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.

I never understood why people assume that those biological self-replicators were in any way analogous to quines. Quines generate information from within themselves. Proto-life would have to assemble copies of itself from molecules available in the environment. It seems like completely different principles of work.

Quines also live in an environment: the programming language they're written in. It's a very different _kind_ of environment; for instance the physical world has preservation of mass while it's really easy to copy bits. But both quines and proto-life can only replicate in the right environment, and they do so via an interaction between the information contained in themselves and the environment in which they are embedded. They seem quite analogous to me.

That's the problem with analogies: they are observer-dependent.

A quine shares no causal property with DNA.

"assembling a copy of yourself" is a description you are imposing on a series of events.

The events in the case of a digital computer are certain oscillations to the electric field. That shares nothing with the chemical assembly of biological material

"Neurons" arent neurons. Replication isnt replication.

AI proponents speak of photographs of animals made of ink (electrical fields in silicon) as-if they were themselves animals. Inviting us to marvel at how life-like the piece of paper is.

2. "We suggest that a self-replication mechanism for artificial intelligence is useful because it introduces the possibility of continual improvement through natural selection."

We're progressing from static neural nets (like the old TensorFlow) to dynamic nets (like PyTorch) and even dynamically generated on-the-fly neural nets (hypernets) which is great for creating architectures that better fit the problem, case by case.

This paper shows another way to generate a net from a net, but this time with self replication. Self replication could be useful for evolutionary optimisation, and, as they say in the paper, could become a rudimentary form of introspection. The idea of a "self-replication loss term" is very exciting, it's rare that we see such innovation in loss functions.

Essentially it's a neural network which is represented by a series of genes. Genetically, new genes come in by having new links form between neurons, disabled, or split (with an in between neuron). This, as you can see, allows for a wide range of network representations. Because these variations are tagged with id's sequentially, we can mate two neural networks by combining their genes in order. They never conflict, and will produce something similar to both parents.