Neural Turing Machines (NTMs) extend neural networks by integrating them with external memory resources, enabling interaction through attention mechanisms. This system, akin to a Turing Machine but differentiable, allows efficient training via gradient descent. Preliminary results show NTMs can infer simple algorithms like copying, sorting, and associative recall from input-output examples. NTMs combine a neural network controller with a memory bank, using attention to read and write memory. The controller is differentiable, enabling end-to-end training. NTMs mimic human working memory, handling short-term information storage and rule-based manipulation. They resemble working memory models by selectively accessing memory through attention. Research in psychology, neuroscience, and AI highlights the importance of working memory in cognition. NTMs draw from these fields, incorporating mechanisms for variable-binding and variable-length structures. Recurrent neural networks (RNNs), including Long Short-Term Memory (LSTM), have shown promise in handling variable-length data, but NTMs enhance this by adding external memory and attention. NTMs use content-based and location-based addressing to access memory. Content-based addressing retrieves data based on similarity, while location-based addressing uses shifts to navigate memory. This dual mechanism allows NTMs to generalize well beyond training data. Experiments demonstrate NTMs' ability to learn and execute simple algorithms. In the copy task, NTMs outperformed LSTMs, maintaining accuracy for longer sequences. In repeat copy, NTMs learned nested functions, though they struggled with counting repetitions. Associative recall tasks showed NTMs could retrieve subsequent items in a sequence, using content-based lookups. Dynamic N-Grams tests revealed NTMs could adapt to new predictive distributions, using memory to track transition statistics. Priority sort tasks confirmed NTMs could sort data based on priorities, using memory to track context-specific counts. NTMs show significant performance improvements over LSTMs in various tasks, leveraging external memory and attention for better generalization. The architecture is differentiable, allowing efficient training and adaptation to new tasks. Overall, NTMs offer a practical mechanism for learning programs, combining the strengths of neural networks and external memory systems.Neural Turing Machines (NTMs) extend neural networks by integrating them with external memory resources, enabling interaction through attention mechanisms. This system, akin to a Turing Machine but differentiable, allows efficient training via gradient descent. Preliminary results show NTMs can infer simple algorithms like copying, sorting, and associative recall from input-output examples. NTMs combine a neural network controller with a memory bank, using attention to read and write memory. The controller is differentiable, enabling end-to-end training. NTMs mimic human working memory, handling short-term information storage and rule-based manipulation. They resemble working memory models by selectively accessing memory through attention. Research in psychology, neuroscience, and AI highlights the importance of working memory in cognition. NTMs draw from these fields, incorporating mechanisms for variable-binding and variable-length structures. Recurrent neural networks (RNNs), including Long Short-Term Memory (LSTM), have shown promise in handling variable-length data, but NTMs enhance this by adding external memory and attention. NTMs use content-based and location-based addressing to access memory. Content-based addressing retrieves data based on similarity, while location-based addressing uses shifts to navigate memory. This dual mechanism allows NTMs to generalize well beyond training data. Experiments demonstrate NTMs' ability to learn and execute simple algorithms. In the copy task, NTMs outperformed LSTMs, maintaining accuracy for longer sequences. In repeat copy, NTMs learned nested functions, though they struggled with counting repetitions. Associative recall tasks showed NTMs could retrieve subsequent items in a sequence, using content-based lookups. Dynamic N-Grams tests revealed NTMs could adapt to new predictive distributions, using memory to track transition statistics. Priority sort tasks confirmed NTMs could sort data based on priorities, using memory to track context-specific counts. NTMs show significant performance improvements over LSTMs in various tasks, leveraging external memory and attention for better generalization. The architecture is differentiable, allowing efficient training and adaptation to new tasks. Overall, NTMs offer a practical mechanism for learning programs, combining the strengths of neural networks and external memory systems.