Neural Network Quine
📖 Description
Neural Network Quine Oscar Chang1and Hod Lipson1 1Data Science Institute, Columbia University, New York, NY 10027 oscar.chang, [email address redacted] Abstract Self-replication is a key aspect of biological life that has been largely overlooked in Arti?cial Intelligence systems. Here we describe how to build and train self-replicating neural net- works. The network replicates itself by learning to output its own weights. The network is designed using a loss func- tion that can be optimized with either gradient-based or non- gradient-based methods. We also describe a method we call regeneration to train the network without explicit optimiza- tion, by injecting the network with predictions of its own pa- rameters. The best solution for a self-replicating network was found by alternating between regeneration and optimization steps. Finally, we describe a design for a self-replicating neu- ral network that can solve an auxiliary task such as MNIST image classi?cation. We observe that th...
📊 Game Impacts
Not verified in gameWhich variables this event was proposed to move, and in which direction. The magnitudes are held in the corpus but are not shown here, because they have not been verified against the shipped game. They come from pdoom-data. They describe what an event was proposed to do, not what the shipped game does with it. Most events in the corpus are flavour: they are shown for colour and do not move any game variable. Only a small minority reach the systems below, and several of the variables listed here are not read by the game at all yet. Treat this table as a design proposal under review, not as a measurement of play. Corrections and arguments are welcome — the suggestion links at the foot of this page go straight to the data repo.
| Variable | Direction | Condition |
|---|---|---|
| Research | proposed: up | Always |
| Papers | proposed: up | Always |
| Vibey Doom | proposed: up | Always |
💭 Reactions
"Advances our understanding of AI safety"
"Academic research release"
🔗 Sources
🤝 Found an Issue?
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