Recent disruptive AI technology provides all the tools to eliminate emotional deficiencies in artificial intelligence applications. Conceptual representation learning meta-models (CRLMM) can convert mathematical outputs into somewhat human mind models. Classical machine learning and deep learning programs mostly produce mathematical outputs. In previous chapters, CRLMM models produced enhanced conceptual representations, closing the gap between machine and human thinking.
The goal is clearly to build a mind, not an output, through several machine learning techniques: an RBM, sentiment analysis, CRLMM, RNN, LSTM, Word2Vec, and PCA. For each step, an application is available on a Windows or Linux platform, along with a video.
RBMs were presented in a Netflix competition for prediction purposes, namely to predict user choices...
