Reading list
Links 10
Title: Transcript of Building Machine Learning Systems for a Trillion Trillion Floating Point Operations
Score: 0.9653441675940131
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Out links: 4197804 Raw text: 4197804
https://www.youtube.com/watch?v=139UPjoq7Kw
so it's my great pleasure to introduce our speaker tonight Horas he hor he graduated from Cornwell uh in 2020 and he works at meta on the PCH team specifically on at the intersection of compilers and machine learning uh if you've used things like torch. compile which is a thing in by torch that mak...
Title: Minecraft team jam @ DLCT
Score: 0.9319544806334017
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Out links: 26537 Raw text: 26537
https://www.youtube.com/watch?v=ErXXKGd1ugk&ab_channel=MLCollective
hello this is deep learning classics and trends it's a reading group we've been running for five years now um and i like doing things um different every now and then just to throw in something creative so this time i thought i was reading so many minecraft papers and projects on twitter and it just ...
Title: Algorithmic Improvement Is Probably Faster Than Scaling Now — LessWrong
Score: 0.9105446270983479
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Out links: 22624 Raw text: 22624Title: Algorithmic Improvement Is Probably Faster Than Scaling Now — LessWrong Description: The Story as of ~4 Years Ago Back in 2020, a group at OpenAI ran a conceptually simple test to quantify how much AI progress was attributable to algo… Keywords: No keywords Text content: Algorithmic Improveme...
Title: My AI Timelines Have Sped Up (Again)
Score: 0.903926146517654
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Out links: 3357373 Raw text: 3357373https://www.alexirpan.com/2024/01/10/ai-timelines-2024.html
Title: My AI Timelines Have Sped Up (Again) Description: In August 2020, I wrote a post about my AI timelines.Using the following definition of AGI: Keywords: No keywords Text content: My AI Timelines Have Sped Up (Again) Sorta Insightful Reviews Projects Puzz...
Title: Thinking Elixir Podcast 230: Hot k8s Takes and Self-Hosting
Score: 0.8961396210256085
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Out links: 1238957 Raw text: 1238957
https://www.youtube.com/watch?v=kluqPI4g52s
hello and welcome to the thinking Elixir podcast where we cover the news of the community and learn from each other my name is Mark Ericson and I'm David bernheisel let's jump into the news first up we're gonna cover a little bit of housekeeping so just want to make sure that you are aware that we a...
Title: Naval Ravikant: The Beginning of Infinity
Score: 0.889190149767518
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Out links: 5436351 Raw text: 5436351
https://www.youtube.com/watch?v=yAj5EnyuakI
uh well thanks for joining me no all thank you for having me let's talk about the four strands of the fabric of reality theory of epistemology...
Title: Training Larger Networks for Deep Reinforcement Learning
Score: 0.8653771783355902
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Out links: 199298 Raw text: 199298https://gwern.net/doc/www/arxiv.org/16217a265b539d6d8256a7c3722eff1b2291bcd6.pdf
Training Larger Networks for Deep Reinforcement Learning Kei Ota 1 2 Devesh K. Jha 3 Asako Kanezaki 2 arXiv:2102.07920v1 [cs.LG] 16 Feb 2021 Abstract The success of deep learning in the computer vision and natural language processing communities can be attributed to training of very deep neural n...
Title: None
Score: 0.8446923041463351
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Out links: 22741 Raw text: 22741https://arxiv.org/pdf/2109.00698.pdf
An Empirical Exploration in Quality Filtering of Text Data Leo Gao EleutherAI [email protected] arXiv:2109.00698v2 [cs.CL] 6 Oct 2021 Abstract While conventional wisdom suggests that more aggressively filtering data from lowquality sources like Common Crawl always monotonically improves the quality o...
Title: Let's Discuss OpenAI's Rubik's Cube Result
Score: 0.8207689396288244
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Out links: 3405584 Raw text: 3405584https://www.alexirpan.com/2019/10/29/openai-rubiks.html
Title: Let's Discuss OpenAI's Rubik's Cube Result Description: Recently, OpenAI announced they had gotten their dexterousmanipulation system to solve a Rubik’s Cube.I thought I wouldn’t have much to say, until I started ... Keywords: No keywords Text content: Let's Discuss OpenAI's Rubik's Cube Resu...
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Score: 0.8200421290709352
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Out links: 22698 Raw text: 22698https://arxiv.org/pdf/2205.10487.pdf
Scaling Laws and Interpretability of Learning from Repeated Data arXiv:2205.10487v1 [cs.LG] 21 May 2022 Danny Hernandez∗ Tom Brown, Tom Conerly, Nova DasSarma, Dawn Drain, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Tom Henighan, Tristan Hume, Scott Johnston, Ben Mann, Chris Olah, Catherine...