Andrew Fairless – Page 18 – Andrew Fairless, Ph.D.

What I Read: LLM research

By Andrew Fairless on October 10, 2023September 4, 2023

https://huyenchip.com//2023/08/16/llm-research-open-challenges.html Open challenges in LLM research8/15/23 8:00 PM “Never before in my life had I seen so many smart people working on the same goal: making LLMs better… I noticedContinue readingWhat I Read: LLM research

What I Read: Multimodal, Embeddings

By Andrew Fairless on October 5, 2023September 4, 2023

https://app.twelvelabs.io/blog/multimodal-embeddings The Multimodal Evolution of Vector EmbeddingsJames LeAugust 9, 2023 “…companies collect and feed hundreds of millions of terabytes of multimodal data into deep learning models. As a result, embeddings…Continue readingWhat I Read: Multimodal, Embeddings

What I Read: Bonsai Networks, RNNs

By Andrew Fairless on October 3, 2023September 4, 2023

https://cprimozic.net/blog/growing-sparse-computational-graphs-with-rnns/ Growing Bonsai Networks with RNNsCasey Primozic “This writeup introduces what I’m calling Bonsai Networks – extremely sparse computational graphs produced by training and pruning RNNs. They provide an interpretableContinue readingWhat I Read: Bonsai Networks, RNNs

What I Read: Models Memorize or Generalize?

By Andrew Fairless on October 2, 2023September 4, 2023

https://pair.withgoogle.com/explorables/grokking/ Do Machine Learning Models Memorize or Generalize?By Adam Pearce, Asma Ghandeharioun, Nada Hussein, Nithum Thain, Martin Wattenberg and Lucas DixonAugust 2023 “Large language models can certainly seem like theyContinue readingWhat I Read: Models Memorize or Generalize?

What I Read: scaling laws, cross-entropy loss

By Andrew Fairless on September 28, 2023September 4, 2023

https://epochai.org/blog/the-direct-approach The Direct ApproachMatthew Barnett and Tamay Besiroglu “Empirical scaling laws can help predict the cross-entropy loss associated with training inputs, such as compute and data. However, in order toContinue readingWhat I Read: scaling laws, cross-entropy loss

What I Read: Giant Steps Can Solve Optimization Faster

By Andrew Fairless on September 27, 2023September 4, 2023

https://www.quantamagazine.org/risky-giant-steps-can-solve-optimization-problems-faster-20230811/ Risky Giant Steps Can Solve Optimization Problems FasterAllison ParshallAugust 11, 2023 “New results break with decades of conventional wisdom for the gradient descent algorithm.”

What I Read: LLMs in Planning

By Andrew Fairless on September 25, 2023September 4, 2023

https://youtu.be/BmyB-4S9QuYAvenging Polanyi’s Revenge (Exploiting the Approximate Omniscience of LLMs in Planning..)Subbarao KambhampatiAug 1, 2023 “…we have to stop confusing the impressive form of the generated knowledge for correct content, andContinue readingWhat I Read: LLMs in Planning

What I Read: Economic Case for Generative AI

By Andrew Fairless on September 21, 2023September 4, 2023

https://a16z.com/2023/08/03/the-economic-case-for-generative-ai-and-foundation-models/ The Economic Case for Generative AI and Foundation Modelsby Martin Casado and Sarah Wang “…we believe there’s a very strong argument that generative AI is not only economically viable,Continue readingWhat I Read: Economic Case for Generative AI

What I Read: Multiple Imputation by Chained Equations

By Andrew Fairless on September 20, 2023September 4, 2023

https://cameronpatrick.com/post/2023/07/mice-in-a-blog-post/ Dirty imputation done dirt cheap: implementing Multiple Imputation by Chained Equations in one blog postCameron Patrick31 July, 2023 “An attempt to understand in detail how Multiple Imputation by ChainedContinue readingWhat I Read: Multiple Imputation by Chained Equations

What I Read: LLM-based Products

By Andrew Fairless on September 19, 2023September 4, 2023

https://eugeneyan.com/writing/llm-patterns/ Patterns for Building LLM-based Systems & Productseugeneyan “This post is about practical patterns for integrating large language models (LLMs) into systems and products.”

Author: Andrew Fairless