Landan Seguin

[Landan Seguin @landanjs]

@maxisawesome538 👋❤️👋

[jasmine collins @jazco]

today we're announcing our @DbrxMosaicAI x @Shutterstock partnership, and a new text-to-image diffusion model: ✨ImageAI!!✨

this model is geared towards enterprise use cases and is trained exclusively on shutterstock's trusted data catalog!

https://t.co/H7FtlZs0ne

[Landan Seguin @landanjs]

@ilyas121_real @summerlinARK @Replit @amasad @MosaicML Our SD training estimates are in the blog post below: $160k in 2 weeks. Next, we will train the thing :) watch out for it! https://t.co/t2CyRNjPRT

[Databricks AI Research @DbrxMosaicAI]

How much does it take to train a Stable Diffusion model from scratch? The answer: 79,000 A100-hours in 13 days, for a total training cost of <$160k. Our tooling reduces the time and cost to train by 2.5x, and is also extensible and simple to use.

https://t.co/GsWM84X8s1

[Databricks AI Research @DbrxMosaicAI]

Just in time for Thanksgiving - we're dropping a new batch of recipes for training image segmentation models. Reduce time-to-train by up to 5.4x, improve quality by up to +4.6 mIoU, and impress everyone at your #efficientML potluck!
https://t.co/iOMvRBLUwX

[Databricks AI Research @DbrxMosaicAI]

New blog post: https://t.co/RvAtpHRB0O

We're setting an up-to-date baseline for semantic segmentation model training: 45.56 mIoU on the ADE20k benchmark in 3.5 hours using 8x NVIDIA A100 GPUs.

Next step: develop and release #EfficientML recipes to speed it up!

[Davis Blalock @davisblalock]

Having trouble keeping up with arXiv?

🎉 Announcing "Davis Summarizes Papers" 🎉

tl;dr: People kept telling me I should make the ~15 paper summaries I do each week into a newsletter, so I did: https://t.co/pYqMTpJ3UD

Free forever, and you can also read all past posts as a blog

[Databricks AI Research @DbrxMosaicAI]

We've shared great research before, but reproducing methods from papers is hard.

Announcing Composer, our library of ML speedups: https://t.co/UiGEU1omOZ.

Train CV models ~4x faster and NLP models ~2x faster at the same accuracy -- with minimal tuning. (1/5)

[Jonathan Frankle @jefrankle]

TLDR: Announcing 🌟COMPOSER🌟, a PyTorch trainer for efficient training *algorithmically*. Train 2x-4x faster on standard ML tasks, a taste of what's coming from @MosaicML. Star it, 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚖𝚘𝚜𝚊𝚒𝚌𝚖𝚕, contribute, be efficient! https://t.co/itTXkoydZn

Thread:

[Landan Seguin @landanjs]

@borisdayma Nice! At @MosaicML, we’ve seen significant differences in perplexity when training a GPT-2 model on OpenWebText with vs without dropout! Look forward to seeing your results 😁

[Landan Seguin @landanjs]

@johncarlosbaez I found this talk (and many others) by Alan Watts enlightening https://t.co/bW8FCDw755 (start at 1:26, ignore the cheesy music and video)
"So after you are dead, the only thing that can happen is the same experience, or the same sort of experience as before you were born"

[MIT CSAIL @MIT_CSAIL]

Left: MIT computer scientist Katie Bouman w/stacks of hard drives of black hole image data.

Right: MIT computer scientist Margaret Hamilton w/the code she wrote that helped put a man on the moon.

(image credit @floragraham)

#EHTblackhole #BlackHoleDay #BlackHole

[Landan Seguin @landanjs]

https://t.co/Dvte7x9V23

[Mason @webdevMason]

Kinda crazy how much more advice there is on persuading people than on being persuaded. Not just when to buy an argument, but also how to get it to percolate through your models of the world & actually behave as though you'd bought it.

[Andrej Karpathy @karpathy]

@michael_nielsen @TheAtlantic It's frustrating how thinking feels like exploring a large idea cave serially with a tiny flashlight. It's a bit shocking how underdeveloped our tooling is in surpassing limitations of thinking / short term memory. Pen & paper was a good first step, haven't taken too many since.