New Book: 0 and 1 – From Elemental Math to Quantum AI
The book is available on our E-store, here. It all started with the number 1. This e-book offers a trip deep into the most elusive and fascinating multi-century old conjecture in number theory: are the binary digits of the fundamental math constants evenly distributed? No one even knows if the proportions of ‘0’ and ‘1’ […]
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10 Must-Read Articles and Books About Next-Gen AI in 2025
You could call it the best kept secret for professionals and experts in AI, as you won’t find these books and articles in traditional outlets. Yet, they are read by far more people than documents posted on ArXiv or published in scientific journals, so not really a secret. Actually, one of these books is also […]
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Piercing the Deepest Mathematical Mystery
Any solution to the mythical problem in question has remained elusive for centuries. It is deemed more difficult than proving the Riemann Hypothesis, yet its formulation can be understood by kids in elementary school. The question is whether or not the digits of mathematical constants such as π, behave like a random sequence. This article […]
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Blueprint: Next-Gen Enterprise RAG & LLM 2.0 – Nvidia PDFs Use Case
In my most recent articles and books, I discussed our radically different approach to building enterprise LLMs from scratch, without training, hallucinations, prompt engineering or GPU, while delivering higher accuracy at a much lower cost, safely, at scale and at lightning speed (in-memory). It is also far easier to adapt to specific corpuses and business […]
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LLM Deep Contextual Retrieval and Multi-Index Chunking: Nvidia PDFs, Case Study
The technology described here boosts exhaustivity and structuredness in LLM prompt results, efficiently exploiting the knowledge graph and contextual structure present in any professional or enterprise corpus. The case study deals with public financial reports from Nvidia, available as PDF documents. In this article, I discuss the preprocessing steps used to turn a PDF repository […]
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New Book: Building Disruptive AI & LLM Technology from Scratch
This book features new advances in game-changing AI and LLM technologies built by GenAItechLab.com. Written in simple English, it is best suited for engineers, developers, data scientists, analysts, consultants and anyone with an analytic background interested in starting a career in AI. The emphasis is on scalable enterprise solutions, easy to implement, yet outperforming vendors […]
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New Book: State of the Art in GenAI & LLMs — Creative Projects, with Solutions
With 23 top projects, 96 subprojects, and 6000 lines of Python code, this vendor-neutral coursebook is a goldmine for any analytic professional or AI/ML engineer interested in developing superior GenAI or LLM enterprise apps using ground-breaking technology. This is not another book discussing the same topics that you learn in bootcamps, college classes, Coursera, or […]
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My Top 10 GenAI Articles of the Year
Here is some good reading for the holiday season. More than just reading as the material includes full Python implementations and datasets. The most up-to-date versions are in my new book Statistical Optimization for GenAI and Machine Learning, available here. As a courtesy, if you buy it by December 31, you are entitled to a […]
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Sampling Outside the Observation Range with Quantile Convolution
All of the GenAI apps that I tested, including my own, have the same problem. They cannot easily generate data outside the observation range. As an example, let’s focus on the insurance dataset discussed in my new book. I use it to generate synthetic data with GAN (generative adversarial networks) and the NoGAN models discussed […]
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New Book: Understanding Deep Learning
By Simon Prince, computer science Professor at the University of Alberta. To be published by MIT Press, Dec 2023. The author shares the associated Jupyter notebooks on his website, here. Very popular, it got over 5,000 likes when the author announced the upcoming book on LinkedIn. I pre-ordered my copy. Summary An authoritative, accessible, and […]
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