What I Learn – Page 25 – Andrew Fairless, Ph.D.

What I Read: Estimate Token Importance in LLM Prompts

By Andrew Fairless on November 16, 2023October 5, 2023

https://www.watchful.io/blog/a-surprisingly-effective-way-to-estimate-token-importance-in-llm-prompts A Surprisingly Effective Way to Estimate Token Importance in LLM PromptsShayan Mohanty “Prompting is our primary means of instructing these AIs, yet the process of crafting effective prompts hasContinue readingWhat I Read: Estimate Token Importance in LLM Prompts

What I Read: Auditing AI, How Much Access

By Andrew Fairless on November 14, 2023October 5, 2023

https://aipolicy.substack.com/p/ai-accountability-transparency-2 Auditing AI: How Much Access Is Needed to Audit an AI System?Chapter 2 in our AI Accountability & Transparency series(By Sarah H. Cen, Cosimo L. Fabrizio, James Siderius, AleksanderContinue readingWhat I Read: Auditing AI, How Much Access

What I Read: Overton Paradox

By Andrew Fairless on November 9, 2023October 5, 2023

https://brilliant.org/lesson/the-overton-paradox/ The Overton Paradox “This apparent contradiction is what we’re calling the Overton Paradox: People become more liberal as they get older, but they are more likely to say theyContinue readingWhat I Read: Overton Paradox

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

Category: What I Learn