  We apply the theory of learning to physically renormalizable systems in an
attempt to develop a theory of biological evolution, including the origin of
life, as multilevel learning. We formulate seven fundamental principles of
evolution that appear to be necessary and sufficient to render a universe
observable and show that they entail the major features of biological
evolution, including replication and natural selection. These principles also
follow naturally from the theory of learning. We formulate the theory of
evolution using the mathematical framework of neural networks, which provides
for detailed analysis of evolutionary phenomena. To demonstrate the potential
of the proposed theoretical framework, we derive a generalized version of the
Central Dogma of molecular biology by analyzing the flow of information during
learning (back-propagation) and predicting (forward-propagation) the
environment by evolving organisms. The more complex evolutionary phenomena,
such as major transitions in evolution, in particular, the origin of life, have
to be analyzed in the thermodynamic limit, which is described in detail in the
accompanying paper.
