BondingAI's new AI Agent for Anomaly Detection & Cybersecurity
With two large litigation firms using it and working on implementation with one of the largest corporate compliance companies (customized version specific to their needs).
The input data comes from an Excel repository, automatically processed by an AI agent part of our BondingAI enterprise solutions. It comes with insights generation via automated SQL queries and pattern detection. In other contexts, the data may come from a PDF repository, the Internet, databases, or a combination of all.
In this article, I showcase animated data produced by the generic anomaly detection agent. The goal is to illustrate granular spatial fraud patterns as they evolve over time in the video, without having to rely on analysts or statisticians to produce striking insights or to clean the data. Each video frame represents a day, with timestamp in the top left corner. The data comes from two different time periods, showing the sharp contrast in fraud patterns between year 2019 and 2022 as you progress in the video.
Once you start the video, use the cursor at the bottom to move backward or forward in time at a different speed, or to stop on any particular day. Read more here.
See also our new book No-Blackbox, Secure, Efficient AI and LLM Solutions. In talks to be published by Wiley, with the following testimonial from a Global Head of AI/ML at JP Morgan Chase: "Your book is great. I have been reading it. It is perfect to be used by regulated industry".
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, fast, accurate, secure, replicable Enterprise AI. Most of the material is proprietary and made from scratch, showcasing the culmination of decades of research away from standard models to establish a new framework in machine learning and AI technology.
I discuss an efficient DNN architecture based on a new type of universal functions in chapter 4, with DNN distillation and protection via watermarking in chapter 5. Then, in chapter 6, I discuss non-DNN alternatives that yield exact interpolation on the training set yet benefit from benign overfitting in any dimension. Accurate predictions are obtained with a simple closed-form expression, without gradient descent or other iterative optimization technique, essentially without training.
Case studies include 96% correct predictions for the next token on a Nvidia PDF repository, automated heart beat clustering and unusually high data compression rates (big data), anomaly detection and fraud litigation linked to large-scale cybsersecurity breach (large Excel repository, automated SQL, time series and geospatial data) as well as predicting next sequence on real-world genome data with home-made LLM technology. Some datasets with 1000 dimensions are generated with the best and fastest tabular data synthesizer on the market, described in details in chapter 2 along with the best model evaluation metric. These cases correspond to different agents linked to the xLLM technology (extreme LLM) developed by the author.
