The Basics of Machine Learning - What Base Role Tells Us?

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In solving many problems, it is a really good idea to keep the basics in mind. And here I guess there's also the higher level interpretation of the generative versus discriminative. But then we can go further and think about are there cases? What are the framework under which this directionality actually matters even more so? That's where the causal machine learning or the causal representation learning is very new.

In episode 59 of The Gradient Podcast, Daniel Bashir speaks to Professor Kyunghyun Cho.

Professor Cho is an associate professor of computer science and data science at New York University and CIFAR Fellow of Learning in Machines & Brains. He is also a senior director of frontier research at the Prescient Design team within Genentech Research & Early Development. He was a research scientist at Facebook AI Research from 2017-2020 and a postdoctoral fellow at University of Montreal under the supervision of Prof. Yoshua Bengio after receiving his MSc and PhD degrees from Aalto University. He received the Samsung Ho-Am Prize in Engineering in 2021.

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Outline:

* (00:00) Intro

* (02:15) How Professor Cho got into AI, going to Finland for a PhD

* (06:30) Accidental and non-accidental parts of Prof Cho’s journey, the role of timing in career trajectories

* (09:30) Prof Cho’s M.Sc. thesis on Restricted Boltzmann Machines

* (17:00) The state of autodiff at the time

* (20:00) Finding non-mainstream problems and examining limitations of mainstream approaches, anti-dogmatism, Yoshua Bengio appreciation

* (24:30) Detaching identity from work, scientific training

* (26:30) The rest of Prof Cho’s PhD, the first ICLR conference, working in Yoshua Bengio’s lab

* (34:00) Prof Cho’s isolation during his PhD and its impact on his work—transcending insecurity and working on unsexy problems

* (41:30) The importance of identifying important problems and developing an independent research program, ceiling on the number of important research problems

* (46:00) Working on Neural Machine Translation, Jointly Learning to Align and Translate

* (1:01:45) What RNNs and earlier NN architectures can still teach us, why transformers were successful

* (1:08:00) Science progresses gradually

* (1:09:00) Learning distributed representations of sentences, extending the distributional hypothesis

* (1:21:00) Difficulty and limitations in evaluation—directions of dynamic benchmarks, trainable evaluation metrics

* (1:29:30) Mixout and AdapterFusion: fine-tuning and intervening on pre-trained models, pre-training as initialization, destructive interference

* (1:39:00) Analyzing neural networks as reading tea leaves

* (1:44:45) Importance of healthy skepticism for scientists

* (1:45:30) Language-guided policies and grounding, vision-language navigation

* (1:55:30) Prof Cho’s reflections on 2022

* (2:00:00) Obligatory ChatGPT content

* (2:04:50) Finding balance