NLP, Speech Tech, Transformer Models, w/ Marc von Wyl, Algolia, E30

01:15 - How does NLP work?04:05 - How do Transformer-based NLP models work?08:20 - How to look at unstructured data to take advantage of it more.12:00 - How to leverage ML to bring more to unstructured data?15:25 - Approach for low resources languages.23:25 - Word embeddings for common reasoning needs.26:55 - Techniques to follow to improve error and ambiguity in training data or for a model in general.30:10 - Are GPTs leading effort in the field in a wrong direction?34:15 -  Is DeepLearning the end of AI?37:20 - What are some good NLP metrics to watch?42:05 - How do we get past transactional queries to conversational queries?52:00 - Is the Turing test still relevant for NLP or has it become obsolete?References:AI-Powered Search referenced in respect of text not being unstructured.Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language ProcessingRethinking Search:Making Experts out of Dilettantes Common sense reasoningTWIML AI podcast 518 with Yejin ChoiDARPA's Explainable AI ProjectEPITA is an engineering school in Paris.Marc's LinkedIn profile.