Andrew Fairless – Page 21 – Andrew Fairless, Ph.D.

What I Read: How make history with LLMs

By Andrew Fairless on November 8, 2023October 5, 2023

https://leighmariebraswell.substack.com/p/how-to-make-history-with-llms-and How to make history with LLMs & other generative modelsLeigh Marie BraswellSep 21, 2023 “Over the past year I’ve talked to many founders, operators, and investors, and I’ve encounteredContinue readingWhat I Read: How make history with LLMs

What I Read: Optimizing LLM in production

By Andrew Fairless on November 6, 2023October 5, 2023

https://huggingface.co/blog/optimize-llm Optimizing your LLM in productionSeptember 15, 2023Patrick von Platen “…efficient LLM deployment…. pros and cons of adopting lower precision, provide a comprehensive exploration of the latest attention algorithms, andContinue readingWhat I Read: Optimizing LLM in production

What I Read: Features Are Important?

By Andrew Fairless on November 2, 2023October 5, 2023

https://www.kdnuggets.com/your-features-are-important-it-doesnt-mean-they-are-good Your Features Are Important? It Doesn’t Mean They Are GoodBy Samuele Mazzanti, Lead Data Scientist at JAKALASeptember 21, 2023 ““Feature Importance” is not enough. You also need to lookContinue readingWhat I Read: Features Are Important?

What I Read: Physical Process, Generative AI

By Andrew Fairless on October 31, 2023October 5, 2023

https://www.quantamagazine.org/new-physics-inspired-generative-ai-exceeds-expectations-20230919/ The Physical Process That Powers a New Type of Generative AISteve Nadis9/19/23 10:15 AM “Some modern image generators rely on the principles of diffusion to create images. Alternatives basedContinue readingWhat I Read: Physical Process, Generative AI

What I Read: LLM Training, RLHF

By Andrew Fairless on October 30, 2023October 5, 2023

https://magazine.sebastianraschka.com/p/llm-training-rlhf-and-its-alternatives LLM Training: RLHF and Its AlternativesSebastian Raschka, PhDSep 10, 2023 “RLHF is an integral part of the modern LLM training pipeline due to its ability to incorporate human preferencesContinue readingWhat I Read: LLM Training, RLHF

What I Read: Nvidia AI Supremacy Temporary

By Andrew Fairless on October 25, 2023October 5, 2023

https://petewarden.com/2023/09/10/why-nvidias-ai-supremacy-is-only-temporary/ Why Nvidia’s AI Supremacy is Only TemporarySeptember 10, 2023Pete Warden “…hopefully I’ve made a convincing case that there are strong structural reasons behind Nvidia’s success. Here’s how I seeContinue readingWhat I Read: Nvidia AI Supremacy Temporary

What I Read: Markov Chains

By Andrew Fairless on October 24, 2023September 9, 2023

https://elevanth.org/blog/2017/11/28/build-a-better-markov-chain/ Markov Chains: Why Walk When You Can Flow?Richard McElreath28 November 2017 “If you are still using a Gibbs sampler, you are working too hard for too little result. Newer,Continue readingWhat I Read: Markov Chains

What I Read: LLMs, single example

By Andrew Fairless on October 23, 2023September 9, 2023

https://www.fast.ai/posts/2023-09-04-learning-jumps/ Can LLMs learn from a single example?Jeremy Howard and Jonathan WhitakerSeptember 4, 2023 “We’ve noticed an unusual training pattern in fine-tuning LLMs. At first we thought it’s a bug,Continue readingWhat I Read: LLMs, single example

What I Read: Composable Data Systems

By Andrew Fairless on October 19, 2023September 9, 2023

https://wesmckinney.com/blog/looking-back-15-years/ The Road to Composable Data Systems: Thoughts on the Last 15 Years and the FutureWes McKinneySeptember 1, 2023 “Julien Le Dem… coined the term “deconstructed database” to describe theContinue readingWhat I Read: Composable Data Systems

What I Read: Differentiable Trees

By Andrew Fairless on October 18, 2023September 4, 2023

https://ericmjl.github.io/blog/2023/8/7/journal-club-differentiable-search-of-evolutionary-trees/ Journal Club: Differentiable Search of Evolutionary TreesEric J. Ma2023-08-07 “…how the authors take a non-differentiable problem and turn it into a differentiable problem through interconversion between mathematical data structures.”

Author: Andrew Fairless