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

Applied AI Pod

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3y ago 56:20

MP3•Maison d'episode

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.

33 episodes

#Tech #Entrepreneur #Business #Self-Improvement #Alexandra Petrus #Artificial Intelligence #Ai Use-cases #Ai Applications At Scale #Computer Vision #Ai Practitioners #Podcasting Education #Nlu #Applied AI #NLP

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

Applied AI Pod

12 subscribers

published 3y ago

MP3•Maison d'episode

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.

33 episodes

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