Podknife - Moin Nadeem (MIT): The extraordinary future of natural language models by Machine Learning Engineered

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Moin Nadeem (MIT): The extraordinary future of natural language models

Podcast |: Machine Learning Engineered
Publisher |: Charlie You
Media Type |: audio
Podknife tags |: Artificial Intelligence,; Business,; Careers,; Data Science,; Interview,; Machine Learning,; Technology
Categories Via RSS |: Business,; Careers,; Science,; Technology
Publication Date |: Nov 03, 2020
Episode Duration |: 01:24:40

Moin Nadeem is a masters student at MIT, where he studies natural language generation. His research interests broadly include natural language processing, information retrieval, and software systems for machine learning.

Learn more about Moin:

https://moinnadeem.com/

https://twitter.com/moinnadeem

Want to level-up your skills in machine learning and software engineering? Join the ML Engineered Newsletter: http://bit.ly/mle-newsletter

Comments? Questions? Submit them here: http://bit.ly/mle-survey

Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Subscribe to ML Engineered: https://mlengineered.com/listen

Timestamps:

01:35 Follow Charlie on Twitter (https://twitter.com/CharlieYouAI)

03:10 How Moin got started in computer science

05:50 Using ML to identify depression on Twitter in high school

11:00 Building a system to track phone locations on MIT’s campus

14:35 Specializing in NLP

17:20 Building an end-to-end fact-checking system (https://www.aclweb.org/anthology/N19-4014/)

25:15 Predicting statement stance with neural multi-task learning (https://www.aclweb.org/anthology/D19-6603/)

27:20 Is feature engineering in NLP dead?

29:40 Reconciling language models with existing knowledge graphs

35:20 How advances in AI hardware will affect NLP research (crazy!)

47:25 Moin’s research into sampling algorithms for natural language generation (https://arxiv.org/abs/2009.07243)

57:10 Under-rated areas of ML research

01:00:10 How research works at MIT CSAIL

01:04:35 How Moin keeps up in such a fast-moving field

01:11:30 Starting the MIT Machine Intelligence Community

01:16:30 Rapid Fire Questions

Links:

FAKTA: An Automatic End-to-End Fact Checking System

StereoSet: Measuring stereotypical bias in pretrained language models

Neural Multi-Task Learning for Stance Prediction

Rich Sutton - The Bitter Lesson

A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation

Strategies for Pre-training Graph Neural Networks

Transformers For Image Recognition at Scale

Moin discusses his research in NLP, how language models can learn to reason with knowledge graphs, and what the future of the field looks like given recent advancements in AI hardware.