5 ms·

The idea is the average person would, sure. A mathematically oriented person would fair far better. Throw all the math problems you want at a LLM for training;

• ben_w 2y ago
  > it will still fail if you step outside of the familiar. To which I say: ᛋᛟ᛬ᛞᛟ᛬ᚻᚢᛗᚪᚾᛋ
  • trashtester 2y ago
    ᛒᚢᛏ ᚻᚢᛗᚪᚾ ᚻᚢᛒᚱᛁᛋ ᛈᚱᛖᚹᛖᚾᛏ ᚦᛖᛗ ᚠᚱᛟᛗ ᚱᛖᚪᛚᛁᛉᛁᚾᚷ ᚦᚻᚪᛏ
    • ben_w 2y ago
      ᛁᚾᛞᛖᛖᛞ᛬ᛁᛏ᛬ᛁᛋ᛬ᚻᚢᛒᚱᛁᛋ ᛁ᛬ᚻᚪᚹᛖ᛬ᛟᚠᛏᛖᚾ᛬ᛋᛖᛖᚾ᛬ᛁᚾ᛬ᛞᛁᛋᚲᚢᛋᛋᛁᛟᚾᛋ᛬ᛋᚢᚲ᛬ᚪᛋ᛬ᚦᛁᛋ᛬ᚲᛚᚪᛁᛗᛋ᛬ᚦᚪᛏ᛬ᚻᚢᛗᚪᚾ᛬ᛗᛁᚾᛞᛋ᛬ᚲᚪᚾ᛬ᛞᛟ᛬ᛁᛗᛈᛟᛋᛋᛁᛒᛚᛖ᛬ᚦᛁᛝᛋ᛬ᛋᚢᚲ᛬ᚪᛋ᛬ᚷᛖᚾᛖᚱᚪᛚᛚᚣ᛬ᛋᛟᛚᚹᛖ᛬ᚦᛖ᛬ᚻᚪᛚᛏᛁᛝ᛬ᛈᚱᛟᛒᛚᛖᛗ edit: Snap, you said the same in your other comment :)
      • trashtester 2y ago
        Switching back to latin letters... It seems to me that the idea of the Universal Turing Machine is quite misleading for a lot of people, such as David Deutsch. My impression is that the amount of compute to solve most problems that can really only be solved by Turing Machines is always going to remain inaccessible (unless they're trivally small). But at the same time, the universe seems to obey a principle of locality (as long as we only consider the Quantum Wave Function, and don't postulate that it collapses). Also, the quantum fields are subject to some simple (relative to LLMs) geometric symmetries, such as invariance under the U(1)xSU(2)xSU(3) group. As it turns out, similar group symmetries can be found in all sorts of places in the real world. Also it seems to me that at some level, both ANN's and biological brains set up a similar system to this physical reality, which may explain why brains develop this way and why both kinds are so good at simulating at least some aspects of the physical world, such as translation, rotation, some types of deformation, gravity, sound, light etc. And when biological brains that initially developed to predict the physical world is then use to create language, that language is bound to use the same type of machinere. And this may be why LLM's do language so well with a similar architecture.
        • vidarh 2y ago
          There are no problems that can be solved only by Turing Machines as any Turing complete system can simulate any other Turing complete system. The point of UTM's is not to ever use them, but that they're a shortcut to demonstrating Turing completeness because of their simplicity. Once you've proven Turing completeness, you've proven that your system can compute all Turing computable functions and simulate any other Turing complete system, and we don't know of any computable functions outside this set.
          • trashtester 2y ago
            When I wrote Turing Machine, I meant it as shorthand for Turing complete system. My point is that any such system is extremely limited due to how slow they become at scale (when running algorithms/programs that require full turing completeness), due to it's "single threaded" nature. Such algorithms simply are not very parallelizable. This means a Turing Complete system becomes nearly useless for things like AI. The same is the case inside a human brain, where signals can only travel at around the speed of sound. Tensor / neuron based systems sacrifice Turing Completeness to gain (many) orders of magnitude more compute speed. I know that a GPU's CAN in principle emulate a Turing Complete system, but they're even slower at it than CPU's, so that's irrelevant. The same goes for human brains. People like Deutsch are so in love with the theoretical universality of Turing Completeness that he seems to ignore that Turing Complete system might take longer to formulate meaningful thought than the lifetime of a human. And possibly longer than the lifetime of the Sun, for complex ideas. The fact that so much can be done by systems that are NOT Turing Complete may seem strange. But I argue that since the laws of Physics are local (with laws described by tensors), it should not be such a surprise that systems that computers that perform tensor computations are pretty good at simulating physical reality.
            • vidarh 2y ago
              > My point is that any such system is extremely limited due to how slow they become at scale (when running algorithms/programs that require full turing completeness), due to it's "single threaded" nature. Such algorithms simply are not very parallelizable. My point is that this isn't true. Every computer you've ever used is a Turing complete system, and there is no need for such a system to be single-threaded, as a multi-threaded system can simulate a single-threaded system and vice versa. > I know that a GPU's CAN in principle emulate a Turing Complete system, but they're even slower at it than CPU's, so that's irrelevant. The same goes for human brains. Any system that can emulate any other Turing complete system is Turing complete, so they are Turing complete. You seem to confuse Turing completeness with a specific way of executing something. Turing completeness is about the theoretical limits on which set of functions a system can execute, not how they execute them.