Loading ...
Sorry, an error occurred while loading the content.

Re: the quest for om.

Expand Messages
  • bobdeloyd
    ... like ... question ... Dear young_and_benevolent: Yes theres that word reality again.. Are we talking about intelligence or game theory? //bob
    Message 1 of 14 , Jul 3 1:03 AM
    • 0 Attachment
      --- In artificialintelligencegroup@y..., young_and_benevolent
      <no_reply@y...> wrote:
      > bobdeloyd wrote:
      > > Once we finally get a "real" artificial intelligence
      > > the rest is evolution....
      >
      > Perhaps all we have to do is mimic something relatively simple,
      like
      > a flatworm, and then set it off on a virtual evolution. The
      question
      > is: How do we make the virtual reality that our AI lives in and
      > evolves through?
      >
      > y&b

      Dear young_and_benevolent:
      Yes theres that word reality again.. Are we talking about
      intelligence or game theory? //bob
    • young_and_benevolent
      ... An artificial intelligence would have to exist in some form of environment for it to enact and react within. This is the reality that the AI is aware
      Message 2 of 14 , Jul 4 3:58 AM
      • 0 Attachment
        young_and_benevolent wrote:
        > How do we make the virtual reality that our
        > AI lives in and evolves through?

        bobdeloyd wrote:
        > Dear young_and_benevolent:
        > Yes theres that word reality again.. Are we talking about
        > intelligence or game theory? //bob

        An artificial intelligence would have to exist in some form of
        environment for it to enact and react within. This is the "reality"
        that the AI is "aware" of. That space could be as simple as a command
        line interface, or as complex as a three dimensional interactive
        environment. (Probably the former, though.)

        So when I say "virtual reality" I mean the environment that the AI is
        aware of. I am using the word "aware" because we strive to build a
        cognitive and sentient intelligence, in the long run.

        y&b
      • young_and_benevolent
        ... Actually, film works at 24 frames per second, and TV at 25/sec. Your eyes, however, work at about 1000 fps. Your brain is cognitive of these pictures in a
        Message 3 of 14 , Jul 4 4:20 AM
        • 0 Attachment
          "red_cell_op" <RED_CELLss@H...> wrote:
          > The human eye takes pictures at approximately 25shots/second.

          Actually, film works at 24 frames per second, and TV at 25/sec. Your
          eyes, however, work at about 1000 fps. Your brain is cognitive of
          these pictures in a vastly different way, so that a "frames per
          second" rating becomes meaningless.

          > On _depth perception_ ...

          Maybe the lasers that land surveyors use to judge distance would be
          easier and more accurate than running an algorithm on an image.

          > If a robot is ordered to find an exit out of a room;
          > and the robot circles about looking for a door or
          > opening and happens to find none--the room is walled
          > completely. How does the robot improvise on its own?
          > E.g. Break down a wall.

          A good question. I guess that it would either be programmed to
          destroy objects in it's way, or it would have to learn to do that by
          itself. How, though, I'm not sure!


          y&b
        • red_cell_op
          http://64.4.22.250/cgi-bin/linkrd? _lang=EN&lah=891ef64a2e5597e1b61127d6d401df26&lat=1025809872&hm___acti on=http%3a%2f%2fcgi%2ezdnet%2ecom%2fslink%3f182325
          Message 4 of 14 , Jul 4 12:57 PM
          • 0 Attachment
            http://64.4.22.250/cgi-bin/linkrd?
            _lang=EN&lah=891ef64a2e5597e1b61127d6d401df26&lat=1025809872&hm___acti
            on=http%3a%2f%2fcgi%2ezdnet%2ecom%2fslink%3f182325
          • red_cell_op
            Here s a quote from Robert O Shea s webpage, at: http://psy.otago.ac.nz/r_oshea/ I have so many interests in human visual perception it makes my eyes blur.
            Message 5 of 14 , Jul 4 1:11 PM
            • 0 Attachment
              Here's a quote from Robert O'Shea's webpage, at:
              http://psy.otago.ac.nz/r_oshea/

