Author Archive | Delip

Some Thoughts on Writing (noun)

TLDR: This is a weird post; I am making an entry from my personal journal public. So, it’s likely useless and worth skipping, esp. if you are not interested in writing. This is also not advice on how to write (I’m in no place to give that), but an exploration of writing as a noun […]

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Unstoppable AI Flywheels and the Making of the New Goliaths

TL;DR: AI creates engines for relentless optimization at all levels. Read the article to figure out how and its consequences to what you’re doing. Some time ago I wrote about how everything is a model when reviewing a paper from Kraska et al (2017) where they show how traditional CS data structures like B-Tree indexes, […]

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Fragility of Everything

The day before heading to Neurips, I packed my luggage as usual at the last minute before going to bed (early am flight). As I was packing, I received a call from India. It was my mom. My dad had developed “breathing issues”, which we later found out was a combination of congestive heart failure, […]

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The Doers and The Clarion Callers

There is an unnecessary drama unfolding on Twitter on “the war” between connectionists and symbolists. This drama is absurd and perpetuated by people whose relevance exists only in the discussion of the “difference” and the “war.” Most of us who are too busy learning or doing, happily occupy the liminal space between symbolism and connectionism. […]

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Joining AI Foundation

TL;DR: I am joining AI Foundation to help build uncanny content generation and detection systems A few friends and family knew this, but today, I am excited to announce widely that I joined the AI Foundation as their VP of Research. I am overseeing their deep learning/machine learning research roadmaps in speech, language, and vision for […]

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Differentiable Dynamic Programs and SparseMAP Inference

Two exciting NLP papers at ICML 2018! ICML 2018 accepts are out, and I am excited about two papers that I will briefly outline here. I think both papers are phenomenally good and will bring back structured prediction in NLP to modern deep learning architectures. Differentiable Dynamic Programming for Structured Prediction and Attention Arthur Mensch […]

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