Ask A Genius 1361: The Limits of Science, Time, and Understanding AI
Author(s): Rick Rosner and Scott Douglas Jacobsen
Publication (Outlet/Website): Ask A Genius
Publication Date (yyyy/mm/dd): 2025/04/29
Rick Rosner: On-time first: I do not think time itself has to have a limit. Time, as we understand it, needs a universe to exist within. A universe can continue indefinitely into the future. However, you never actually reach infinity. You keep extending further and further without ever arriving at an infinite point.
You can have a universe that is any finite age, no matter how old it gets. Every possible age is finite—even if it gets staggeringly large—because infinity is never achieved.
Part of the answer regarding the limit of science comes from an essay by Richard Feynman, who wrote about it around sixty years ago. He outlined that the limits of science partly depend on how complicated the universe is, how much explanation it requires, and how scrutable—how decipherable—it is.
Feynman proposed three possibilities:
- Attainable complexity: Science could eventually complete most of the work of explaining the universe. There would still be a few details to fill in, but the major framework would be primarily understood.
- Inscrutable universe: The universe could be so complex that, no matter how much science progresses, we would never fully understand it. It would be permanently beyond human comprehension.
- A mix: The universe could be partly understandable—some areas are easy to figure out, others are permanently hard. Science would continue progressing steadily but never “complete” the picture entirely.
The actual universe falls into the third category. We can figure out significant aspects of how things work, but as our computational and analytical powers grow, there will always be new mysteries and increasingly subtle phenomena to investigate.
With the rise of big data and machine learning, we are also learning that there are emergent properties—patterns that appear only when analyzing massive datasets with powerful analytic engines. Even if most fundamental physics is pinned down, these emergent phenomena open new layers of understanding.
This week, Sam Altman announced that, at least in his opinion, we have achieved AGI—Artificial General Intelligence.
Sam Altman says these new models are innovative. They describe the capabilities using all sorts of terms—none of which I fully understand yet—so I should probably educate myself about AI.
There are all these descriptors and indices for what AI can do analytically, and I do not know what most of the terms mean. Do you?
Scott Douglas Jacobsen: They say everyday things in weird ways. For example, instead of just saying “compute” as a verb, they use “compute” as a unit of measurement. So instead of asking, “How do you compute this?” they will say, “How much compute does it have?” or “How much compute will you need to perform this task?” They talk about a 10x decrease in computing costs every so many periods. But they also have other indices that measure nimbleness or adaptability.
Rosner: It seems like a pain to learn now, but it is similar to when people had to educate themselves about cars in 1904. Back then, you had to learn what a piston was, what a carburetor was, and what a spark plug was. It was all new mechanical vocabulary.
Similarly, we will need to learn about the mechanics and characteristics of this new AI technology. A lot of it sounds like bullshit right now, but over time it will become more tangible and less obscure.
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