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The Evolution of Decision Intelligence - AI Coach" traces the development of data-informed decision-making. It begins by exploring the foundational concepts of statistical process control, data analysis, and bounded rationality in the mid-20th century.

The podcast then examines the emergence of Decision Support Systems in the 1970s and 1980s as early technological aids. Subsequently, it details the rise and maturation of Data-Driven Decision Making through the 1990s and 2000s, highlighting the impact of business intelligence and analytics.

Finally, the excerpts focus on the more recent evolution and shaping of Decision Intelligence in the 2010s and 2020s, which integrates AI and decision theory for optimised outcomes.

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This podcasy from AI Coach distinguishes between Data-Driven Decision-Making (DDDM) and Decision Intelligence (DI), explaining that while DDDM uses data to inform specific choices, DI is a broader framework for designing how decisions are made across an organisation.

The podcast outlines the processes for both approaches, highlighting that DI incorporates and extends DDDM by focusing on automation, scalability, and learning within decision systems.

Key differences in scope, purpose, automation, and tools are presented, alongside examples illustrating their respective roles in real-world scenarios. Ultimately, the article argues that DI represents an evolution, enabling more adaptive and intelligent decision ecosystems by embedding data-driven practices within a systemic approach.

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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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:

These components work together to enable organisations to make data-driven decisions more efficiently.

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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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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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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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The rise of Large Language Models (LLMs) like OpenAI’s GPT and the growing interest in autonomous agents have reshaped how software-as-a-service (SaaS) applications are conceptualized and built. Leveraging these technologies can enable dynamic, adaptive, and intelligent software solutions. This podcast explores how to build a SaaS application using agents and LLMs, from architecture design to deployment in 7 steps:

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This podcast explores vector databases, their architecture, and applications in AI. It explains how vectors represent high-dimensional data, detailing core database architecture, indexing techniques (like HNSW and FAISS), and distance metrics. The article then examines various applications including semantic search, recommendation systems, and anomaly detection, across diverse sectors. Finally, it discusses challenges like scalability and data privacy, outlining future directions in indexing, real-time processing, and ethical considerations, and provides an overview of popular vector databases such as Pinecone and Weaviate.

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This podcast from AI Coach explores key artificial intelligence trends predicted for 2025, focusing on technologies offering businesses a competitive edge.

It examines foundational technologies like agentic AI and multimodal AI, alongside advancements in customer experience, cybersecurity, and infrastructure.

The podcast also highlights emerging technologies such as edge AI, small language models, and generative video, while addressing crucial considerations around energy efficiency, responsible AI, and ethical implementation.

Finally, it outlines essential technical, business, and ethical skills needed to leverage these AI trends effectively.