Growing on Purpose: The Work That Makes You. Jeremy Howard on human flourishing in the time of AI.
Summary
Jeremy Howard, co-founder of fast.ai and CEO of Answer.ai, discusses human flourishing in the age of AI, emphasizing augmenting human creativity over replacement. Drawing on Self-Determination Theory (SDT), he highlights the importance of autonomy and mastery for eudaimonia, or fully actualizing one's capacities. Howard warns that AI, while capable of supporting growth, can also diminish these qualities through "dark flow" or an "illusion of control," especially when vendors prioritize outputs. He illustrates historical examples of human-computer augmentation, from Ivan Sutherland's Sketchpad (1963) to Douglas Engelbart's "Mother of All Demos" (1968) and Kenneth Iverson's APL. Howard then demos Solve It, a tool designed to foster deep engagement, showing how it facilitated learning Recursive Language Models and interactively rebuilding a CSS styling framework, enabling users to understand, replicate, and experiment with complex technical concepts.
Key takeaway
For AI Engineers and Machine Learning Engineers seeking genuine skill development, you should actively use AI tools to augment your learning and craft, rather than merely outsourcing tasks. Prioritize tools that enable deep engagement, interactive experimentation, and the ability to understand and replicate underlying principles. Be vigilant against "dark flow" scenarios where AI provides an illusion of control without fostering true mastery, ensuring your work contributes to your professional growth and well-being.
Key insights
Human flourishing with AI requires focusing on augmenting creativity and fostering autonomy and mastery, not just output.
Principles
- Authentic motivation enhances well-being, performance, and creativity.
- "Dark flow" creates addiction and an illusion of control, hindering growth.
- AI tools can either decay or support autonomy and mastery.
Method
Engage with AI by asking questions, interactively experimenting with code, and replicating concepts to deepen understanding and build genuine ability.
In practice
- Use AI to clarify complex figures and generate concrete examples.
- Reimplement technical concepts within an AI-assisted environment.
- Experiment with code and styles interactively to test understanding.
Topics
- Human-AI Collaboration
- Self-Determination Theory
- AI Augmentation
- Flow State
- Recursive Language Models
- Solve It Platform
Best for: AI Engineer, Machine Learning Engineer, AI Student
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Editorial summary, takeaway, and curation by AIssential. Original article published by Jeremy Howard.