We live without reconsidering everything from the beginning every time. We can judge more quickly by using methods that worked before, rules we learned, and other people’s experiences as knowledge. The value of knowledge lies in carrying the past into the present. Yet that convenience also contains an opposite risk. What was correct in the past is not necessarily correct today under the same conditions. There is a difference between using knowledge and forcing the present into a pattern from the past.
For example, even if a study method produced good results before, the next task may require different abilities or allow different amounts of time. The fact that "it worked before" matters, but that alone does not determine that the same method is optimal now. Turning a successful example directly into a rule fixes the answer before present differences are checked. The stronger the knowledge, the easier it can become to overlook small changes that do not fit it.
Because AI can use vast amounts of past information, it is deeply involved with this problem. It is good at finding similar situations in past cases and showing general tendencies. But similarity and identity are different. If the present person, purpose, environment, or constraint differs, the way past knowledge should be used also changes. The more AI knows about the past, the more persuasive "this is how it has been" can sound, so present differences need to be handled consciously.
To avoid sliding knowledge directly into the present, one approach is to treat knowledge not as an answer but as material for comparison. We can ask: "In the past, this result was common under these conditions. Do those conditions still hold now?" Adding this one step changes knowledge from a rule governing the present into a hypothesis for understanding it. The point is not to discard the past, but to place a distance of verification between past and present.
This distance is necessary even when knowledge is highly reliable. Even well-established knowledge has conditions defining its range of application. A general rule may be correct without applying directly to every individual situation. The important question is not only "Is this knowledge correct?" but also "Are the conditions for using this knowledge present here?" Even correct knowledge can lead to a wrong judgment if its conditions of application are ignored.
The same applies to human experience. Long accumulated experience helps us understand situations quickly. Yet the richer our experience, the more easily we may apply a familiar pattern to a new event. Precisely when we feel "this is the same as before," we need to check what is the same and what is different. Possessing knowledge and not being bound by it are not contradictory. Rather, that attitude is necessary if knowledge is to remain useful over time.
When AI holds past information about a person, this principle becomes even more important. Previous preferences, past judgments, and repeated behavior can provide clues for understanding the present. But people change. If past tendencies are turned into a fixed image that "this is the kind of person they are," AI may appear to understand while moving away from who the person is now. The past can be material for predicting the present, but it is not the present person themselves.
The role of knowledge in Sai＊aus is likewise not merely to increase the amount accumulated. What matters is being able to handle the differences between what has been accumulated and the current dialogue. Earlier understanding can be used while being corrected by present words. Even useful past information should not be placed above a person’s current intention. With that relationship, knowledge can be used not to create a fixed image of a person, but also to discover change.
Past correct answers help today’s judgment. But they do not decide today’s correct answer in advance. Knowledge is not a substitute for reality; it is experience that can be carried with us in order to check reality. If distance remains between past and present, we can keep accumulated knowledge while accepting new differences. Using what we know while still looking at the present again: that attitude turns knowledge from a rigid answer into living understanding.
Not answers. Understanding.