AI Propaganda

AI Propaganda

Author: Noah Gift February 18, 2025 Duration: 8:37

AI Propaganda and Market Reality

Key Points

  • LLMs are pattern matching systems, not true AI - similar to established clustering and regression techniques
  • Innovation follows non-linear path, contrary to VC expectations
  • VCs require exponential returns - 1/100 investments must generate massive profits
  • Perfect competition emerging in AI market - open source models reaching parity with commercial ones

Technical Context

  • LLMs extend existing data science tools:
    • K-means clustering
    • Linear regression
    • Recommendation engines
  • Pattern matching in multi-dimensional space ≠ intelligence

Market Dynamics

  • VCs invested expecting exponential growth
  • Getting logarithmic returns instead
  • Fear driving two contradictory narratives:
    • "Use AI or lose job"
    • "AI will take your jobs"

Historical Parallel

Steam engine (1700s) → combustion engine → electric cars (1910-2025)
Demonstrates long adoption curves for transformative tech

Recommendation

Use LLMs pragmatically:

  • Beneficial for code tasks
  • Prefer open source implementations
  • Ignore hype from vested interests

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Noah Gift guides you through a year-long journey with 52 Weeks of Cloud, a weekly exploration designed for anyone building, managing, or simply curious about modern cloud infrastructure. Each episode digs into a specific technical topic, moving beyond surface-level explanations to offer practical insights you can apply. You’ll hear detailed discussions on the platforms that power the industry-like AWS, Azure, and Google Cloud-and how to navigate multi-cloud strategies effectively. The conversation regularly delves into the orchestration of these systems with Kubernetes and the specialized world of machine learning operations, or MLOps, including the integration and implications of large language models. This isn't just theory; it's a focused look at the tools and methodologies shaping how software is deployed and scaled today. By committing to this podcast, you're essentially getting a structured, expert-led curriculum that breaks down complex subjects into manageable weekly segments, all aimed at building a comprehensive and practical understanding of the cloud ecosystem.
Author: Language: English Episodes: 225

52 Weeks of Cloud
Podcast Episodes
Introduction to Data Engineering on AWS [not-audio_url] [/not-audio_url]

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Hey readers 👋, if you enjoyed this post, I wanted to share some of my favorite resources to continue your learning journey in technology!Hands-On Courses for Data, Cloud, and AI 🚀Cloud Machine Learning Engineering and ML…
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EP: 48.0 AWS Security Certification module6 logging monitoring [not-audio_url] [/not-audio_url]

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AWS Security Ep. 2, Module 5: Data Protection [not-audio_url] [/not-audio_url]

Duration: 24:33
If you enjoyed this video, your learning journey has just begun! Dive deeper into the fascinating world of technology with these hand-picked resources:📊 Data Visualization and Python:Data Visualization with Python: https…
AWS Security Ep. 2, Module 4: Securing Infrastructure [not-audio_url] [/not-audio_url]

Duration: 38:33
If you enjoyed this video, your learning journey has just begun! Dive deeper into the fascinating world of technology with these hand-picked resources:📊 Data Visualization and Python:Data Visualization with Python: https…
AWS Security Ep. 2, Module 3:  IAM [not-audio_url] [/not-audio_url]

Duration: 21:19
If you enjoyed this video, here are additional resources to look at: MLOps Platforms: AWS SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke Open Source Platforms for MLOps: https://www.coursera.…
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Duration: 1:01:59
If you enjoyed this video, here are additional resources to look at: Buy book here: https://www.amazon.com/The-Enterprise-Data-Catalog/dp/149209871X Open Source Platforms for MLOps: https://www.coursera.org/learn/open-so…
O'Reilly Author Adi Polak [not-audio_url] [/not-audio_url]

Duration: 54:05
If you enjoyed this video, here are additional resources to look at: Open Source Platforms for MLOps: https://www.coursera.org/learn/open-source-mlops-platforms-dukePython Essentials for MLOps: https://www.coursera.org/l…