Connectionists: Brain-Mind Magazine Vol. 1, No. 1, June 2012

[Juyang Weng]

      Brain-Mind Magazine
      Vol. 1, No. 1, June 2012
      http://www.brain-mind-magazine.org/


      Table of Contents

    Start a Different Kind of Magazine
    <http://www.brain-mind-magazine.org/download-article.php?file=V1-N1-BMMsubm.pdf>
                  1
    by /Juyang Weng /

    Brain-Mind Institute: For Future Leaders of Brain-Mind Research
    <http://www.brain-mind-magazine.org/download-article.php?file=V1-N1-BMI-Flyer-3-A4Size.pdf>
                  2

    Open Letter to the US President Obama: Is the US Foreign Policy
    Scientifically Shortsighted?
    <http://www.brain-mind-magazine.org/download-article.php?file=V1-N1-Obama.pdf>banner             3
    - 4
    by /Juyang Weng /
    *Abstract: *All minds are groupish and shortsighted in nature. The
    aftermath of Richard Nixon's China visit demonstrated that a
    scientifically correct foreign policy is to make friends with foes,
    counter intuitive to many souls. Our brains blinded us. Scientific
    principles, e.g., checks-and-balances of government power, seem more
    convincing and effective in converting foes than shallow and
    ideological slogans like "human rights".
    *Index terms: *brain, science of mind, foreign policy

    Brain Stories 1: Naivety in Everybody
    <http://www.brain-mind-magazine.org/download-article.php?file=V1-N1-BrainStories1.pdf>banner             5
    - 6
    by /Brian N. Huang /
    *Abstract: *Meant for layman readers, this series uses real world
    stories to explain how a single brain works computationally inside
    its skull and how multiple brains work together to give rise to
    group intelligence. Hopefully, this series is useful for us humans
    to see the weakness of our current governing systems, in developed
    countries and developing countries alike, and how a country can
    develop earlier and better. It also explains some key mechanisms to
    make a robot learn skills that its human programmer does not have.
    This installment is about naivety, in childhood and adulthood; in
    you and in your officials. Brains are naive for various tasks,
    making strategic errors with high costs. This installment raises a
    few nation-scale naiveties to be discussed in future installments of
    this series.
    *Index terms: *brain, mind, law, group intelligence

    Problem Solving to Problem Posing
    <http://www.brain-mind-magazine.org/download-article.php?file=V1-N1-ProblemPosing.pdf>banner             7
    - 8
    by /Yoonsuck Choe /and/Timothy A. Mann /
    *Abstract: *Artificial intelligence and machine learning approaches
    are both very good at problem solving. However, the various methods
    accumulated in these fields have not been able to give us truly
    autonomous agents. The main shortcoming is that the problems
    themselves are formulated by human designers and subsequently fed to
    the problem solving or learning algorithms. The algorithms do not
    question the validity of the problems nor do they formulate new
    problems. This latter task is called "problem posing", and is in
    fact an active area in education research. In this article, we will
    discuss the importance and relevance of problem posing to autonomous
    intelligence and speculate on key ingredients for effective problem
    posing in an AI and machine learning context.
    *Index terms: *problem posing, education, artificial intelligence

    Why Should the CVPR Community See that Output Is Not Only Output?
    <http://www.brain-mind-magazine.org/download-article.php?file=V1-N1-VisionSee.pdf>banner             9
    - 10
    by /Christopher S. Masfis /
    *Abstract: *The currently prevailing methods in the computer vision
    and pattern recognition (CVPR) community require images for system
    training. Many of such methods require manually drawn object
    contours to segment the pixels from each object of interest from
    those pixels in other parts of the image. Does a human child require
    such object-contour annotation to learn how to detect, recognize,
    and segment objects from cluttered natural scenes? Weng argued for a
    negative answer. He explained that a brain autonomously learns
    directly from its physical environment using not only non-annotated
    continuous video of dynamic and cluttered scenes, but also its
    video-synchronized actions --- output is also input.
    Body-environment interactions give rise to brain representations. In
    particular, contour annotation is not necessary for a brain, neither
    for a machine.
    *Index terms: *pattern recognition, computer vision, brain


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