The large language based-model chatbot ChatGPT gained a lot of popularity since its launch and has been used in a wide range of situations. This research centers around a particular situation, when the ChatGPT is used to produce news that will be consumed by the population, causing the facilitation in the production of fake news, spread of misinformation and lack of trust in news sources. Aware of these problems, this research aims to build an artificial intelligence model capable of performing authorship attribution on news articles, identifying the ones written by the ChatGPT. To achieve this goal, a dataset containing equal amounts of human and ChatGPT written news was assembled and different natural processing language techniques were used to extract features from it that were used to train, validate and test three models built with different techniques. The best performance was produced by the Bidirectional Long Short Term Memory (LSTM) Neural Network model, achiving 91.57\% accuracy when tested against the data from the testing set.

With recent breakthroughs in artificial neural networks, deep generative models have become one of the leading techniques for computational creativity. Despite very promising progress on image and short sequence generation, symbolic music generation remains a challenging problem since the structure of compositions are usually complicated. In this study, we attempt to solve the melody generation problem constrained by the given chord progression. This music meta-creation problem can also be incorporated into a plan recognition system with user inputs and predictive structural outputs. In particular, we explore the effect of explicit architectural encoding of musical structure via comparing two sequential generative models: LSTM (a type of RNN) and WaveNet (dilated temporal-CNN). As far as we know, this is the first study of applying WaveNet to symbolic music generation, as well as the first systematic comparison between temporal-CNN and RNN for music generation. We conduct a survey for evaluation in our generations and implemented Variable Markov Oracle in music pattern discovery. Experimental results show that to encode structure more explicitly using a stack of dilated convolution layers improved the performance significantly, and a global encoding of underlying chord progression into the generation procedure gains even more.