Theoretical Robopsychology: Samu Has Learned Turing Machines
원문 초록
From the point of view of a programmer, the robopsychology is a synonym for the activity is done by developers to implement their machine learning applications. This robopsychological approach raises some fundamental theoretical questions of machine learning. Our discussion of these questions is constrained to Turing machines. Alan Turing had given an algorithm (aka the Turing Machine) to describe algorithms. If it has been applied to describe itself then this brings us to Turing's notion of the universal machine. In the present paper, we investigate algorithms to write algorithms. From a pedagogy point of view, this way of writing programs can be considered as a combination of learning by listening and learning by doing due to it is based on applying agent technology and machine learning. As the main result we introduce the problem of learning and then we show that it cannot easily be handled in reality therefore it is reasonable to use machine learning algorithm for learning Turing machines.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks
Evolutionary bagging for ensemble learning
Neural network accelerator for quantum control
Deep Reinforcement Learning for Multi-Agent Interaction
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning
Unsupervised machine learning framework for discriminating major variants of concern during COVID-19
토론
이 논문과 연결된 포럼 글이 아직 없습니다.