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Bob Marks on what happens when people try to write creative computer programs

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From an interview with World Radio:

It’s actually the computer programmer, which is supplying that creativity.

So that’s where any creativity comes from—any smarter program. Somehow I don’t believe that it will happen.

I also know that people who looked at writing smarter programs using genetic algorithms and evolutionary programming have abandoned their search in large because they’ve tried a bunch of different things and nothing seems to work. They can’t get smarter programs that way.

But I also know people that are very excited about trying other ways. I don’t think they’re going to work, though.

Can we write creative computer programs?” at Mind Matters News


It seems that the programmer would have to make the computer smarter than he is, which means smarter than itself. That’s a challenge.

Computer engineer Robert J. Marks is one of the authors of Introduction to Evolutionary Informatics. He will also be addressing these topics at the COSM technology summit, October 23–25, 2019, in Bellevue, Washington.

See also: Some of Marks’s takes on recent AI news items:

Random thoughts on recent AI headlines: Google gives away “free” cookies… Also, why AI can’t predict the stock market or deal with windblown plastic bags

Random thoughts on recent AI headlines (March 18, 2019): There is usually a story under those layers of hype but not always the one you thought

and

Top Ten AI hypes of 2018: More help, less hype, please!

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6 Replies to “Bob Marks on what happens when people try to write creative computer programs

  1. 1
    Axel says:

    Well, you know, KF, I was only musing this morning : given the very nature of randomness, how could we have failed to see that the universe in all its wonderful variety and majesty was just as it should have been.

    I’m sure that to every self-respecting atheist, it is all too obvious that randomness has a built-in ‘fine-tuning’ faculty’, and it could not have created our universe, from the simplest virus to the trillions of galaxies, from sunsets to humming-birds, other than it is !

    Don’t you all feel foolish, now, how we all missed the obvious – and that, staring at us in plain sight – except the oh so wily A/Mats ! Just don’t try and teach it to a child. It is far too sophisticated for it to be able to grasp.

  2. 2
    Axel says:

    They would find it as difficult to wrestle with as Henry Ford’s famous concession : ‘You can have your car in whatever colour you choose – as long as it’s black…’

  3. 3
    ET says:

    Don’t forget they need a hardware platform capable of carrying out their programs…

  4. 4
    kairosfocus says:

    News, Marks’ money shot remark:

    people who looked at writing smarter programs using genetic algorithms and evolutionary programming have abandoned their search in large because they’ve tried a bunch of different things and nothing seems to work. They can’t get smarter programs that way.

    KF

  5. 5
    kairosfocus says:

    Axel, the forcing superlaws to do such would be extraordinarily fine tuned. KF

  6. 6
    bornagain77 says:

    “There exists no (computer) model successfully describing undirected Darwinian evolution. Period.”

    As to the failure of genetic algorithms and evolutionary programming in particular, here is a bit more detail from another article from Dr. Marks

    Top Ten Questions and Objections to ‘Introduction to Evolutionary Informatics’ – Robert J. Marks II – June 12, 2017
    Excerpt: There exists no (computer) model successfully describing undirected Darwinian evolution. Period. By “model,” we mean definitive simulations or foundational mathematics required of a hard science.,,,
    We show that no meaningful information can arise from an evolutionary process unless that process is guided. Even when guided, the degree of evolution’s accomplishment is limited by the expertise of the guiding information source — a limit we call Basener’s ceiling. An evolutionary program whose goal is to master chess will never evolve further and offer investment advice.,,,
    There exists no model successfully describing undirected Darwinian evolution. Hard sciences are built on foundations of mathematics or definitive simulations. Examples include electromagnetics, Newtonian mechanics, geophysics, relativity, thermodynamics, quantum mechanics, optics, and many areas in biology. Those hoping to establish Darwinian evolution as a hard science with a model have either failed or inadvertently cheated. These models contain guidance mechanisms to land the airplane squarely on the target runway despite stochastic wind gusts. Not only can the guiding assistance be specifically identified in each proposed evolution model, its contribution to the success can be measured, in bits, as active information.,,,
    Models of Darwinian evolution, Avida and EV included, are searches with a fixed goal. For EV, the goal is finding specified nucleotide binding sites. Avida’s goal is to generate an EQU logic function. Other evolution models that we examine in Introduction to Evolutionary Informatics likewise seek a prespecified goal.,,,
    The most celebrated attempt of an evolution model without a goal of which we’re aware is TIERRA. In an attempt to recreate something like the Cambrian explosion on a computer, the programmer created what was thought to be an information-rich environment where digital organisms would flourish and evolve. According to TIERRA’s ingenious creator, Thomas Ray, the project failed and was abandoned. There has to date been no success in open-ended evolution in the field of artificial life.5,,,
    We show that the probability resources of the universe and even string theory’s hypothetical multiverse are insufficient to explain the specified complexity surrounding us.,,,
    If a successful search requires equaling or exceeding some degree of active information, what is the chance of finding any search with as good or better performance? We call this a search-for-the-search. In Introduction to Evolutionary Informatics, we show that the search-for-the-search is exponentially more difficult than the search itself!,,,
    ,,,we use information theory to measure meaningful information and show there exists no model successfully describing undirected Darwinian evolution.,,,
    ,,, if the fitness continues to change, it is argued, the evolved entity can achieve greater and greater specified complexity,,,
    ,,, We,, dub the overall search structure ‘stair step active information’. Not only is guidance required on each stair, but the next step must be carefully chosen to guide the process to the higher fitness landscape and therefore ever increasing complexity.,,,
    Such fine tuning is the case of any fortuitous shift in fitness landscapes and increases, not decreases, the difficulty of evolution of ever-increasing specified complexity. It supports the case there exists no model successfully describing undirected Darwinian evolution.,,,
    Turing’s landmark work has allowed researchers, most notably Roger Penrose,26 to make the case that certain of man’s attributes including creativity and understanding are beyond the capability of the computer.,,,
    ,,, there exists no model successfully describing undirected Darwinian evolution. According to our current understanding, there never will be.,,,
    https://evolutionnews.org/2017/06/top-ten-questions-and-objections-to-introduction-to-evolutionary-informatics/

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