Reading List - doraithodla/notes GitHub Wiki

Dec 19

Petroski was the author of The Pencil: A History of Design and Circumstance, the authoritative history of the pencil.

Nov 23

Teaching Small Language Models how to Learn A Microsoft paper. The product prototype name is Orca 2. Heard a podcast about it in Seattle.

Oct 27

## Naval's recommended reading
Evolution of everything
Fynmann - 6 easy steps -- Naval Ravikanth - The Almanac

Oct 21

Oct 20

The foundation model Transparency Index
The Geometry of Truth Podcasts

24th Sep - [The OpenAI API can be applied to virtually any task that requires understanding or generating natural language and code](The OpenAI API can be applied to virtually any task that requires understanding or generating natural language and code.) A Beginner's Guide to Neural Mechanisms

Articles

RAG is more than embedding search
The path to detecting extraterrestrial life with astrophotonics Exoplanets, astrophotonics, integrated photonics, photonic lanterns, beam combiners, spectrographs
Three waves of AI - a DARPA doc
Study Hacks Blog by Cal Neport
Cargo Cult AI
The Phsics of Readability
Introduction to Vectorization
AI Teachers and Education
Topics to Learn/Write About

Books (Recommended)

LEGO mindstorms
Hypercard - Bill Atkinson
ML Architecture
The hundred page Machine Learning Book
Start with No
The Cold Start Problem by Andrew Chen
Human Compatible
The case against education
The Mom Test
Story Brand
One Page Marketing
Emyth Revisited
Scaling People
Build by Tony Fadell
Disciplined Enterpreneurship by Bill Aulet
The Enterpreneurship Equation by Carol Roth
Turning the Flywheel by Jim Collins
Built to Sell -Meta - Tools for finding books Blogs/Articles/Papers

Books

Understanding Deep Learing
Art of Probability by Hamming.
Calculus by Spivak.
Naive Set Theory by Halmos
Python Natural Language Processing - Advanced Machine Learning and Deep Learning Techniques For Natural Language Processing PDF
Hugging Face - https://huggingface.co/learn/nlp-course/chapter1/1?fw=pt

Blog Posts/Articles

https://github.com/bentoml/OpenLLM - An open platform for operating large language models (LLMs) in production.Fine-tune, serve, deploy, and monitor any LLMs with ease.
tps://zura.wiki/post/never-write-a-commit-message-again-with-the-help-of-gpt-3/
#The Rise of the Millionaire LinkedIn Influencer - https://www.vice.com/en/article/4ax5wp/the-rise-of-the-millionaire-linkedin-influencer

Papers Simulation Intelligence The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science. https://arxiv.org/abs/2112.03235

Ben Gilad's 'Early Warning' book
From word models to World Models - https://arxiv.org/pdf/2306.12672.pdf?fname=cm&font=TypeI
Videos
The theory of computation # Cotnext Free Gramma

Word Vector Representations: word2vec

News

-Salesforce Launches GPT for Data Insights

Listening/Watching List

What gets measured gets machine learned

From HN on how to keep current on AI.

Finished

Title: Latent Space Jun 9, 2023 From RLHF to RLHB: The Case for Learning from Human Behavior - with Jeffrey Wang and Joe Reeve of Amplitude

Latent Space

Amplitude Podcase Notes

To Read

Not all, but some https://www.scribd.com/read/662388090/Summary-Review-of-The-100-Best-Non-Fiction-Books