These updates are motivated by a Maximum Entropy Principle and
they are prevalent in evolutionary processes where the parameters
are for example concentrations of species and the factors are survival rates.
The simplest such update is Bayes rule and we give
an in vitro selection algorithm for RNA strands that
implements this rule in the test tube where
each RNA strand represents a different model.
In one liter of the RNA soup there are approximately 10^15 different strands
and therefore this is a rather high-dimensional implementation of Bayes rule.
We investigate multiplicative updates for the purpose
of learning online while processing a stream of examples.
The ``blessing'' of these updates is that they learn very fast
in the short term because the good parameters grow exponentially.
However their ``curse'' is that they learn too fast and
wipe out parameters too quickly. This can have a negative
effect in the long term. We describe a number of
methods developed in the realm of online learning
that ameliorate the curse of the multiplicative updates.
The methods make the algorithm robust against data
that changes over time and prevent the currently good
parameters from taking over.
We also discuss how the curse is circumvented by nature.
Surprisingly, some of nature's methods parallel the ones
developed in Machine Learning, but nature also has some additional tricks.
This will be a high level talk.
No background in online learning will be required.