              I have so many interests in human visual perception it makes my eyes
              blur. Currently I'm working on projects on: Binocular rivalry
              (including
              spread of rivalry, the nature of rivalry suppression, and rivalry in
              split-brain observers); Early history of binocular vision; Interocular
              transfer of aftereffects; Meteorological optics (including why we
              perceive the bowl of the sky, perception of sun rays and their linear
              perspective, and effects of height on perceived eye level); Size and
              depth perception over large distances; Spatial frequency, blur,
              contrast, and luminance as depth cues; Colour constancy with
              reflected and emitted light; Kinetic depth effect; Perception of
              contrast and blur in the peripheral visual field; Colour spreading in
              the McCollough effect; and Vernier acuity with opposite-contrast and
              dichoptic figures.

              At the same page, you can also download the PDF of an article
              describing
              some of the work covered by the talk:

              O'Shea, R. P., & Corballis, P. M. (2001).
              Binocular rivalry between complex stimuli in split-brain observers.
              Brain and Mind, 2, 151-160.

              You can check out the webpage of O'Shea's collaborator, Paul
              Corballis,
              here:
              http://www.dartmouth.edu/~cogneuro/corballis.html

              Raj
              ---------- Forwarded message ----------
              Date: Tue, 02 Jul 2002 16:01:14 -0400
              From: George Alvarez <geoalvarez@w...>
              To: VisionLabTalks <geoalvarez@w...>
              Subject: Harvard Vision Lab Talk Wednesday: Robert P. O'Shea,
              Wednesday,
              July 3rd

              *************************************************************

              Harvard Vision Lab Seminar Series Announcement

              *************************************************************

              Binocular rivalry in split-brain observers

              Robert P. O'Shea & Paul M. Corballis
              Department of Psychology, University of Otago

              Wednesday, July 3rd
              12:00 noon
              Rm 765, William James Hall, Harvard University
              33 Kirkland Street, Cambridge

              A split-brain observer has had the corpus callosum, the major tract
              between the left and the right hemispheres, cut to relieve epilepsy.
              One can selectively stimulate the left or right hemisphere by
              presenting stimuli to the right or left of fixation respectively.
              Likewise, one can elicit responses from the left or right hemisphere
              by requiring the observer to press keys with the right or left hand
              respectively. On many tasks, these fascinating individuals behave as
              though each hemisphere is acting independently of the other. Are there
              differences in rivalry between the isolated hemispheres? To answer
              this, we have studied two split-brain observers, VP and JW.

              We first trained split-brain and intact-brain observers to respond to
              real alternations between nonrival stimuli by pressing keys with the
              ipsilateral hand. When we presented rival stimuli to the isolated
              hemispheres of split-brain observers, their key presses showed that
              their experiences of rivalry were similar to those of intact-brain
              observers. When we presented stimuli to the left hemisphere of the
              split-brain observers, they were also able to describe the chaotic
              appearance of rivalry alternations.

              Over many experiments, mainly on JW, we conclude that rivalry is
              essentially normal when processed in each isolated hemisphere,
              although periods of dominance are slower from the left hemisphere than
              from the right. Rivalry is normal from stimuli such as sinusoidal
              gratings, coloured faces, random dots, and Diaz-Caneja displays. The
              distributions of periods of dominance follow the classical gamma
              shape. The only case in which lacking a corpus callosum made a
              difference was that the synchronization of rivalry in two regions of
              the visual field did not happen when the two regions were processed by
              different hemispheres. We think that the longer rivalry periods from
              the left hemisphere reflect only its response bias. We conclude from
              the qualitative similarity of rivalry in the two isolated hemispheres
              that the rivalry mechanism is low in the visual system.

