Blog: Text Generation through AI
Text Generation is one of the main Language Understanding and Language processing problems in the field of Artificial Intelligence. The goal of being able to understand common human languages and be able to produce a understandable adequate response is closely related to what artificial intelligence is all about because it constitutes the concept of making a computer behave like a human or act intelligently, it involves understanding previous language and being able to develop a concept of what the expected response or what a human response would be like. Text generation is one of the greatest achievements and challenges for AI nowadays. One of the biggest and most popular approaches that is commonly used today is a neural network approach due to the ability of neural networks to process complex inputs due to the many nodes and layers that compose them. The nodes is where the computation occurs, each node combines inputs from the previous layer with a set of weights assigning significance to each input that is taken into consideration. The output from such processing is then passed to an activation function, to determine whether the output should be passed down to the next layer. This is very useful when processing language because it allows us to have deeper processing of the language or input to allow a better understanding and better accuracy when creating a prediction or determining a response. The node function can be related to preprocessing the words that can be converted to tokens, determining probabilities and analysing meaning of words and later in combinations of words. This process is fundamental or at least very related to how text generation should work because to generate text based on previous input or data there must be many steps of processing to allow the computer to relate how words and sentences or specifically language works. Understanding neural networks is fundamental in the further development of artificial intelligent techniques as well as prediction techniques and therefore improve language processing and text generation accuracy.