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

Applied AI Pod

• 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 Processing
• Rethinking Search:Making Experts out of Dilettantes Common sense reasoning
• TWIML AI podcast 518 with Yejin Choi
• DARPA's Explainable AI Project
• EPITA is an engineering school in Paris.
• Marc's LinkedIn profile.