New Book: No-Blackbox, Secure, Efficient AI and LLM Solutions
Large language models and modern AI is often presented as technology that needs deep neural networks (DNNs) with billions of Blackbox parameters, expensive and time consuming training, along with GPU farms, yet prone to hallucinations. This book presents alternatives that rely on explainable AI, featuring new algorithms based on radically different technology with trustworthy, auditable, […]
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Cybersecurity Use Case: AI Agent for Anomaly Detection
The case discussed here concerns fraudulent paid clicks to defraud a Google advertiser. The sophisticated click fraud scheme involving clicking viruses, data centers and other means, is undetected by Google. I worked with the law firm involved in the litigation, to build an agent able to pinpoint the sources of fraudulent traffic. The agent processes […]
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How to Build and Optimize High-Performance Deep Neural Networks from Scratch
With explainable AI, intuitive parameters easy to fine-tune, versatile, robust, fast to train, without any library other than Numpy. In short, you have full control over all components, allowing for deep customization, and much fewer parameters than in standard architectures. Introduction I explore deep neural networks (DNNs) starting from the foundations, introducing a new type […]
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Differences between transformer-based AI and the new generation of AI models
I frequently refer to OpenAI and the likes as LLM 1.0, by contrast to our xLLM architecture that I present as LLM 2.0. Over time, I received a lot of questions. Here I address the main differentiators. First, xLLM is a no-Blackbox, secure, auditable, double-distilled agentic LLM/RAG for trustworthy Enterprise AI, using 10,000 fewer (multi-)tokens, […]
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BondingAI Acquires GenAItechLab, Add Core Team Members
BondingAI acquisition of GenAItechLab.com was recently completed, including all the IP related to the xLLM technology, the material published on MLtechniques and the most recent technology pertaining to deep neural networks watermarking. GenAItechLab was founded in 2024 by Vincent Granville, a world-class leader and well-known scientist building innovative and efficient AI solutions from scratch, hallucination-free, […]
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Language Models: A 75-Year Journey That Didn’t Start With Transformers
Introduction Language models have existed for decades — long before today’s so-called “LLMs.” In the 1990s, IBM’s alignment models and smoothed n-gram systems trained on hundreds of millions of words set performance records. By the 2000s, the internet’s growth enabled “web as corpus” datasets, pushing statistical models to dominate natural language processing (NLP). Yet, many […]
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How to Get AI to Deliver Superior ROI, Faster
Reducing total cost of ownership (TCO) is a topic familiar to all enterprise executives and stakeholders. Here, I discuss optimization strategies in the context of AI adoption. Whether you build in-house solutions, or purchase products from AI vendors. The focus is on LLM products, featuring new trends in Enterprise AI to boost ROI. Besides the […]
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How to design LLMs that don’t need prompt engineering
Standard LLMs rely on prompt engineering to fix problems (hallucinations, poor response, missing information) that come from issues in the backend architecture. If the backend (corpus processing) is properly built from the ground up, it is possible to offer a full, comprehensive answer to a meaningful prompt, without the need for multiple prompts, rewording your […]
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The Rise of Specialized LLMs for Enterprise
In this article, I discuss the main problems of standard LLMs (OpenAI and the likes), and how the new generation of LLMs addresses these issues. The focus is on Enterprise LLMs. LLMs with Billions of Parameters Most of the LLMs still fall in that category. The first ones (ChatGPT) appeared around 2022, though Bert is […]
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Watermarking and Forensics for AI Models, Data, and Deep Neural Networks
In my previous paper posted here, I explained how I built a new class of non-standard deep neural networks, with various case studies based on synthetic data and open-source code, covering problems such as noise filtering, high-dimensional curve fitting, and predictive analytics. One of the models featured a promising universal function able to represent any […]
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