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Readers! A November fund-raising drive!

 

It is unfortunately time for another November fund-raising campaign to support my work here at Behind the Black. I really dislike doing these, but 2025 is so far turning out to be a very poor year for donations and subscriptions, the worst since 2020. I very much need your support for this webpage to survive.

 

And I think I provide real value. Fifteen years ago I said SLS was garbage and should be cancelled. Almost a decade ago I said Orion was a lie and a bad idea. As early as 1998, long before almost anyone else, I predicted in my first book, Genesis: The Story of Apollo 8, that private enterprise and freedom would conquer the solar system, not government. Very early in the COVID panic and continuing throughout I noted that every policy put forth by the government (masks, social distancing, lockdowns, jab mandates) was wrong, misguided, and did more harm than good. In planetary science, while everyone else in the media still thinks Mars has no water, I have been reporting the real results from the orbiters now for more than five years, that Mars is in fact a planet largely covered with ice.

 

I could continue with numerous other examples. If you want to know what others will discover a decade hence, read what I write here at Behind the Black. And if you read my most recent book, Conscious Choice, you will find out what is going to happen in space in the next century.

 

 

This last claim might sound like hubris on my part, but I base it on my overall track record.

 

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It’s a sweep!

Google’s AlphaGo computer program today completed a three game sweep of Go professional champion Lee Sedol.

This might be the best quote from the article:

The algorithm seems to be holding back its power. Sometimes it plays moves that lose material because it is seeking simply to maximise its probability of reaching winning positions, rather than — as human players tend to do — maximise territorial gains. Jackson thinks that some of these odd-looking moves may have fooled Lee into underestimating the machine’s skills at the beginning of game 1 — which, I suppose, makes AlphaGo a kind of computerized hustler.

Genesis cover

On Christmas Eve 1968 three Americans became the first humans to visit another world. What they did to celebrate was unexpected and profound, and will be remembered throughout all human history. Genesis: the Story of Apollo 8, Robert Zimmerman's classic history of humanity's first journey to another world, tells that story, and it is now available as both an ebook and an audiobook, both with a foreword by Valerie Anders and a new introduction by Robert Zimmerman.

 

The print edition can be purchased at Amazon or from any other book seller. If you want an autographed copy the price is $60 for the hardback and $45 for the paperback, plus $8 shipping for each. Go here for purchasing details. The ebook is available everywhere for $5.99 (before discount) at amazon, or direct from my ebook publisher, ebookit. If you buy it from ebookit you don't support the big tech companies and the author gets a bigger cut much sooner.


The audiobook is also available at all these vendors, and is also free with a 30-day trial membership to Audible.
 

"Not simply about one mission, [Genesis] is also the history of America's quest for the moon... Zimmerman has done a masterful job of tying disparate events together into a solid account of one of America's greatest human triumphs."--San Antonio Express-News

6 comments

  • Wayne

    “….The algorithm seems to be holding back its power. Sometimes it plays moves that lose material because it is seeking simply to maximise its probability of reaching winning positions, rather than — as human players tend to do — maximise territorial gains…..”

    This sounds rather anthropomorphic to me– “it’s holding back it’s power.”
    Correct me if I’m off-base, does it not “only do what it has been programmed to do?”

    — Or am I missing the point & it’s more into the AI machine-learning realm?
    (I can write a little HTML & troubleshoot my computer, darn well, but not familiar with modern programming much past BASIC & COBOL. That dates me eh!)
    — Am familiar with the basics of Game Theory but not in the computer-applied realm.)

  • Garry

    My interpretation is that the algorithm made decisions that looked much different from what a human would decide. No matter how calculating a human player is, in some cases he lets intuition dictate his decisions. In many realms, this is what makes humans superior to any kind of AI (as it exists now).

    When we reduce the universe to a relatively simple game, however, computational power has advanced to the point where it can defeat even the most skilled human players; moves that “look” wrong can cloud our judgments and make us reject them too early.

    I apologize for not having a link, but I read somewhere that Lee defeated the computer in the 4th game. I look forward to reading about implications in the field of human vs. machine learning.

  • Wayne

    Garry–
    Thanks for expanding on that.
    I understand basic game-theory, just not how or what element’s are used in the program.
    (not familiar with the actual game either– a drawback for me on understanding.)

    Is this designed to learn from repetitive play or is it following strictly deterministic processes or both? (I realize I’m not using these terms, precisely.)

    –I’m currently (trying!) getting a handle on P Vs. NP problems in computation, which is not directly related but I could see where it would be a factor of sorts, “depending.”

    As I understand it– Chess for example, can be “beaten” with simple brute force calculation, but in certain set-ups of the pieces, a program would miss (not “recognize that..) moves-in-the-future not yet made would cause it to lose, where-as any novice chess-player can overtly “see” it taking place ahead of time..

    I don’t have a grasp of the conditional-rules a program would need to be able to calculate complex, multiple “if-then” linear or non-linear solution’s.

    Sorry– totally babbling & not adequate describing what I think I might mean. (HAR)

    Tangent– I’m a big fan of “emergent-order,” where-in seemingly chaotic situations, can expand out to “infinity” but always contract back down to solvable situations and predictable solutions.
    — I’m thinking certain Fractal sets and or Penrose tiling problems & solutions… (but again, my eyes are bigger than my brain on all this.)
    –fascinating stuff– nonetheless!!
    Haven’t spent enough time on modern AI concepts to even form my questions clearly!!
    — currently wrapped up in Leonard Susskind’s fascinating presentation of “Boltzmann & The Arrow of Time” part 1 of 3, as it relates to entropy & time-reversible process’s as they apply or don’t, to Cosmology… too much on my plate! HAR!

    (thanks nonetheless!)

  • It seems that as a species we have developed a new way of looking at the universe. The non-biological program keeps ‘its eyes on the prize’ and sacrifices short-term gain for long-term success. Biological entities (like humans) tend to be more concerned with short-term goals and try to make them work for long-term strategies. I would hope that military colleges take note of this at once both horrifying and fascinating development.

  • Dick Eagleson

    From what I’ve read about the software being used by the supercomputer that beat Mr. Sedol, it is a generic machine-learning AI that is not optimized for the game of Go or even for playing games in general. That would seem to make this result even more significant than Deep Blue’s defeat of Gary Kasparov at chess nearly 20 years ago. Deep Blue’s software was entirely chess-centric and, if I recall correctly, even the hardware was not general-purpose. I believe each computing node included a hardware chess move generator.

  • Wayne

    Dick:
    Thank you! (I can actually follow that…)
    Now, I am more impressed!

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