3 ms·

It's mostly interesting from an artificial life perspective. I'm just going to quote from the motivations sections here: Biological life began with the first s

It's mostly interesting from an artificial life perspective. I'm just going to quote from the motivations sections here: 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.

romaniv 9y ago
>Biological life began with the first self-replicator 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.
- jachee 9y ago
  The title was updated in the last 3 hours to include the word quine instead of self-replicator. GP comment was written in the previous context.
- justinpombrio 9y ago
  > Proto-life would have to assemble copies of itself from molecules available in the environment. 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.
  - mjburgess 9y ago
    A rock is analogues to a car from the point of view of a child. That's the problem with analogies: they are observer-dependent. A quine shares no causal property with DNA.
    - TeMPOraL 9y ago
      Oh, but it does, if you treat "assembling a copy of yourself from atoms in the environment" as equivalent to the implementation details of printing to standard output.
      - mjburgess 9y ago
        > causal property "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
- mjburgess 9y ago
  The whole field of artificial intelligence is missold on analogy. "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.

romaniv 9y ago

>Biological life began with the first self-replicator 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.

jachee 9y ago
The title was updated in the last 3 hours to include the word quine instead of self-replicator. GP comment was written in the previous context.
justinpombrio 9y ago
> Proto-life would have to assemble copies of itself from molecules available in the environment. 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.
- mjburgess 9y ago
  A rock is analogues to a car from the point of view of a child. That's the problem with analogies: they are observer-dependent. A quine shares no causal property with DNA.
  - TeMPOraL 9y ago
    Oh, but it does, if you treat "assembling a copy of yourself from atoms in the environment" as equivalent to the implementation details of printing to standard output.
    - mjburgess 9y ago
      > causal property "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
mjburgess 9y ago
The whole field of artificial intelligence is missold on analogy. "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.