NextGen AI Technology
“NextGen AI Technology” delves into the intricacies of advancing artificial intelligence systems to the next level. The document explores cutting-edge technologies like Generative AI, Knowledge Base Infrastructure, and Secure Semantic Memory. It emphasizes the importance of moving beyond static data libraries to dynamic, living data interactions. The focus is on enhancing AI capabilities to understand and respond to complex queries, ensuring precision and relevance in responses. Through a combination of advanced tools like Spark, EMR, Airflow, and Vector Embeddings, the document showcases a forward-looking approach to AI development that prioritizes understanding over mere indexing.
Short Introduction to Signal Processing and Convolution
In less than 3 pages, this tutorial covers signal processing and convolution quite thoroughly, even advanced concepts. I illustrate the techniques with the Riemann zeta function and the kernel method,...
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Cybersecurity Use Case: AI Agent for Anomaly Detection – Part 2
In the first part of this series, here, I feature a click fraud case that we are working on, litigated by one of the largest law firms in the US. The input data comes from an Excel repository, automat...
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30 Articles Shaping the Future of Enterprise AI in 2026
Over several decades, I unlearned everything that I learned in college classes, and built a new discipline from scratch, as much different from traditional AI than it is from standard machine learning...
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Simple Normality Test with Application to Random Number Generation
Numbers such as π, e, log 2 or √2 have binary digits (bits) that look randomly distributed. They are very good candidates to generate randomness especially in cryptography. One way to assess their ran...
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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 ...
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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 ...
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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 d...
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xLLM by BondingAI: 5–min Demo
This document features screenshots from a quick live demo intended to potential investors and clients interested in seeing how our production platform works. Contact the author for a live presentation...
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Main differentiators between LLM 2.0 and LLM 1.0
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...
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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 ...
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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 ...
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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-ho...
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