How does AI stay current?
I asked this question of Gemini yesterday. I started with an easy one, asking about how it stays up to date in the field of astronomy. The reply was interesting but not particularly deep. The AI was trained in the fundamentals of physics first, then it was fed large data sets. The laws of physics don't change (it claimed; the reality is more complex but I let that slide), so there's no need to update. As we get new data, that replaces the old data.
This creates an interesting historical problem. Historians are always interested to know the state of understanding in a particular time period. Even astronomers might want to know this. If new data sets supplant old ones, we lose the ability to reconstruct that earlier understanding. Then again, how much of the past do we want to drag along with us?
That was mere prep. I wanted something with some more difficult and subtle, so I asked more or less the same thing about history--specifically, the Middle Ages. Historical facts rarely change, so that's sort of parallel. The AI gets trained on "the facts". But what about interpretations--about, for example, the Dark Ages?
Since I had already asked about astronomy, the responses were given in the form of comparison: here's how AI was trained in astronomy, here in history; here's how updates work; here's how anomalies are handled, and so on. That last was interesting. For hard science, the AI spoke of "data gaps" but for history it was "human bias".
So, how does AI handle evolving historical understanding? Two tools were listed. One is temporal weighting (also called "temporal decay"--a term I love). By this, recent publications have more weight than old ones. I'm ok with that; history students are routinely taught to be familiar with current scholarship, which generally (not always!) carries more weight with the thesis advisor or book reviewer.
The other is historiography, which AI called "meta-discourse". The language here is worth quoting:
"Advanced Large Language Models are highly sensitive to conditional probability. They learn to recognize phrases that signal a shift in scholarly opinion, such as:
"Historians used to believe X, but recent archaeological evidence suggests Y."
Because the AI has ingested thousands of modern articles debating this exact terminology, it maps the concept of "The Dark Ages" not as an objective fact about the year 600 AD, but as a concept invented by 14th-century humanists like Petrarch to make their own era look better.
Therefore, if you ask a well-trained AI to describe Europe in 700 AD, it will use modern consensus terms (Early Middle Ages). But if you explicitly ask it about the term "Dark Ages," it navigates to its historiographical data cluster to explain why the term was used and why it was rejected."
Lastly, in the section about data gaps versus human bias I found this wonderful sentence: "The mathematical engine treats both domains the same, but historians face a logical trap that astronomers do not: the data itself lies."
I'm still smiling over that one.
The response went on to explain: "If a telescope misses a star, it is just a blind spot in data collection. But if a historical diary leaves out an event, it might be due to political fear, cultural shame, or deliberate propaganda. AI cannot naturally detect human motive; it only calculates probabilities. If trained on a biased archive, the AI's logic will simply amplify that bias, mistaking statistical probability for historical truth. This is why, just like in space science, human interpretation remains entirely unreplaceable."
There you go, all you anti-AI folk. The machine itself admits to our superiority (in this one field, anyway).
Anyway, all the above is offered for your consideration. There's a further fascinating (to me) conversation about another source of bias, but this post is already terribly long.
I asked this question of Gemini yesterday. I started with an easy one, asking about how it stays up to date in the field of astronomy. The reply was interesting but not particularly deep. The AI was trained in the fundamentals of physics first, then it was fed large data sets. The laws of physics don't change (it claimed; the reality is more complex but I let that slide), so there's no need to update. As we get new data, that replaces the old data.
This creates an interesting historical problem. Historians are always interested to know the state of understanding in a particular time period. Even astronomers might want to know this. If new data sets supplant old ones, we lose the ability to reconstruct that earlier understanding. Then again, how much of the past do we want to drag along with us?
That was mere prep. I wanted something with some more difficult and subtle, so I asked more or less the same thing about history--specifically, the Middle Ages. Historical facts rarely change, so that's sort of parallel. The AI gets trained on "the facts". But what about interpretations--about, for example, the Dark Ages?
Since I had already asked about astronomy, the responses were given in the form of comparison: here's how AI was trained in astronomy, here in history; here's how updates work; here's how anomalies are handled, and so on. That last was interesting. For hard science, the AI spoke of "data gaps" but for history it was "human bias".
So, how does AI handle evolving historical understanding? Two tools were listed. One is temporal weighting (also called "temporal decay"--a term I love). By this, recent publications have more weight than old ones. I'm ok with that; history students are routinely taught to be familiar with current scholarship, which generally (not always!) carries more weight with the thesis advisor or book reviewer.
The other is historiography, which AI called "meta-discourse". The language here is worth quoting:
"Advanced Large Language Models are highly sensitive to conditional probability. They learn to recognize phrases that signal a shift in scholarly opinion, such as:
"Historians used to believe X, but recent archaeological evidence suggests Y."
Because the AI has ingested thousands of modern articles debating this exact terminology, it maps the concept of "The Dark Ages" not as an objective fact about the year 600 AD, but as a concept invented by 14th-century humanists like Petrarch to make their own era look better.
Therefore, if you ask a well-trained AI to describe Europe in 700 AD, it will use modern consensus terms (Early Middle Ages). But if you explicitly ask it about the term "Dark Ages," it navigates to its historiographical data cluster to explain why the term was used and why it was rejected."
Lastly, in the section about data gaps versus human bias I found this wonderful sentence: "The mathematical engine treats both domains the same, but historians face a logical trap that astronomers do not: the data itself lies."
I'm still smiling over that one.
The response went on to explain: "If a telescope misses a star, it is just a blind spot in data collection. But if a historical diary leaves out an event, it might be due to political fear, cultural shame, or deliberate propaganda. AI cannot naturally detect human motive; it only calculates probabilities. If trained on a biased archive, the AI's logic will simply amplify that bias, mistaking statistical probability for historical truth. This is why, just like in space science, human interpretation remains entirely unreplaceable."
There you go, all you anti-AI folk. The machine itself admits to our superiority (in this one field, anyway).
Anyway, all the above is offered for your consideration. There's a further fascinating (to me) conversation about another source of bias, but this post is already terribly long.
Inkling
Myth Weaver
