After reading Li Mu’s article “Optimizing Life Using Stochastic Gradient Descent” and feeling deeply inspired, I felt compelled to share some thoughts, especially after exploring Wolfram’s book “This is ChatGPT”. While Li Mu’s work was written before ChatGPT’s rise, he used stochastic gradient descent to discuss life principles. This note, however, will draw from the principles behind ChatGPT to explore how GPT models can inspire human learning and the development of personal knowledge systems.
Here are the key takeaways:
- Set Clear Goals
All machine learning requires a goal function, and so does human learning. Before learning, you must set a clear goal. Just as GPT minimizes its loss function to improve performance, human learning should focus on minimizing the gap between your personal knowledge (BOK2) and the objective human knowledge (BOK1). - Aim High
GPT’s goal of simulating all human language through a massive neural network is ambitious. Similarly, human learning shouldn’t focus solely on passing exams. Instead, build a comprehensive personal knowledge system (BOK2), striving to make it as robust and wide as possible. - Learn from Examples
Machines learn from data examples, not rules. Humans should do the same. Learn from books, articles, conversations, or videos—these are your data points. - Quality of Data Matters
In machine learning, “garbage in, garbage out” is a key principle. The same goes for humans—use high-quality sources to learn. Avoid “junk” materials and curate your learning resources carefully. - Learn from Mistakes
GPT learns by adjusting based on errors. Likewise, humans should view errors as valuable feedback. Embrace mistakes as part of the learning process, rather than fearing them. - Learning Changes You
As GPT adjusts its parameters through training, human learning similarly reshapes the brain. Each time you engage in deep learning, your brain forms new connections, leading to actual biological change. - Reading as a Learning Method
GPT’s learning process involves reading vast amounts of text. Humans, too, should prioritize reading. Make reading a core part of your learning routine—aim for at least one hour per day. - Feynman Technique for Understanding
GPT understands language through complex embedding processes. Humans can apply the Feynman technique to grasp concepts better by breaking them down and teaching them in simple terms, refining understanding through multiple iterations. - Iterative Learning
Like GPT’s iterative process of improving predictions, human learning benefits from repeated effort and refinement. The more you revisit and refine a concept, the clearer your understanding becomes. - Scaling Laws Lead to Results
GPT’s effectiveness relies on scaling laws—larger models perform better. Similarly, human learning thrives on accumulating knowledge over time. The more knowledge you gather, the stronger your intellectual capacity becomes. - Reduce Over-Intervention as Parents
Just as deep learning algorithms benefit from less manual interference, children’s learning thrives when parents provide guidance without micromanaging. Focus on fostering a nurturing environment for independent learning. - Attention is the Key
GPT relies on attention mechanisms to focus on relevant parts of the input. Humans must also cultivate the ability to focus on important information while blocking distractions, especially in today’s world of constant notifications. - Fine-Tuning After Training
GPT’s training continues after its initial training through fine-tuning and human feedback. Similarly, humans can enhance their learning through periodic reflections, feedback, and adjustments based on personal goals. - Language as Thought
The success of GPT shows that human language, while complex, can be understood and processed through structured algorithms. Language is not just communication but also the medium of thought—therefore, human learners must prioritize language skills. - Balancing Fast and Slow Learning
Just as GPT has an optimal learning rate, humans should balance rapid acquisition of new knowledge with deeper, more reflective understanding. Find a balance between speed and depth to optimize learning. - No One is Born in Rome
Learning is a lifelong process, and no one has a shortcut. Whether you’re 6 or 60, everyone starts from scratch and can improve by continuously engaging with the material. There’s no fixed “right” time to begin learning. - Everyone Has Their Own Learning Pace
Avoid the societal pressure of “catching up” by arbitrary deadlines. Everyone’s learning journey is unique, and it’s more important to focus on personal progress than compare oneself to others. - Simplicity is the Ultimate Complexity
The most effective learning systems, whether in AI or human brains, rely on simple structures. Avoid overcomplicating your methods; focus on foundational principles and iterative learning to build deep understanding.
These principles drawn from ChatGPT’s architecture offer profound insights into how we can improve human learning. By applying these lessons—goal-setting, iterative learning, and focus on quality—anyone can build a more powerful, structured knowledge system.
Please indicate: Thinking In Educating » How to Learn Anything Using ChatGPT Principles? Reflections after Reading Wolfram’s Book “This is ChatGPT”