Toronto, Ontario
Joined September 2020
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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
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.
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CM retweeted
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.
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.
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
CM retweeted
It will always be the highlight of my career to have been able to see firsthand a model make progress towards the problem that got me interested in mathematics a decade ago. It was a huge honour to have been part of this team.
A uniform zero-free strip.
github.com/openai/math/blob/…
didn’t even stand true for 3 weeks, apparently ζ(5) was proven to be irrational recently which is the biggest stride in irrational/transcendental NT since Apery imo
Noticing a trend of 5-hour take-homes that you absolutely cannot complete in 5hrs just to “test you at your limit” becoming more common for entry-level ML roles
I’ve basically never completed these take-homes fully (or well, even) but still get passed through
advice that's worked for me well, and works better in the future: never EVER have a difficult school schedule.
Past your foundations, take the easiest courses required to graduate. Keep lots of free time outside school. Do your learning with internships/projects/hackathons.
