Deep Learning Research intern @blocks (Dessa). Did pure math in a past life. cashmere-y, grad student @UofT 🍁

Toronto, Ontario
Joined September 2020
Career update: I’m joining @blocks for the Summer as a DL Research Intern! I’ll be working on some very cool and novel approaches to generative audio modelling, would be happy to get to know more people in this space!
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Man, some people are so smart. It’s probably possible to reverse engineer the finetuning optimizer variant of Muon that Tinker released a few days ago as well
Tinker's new StableReinforce objective is Reinforce + Score Centering. Here's how I reverse-engineered it 🧵
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Hard agree, especially with the last point. There is a lot more you can squeeze out from GPUs than people first imagine. Like a LOT more
Startup @flappyairplanes is betting the future of AI is making models more efficient. cnb.cx/3UiruGm
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Key takeaway from the launch: We ended up sounding more like AI than our model did
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dessa by @jack's company Block incredibly impressive best speech to speech model ive seen bar none
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CM retweeted
new from us at @blocks
Meet Dessa, our AI model trained from scratch on nothing but speech. We believe speech is the natural interface for technology. So we built Dessa. And we’re inviting AI safety researchers to explore Dessa. Learn more: block.xyz/dessa
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Meet Dessa, our AI model trained from scratch on nothing but speech. We believe speech is the natural interface for technology. So we built Dessa. And we’re inviting AI safety researchers to explore Dessa. Learn more: block.xyz/dessa
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It’s time to recalibrate whatever we knew about voice AI. I’m excited to share what we’ve been working on at @blocks This is the GPT3 moment for speech.
Meet Dessa, our AI model trained from scratch on nothing but speech. We believe speech is the natural interface for technology. So we built Dessa. And we’re inviting AI safety researchers to explore Dessa. Learn more: block.xyz/dessa
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RT @blocks: Meet Dessa, our speech model trained from scratch on human conversation. Hear it for yourself: block.xyz/dessa http…
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Always love the YC events in Toronto, so many talented ppl here
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I will say, I’m in love with pre-training optimizers. The fact that you can only use first order derivative info, that your implementation must be distributed-friendly, that it must maintain sample efficiency under scaling It produces so many beautiful ideas
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If you order a waymo from Scarbs into downtown Toronto when it’s snowing and there’s salt on the road you get banned by anthropic for AI abuse btw
We’re bringing the magic of Waymo to Toronto! 🇨🇦 In the coming days, you’ll begin seeing Waymo’s all-electric vehicles driving through Toronto’s streets as we lay the groundwork for our ride-hailing service. Our goal is to begin serving riders in 2028. Sign up for future updates: waymo.com/waymo-in-canada
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Extremely biased take but the best possible thing you could’ve done in 2018-2025 was a math/physics degree and then switch to working in ai research
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Replying to @davidbessis
Ah. Another mathematician. The comments are so predictable you can figure out who is a mathematician by what they think of the proof drops.
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Am I the only one who finds it a bit weird just how obsessed people here are with how mathematicians are reacting to AI models? Like why is a CS grad who barely scratched a 70 on lin alg writing 5 paragraphs on how mathematicians should restructure their field
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i'm not saying they can't have opinions on it, i'm not a mathematician anymore either and i have my thoughts on this but obv the q of how mathematics as a social practice should be is one we let the mathematicians figure out? surely they know the institution better than others
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like it's obv that terry tao, 1 of the *least* arrogant mathematician of our times (I personally attest to this as a co-author of his), who's collaborated with so many, helped many research-groups/careers/subfields flourish knows what's best for math better than most of us do
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We built an 8000 sqft. high-throughput wet-lab in 30 days, to solve molecular discovery - starting with pesticides. Most teams in drug-discovery don’t receive target-organism verification until clinical trials. Pesticides allow us to rapidly validate our model in the target organism directly. With the help of our high-throughput lab, we were able to generate compounds that killed a $13 billion pest, the Fall Armyworm. We believe that the bitter lesson is coming for biology.
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True post-AGI company btw, you’re gonna need the best pesticides for when all white-collar work is solved and we have our own farms
We built an 8000 sqft. high-throughput wet-lab in 30 days, to solve molecular discovery - starting with pesticides. Most teams in drug-discovery don’t receive target-organism verification until clinical trials. Pesticides allow us to rapidly validate our model in the target organism directly. With the help of our high-throughput lab, we were able to generate compounds that killed a $13 billion pest, the Fall Armyworm. We believe that the bitter lesson is coming for biology.
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I spent basically all of today (like ~7 hours now) fixing a bug with a +22/-7 diff. As opposed to a +500 (literally) fix that was AI slopped, idek if this is worth it anymore
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