arXiv:1901.03429cs.LG
On the Turing Completeness of Modern Neural Network Architectures
Abstract
Alternatives to recurrent neural networks, in particular, architectures based on attention or convolutions, have been gaining momentum for processing input sequences. In spite of their relevance, the computational properties of these alternatives have not yet been fully explored. We study the computational power of two of the most paradigmatic architectures exemplifying these mechanisms: the Transformer (Vaswani et al., 2017) and the Neural GPU (Kaiser & Sutskever, 2016). We show both models to be Turing complete exclusively based on their capacity to compute and access internal dense representations of the data. In particular, neither the Transformer nor the Neural GPU requires access to an external memory to become Turing complete. Our study also reveals some minimal sets of elements needed to obtain these completeness results.
> No AI can code in Lean 4 reliably I wonder if this is due to the nature of the language. Lean 4 lets you redefine its syntax in ways that most other languages do not allow[1], so effectively you are dealing with a recursive language, that could require a Touring complete token…
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