From Deep Number Theory to Powerful Enterprise AI Solutions
From Deep Number Theory to Powerful Enterprise AI Solutions

Download my new book featuring the path from piercing some of the most challenging math conjectures, to AI applications built nowhere else, covering Fintech, time series, high-quality data synthesis, cybersecurity, fraud detection, high-performance and scientific computing, quantum and dynamical systems, detection of weak or hidden patterns, random numbers and tests that outperform all others including one based on deep neural networks, large scale simulations, gigantic data sets and data auditing, new types of chaos including modeling, measurement and synthesis, computer and AI-assisted proofs, applications to LLMs, as well as probabilistic and deterministic AI based on a new type of universal functions.
The book is available here. In 184 pages, it showcases many new algorithms and solutions that even AI could not imagine, with numerous high-resolution illustrations and data animations, use cases, datasets, enterprise-grade Python code, seminal results based on years of research, yet all written in simple English with a particular attention to formatting, readability, modern references, indexing, and clickable links--both internal and external.
No one has ever written such a book. AI and computer science authors usually lack the deep math background, while pure mathematicians lack experience and perspective on real-world applications. This manuscript offers both: depth and breadth in mathematics and computer science well beyond PhD level, together with novel, practical applications. It is perhaps the last book written without external AI, the author being an AI builder first and foremost rather than a user.
If you are an executive, share it with your top mathematicians in your team. If you are an AI engineer, quant or developer with good math background, you will understand and enjoy most of the material. If you are a mathematician, you'll be surprised by the range of applications and how number theory can be monetized across multiple industries, in ways few if any can even imagine.
Despite significant efforts to make the book accessible to a large audience, it covers new, unpublished and at times, very advanced topics. I would be happy to meet with your team to discuss in layman's terms how the material can be adapted to your needs and help you build AI that significantly outperforms what is available on the market, generating real ROI while delivering accurate, audit-proof results giving you full control over all the components, without external API calls. Download the free book here, and we will get back to you.
Download my new book featuring the path from piercing some of the most challenging math conjectures, to AI applications built nowhere else, covering Fintech, time series, high-quality data synthesis, cybersecurity, fraud detection, high-performance and scientific computing, quantum and dynamical systems, detection of weak or hidden patterns, random numbers and tests that outperform all others including one based on deep neural networks, large scale simulations, gigantic data sets and data auditing, new types of chaos including modeling, measurement and synthesis, computer and AI-assisted proofs, applications to LLMs, as well as probabilistic and deterministic AI based on a new type of universal functions.
The book is available here. In 184 pages, it showcases many new algorithms and solutions that even AI could not imagine, with numerous high-resolution illustrations and data animations, use cases, datasets, enterprise-grade Python code, seminal results based on years of research, yet all written in simple English with a particular attention to formatting, readability, modern references, indexing, and clickable links--both internal and external.
No one has ever written such a book. AI and computer science authors usually lack the deep math background, while pure mathematicians lack experience and perspective on real-world applications. This manuscript offers both: depth and breadth in mathematics and computer science well beyond PhD level, together with novel, practical applications. It is perhaps the last book written without external AI, the author being an AI builder first and foremost rather than a user.
If you are an executive, share it with your top mathematicians in your team. If you are an AI engineer, quant or developer with good math background, you will understand and enjoy most of the material. If you are a mathematician, you'll be surprised by the range of applications and how number theory can be monetized across multiple industries, in ways few if any can even imagine.
Despite significant efforts to make the book accessible to a large audience, it covers new, unpublished and at times, very advanced topics. I would be happy to meet with your team to discuss in layman's terms how the material can be adapted to your needs and help you build AI that significantly outperforms what is available on the market, generating real ROI while delivering accurate, audit-proof results giving you full control over all the components, without external API calls. Download the free book here, and we will get back to you.
About the Author

Vincent Granville is a pioneering AI builder, co-founder at Data Science Central (acquired by TechTarget), co-founder and CAIO at Bonding AI, author, patent owner, expert witness and investor including Limited Partner at CalculusVC, a Bay Area VC firm. Vincent worked with Visa, Wells Fargo, eBay, NBC, Microsoft, CNET and several startups. He is also a top AI influencer for NVIDIA and other brands. His AI newsletter has 200,000 subscribers.
Vincent is a former post-doc at University of Cambridge. He published in Journal of Number Theory, Journal of the Royal Statistical Society (Series B), and IEEE Transactions on Pattern Analysis and Machine Intelligence (500+ citations). He is the author of multiple books, available here, including "Synthetic Data and Generative AI" (Elsevier, 2024). Vincent lives in Washington state, and enjoys doing research on stochastic processes, dynamical systems, probabilistic and computational number theory.
Vincent Granville is a pioneering AI builder, co-founder at Data Science Central (acquired by TechTarget), co-founder and CAIO at Bonding AI, author, patent owner, expert witness and investor including Limited Partner at CalculusVC, a Bay Area VC firm. Vincent worked with Visa, Wells Fargo, eBay, NBC, Microsoft, CNET and several startups. He is also a top AI influencer for NVIDIA and other brands. His AI newsletter has 200,000 subscribers.
Vincent is a former post-doc at University of Cambridge. He published in Journal of Number Theory, Journal of the Royal Statistical Society (Series B), and IEEE Transactions on Pattern Analysis and Machine Intelligence (500+ citations). He is the author of multiple books, available here, including "Synthetic Data and Generative AI" (Elsevier, 2024). Vincent lives in Washington state, and enjoys doing research on stochastic processes, dynamical systems, probabilistic and computational number theory.
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