Biological life began with the first self-replicator (Marshall, 2011), and natural selection kicked in to favor organisms that are better at replication, resulting in a self-improving mechanism. Analogously, we can construct a self-improving mechanism for artificial intelligence via natural selection if AI agents had the ability to replicate and improve themselves without additional machinery.
Neural networks are capable of learning powerful representations across many different domains of data (Bengio et al., 2013). But can a neural network learn a good representation of itself? Self-replication involves some level of self-awareness, and is a small step towards developing introspective capabilities in neural networks.
In a HyperNetwork (Ha et al., 2017), a small recurrent neural network is used to generate the weights for a larger one, which can be viewed as a meta-controller enforcing a soft weight-sharing constraint between layers of a recurrent neural network. Similarly, we can view self-replication as a mechanism that enforces a soft weight-sharing constraint between a network and past versions of itself, which is helpful for lifelong learning (Silver et al., 2013) and potential discovery of new neural network architectures.
Learning how to enhance or diminish the ability for AI programs to self-replicate is useful for computer security. For example, we might want an AI to be able to execute its source code without being able to read or reverse-engineer it, either through its own volition or interaction with an adversary.
Self-replication functions as the ultimate mechanism for self-repair in damaged physical systems (Zykov et al., 2005). The same may apply to AI, where a self-replication mechanism can serve as the last resort for detecting damage, or returning a damaged or out-of-control AI system back to normal.