Tim Dettmers

In one of my classes I asked the question I was afraid to ask but I just needed the answer to: “Who is afraid of not getting a job after graduating?”

If you look at how people cook coding agents today, they have an industrial kitchen: large-scale reinforcement learning systems with many components for efficiency spanning hundreds of GPUs, complex repository mixing, and large teams working on all angles…

If you are reading this, you probably feel the FOMO.

If you are reading this, you probably have strong opinions about AGI, superintelligence, and the future of AI.

Deep learning is a field with intense computational requirements, and your choice of GPU will fundamentally determine your deep learning experience.

When I attended NAACL, I wanted to do a little test.

If you are reading this, then you probably finished the long and arduous journey to grad school.

I recently had a discussion about creativity with a colleague.

This blog post is about my work, Sparse Networks from Scratch: Faster Training without Losing Performance, with Luke Zettlemoyer on fast training of neural networks which we keep sparse throughout training.

Deep Learning is very computationally intensive, so you will need a fast CPU with many cores, right?

I studied in depth how to be successful in my PhD applications and it paid off: I got admitted to Stanford, University of Washington, UCL, CMU, and NYU.