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Published Jul 24, 2024

Hyperventilating over the Gartner AI Hype Cycle

Chris Benson, Daniel Whitenack, and Demetrios Brinkmann humorously dissect the AI buzzword frenzy and critically analyze the Gartner Hype Cycle for AI, exploring its alignment with reality, unexpected omissions, and the future trajectory of AI innovations, including wearable technology.
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Episode Highlights

  • Hype vs. Reality

    The discussion on the Gartner Hype Cycle for AI highlights the disparity between hype and real-world applications. and explore how cloud AI services, despite being highly productive, lack the allure that fuels hype 1. notes the misleading nature of the term "AI engineer," which can range from front-end developers dabbling in AI to seasoned ML engineers 1. This reflects a broader trend where AI's integration into software doesn't always meet the magical expectations set by hype 2.

    You mean I can't just buy an AI model and stick it out there and magic things happen.

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    The conversation underscores the need for a realistic understanding of AI's capabilities and limitations.

       

    Unexpected Omissions

    The absence of certain technologies from the Gartner Hype Cycle raises questions about its comprehensiveness. and express surprise at the omission of multimodal AI, which they believe should be at the peak of inflated expectations 3. suggests that AI gateways, which allow for efficient model selection, are gaining traction but are not represented 3. This omission highlights the evolving landscape of AI, where emerging technologies may not yet be fully recognized.

    Multimodal models should be on the peak of inflated expectations.

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    The discussion points to the dynamic nature of AI trends and the challenge of capturing them in a single chart.

       

    Criticizing AI Terms

    The panel critiques the clarity and necessity of certain AI terms featured on the hype cycle. questions the meaning of terms like "embodied AI" and "first principles AI," suggesting they may be more buzzwords than substantive concepts 4. notes the inconsistency in how related terms are positioned on the chart, such as generative AI and foundation models 5. This inconsistency reflects the broader issue of defining and categorizing AI technologies.

    I really would love to know what first principles AI is, because that feels like buzzword bingo to the fullest.

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    The conversation highlights the need for clearer definitions and understanding of AI terminology.

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