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<h3 class="color-in-selected-button" align="center">Brain-Mind
Magazine<br>
Vol. 1, No. 1, June 2012<br>
</h3>
<h3 class="color-in-selected-button" align="center">Table of
Contents</h3>
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<a
href="http://www.brain-mind-magazine.org/download-article.php?file=V1-N1-BMMsubm.pdf">Start
a Different Kind of Magazine</a>
1 <br>
by <em>Juyang Weng </em><br>
<br>
<a
href="http://www.brain-mind-magazine.org/download-article.php?file=V1-N1-BMI-Flyer-3-A4Size.pdf">Brain-Mind
Institute: For Future Leaders of Brain-Mind Research</a>
2 <br>
<br>
<a
href="http://www.brain-mind-magazine.org/download-article.php?file=V1-N1-Obama.pdf">Open
Letter to the US President Obama: Is the US Foreign Policy
Scientifically Shortsighted?</a><img
src="cid:part4.02090504.00080703@cse.msu.edu" alt="banner"
style="float:left;margin:0 5px 0 0;" height="100"
width="100"> 3 - 4<br>
by <em>Juyang Weng </em><br>
<strong>Abstract: </strong>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”. <br>
<strong>Index terms: </strong>brain, science of mind,
foreign policy <br>
<br>
<a
href="http://www.brain-mind-magazine.org/download-article.php?file=V1-N1-BrainStories1.pdf">Brain
Stories 1: Naivety in Everybody</a><img
src="cid:part6.02090703.03000100@cse.msu.edu" alt="banner"
style="float:left;margin:0 5px 0 0;" height="100"
width="100"> 5 - 6<br>
by <em>Brian N. Huang </em><br>
<strong>Abstract: </strong>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. <br>
<strong>Index terms: </strong>brain, mind, law, group
intelligence <br>
<br>
<a
href="http://www.brain-mind-magazine.org/download-article.php?file=V1-N1-ProblemPosing.pdf">Problem
Solving to Problem Posing</a><img
src="cid:part8.02050504.04080305@cse.msu.edu" alt="banner"
style="float:left;margin:0 5px 0 0;" height="100"
width="100"> 7 - 8<br>
by <em>Yoonsuck Choe </em>and<em> Timothy A. Mann </em><br>
<strong>Abstract: </strong>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. <br>
<strong>Index terms: </strong>problem posing, education,
artificial intelligence <br>
<br>
<a
href="http://www.brain-mind-magazine.org/download-article.php?file=V1-N1-VisionSee.pdf">Why
Should the CVPR Community See that Output Is Not Only
Output?</a><img
src="cid:part10.06010501.06040901@cse.msu.edu"
alt="banner" style="float:left;margin:0 5px 0 0;"
height="100" width="100"> 9 - 10<br>
by <em>Christopher S. Masfis </em><br>
<strong>Abstract: </strong>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. <br>
<strong>Index terms: </strong>pattern recognition, computer
vision, brain <br>
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