Evolution is not time reversible so it can't run on a quantum computer. Quantum processes can also produce uncomputable sequences since they can produce infinite random bits.
But that aside, let's say you have a simple quantum learner that can predict any quantum computable sequence, which is any sequence that can be produced on a Turing machine with a source of random bits (because a quantum computer can be otherwise simulated classically). Then I can still produce a simple sequence that you can't predict any better than random guessing. I run a copy of your quantum predictor, which produced an output from the same distribution, and return a different symbol. Either the distribution is uniform and you are guessing, or it's not uniform and you will do worse than guessing. On Fri, Aug 9, 2019, 9:26 PM Ben Goertzel <[email protected]> wrote: > What if my program was created by quantum evolutionary learning, and > carries out its predictions while running in an uncollapsed quantum > state, coupled with the classical system reading-out its predictions > in a way that doesn't collapse its internal memory states... > > Then I can set it up so you can't measure what algorithm my program is > running *without collapsing the state, which I could notice* -- and > even if you emulated my process of quantum evolutionary learning, you > couldn't tell what random program it had produced for me. > > So your approach doesn't work for quantum computers... but our > physical universe is a quantum system... > > -- Ben > > On Sat, Aug 10, 2019 at 9:08 AM Matt Mahoney <[email protected]> > wrote: > > > > Suppose you have a simple learner that can predict any computable > sequence of symbols with some probability at least as good as random > guessing. Then I can create a simple sequence that your predictor will get > wrong 100% of the time. My program runs a copy of your program and outputs > something different from your guess. > > > > All the empirical evidence supports this. Good compressors have a lot of > code to handle lots of special cases. > > > > On Fri, Aug 9, 2019, 8:15 PM Ben Goertzel <[email protected]> wrote: > >> > >> > >> > >>> > >>> > >>> Legg proved there is no such thing as a simple, universal learner. So > we can stop looking for one. > >> > >> > >> > >> To be clear, these algorithmic information theory results don't show > there is no such thing as a simple learner that is universal in our > physical universe... > >> > >> I'm not saying there necessarily is one, just pointing out that the > math is not so practically applicable as your statement implies... > > Artificial General Intelligence List / AGI / see discussions + > participants + delivery options Permalink > > -- > Ben Goertzel, PhD > http://goertzel.org > > “The only people for me are the mad ones, the ones who are mad to > live, mad to talk, mad to be saved, desirous of everything at the same > time, the ones who never yawn or say a commonplace thing, but burn, > burn, burn like fabulous yellow roman candles exploding like spiders > across the stars.” -- Jack Kerouac ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T1ff21f8b11c8c9ae-M084f06b7c82701d7a607c151 Delivery options: https://agi.topicbox.com/groups/agi/subscription
