The Nonlinear Library LW - Claude 3 Opus can operate as a Turing machine by Gunnar Zarncke
Apr 18, 2024
01:21
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Claude 3 Opus can operate as a Turing machine, published by Gunnar Zarncke on April 18, 2024 on LessWrong.
Posted on Twitter:
Opus can operate as a Turing machine.
given only existing tapes, it learns the rules and computes new sequences correctly.
100% accurate over 500+ 24-step solutions (more tests running).
for 100% at 24 steps, the input tapes weigh 30k tokens*.
GPT-4 cannot do this.
Here is the prompt code for the Turing machine: https://github.com/SpellcraftAI/turing
This is the fully general counterpoint to the @VictorTaelin's A::B challenge (he put money where his mouth is and got praise for that from Yudkowsky).
Attention is Turing Complete was a claim already in 2021:
Theorem 6 The class of Transformer networks with positional encodings is Turing complete. Moreover, Turing completeness holds even in the restricted setting in which the only non-constant values in positional embedding pos(n) of n, for n N, are n, 1/n, and 1/n2 , and Transformer networks have a single encoder layer and three decoder layer
Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org
Posted on Twitter:
Opus can operate as a Turing machine.
given only existing tapes, it learns the rules and computes new sequences correctly.
100% accurate over 500+ 24-step solutions (more tests running).
for 100% at 24 steps, the input tapes weigh 30k tokens*.
GPT-4 cannot do this.
Here is the prompt code for the Turing machine: https://github.com/SpellcraftAI/turing
This is the fully general counterpoint to the @VictorTaelin's A::B challenge (he put money where his mouth is and got praise for that from Yudkowsky).
Attention is Turing Complete was a claim already in 2021:
Theorem 6 The class of Transformer networks with positional encodings is Turing complete. Moreover, Turing completeness holds even in the restricted setting in which the only non-constant values in positional embedding pos(n) of n, for n N, are n, 1/n, and 1/n2 , and Transformer networks have a single encoder layer and three decoder layer
Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org