A Neural Turing Machine (NTM) is a unique type of neural network architecture that incorporates external memory resources to perform tasks such as copying, sorting, and associative recall. This machine has a controller and a memory bank that work together for better performance.
Architecture
The architecture of an NTM has two primary components: a neural network controller and an external memory bank. The controller connects the input and output vectors to the external memory matrix, which is capable of selectively reading and writing data. The NTM's output that performs these operations is referred to as a "head."
Unlike a traditional neural network, every component of an NTM is differentiable, allowing it to learn and improve its performance using gradient descent. Instead of addressing a single element in memory, the NTM uses "fuzzy" read and write operations to interact with the entire memory matrix to a greater or lesser extent.
Attentional Focus Mechanism
The degree of blurriness of the read and write operations is determined by an attentional focus mechanism, which constrains each operation to interact with a small portion of the memory and ignore the rest. Because interaction with memory is so sparse, the NTM is biased towards storing data without interference. The memory location brought into attentional focus is determined by the outputs emitted by the heads. This output defines a normalized weighting over the rows in the memory matrix that determines the degree to which the head reads or writes at each location.
Each weighting, one per read or write head, enables the head to attend sharply to memory at a single location or weakly at memory at many locations. The attentional focus mechanism enables the NTM to write and read data without interfering with previously stored information.
Applications
An NTM has broad applications in the field of artificial intelligence, including image recognition, natural language processing, sequence generation, and decision-making processes. Its ability to incorporate external memory resources distinguishes this neural network architecture from traditional neural networks.
One example of an NTM application is the learning of how to sort data. An NTM reads a sequence of data and sorts it by writing the sorted output to memory. The sorted data is then read from memory as the output of the NTM.
Neural Turing Machines are a type of neural network architecture that allows for a more efficient use of external memory resources. This architecture has improved the performance of various artificial intelligence applications including image recognition, natural language processing, and sequence generation. The attentional focus mechanism of the NTM allows the network to read and write data without interfering with previously stored information.
