Natural intelligence (NI) is the opposite of artificial
intelligence: it is all the systems of control that are not
artefacts, but rather are 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:
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.
- 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?
models of natural Intelligence (AmonI) group at Bath is
dedicated to understanding natural intelligence. Building AI
models of NI systems requires designing
intelligent systems. Understanding human culture is
also key to understanding AI ethics.
Below are selected publications of mine contributing to
understanding NI. For full
references and a complete list of publications, see my publications page. My main current
research is on identity,
social structure, and public goods investment. For
focused lists concerning task
learning in nature, the evolution
primate social structure and the evolution
culture, see my understanding
intelligence web page. For a general overview of my
views on natural intelligence and how it relates to artificial
intelligence, see this book chapter:
Last updated February 2018.