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Putting the Bitter Lesson into practice in day-to-day AI work

The talk’s speaker says to add structure for progress now, then remove it when the conditions justifying it change.

Hyung Won ChungHW
1 Source, 33m ago, first seen 33m ago

TLDR

The speaker says their talk offered a practical interpretation of the Bitter Lesson: make progress with today’s methods, compute, data and algorithms, but revisit why added structure is needed as conditions change. The person who shared the talk called it the best they watched during their PhD and said they built their thesis conclusions and future work on it.

Combined views

774

1 Source, first seen 33m ago

8 likes4 saves1 reposts

Combined views

774

1 Source, first seen 33m ago

8 likes4 saves1 reposts

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Featured Source

Sentiment

Positive——Negative

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1 Source

Hyung Won Chung@hwchung27In AI, most artifacts have a short shelf life. So I’m glad this talk I gave 2.5 years ago is still relevant to some people. The Bitter Lesson has become much more popular since then, but I’ve seen a gap between how readily people agree with it and how much it actually guides their day-to-day work. That’s partly because it’s a statement about what wins in the long run, which doesn’t easily translate into day-to-day decisions, especially under enormous pressure to deliver. What I tried to offer in this talk was a practical interpretation. That interpretation starts with acknowledging that we have to make progress with the methods, compute, data, and algorithms available today. So we add structure that enables progress now. But we should remember what justified that structure and revisit it when those conditions change. When it’s no longer needed, remove it. Adding structure while understanding when to take it out may not look "Bitter Lesson pilled" at first glance, but I think this is how we actually put the lesson into practice.33m
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    1 Source

    Hyung Won Chung@hwchung27In AI, most artifacts have a short shelf life. So I’m glad this talk I gave 2.5 years ago is still relevant to some people. The Bitter Lesson has become much more popular since then, but I’ve seen a gap between how readily people agree with it and how much it actually guides their day-to-day work. That’s partly because it’s a statement about what wins in the long run, which doesn’t easily translate into day-to-day decisions, especially under enormous pressure to deliver. What I tried to offer in this talk was a practical interpretation. That interpretation starts with acknowledging that we have to make progress with the methods, compute, data, and algorithms available today. So we add structure that enables progress now. But we should remember what justified that structure and revisit it when those conditions change. When it’s no longer needed, remove it. Adding structure while understanding when to take it out may not look "Bitter Lesson pilled" at first glance, but I think this is how we actually put the lesson into practice.33m
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