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Cake day: December 11th, 2024

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  • I think we generally agree with each other. The existence of an omniscient AI or deity doesn’t change the “experience” of free will. It doesn’t “invalidate choice” from the point of view of the observed. It does “invalidate choice” from the point of view of the observer, who can now say “This thing exhibits no unpredictable behavior to me”. You and I both think we have free will, because we can’t predict our own behavior. Our experience is unchanged, whether or not some other observer exists or could exist that could predict our behavior.

    Agreeing on a frame of reference is exactly my point. “Does something have free will?” requires the follow-up question, “According to whom?”. Just like “I’m far from that rock” requires the followup question, “According to whom?”. The ant might think you’re far from the rock, something else might think you’re near the rock.

    To boil it down a bit more, my point is just that you can always replace the phrase “free will” in speech with “unpredictable behavior” without loss of meaning, because that is what people actually mean when they say it, whether they realize that or not.




  • Free will is incompatible with omniscience. People really want it to work, but it doesn’t.

    Free will is observer-dependent, and is short for “I can’t predict the behavior of this thing”. For an omniscient observer, there is no thing that it can say that about.

    Free will is not an inherent property of a thing, and that’s what trips people up so much.

    To ponder it a bit, does a rock have free will? A dog? A human? A super-intelligent AI that we can’t hope to comprehend? Why or why not for each step?

    The definition above explains it all. Of course a rock doesn’t, we can predict its behavior with physics! Maybe a monkey does, people disagree on that. Of course human do though, because I do!

    Now ponder what the super-intelligent AI would think. “Of course the first three don’t have free will, their behavior is entirely predictable with physics”






  • I don’t think it’ll be LLMs (which is what a lot of people jump to when you mention “AI”), they have much higher latencies than microseconds. It will be AI of some sort, but probably won’t be considered AI due to the AI effect:

    The AI effect is the discounting of the behavior of an artificial intelligence program as not “real” intelligence.

    The author Pamela McCorduck writes: “It’s part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, ‘that’s not thinking’.”

    Researcher Rodney Brooks stated: “Every time we figure out a piece of it, it stops being magical; we say, ‘Oh, that’s just a computation.’”

    LLMs might be useful for researchers diving down a particular research/experiment rabbit hole.


  • I don’t have any useful speculation to contribute, but here’s a classic chart showing various funding levels towards that goal:

    Coming from a slashdot thread from 2012 where some fusion researchers did an AMA type thing:

    https://hardware.slashdot.org/story/12/04/11/0435231/mit-fusion-researchers-answer-your-questions

    Here’s also a recent HN thread about achieving more energy than we put in:

    https://news.ycombinator.com/item?id=33971377

    The crucial bit is this

    Their total power draw from the grid was 300 megajoules and they got back about 3 megajoules, so don’t start celebrating yet

    The critical ELI5 message that should have been presented is that they used a laser to create some tiny amount of fusion. But we have been able to do that for a while now. The important thing is that they were then able to use the heat and pressure of the laser generated fusion to create even more fusion. A tiny amount of fusion creates even more fusion, a positive feedback loop. The secondary fusion is still small, but it is more than the tiny amount of laser generated fusion. The gain is greater than one. That’s the important message. And for the future, the important takeaway is that the next step is to take the tiny amount of laser fusion to create a small amount of fusion, and that small amount of fusion to create a medium amount of fusion. And eventually scale it up enough that you have a large amount of fusion, but controlled, and not a gigantic amount of fusion that you have in thermonuclear weapons, or the ginormous fusion of the sun.

    So it’s still really encouraging, but just a warning that headlines don’t capture the full picture. Bonus fun fact from that thread:

    Theoretical models of the Sun’s interior indicate a maximum power density, or energy production, of approximately 276.5 watts per cubic metre at the center of the core, which is about the same power density inside a compost pile.


  • The Bitter Lesson talks about speech recognition instead of synthesis, but I would guess that it’s a similar dynamic:

    In speech recognition, there was an early competition, sponsored by DARPA, in the 1970s. Entrants included a host of special methods that took advantage of human knowledge—knowledge of words, of phonemes, of the human vocal tract, etc. On the other side were newer methods that were more statistical in nature and did much more computation, based on hidden Markov models (HMMs). Again, the statistical methods won out over the human-knowledge-based methods. This led to a major change in all of natural language processing, gradually over decades, where statistics and computation came to dominate the field. The recent rise of deep learning in speech recognition is the most recent step in this consistent direction. Deep learning methods rely even less on human knowledge, and use even more computation, together with learning on huge training sets, to produce dramatically better speech recognition systems. As in the games, researchers always tried to make systems that worked the way the researchers thought their own minds worked—they tried to put that knowledge in their systems—but it proved ultimately counterproductive, and a colossal waste of researcher’s time, when, through Moore’s law, massive computation became available and a means was found to put it to good use.

    Also posted over in !discuss@discuss.online here, since I was reminded of the essay