Chetan Verma

We are hiring Research Scientists for our Frontiers-of-AI team at Google DeepMind Bangalore, Singapore, Mountain View. If you're passionate about cutting-edge AI research and building thinking, efficient, elastic, customized, and safe LLMs, we'd love to hear from you. We are looking for candidates with a PhD and a strong demonstrated record of ideating and executing deep research projects. If interested, please apply here: job-boards.greenhouse.io/deepmind/jobs/…

cc: @tadityasrinivas @inderjit_ml @cho_jui_hsieh @jainprateek_

Please come talk to me at #KDD2025 if you're interested in learning more :)

📢 Excited to present our paper at ACM KDD 2025 Conference Matryoshka Model Learning for Improved Elastic Student Models lnkd.in/gYgXrngq 🪆🙌↓

@friedberg Emergency pod, but no Jason.

Over the last ~2 hours I curated a new Podcast of 10 episodes called "Histories of Mysteries". Find it up on Spotify here: open.spotify.com/show/3K4LRyMCP… 10 episodes of this season are: Ep 1: The Lost City of Atlantis Ep 2: Baghdad battery Ep 3: The Roanoke Colony Ep 4: The Antikythera Mechanism Ep 5: Voynich Manuscript Ep 6: Late Bronze Age collapse Ep 7: Wow! signal Ep 8: Mary Celeste Ep 9: Göbekli Tepe Ep 10: LUCA: Last Universal Common Ancestor Process: - I researched cool topics using ChatGPT, Claude, Google - I linked NotebookLM to the Wikipedia entry of each topic and generated the podcast audio - I used NotebookLM to also write the podcast/episode descriptions. - Ideogram to create all digital art for the episodes and the podcast itself - Spotify to upload and host the podcast I did this as an exploration of the space of possibility unlocked by generative AI, and the leverage afforded by the use of AI. The fact that I can, as a single person in 2 hours, curate (not create, but curate) a podcast is I think kind of incredible. I also completely understand and acknowledge the potential and immediate critique here, of AI generated slop taking over the internet. I guess - have a listen to the podcast when you go for walk/drive next time and see what you think.

The Transformer architecture has changed surprisingly little from the original paper in 2017 (over 7 years ago!). The diff: - The nonlinearity in the MLP has undergone some refinement. Almost every model uses some form of gated nonlinearity. A silu or gelu nonlinearity is common. - The placement of normalization layers. This tends to vary a little from architecture to architecture. Sometimes more normalization layers per Transformer block (e.g.Gemma 2). Sometimes keys and queries are normalized (e.g. Command+R). - The type of normalization layer. RMS norm is commonly used instead of Layer Norm. All of Llama 3, Phi 3 and Gemma 2 use RMS norm now. Seems like vanilla Layer Norm is becoming a little less common. - Group-query attention is now a staple as it really speeds up inference for larger KV cache's (e.g. longer prompts / generations). - And of course the positional encodings have changed from sinusoidal to rotary (aka RoPE). Not too much variation otherwise.

grug be true. warning: stomach hurt laughing grugbrain.dev

Every tech groupchat rn

You can give ChatGPT a picture of your team’s whiteboarding session and have it write the code for you. This is absolutely insane.

15 visuals every investor should memorize: 1: In the long run, stocks win:

friendships were forged

@sdachen Yeah if your LinkedIn doesn’t have “He …” then you still haven’t made it, Scott :)

@chtnverma I wrote it in an compacted (news headlines) style where "I" is the implied/omitted subjects of all sentences. I suppose this doesn't count. 😂

have you really made it if your linkedin isn't written in 3rd person?

We pay too much attention to the most confident voices—and too little attention to the most thoughtful ones. Certainty is not a sign of credibility. Speaking assertively is not a substitute for thinking deeply. It's better to learn from complex thinkers than smooth talkers.

Twitter’s source code is full of libs. It’s time to get rid of them