Title: AI is the New Compiler (Why 'Learning AI' is the Wrong Goal)
Introducton: Turng's Vson & The Goal
The video begins by discussing Alan Turing's vision of AI and its ability to surprise us, despite our understanding of computing and abstraction. The speaker aims to show that AI is the new compiler, a new abstraction language, and understanding it is crucial for the future of jobs, companies, and investments.
AI s the New Compler
The speaker introduces the idea that AI is the new compiler, a new abstraction language that we are using. He aims to teach viewers about this concept and its importance in the future.
Why 95% of AI Intatves Are Falng
The speaker discusses the current state of AI initiatives, noting that 95% are failing according to an MIT study. He compares the AI bubble to the internet bubble of the early 2000s and emphasizes the importance of understanding fundamental truths to navigate this landscape.
Operatng on Fundamental Truths
The speaker emphasizes the importance of operating on fundamental truths to understand and navigate the AI landscape. He promises to teach viewers how to use AI effectively, regardless of their programming background.
Who Am I? (My Background)
The speaker introduces himself, sharing his background in the US Marine Corps, political science, and AI research. He highlights his experience working with AI and neuro-politics, and his current work teaching workshops on AI implementation.
Let's Talk About Prompt Engneerng
The speaker discusses prompt engineering, clarifying misconceptions and explaining its importance. He shares an example of using AI to test personality scales and emphasizes the logic and programming behind effective prompt engineering.
Advanced Prompt Engneerng: A Real-World Example
The speaker provides a real-world example of advanced prompt engineering, explaining how he used AI to test personality scales by separating each question and asking them individually to the model.
The 'Fnal Prompt' Explaned
The speaker explains the concept of the 'final prompt' and how it is used to control AI output. He demonstrates how he uses Python to automate the prompting process and create reproducible results at a large scale.
Smplfyng the Concept: AI as a Smart Mad Lbs Game
The speaker simplifies the concept of AI by comparing it to a smart Mad Libs game. He explains how AI can be used to control output based on direct input and how this can be applied to various tasks.
Controllng AI Output wth Your Own Data
The speaker discusses how to control AI output using your own data. He explains how to create templates to control the output and how to automate the prompting process.
Fxed vs. Varable Parts of a Prompt
The speaker explains the difference between fixed and variable parts of a prompt. He emphasizes the importance of having fixed instructions and variable parts that change based on the context.
The Compler Analogy: From Machne Language to AI
The speaker draws an analogy between AI and traditional programming languages, explaining how AI is creating another layer to the pyramid of abstraction. He argues that AI is automating parts of the coding process and adding an abstraction layer.
The Future of Programmng & Abstracton Layers
The speaker discusses the future of programming and abstraction layers, explaining how AI is automating parts of the workflow and creating an automation of business templates. He emphasizes the importance of understanding these concepts for the future.
Sneak Peek: Part 2 & The 4 Core Concepts of AI Orchestraton
The speaker provides a sneak peek into part 2 of the video, discussing the four core concepts of AI orchestration: defining a role or persona, setting structure, adding validation, and handling errors.
Concluson & Fnal Thoughts
The speaker concludes the video by summarizing the key points and emphasizing the importance of understanding AI as the new compiler. He encourages viewers to ask questions and looks forward to part 2 of the video.
