We selected @sahilsingla47 as @fastdotai
's first ever international fellow, back in 2017! After 5 years, he now has his PhD. 😀 I'm not at all surprised that Sahil has gone on to great things. We were so impressed by his tenacity and brilliant work.
RESEARCH
-
Fast.ai’s First International Fellow Earns PhD
By
–
-
Synthetic Data Advances Financial Model Development at BNY Mellon
By
–
New ways of generating synthetic data to develop robust financial models is just one way that Mohit Jain at @BNYMellonWealth is driving data science innovation. Learn tips from him and 9 others in our new ebook! https://
domino.buzz/3HqlEd5 -
Cohere For AI celebrates six months of achievements
By
–
Cohere For AI @forai_ml is celebrating six months of an incredible journey! Take a look back at everything that happened during this time
-
CALM: Dynamic Computational Effort for Language Models
By
–
Presenting Confident Adaptive Language Modeling (CALM), a novel method that allows language models to dynamically modify computational effort when generating text. Learn how CALM can accelerate text generation while preserving output quality → https://t.co/Nm7yyT8sMA pic.twitter.com/MpuaPBzDMU
— Google AI (@GoogleAI) 16 décembre 2022Presenting Confident Adaptive Language Modeling (CALM), a novel method that allows language models to dynamically modify computational effort when generating text. Learn how CALM can accelerate text generation while preserving output quality → https://
goo.gle/3HJKzbM -
Stable Diffusion artifacts and training distribution drift analysis
By
–
Yep, resembles the artifacts I had here. I get a lot of zig zags too.
— Gene Kogan (@genekogan) 16 décembre 2022
I think our working hypothesis was that the output image gradually careens out of the training distribution, so even after noising it, SD doesn't quite know what to do with it.https://t.co/r1YZNzonfUYep, resembles the artifacts I had here. I get a lot of zig zags too. I think our working hypothesis was that the output image gradually careens out of the training distribution, so even after noising it, SD doesn't quite know what to do with it.
-
ROSCOE: New Metrics Suite for Evaluating Step-by-Step Reasoning
By
–
ROSCOE is a first-of-its-kind suite of metrics for scoring step-by-step reasoning. By publishing this study we hope to provide a foundation that enables scalable systematic evaluation and benchmarking of new language models. See the paper on arXiv https://
arxiv.org/abs/2212.07919 -
Thanking researchers for advancing AI study and progress
By
–
Thank you to @OlgaNLP
, @moyapchen
, @spencerpoff
, @LukeZettlemoyer
, @CMacota99
, Maryam F. and @real_asli for all of their work to move this space forward with this study! -
Ludwig AI Maintainer Discusses Declarative ML Approaches
By
–
ICYMI: @ludwig_ai maintainer Justin Zhao joined @DataTalksClub to discuss all things declarative #ML. Topics covered:
• State of ML and #AutoML
• Overview of declarative approaches
• Live Ludwig demo
• Rapid config-driven iteration https://
pbase.ai/3HFNSAI #deeplearning -
Constitutional AI: Moving Beyond Researcher-Defined Constitutions
By
–
In our paper we used an ad hoc constitution drafted purely for research purposes. Ultimately, we think constitutions shouldn’t be just defined by researchers in isolation, but by groups of experts from different disciplines working together.
-
Constitutional AI: Making Implicit Principles Explicit in AI Systems
By
–
While the name “Constitutional AI” may sound ambitious, we chose it to emphasize that powerful, general-purpose AI systems will always be operating according to *some* principles, even if they are left implicit, or encoded in privately held data.