{"id":189,"date":"2026-09-17T14:38:00","date_gmt":"2026-09-17T14:38:00","guid":{"rendered":"https:\/\/aiprimus.ai\/?p=189"},"modified":"2026-09-16T02:37:20","modified_gmt":"2026-09-16T02:37:20","slug":"ai-training-tao-te-ching-2","status":"publish","type":"post","link":"https:\/\/aiprimus.ai\/en\/ai-training-tao-te-ching-2\/","title":{"rendered":"The Highest-Level Art of Training AI"},"content":{"rendered":"
\"\"<\/figure>\n\n\n\n

You Think AI Was Invented in America? Some of Its Deepest Ideas Echo an Ancient Chinese Classic.<\/h3>\n\n\n\n

I know someone who teaches AI to companies.<\/p>\n\n\n\n

Last year, he was hired to train a group of employees on how to use large language models.<\/p>\n\n\n\n

He prepared a hundred-slide presentation.<\/p>\n\n\n\n

Neural networks.
Transformers.
Attention mechanisms.
Loss functions.<\/p>\n\n\n\n

He explained everything he could think of.<\/p>\n\n\n\n

Then, in the middle of the session, a business owner in his fifties raised his hand.<\/p>\n\n\n\n

He asked one question:<\/p>\n\n\n\n

\n

\u201cCan you tell me how this thing actually learns to become intelligent?\u201d<\/strong><\/p>\n<\/blockquote>\n\n\n\n

My friend froze.<\/p>\n\n\n\n

He had prepared a hundred slides.<\/p>\n\n\n\n

But somehow, he had not prepared for that question.<\/p>\n\n\n\n

The funny thing is that you can begin answering it through a few lines from the Tao Te Ching<\/strong>, written more than two thousand years ago.<\/p>\n\n\n\n

One of its most important ideas is:<\/p>\n\n\n\n

\n

\u201cBeing and non-being give rise to each other.\u201d<\/strong>
\u2014 Tao Te Ching<\/em>, Chapter 2<\/p>\n<\/blockquote>\n\n\n\n

Don’t rush to call this mystical.<\/p>\n\n\n\n

Look at how modern AI actually learns.<\/p>\n\n\n\n

You may discover that some of the deepest principles of machine learning have a surprisingly familiar shape.<\/p>\n\n\n\n

\n\n\n\n

01\uff5cHow AI Learns Language: Hide the \u201cSomething\u201d and Let It Infer the \u201cNothing\u201d<\/h2>\n\n\n\n

Have you ever done a fill-in-the-blank exercise?<\/p>\n\n\n\n

Imagine a sentence:<\/p>\n\n\n\n

\n

\u201cTom was in a great mood today. He had a ______ on his face all morning.\u201d<\/p>\n<\/blockquote>\n\n\n\n

You might fill in:<\/p>\n\n\n\n

smile.<\/strong><\/p>\n\n\n\n

Simple.<\/p>\n\n\n\n

But think about what your brain actually did.<\/p>\n\n\n\n

You didn’t just memorize the word smile<\/em>.<\/p>\n\n\n\n

You understood the relationship between:<\/p>\n\n\n\n

    \n
  • \u201cgreat mood\u201d<\/li>\n\n\n\n
  • \u201cface\u201d<\/li>\n\n\n\n
  • human expression<\/li>\n\n\n\n
  • emotional context<\/li>\n\n\n\n
  • the structure of the sentence<\/li>\n<\/ul>\n\n\n\n

    Then you inferred the missing word.<\/p>\n\n\n\n

    This is remarkably close to one of the fundamental ideas behind modern language-model training.<\/p>\n\n\n\n

    During training, parts of the input can be hidden or withheld, and the model is asked to predict what belongs there.<\/p>\n\n\n\n

    The visible information becomes the context.<\/p>\n\n\n\n

    The missing information becomes the target.<\/p>\n\n\n\n

    The model repeatedly learns to infer what is absent from what is present.<\/p>\n\n\n\n

    In the language of the Tao Te Ching<\/em>:<\/p>\n\n\n\n

    \n

    \u6709\u65e0\u76f8\u751f \u2014 being and non-being give rise to each other.<\/strong><\/p>\n<\/blockquote>\n\n\n\n

    The interesting part is not simply that AI sees enormous amounts of data.<\/p>\n\n\n\n

    It is that AI learns through the relationship between what is present and what is missing<\/strong>.<\/p>\n\n\n\n

    It makes predictions.<\/p>\n\n\n\n

    It receives feedback.<\/p>\n\n\n\n

    It adjusts.<\/p>\n\n\n\n

    Then it tries again.<\/p>\n\n\n\n

    Millions, billions, and trillions of times.<\/p>\n\n\n\n

    No one needs to sit beside the model and explain every possible meaning of every word.<\/p>\n\n\n\n

    Instead, the training environment creates conditions in which the model can gradually discover statistical structure, relationships, patterns, and representations.<\/p>\n\n\n\n

    That is a profound idea for education as well.<\/p>\n\n\n\n

    The highest-level training does not simply provide answers. It creates a situation in which the learner has to discover the answer.<\/strong><\/p>\n\n\n\n