              Wednesday, July 3rd
              12:00 noon
              Rm 765, William James Hall, Harvard University
              33 Kirkland Street, Cambridge
            • red_cell_op
              http://64.4.22.250/cgi-bin/linkrd? _lang=EN&lah=74d76f6202fc28863821a712ea0cd4a7&lat=1025813685&hm___acti on=http%3a%2f%2fcgi%2ezdnet%2ecom%2fslink%3f182325
              Message 6 of 14 , Jul 4 1:16 PM
              • 0 Attachment
                http://64.4.22.250/cgi-bin/linkrd?
                _lang=EN&lah=74d76f6202fc28863821a712ea0cd4a7&lat=1025813685&hm___acti
                on=http%3a%2f%2fcgi%2ezdnet%2ecom%2fslink%3f182325
              • red_cell_op
                Young: Actually, film works at 24 frames per second, and TV at 25/sec. Your eyes, however, work at about 1000 fps. Your brain is cognitive of these
                Message 7 of 14 , Jul 4 2:36 PM
                • 0 Attachment
                  Young:> Actually, film works at 24 frames per second, and TV at
                  25/sec. Your > eyes, however, work at about 1000 fps. Your brain is
                  cognitive of > these pictures in a vastly different way, so that
                  a "frames per> second" rating becomes meaningless.
                  >

                  Borgia: I will re-check my notes about 25/sec shots(of the human
                  eye)....but this comparison of shots/second is moot--that is only
                  tangent to the point i was trying to make, which is: isolating each
                  frames and executing algorithms on them in _real time_. I have
                  forgotten about the name of a super-fast cameras that can take
                  thousands of shots/second--much faster, and with better resolution
                  than the human eye. The rate is not all that important as much as the
                  computing power that will process the _stills_ in real time. On how
                  the brain process these visual data, is irrelevant in my view--we
                  don't have to simulate the brain. Just let us do something that
                  works, brain-imitation or no brain-imitation, regardless.

                  > > On _depth perception_ ...
                  >
                  > Maybe the lasers that land surveyors use to judge distance would be
                  > easier and more accurate than running an algorithm on an image.

                  Borgia: The _depth perception_ part of the post _the search for om_
                  has nothing to do with "running algorithm on an image". I do not
                  recall typing "running algorithm on an image to determine depth
                  perception", what i recalled doing was proposing two approaches from
                  physics for depth perception. Thanks for the thought though.

                  > > If a robot is ordered to find an exit out of a room;
                  > > and the robot circles about looking for a door or
                  > > opening and happens to find none--the room is walled
                  > > completely. How does the robot improvise on its own?
                  > > E.g. Break down a wall.
                  >
                  YOung:> A good question. I guess that it would either be programmed
                  to > destroy objects in it's way, or it would have to learn to do
                  that by > itself. How, though, I'm not sure!


                  Borgia:From my own personal experience i think creativity =
                  integration. You see an apple falling down and then integrate that
                  visual data with other data to get Newton's gravitation. Think about
                  how we CREATIVELY solve problems, it seems to be one and only one
                  way: integrating relevant but seemily disparate data into a new
                  synthesis. Can a robot on seeing a woman slicing an apple on a street
                  corner break down this visual input into some version of this crude
                  formalization:"sharp object(of certain characteristics y) + force +
                  an object(of certain characteristics x))--> a split x + object y"??.
                  Then, how can one write algorithms that will attempt to match this
                  _solution pattern_ with a _problem pattern_(e.g. getting out of a
                  walled room)?. To find a solution to something, first, the problem
                  has to be defined. Is an algorithm capable of partially formalizing
                  environmental events, feasible?
                  After the problem of the walled room has been formalized:
                  (a)Get from point_A_(inside the walled enclosure) to point _B_(out of
                  the walled enclosure)
                  (b)How? Tranverse a sectional area without walls
                  (c)There is no such area.
                  [The problem has been determined: no such area. Any "~x" that
                  fustrates acquiring an objective "z":is a problem.

