AI Coach - Anil Nathoo

• #39 - Training Agents to Self-Educate and then Train Other Agents

  This podcast series explores the potential of self-learning Agents and Teaching AI systems.It details how AI can learn through reinforcement and self-supervised learning, utilising technologies like vector databases and knowledge graphs to store and retrieve information effectively.It then guides listeners through designing an AI tutor, including adaptive learning systems and interactive interfaces, and finally discusses the future of AI-powered education, addressing ethical considerations and potential advancements.The accompanyingarticle offer a comprehensive roadmap for building AI tutors and provide a detailed technical overview.

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• #38 - Digital Twin for Decision Intelligence (DT4DI)

  Click here to read the article. This podcast explains the DT4DI is a framework that combines digital twins, artificial intelligence (AI) and decision intelligence to improve decision-making processes. It utilises digital twins, which are virtual replicas of physical systems that are updated with real-time data, to simulate scenarios and predict outcomes. By combining AI and machine learning, DT4DI enables companies to convert data into actionable insights, which helps with strategic decision-making. This approach helps organisations to move beyond traditional business intelligence by using advanced technologies to become knowledge-driven and results-oriented. The key components of a DT4DI system are: digital twin (DT) platforms artificial intelligence (AI) and machine learning (ML) decision intelligence (DI) framework data management and analytics These components work together to enable organisations to make data-driven decisions more efficiently.

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• #37 - Mastering Machine Learning: A Beginner's Guide

  Click here to read the article. This is an introductory guide to machine learning (ML), covering fundamental concepts and various types including supervised, unsupervised, self-supervised, reinforcement, and semi-supervised learning. The guide details the applications of each type, highlighting key differences between supervised and unsupervised approaches. It also addresses common challenges in ML such as data bias and the need for responsible AI development. Finally, it explores the future of the field, focusing on emerging trends like generative AI and edge computing, and provides learning resources for aspiring practitioners.

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• #36 - KAG Framework - Builder, Solver & Model

  Click here to read the article. This podcast describes the Knowledge Augmented Generation (KAG) Framework. There are 3 main components: KAG-Builder (for offline indexing), KAG-Solver (for hybrid reasoning), and KAG-Model (for optimisation). The framework leverages Natural Language Understanding (NLU), Natural Language Inference (NLI), and Natural Language Generation (NLG) – core NLP processes – to enable the system to understand, reason with, and generate human-like text. NLU interprets input, NLI establishes logical connections, and NLG produces coherent outputs. In essence, KAG integrates knowledge construction, reasoning, and model optimisation for advanced text processing.

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• #35 - Knowledge Augmented Generation (KAG) explained

  Click her to read the article. This podcast covers Knowledge Augmented Generation (KAG), a novel AI framework designed to improve Large Language Models (LLMs) in professional fields. KAG addresses limitations of existing retrieval-based systems by incorporating knowledge graphs and advanced logical reasoning capabilities. This allows for more accurate and contextually relevant responses, particularly in complex, multi-step queries. The framework comprises three core components: KAG-Builder, KAG-Solver, and KAG-Model, working together to process information and generate professional-level outputs. The system's efficacy is demonstrated through successful applications in e-government and e-health, significantly outperforming traditional AI systems in accuracy and logical reasoning.

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