Data Science Central

Using Archetypes to Decode The Four Types of AI: Generative, Analytical, Causal, and Autonomous AI By Bill Schmarzo on Data Science Central #dsc #datascience #AI https://lnkd.in/ecsW5b-W

GenAI and LLM: Key Concepts You Need to Know, with new trends and emphasis on better results, lower costs, and faster implementations even without NN

Reducing insurance loss ratios with data science and AI algorithms By J. Joseph Rusnak on Data Science Central #dsc #datascience #AI https://lnkd.in/gxicmkF4

What I Learned From 25 Years of Machine Learning

New Book: Building Disruptive AI & LLM Technology from Scratch https://lnkd.in/e-JQnxj6 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 both in term of speed and quality, by several orders of magnitude. Each topic comes with GitHub links, full Python code, datasets, illustrations, and real-life case studies, including from Fortune 100 company. Some of the material is presented as enterprise projects with solution, to help you build robust applications and boost your career. You don’t need expensive GPU and cloud bandwidth to implement them: a standard laptop works. Part 1: Hallucination-Free LLM with Real-Time Fine-Tuning Part 2: Outperforming Neural Nets and Classic AI Part 3: Innovations in Statistical AI About the author Vincent Granville is a pioneering GenAI scientist and machine learning expert, co-founder of Data Science Central (acquired by a publicly traded company in 2020), Chief AI Scientist at ML Techniques and GenAI Techlab, former VC-funded executive, author (Elsevier) and patent owner — one related to LLM. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET. ?? See content and get your copy, at https://lnkd.in/e-JQnxj6

How to address concept drift in machine learning By Zachary Amos on Data Science Central #dsc #datascience #machinelearning https://lnkd.in/eHJuFadb

Data Economic Multiplier Effect Explained By Bill Schmarzo on Data Science Central #dsc #data #economy #economics https://bit.ly/3BK0HZY

Critical Decision Making for Enterprise AI By Dan Wilson on Data Science Central #dsc #podcast #AI #business https://lnkd.in/eqWwqWdD

30 Features that Dramatically Improve LLM Performance – Part 3: https://lnkd.in/gBnbc6Cx This is the third and final article in this series, featuring some of the most powerful features to improve RAG/LLM performance. In particular: speed, latency, relevancy (hallucinations, lack of exhaustivity), memory use and bandwidth (cloud, GPU, training, number of parameters), security, explainability, as well as incremental value for the user. I implemented these features in my in-memory xLLM system. See details in my recent book, at https://lnkd.in/g82c9Z8D. The featured image shows the xLLM Web API, now live and available to everyone, based on anonymized sample enterprise corpus from Fortune 100 company. I will share the link when the documentation is finalized. Sign-up to my newsletter at https://lnkd.in/gvvF72aG, to not miss it. In this article, you will find: ?? Self-tuning and customization, with user able to fine-tune in real time and select intuitive parameter values ?? Local, global parameters, and real-time debugging offered to user ?? Displaying relevancy scores for each item in prompt results, and score customization by the user ?? Intuitive hyperparameters: system based on explainable AI ?? Sorted n-grams and token order preservation, further reducing the size of backend tables ?? Blending standard tokens with tokens from the knowledge graph ?? Boosted weights for knowledge-graph tokens ?? Versatile command prompt allowing you to fine-tune parameters, check the size of backend tables, and a lot more ?? Boost long multitokens and rare single tokens to reflect their importance and higher quality Read full content at https://lnkd.in/gBnbc6Cx.

How to Make Black-box Systems more Transparent