Monolithic AI vs Modular AI

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  • Опубликовано: 17 янв 2025

Комментарии • 11

  • @code4AI
    @code4AI  Месяц назад

    Please note, with the automatic dubbing from RUclips /Google you hear a synthetic voice in your regional language. To hear my original voice in English, switch to "Default" or "English" in the settings. Thank you.

  • @davidtindell950
    @davidtindell950 Месяц назад +4

    thank you for your ENTHUSIASM ! ! ! !😊

  • @CharlotteLopez-n3i
    @CharlotteLopez-n3i Месяц назад +3

    Love the emphasis on modularity and specification in building reliable AI systems! The organic solar cells example is a great illustration of how a model-based approach can lead to more efficient solutions.

  • @aennmatyasbarra-hunyor5506
    @aennmatyasbarra-hunyor5506 Месяц назад +1

    Great series

  • @AshWickramasinghe
    @AshWickramasinghe Месяц назад

    Working a system that has the same core concept 😅 we shouldn't need to wait for a few companies to give us access to their expensive monolithic models for us to use GenAI for our daily tasks.

  • @I_am_who_I_am_who_I_am
    @I_am_who_I_am_who_I_am Месяц назад

    😅 here is enterprise AI coming 😂😂😂😂
    Microservices 😂😂😂😂

  • @pensiveintrovert4318
    @pensiveintrovert4318 Месяц назад +3

    So their genius idea is that a specialist is better at its specialty than a generalist?
    I can expect this kind of insight from a chatGPT when I ask it to explain something to a five year old.
    That is why mixture of experts works better than a monolith model.

    • @code4AI
      @code4AI  Месяц назад +5

      No. The idea is, that you have to design your AI architecture and the different NN configurations in your layers in a specific way, and that your specific task is the main design criterium for a dedicated and optimized AI architecture.

    • @ibgib
      @ibgib Месяц назад

      This is highly close to what I've been building: semantic version control. It's like a generalized git. Specification is one layer on top of version control. The protocol enables content addressing (like git commit hashes) to version specs, models, training data, metadata like agentic architectures, and even derivative data. This very instant, I'm working on agent-based JIT UX. Still looking for investment!