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MACHINE LEARNING
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Large Language Models Achieve Human-Level Prompt Engineering
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Large Language Models Are Human-Level Prompt Engineers Zhou et al.: https://
arxiv.org/abs/2211.01910 #MachineLearning #DeepLearning #ArtificialIntelligence -
Observability AI ML AIOps Webinar Reduce Complexity
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Reduce #Complexity & #Apps #Tools #Cloud #Data Proliferation! Join @KoutsoukosNik & I – 2PM GMT Today! Everything #Observability & #AI #ML #AIOps https://
linkedin.com/video/event/ur
n:li:ugcPost:6992857196573151232/
… #AI #CyberSecurity #100DaysOfCode #innovation @solarwinds @mikeflache @Chels_LA @Paula_Piccard @Shi4Tech -
Inverse Scaling Observed Up to 62B Model Parameters
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We also showed inverse scaling up to 62B on our model / prompt setup
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Baidu Accelerates AlphaFold2 Training by 38.67% Percent
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Baidu’s Parallel Evoformer and Branch Parallelism Strategy Accelerates AlphaFold2 Training by 38.67% https://
syncedreview.com/2022/11/03/bai
dus-parallel-evoformer-and-branch-parallelism-strategy-accelerates-alphafold2-training-by-38-67/
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Team Receives Honorable Mention at ECCV2022 for Pose-NDF
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Proud of the team and the honorable mention they received last week at #ECCV2022 for their great work on Pose-NDF! You can learn more about the model and read the paper at the link in Garvita's tweet
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Powerful Tool for K-Shot Datasets and Prompt Chaining with Collapsible Tree UI
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@dust4ai takes some work to wrap your head around, but it's very powerful — gives a collapsible tree UI for representing k-shot example datasets, prompt templates, and prompt chaining with intermediate JS code. Replaces a lot of code around prompt APIs. http://
dust.tt -
Physical Manipulation: The Harder Problem Than AI Judgment
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Most work on the impacts of automation hasn’t considered what happens if solving for basic judgement & creativity was the easy problem, but actually moving around & touching stuff was the hard problem.
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Reincarnating RL: Efficient Reinforcement Learning from Prior Computation
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Training #ReinforcementLearning algorithms from scratch is computationally intensive and time consuming. We propose an alternate approach, Reincarnating RL, that integrates prior computation into the RL training workflow. Learn more and grab the code at https://t.co/XwfX0uakJZ pic.twitter.com/ktaQznCD1a
— Google AI (@GoogleAI) 3 novembre 2022Training #ReinforcementLearning algorithms from scratch is computationally intensive and time consuming. We propose an alternate approach, Reincarnating RL, that integrates prior computation into the RL training workflow. Learn more and grab the code at https://
goo.gle/3Ws2TLk -
Bay Area Data Science Community ML Meetup Fireside Chat
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Bay Area Data Science Community: Don’t miss our first #ML Meetup! Join us next Tues at 5pm for food, drinks, and an engaging fireside chat with @DalianaLiu and @w4nderlus7
, CEO of Predibase and founder of @ludwig_ai
. Save your spot: