Wojciech Zaremba, Ilya Sutskever
2015 · arXiv
The Neural Turing Machine (NTM) is more expressive than all previously considered models because of its external memory. It can be viewed as a broader effort to use abstract external Interfaces and to learn a parametric model that interacts with them. The capabilities of a model can be extended by providing it with proper Interfaces that interact with the world. These external Interfaces include memory, a database, a search engine, or a piece of software such as a theorem verifier. Some of these Interfaces are provided by the developers of the model. However, many important existing Interfaces, such as databases and search engines, are discrete. We examine feasibility of learning models to interact with discrete Interfaces. We investigate the following discrete Interfaces: a memory Tape, an input Tape, and an output Tape. We use a Reinforcement Learning algorithm to train a neural network that interacts with such Interfaces to solve simple algorithmic tasks. Our Interfaces are expressive enough to make our model Turing complete.
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Reinforcement learning is Turing complete [1], so if AI is possible at all, then it can be realised through RL. cognitive psychology You are overselling the insights of this discipline. Has cognitive psychology solved its replication problems? Where is the world-beating AI that is based on "concept spaces", "sparse representations", "small-world networks" and "learning and memory" neuroscience? [1] https://arxiv.org/abs/1505.00521
It's very experimental. The great thing about them is that they open the possibility of building new types of interfaces like memory, stack, tape, database or search engine that work with neural networks in inference. See https://arxiv.org/pdf/1505.00521v3.pdf
In "Learning to Execute" by Zaremba & Sutskever http://arxiv.org/abs/1410.4615 An RNN learns snippets of python Their next paper is "Reinforcement Learning Neural Turing Machines" http://arxiv.org/abs/1505.00521 based on Graves "Neural Turing Machines" http://arxiv.org/abs/1410.5401, which attempts to infer algorithms from the result. In a lost BBC interview from 1951 Turing reputedly spoke of evolving cpu bitmasks for computation.
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