Summary: Objectively identifying hateful or abusive speech on social media platforms would allow those platforms to better control it. However to be objective and without bias that identification would have to be independent of the author especially where elected officials are involved.
Added by William Vorhies on June 8, 2020 at 2:25pm — No Comments
Summary: 99% of our application of NLP has to do with chatbots or translation. This is a very interesting story about expanding the bounds of NLP and feature creation to predict bestselling novels. The authors created over 20,000 NLP features, about 2,700 of which proved to be predictive with a 90% accuracy rate in predicting NYT bestsellers.
Summary: Move over RNN/LSTM, there’s a new algorithm called Calibrated Quantum Mesh that promises to bring new levels of accuracy to natural language search and without labeled training data.
Added by William Vorhies on July 29, 2019 at 8:02am — No Comments
Summary: Our starting assumption that sequence problems (language, speech, and others) are the natural domain of RNNs is being challenged. Temporal Convolutional Nets (TCNs) which are our workhorse CNNs with a few new features are outperforming RNNs on major applications today. Looks like RNNs may well be history.
Summary: This is the second in our chatbot series. Here we explore Natural Language Understanding (NLU), the front end of all chatbots. We’ll discuss the programming necessary to build rules based chatbots and then look at the use of deep learning algorithms that are the basis for AI enabled chatbots.
Summary: This is the first in a series about Chatbots. In this first installment we cover the basics including their brief technological history, uses, basic design choices, and where deep learning comes into play. In subsequent articles we’ll describe in more detail about how they are actually programmed and best practice dos and don’ts.