                  Can the robot then formalize this scenario into this format:
                  X(passage) + Y(robot; moving) = Z(objective--get from A to B)?

                  Since the room is walled, then there is no X, only ~X. "~X"
                  being "walls".

                  Can the robot then proceed to this stage: ~X + Y = ~Z.--this will now
                  be the _state of events_ in the robots' cpu, There are logically two
                  options: Opt for ~Z--and not leave the room(that will be against its
                  instruction, thus the robot can't do that) Or two, eliminate ~X--this
                  fits with its instructions.
                  How do you eliminate ~X?
                  Scan ~X(the walls), from the scanning the robot will gather some
                  scientific data from its scans--physical and chemical properties of
                  ~X.
                  Then, First priority:(i)how do you eliminate walls or things bearing
                  close resemblance to the physical/chemical properties of walls as
                  determined by the robot's scans?
                  Second priority:(ii)How don you eliminate any object?

                  Inorder to answer these questions by itself(the robot), algorithms
                  then prompt the robot to conduct memory search for visual data
                  involving scenes of any form of separation involving physical
                  objects?--cutting, slicing, dicing, twisting, cracking, burning, ,
                  chemical dissolution in a degree relevantly close to the
                  physical/chemical objects of walls and digressing from that point
                  away. etcetera. Then, formalize these scenes into a X + Y = Z, partly
                  using _cause and effect_/physics, and attempt to implement the
                  _formalization_ on the walls so as to create a passage way out of a
                  walled room?

                  Does this makes any sense?

                  --Borgia, C.
                • red_cell_op
                  Can a chip help computers see in 3D? 09:07 Wednesday 3rd July 2002 Stephen Shankland, CNET News.com A Silicon Valley start-up believes it can give stereo
                  Message 8 of 14 , Jul 4 2:50 PM
                  • 0 Attachment
                    Can a chip help computers see in 3D?
                    09:07 Wednesday 3rd July 2002
                    Stephen Shankland, CNET News.com


                    A Silicon Valley start-up believes it can give stereo vision to video
                    cameras by encoding a processing scheme into a custom chip. It could
                    ready the way for robots with depth perception
                    A Silicon Valley start-up believes it can improve computer vision by
                    combining a custom-designed chip with the way humans see.

                    Human brains judge how far away objects are by comparing the slightly
                    different view each eye sees. Tyzx hopes to build this stereo vision
                    process into video cameras.



                    The Palo Alto, California-based start-up has encoded a processing
                    scheme into a custom chip called DeepSea, allowing the processor to
                    determine not only the color of each tiny patch of an image but also
                    how far away that patch is from the camera.

                    The technology could be a boon for surveillance systems,
                    strengthening the ability to track people in banks, stores or
                    airports. But stereo vision could have wider uses as well, helping
                    focus a computer's attention and cutting down on the amount of data
                    that needs to be crunched.

                    For instance, a vacuuming robot trying to discern a table leg through
                    pattern recognition could avoid getting caught up in examining the
                    wallpaper in the background. Similarly, vehicles could use the
                    technology to detect obstacles in their path while filtering out
                    visual noise.

                    "The biggest value is the segmentation. It separates out the portion
                    of the image that interests you," said Takeo Kanade, a stereo vision
                    computing pioneer at Carnegie Mellon University and a member of an
                    independent Tyzx advisory board. "You have not only appearance but
                    also distance to each point. That makes the subsequent processing,
                    such as object detection and recognition, significantly easier."

                    Tyzx's first customers are mostly research labs, with other potential
                    business partners evaluating the technology, chief executive Ron Buck
                    said in an interview. Those who have bought the systems include MD
                    Robotics, the company that makes the robotic arm for the Space
                    Shuttle and, in the future, for the International Space Station. And
                    ChevronTexaco is employing the equipment for "augmented reality"
                    work -- supplementing what ordinary people see with computer imagery
                    for tasks such as operating oil platform cranes in bad weather.

