Max Spero
speaker
224 appearances
1 recordings
1 series
first heard Jul 2026
last heard 16 Jul
Max Spero’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Jul 2026 with 1.
Appearances
Now, today, it's 1 in 10,000, which is 0.01%.
And I think that's low enough that people are able to confidently point at Pangram results and be like, oh, this is what it says.
So it's a method in machine learning called active learning.
Essentially what happens is we take a model that's okay, it's decent, and then we say, scan this really, really large corpus of human written text and find out
which examples we have errors on.
And what this essentially does is it finds examples that are close to the boundary between human and AI.
And then we take those and then we say for each of these documents, say it's like a...
a Yelp review on Denny's.
Then we'll ask AI to also create a Yelp review about Denny's in the same style.
And then so now we have a human side and then an AI synthetic mirror.
And so we train on these, the human example plus the AI synthetic mirror, and our model is able to learn the difference in stylistic choices between these two examples.
How did you tap into that?
Where did that idea originate from?
from like these core machine learning ideas where you want as large of a data set as possible and you want as diverse of a data set as possible.
So I think that's sort of how we landed on synthetic mirrors because otherwise, if you ask, if I just ask AI for 10,000 essays, I'm going to get like 9,000 essays that sound like very, very similar.
So instead of what we have to do to diversify our data is to
to have the AI essays mirror a human essay.
Yeah.
And training on these hardest examples is a really important part of it as well.
Because otherwise, there's just not enough signal.
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