Month: September 2024

  • Mind Readings: Making a Podcast with Generative AI, Part 1

    In today’s episode, you’ll discover how to create engaging podcast content with the help of generative AI. You’ll learn how to set up your audio, prime ChatGPT to conduct interviews like a pro, and avoid common podcasting pitfalls. Tune in to unlock the secrets to producing compelling audio content that captivates your audience! Can’t see…

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  • Mind Readings: Making a Podcast with Generative AI, Part 1

    Summary In today's episode, I walk through how to set up an AI-generated podcast interview using ChatGPT's advanced audio mode instead of relying on Google's Notebook LM. Here's what this means for you. You'll be able to create interactive podcast-style content where you control the conversation and inject your own voice rather than settling for…

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  • Almost Timely News: 🗞️ Small Language Models and Generative AI (2024-09-29)

    Almost Timely News: 🗞️ Small Language Models and Generative AI (2024-09-29) :: View in Browser The Big Plug Here are my two latest talks! 👉 Watch my new 90-minute master class workshop (no slides!) from INBOUND 2024 on how to build a data-driven customer journey map using generative AI. (link fixed from last week) 👉…

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  • You Ask, I Answer: RAG vs Fine Tuning in Generative AI?

    In today’s episode, you’ll dive into the fascinating world of generative AI and learn about two powerful techniques: RAG and fine-tuning. You’ll gain a clear understanding of how these techniques differ and when to use each one to enhance the capabilities of AI models. I’ll provide easy-to-understand analogies to help you grasp the concepts and…

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  • You Ask, I Answer: RAG vs Fine Tuning in Generative AI?

    Summary In today's episode, I break down the difference between RAG and fine-tuning, two core strategies for improving generative AI model outputs. Here's what this means for you. You'll gain a clear decision framework for choosing the right approach when your AI model isn't delivering the results you need. You'll also learn these concepts: how…

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  • You Ask, I Answer: Why Can’t AI Make a Nice Slide Deck?

    In today’s episode, you’ll uncover the reasons behind the challenges AI faces in creating truly compelling slide decks. You’ll learn about the limitations of current AI architectures and how they hinder the seamless integration of text and visuals. I’ll also explore the fascinating interplay between reason and creativity and how it affects AI’s ability to…

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  • You Ask, I Answer: Why Can’t AI Make a Nice Slide Deck?

    Summary In today's episode, I explain why generative AI still struggles to produce a compelling slide deck even as image and text tools keep improving. Here's what this means for you. You gain a clear mental model for the architectural and conceptual gaps that hold AI back from matching human creativity in presentations. You'll also…

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  • You Ask, I Answer: Why is Marketing Data Rich but Insight Poor?

    In today’s episode, you’ll explore the intriguing paradox of why marketers, despite having access to vast amounts of data, often struggle to extract meaningful insights. You’ll discover the crucial role that well-defined questions play in guiding data analysis and learn why simply having data isn’t enough. I’ll share a practical framework that helps you transform…

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  • You Ask, I Answer: Why is Marketing Data Rich but Insight Poor?

    Summary In today's episode, I explore why marketers sit on piles of data yet rarely surface meaningful insights. Here's what this means for you. You'll discover that defining clear questions before touching your data is the missing link between raw numbers and real answers. You'll also learn these concepts: how a kitchen analogy maps data…

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  • First Look: Llama 3.2 Vision Instruct 11B and 90B Vision Language Models (VLM) in IBM WatsonX.ai

    Summary In today's episode, I test Meta's new Llama 3.2 multimodal models on IBM Watson X, comparing the 90 billion and 11 billion parameter versions across image description and data extraction tasks. Here's what this means for you. You can run capable vision models locally on your own hardware for the cost of electricity, skipping…

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