Exploring AI and its applications

This week, I came across many new terminologies and AI applications. 

Bayesian optimization algorithm (BOA) is one of the most efficient and accurate algorithms that can tune hyperparameters of a machine learning model. The long short-term memory (LSTM) is capable of identifying long term dependencies in sequential data (thus, it is useful in natural language processing and other similar applications). These networks can also classify what information to retain and what to delete, which helps them identify those long term dependencies. Moving on, convolutional neural networks (CNNs) analyze visual data like images. Graphical neural networks (GNNs) make inferences based on graphs. Deep Q-Networks (DQNs) are a type of reinforcement learning that uses a deep neural network to determine the most optimal action to take. The Q function is something that estimates the value of taking a certain action. 

I have also continued to gather many publications from sites like the Journal of Marketing Analytics and American Marketing Association about the intersection between AI and customer experience and am excited to start reading! 

As for my final product, my ideas range from creating a final report of all my findings, expanding my current digital framework, or creating a chatbot that can generate personalized ads.