On Computable Numbers, with an Application to the Entscheidungsproblem.

By A. Turing. 1937

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Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “A general approach (Schmidhuber, 1997) for both SL and RL seeks to compactly encode weights of large NNs (Schmidhuber, 1997) through programs written in a universal programming language (Church, 1936; Gödel, 1931; Post, 1936; Turing, 1936).”
PowerPlay: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem (2011, neutral): “Assume the architecture of the solver is a universal computer (Gödel, 1931; Church, 1936; Post, 1936; Turing, 1936).”
First Experiments with PowerPlay (2012, background): “All problem solvers can be uniquely encoded [7] or implemented on universal computers such as universal Turing Machines (TM) [31].”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, background): “Given some universal programming language [67, 35, 282, 194] for a universal computer, the algorithmic information content or Kolmogorov complexity of some computable object is the length of the shortest program that computes it.”
Optimal Ordered Problem Solver (2002, neutral): “We focus on S being the set of integers and Q := { 1 , 2 , . . . , n Q } representing a set of n Q instructions of some universal programming language (G¨odel, 1931, Turing, 1936).”
One Big Net For Everything (2018, background): “To become a general problem solver that is able to run arbitrary problem-solving programs, the controller of a robot or an artiﬁcial agent must be a general-purpose computer [15, 7, 92, 44].”
Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes (2008, background): “More recent work [59, 63] (1997) greatly increased the computational power of controller and predictor by implementing them as co-evolving, symmetric, opposing modules consisting of self-modifying probabilistic programs [88, 89] written in a universal programming language [13, 100 ].”
Computation and Computational Thinking (2012, background): “The most important model of sequential computation studied in computer science is the Turing machine, first proposed by Alan Turing in 1936 [3].”
On the proper treatment of connectionism (1988, background): “This is what has been formalized in the theory of effective procedures (Turing 1936).”
The Gremlin graph traversal machine and language (invited talk) (2015, background): “If so, that machine can be programmed to execute any known algorithm [18].”
What the radiologist should know about artificial intelligence – an ESR white paper (2019, background): “In 1950, British computer scientist Alan Turing enunciated the basis of the Turing test: a computer passes the test if a human interrogator, after posing a number of written questions, cannot tell whether the written responses come from a person or a computer [4, 5].”