
of NI we
think about how
animal or human brains function, but there is more to natural
intelligence than
neuroscience. Nature also demonstrates
non-neural control in plants and protozoa, as well as distributed
intelligence in colony species like ants, hyenas and humans. Our
behaviour co-evolves with the rest of our bodies, and in response to
our
changing environment. Understanding natural intelligence requires
understanding all of these influences on behaviour and their
interactions.
One of the best
methods for understanding how NI systems work is to try to
replicate their behaviour in simulation. Just as learning
to
paint forces you to understand the details of what you are seeing,
building
a working model forces you to understand the intricacies of what the
target intelligent system is doing. For example:
- What environment does it work in?
- What aspects of that environment does it rely on?
- What does it need to do itself?
- How much does it need to learn and remember?
- What can it learn just from its senses?
- How much does it need to innovate?
An AI model of an
organism is a very-well-specified hypothesis about
how that organism thinks and behaves. Like any hypothesis, we
assess an AI model by testing its predictions against the performance
of the real system and by evaluating the plausibility of its
assumptions. The predictions of a model are its behaviour, which we
simply
record after running simulations. Its
assumptions are its components; for example, the computations it makes,
the
information it has access to, the things it perceives and remembers.
We can use
standard statistical tests to see how close we come to modelling
behaviour in order to argue the validity of our
assumptions.
The Artificial
models
of natural Intelligence (AmonI) group at Bath is dedicated to
understanding natural intelligence. Building AI models of NI
systems requires designing intelligent systems.
These are selected publications generally contributing to understanding
NI. For full references and a
complete list of
publications, see my publications page.
For focused lists concerning task
learning, the evolution
of primate social structure and the evolution
of culture, see my understanding
primate intelligence web page.
Joanna Bryson
Last updated 29 February, 2008