    AI learns this way.<\/p>\n\n\n\n

    Humans do too.<\/p>\n\n\n\n

    \n\n\n\n

    02\uff5cHow Do You Teach Something Difficult?<\/h2>\n\n\n\n

    Don’t Start With the Difficult Part.<\/h3>\n\n\n\n

    Imagine teaching mathematics to a six-year-old.<\/p>\n\n\n\n

    Would you begin with calculus?<\/p>\n\n\n\n

    Of course not.<\/p>\n\n\n\n

    You start with numbers.<\/p>\n\n\n\n

    Then addition and subtraction.<\/p>\n\n\n\n

    Then multiplication.<\/p>\n\n\n\n

    Then more complicated concepts.<\/p>\n\n\n\n

    Why?<\/p>\n\n\n\n

    Because difficulty does not exist independently of simplicity.<\/p>\n\n\n\n

    The difficult is built layer by layer on top of the easy.<\/strong><\/p>\n\n\n\n

    This idea has a modern counterpart in machine learning:<\/p>\n\n\n\n

    curriculum learning.<\/strong><\/p>\n\n\n\n

    Instead of presenting a model with the most complicated examples immediately, training can progress from simpler patterns toward increasingly difficult ones.<\/p>\n\n\n\n

    Think about teaching a child to ride a bicycle.<\/p>\n\n\n\n

    First, training wheels.<\/p>\n\n\n\n

    Then one training wheel.<\/p>\n\n\n\n

    Eventually, none.<\/p>\n\n\n\n

    The simple stage does not disappear.<\/p>\n\n\n\n

    It becomes the foundation of the more advanced stage.<\/p>\n\n\n\n

    This echoes another famous line from the Tao Te Ching<\/em>:<\/p>\n\n\n\n

    \n

    \u201cThe difficult and the easy complete each other.\u201d<\/strong><\/p>\n<\/blockquote>\n\n\n\n

    We often look at experts and see only the final result.<\/p>\n\n\n\n

    We see someone who can analyze a complex business problem, write sophisticated software, trade markets, or design an AI system.<\/p>\n\n\n\n

    What we don’t see are the thousands of simple patterns underneath that ability.<\/p>\n\n\n\n

    The same is true of AI.<\/p>\n\n\n\n

    A powerful model is not built by throwing complexity at it.<\/p>\n\n\n\n

    It is built by allowing increasingly complex structures to emerge from simpler ones.<\/p>\n\n\n\n

    So the next time learning feels painfully difficult, don’t immediately conclude that you are not smart enough.<\/p>\n\n\n\n

    Ask a different question:<\/p>\n\n\n\n

    \n

    Did I skip the \u201ceasy\u201d layer and jump directly into the \u201cdifficult\u201d one?<\/strong><\/p>\n<\/blockquote>\n\n\n\n

    Sometimes the problem isn’t intelligence.<\/p>\n\n\n\n

    It’s sequencing.<\/p>\n\n\n\n

    \n\n\n\n

    03\uff5cHow Does AI Learn to Recognize Beauty? Through Comparison.<\/h2>\n\n\n\n

    This is where things get particularly interesting.<\/p>\n\n\n\n

    Imagine someone who has never seen a cat or a dog.<\/p>\n\n\n\n

    How would you teach them to tell the difference?<\/p>\n\n\n\n

    You could show them a picture of a cat and say:<\/p>\n\n\n\n

    \n

    \u201cThis is a cat. It has four legs, a tail, pointed ears\u2026\u201d<\/p>\n<\/blockquote>\n\n\n\n

    But there is a problem.<\/p>\n\n\n\n

    A dog also has four legs.<\/p>\n\n\n\n

    A dog can also have a tail.<\/p>\n\n\n\n

    A dog can also have pointed ears.<\/p>\n\n\n\n

    The more you try to define something through absolute rules, the harder the distinction can become.<\/p>\n\n\n\n

    So try another method.<\/p>\n\n\n\n

    Show them ten cats.<\/p>\n\n\n\n

    Then show them ten dogs.<\/p>\n\n\n\n

    Don’t immediately give them the rule.<\/p>\n\n\n\n

    Let them compare.<\/p>\n\n\n\n

    After enough comparisons, subtle patterns begin to emerge.<\/p>\n\n\n\n

    Perhaps the proportions are different.<\/p>\n\n\n\n

    Perhaps the movement is different.<\/p>\n\n\n\n

    Perhaps the face, posture, or behavior has a recognizable structure that is difficult to express as a simple rule.<\/p>\n\n\n\n

    The distinction emerges from the relationship.<\/p>\n\n\n\n

    Modern AI uses this principle extensively.<\/p>\n\n\n\n

    Consider contrastive learning<\/strong>.<\/p>\n\n\n\n

    A model can be trained to bring related representations closer together and push unrelated representations apart.<\/p>\n\n\n\n

    Over many examples, the model begins to construct a meaningful representation of the world.<\/p>\n\n\n\n

    Systems such as CLIP<\/strong> demonstrated how powerful this approach can be by learning relationships between images and text.<\/p>\n\n\n\n

    A model doesn’t need a human to write down every possible rule for:<\/p>\n\n\n\n

    \n

    \u201ca red cat\u201d<\/p>\n<\/blockquote>\n\n\n\n

    Instead, it can learn from enormous numbers of examples linking visual information and language.<\/p>\n\n\n\n

    The Tao Te Ching<\/em> expresses a related insight:<\/p>\n\n\n\n

    \n

    \u201cLong and short define each other; high and low lean into each other.\u201d<\/strong><\/p>\n<\/blockquote>\n\n\n\n

    Long becomes meaningful because there is short.<\/p>\n\n\n\n

    High becomes meaningful because there is low.<\/p>\n\n\n\n

    Difference creates information.<\/p>\n\n\n\n

    Contrast creates structure.<\/p>\n\n\n\n

    And comparison creates judgment.<\/p>\n\n\n\n

    This principle extends far beyond AI.<\/p>\n\n\n\n

    Many sophisticated abilities are not learned from isolated answers. They emerge from comparison, contrast, feedback, and opposition.<\/strong><\/p>\n\n\n\n

    That is why great thinkers constantly compare:<\/p>\n\n\n\n