                    The company hopes to win customers in the military and surveillance
                    industries, and, as costs go down, to expand into
                    broader "intelligent environments" where, for example, doors could
                    open automatically or a house could send a medical alert if someone
                    has been sitting still for an unusually long time. But Tyzx faces a
                    solid challenge translating the idea into a workable product.

                    "I believe it's a great idea," Kanade said. "Conceptually it's easy,
                    but computationally it's not."

                    Tyzx is backed by Vulcan Ventures, the investment firm of Microsoft
                    co-founder Paul Allen. It has less than 20 employees, some of whom
                    have years of experience in the field.

                    John Woodfill and Gaile Gordon launched the company in early 2001,
                    but much of their work precedes that date. A key formula used in the
                    custom chip dates back to 1990, and Tyzx has had prototype chips for
                    about a year, Buck said. It's only recently, though, that Tyzx's
                    ideas have become economically feasible.

                    Eyes on the prize
                    Stereo vision may indeed be a leap ahead for computers, but there's
                    still a long way to go before machines can achieve the sophistication
                    of human sight.

                    "Because vision comes so naturally to us, we don't appreciate the
                    problem intuitively," said David Touretzky, a computational
                    neuroscientist at Carnegie Mellon. "I don't think we got that
                    appreciation until people started trying to build computer systems to
                    see."

                    A large fraction of the brains of primates such as monkeys, apes and
                    humans is devoted to processing visual information, Touretzky said.
                    There are more than 20 different specialised areas for tasks such as
                    recognizing motion, color, shapes and spatial relationships between
                    objects.

                    "These areas are all interconnected in ways not fully understood
                    yet," Touretzky said, but together these parts of the brain can
                    discern the difference between the edge of a shadow and the edge of
                    an object or compensate for color shifts that occur when the sun
                    comes out.

                    Tyzx isn't the only company trying to capitalize on stereo computer
                    vision. Microsoft Research is working on technology that extracts 3D
                    information from 2D pictures. Point Grey Research already has cameras
                    on the market, though its processing algorithms require a full-
                    fledged computer.

                    In Japan, a company called ViewPlus is working in collaboration with
                    Point Grey Research. Its products, though, combine as many as 60
                    cameras into a spherical system that produces 20 simultaneous video
                    information streams.

                    These other companies are taking a fundamentally different approach
                    to Tyzx in one respect: Their systems compare more than two images.

                    Carnegie Mellon's Kanade said it might seem that comparing three
                    images would be a harder computational task, but in fact having more
                    data to work with can actually make the process simpler.

                    DeepSea processing
                    The key development at Tyzx is its custom chip, which runs an
                    algorithm called census correspondence that quickly finds
                    similarities across two streams of video images broken up into a
                    square grid of 512 pixels, or picture elements. The chip can perform
                    this comparison 125 times per second with a video image measuring 512
                    by 512 pixels, but the 33MHz DeepSea consumes much less power than
                    full-fledged processors such as Intel's Pentium.

                    "It allows incredibly compute-intensive searching for matching pixels
                    to happen very fast at a very low price. It allows us to bring stereo
                    vision to computers," chief executive Buck said.

                    Another important development needed to reach Tyzx's low-price
                    targets is camera sensors built using the comparatively inexpensive
                    complimentary metal-oxide semiconductor (CMOS) technology -- the same
                    process used to build most computer chips, Buck said. Digital cameras
                    today use more elaborate -- but more expensive -- "charge-coupled
                    devices", or CCDs.

                    Kanade has an appreciation for the difficulties involved. About 10
                    years ago he built an expensive but pioneering stereo vision system
                    with many processors that could determine range information by
                    comparing the images from multiple cameras.

                    Since then, more powerful computer processing abilities have elevated
                    the potential of the field, which Kanade believes will take off once
                    stereo cameras are as cheap as today's ordinary video cameras.

                    "I'm very impressed with the various attempts which made real-time
                    stereo possible. I think the Tyzx effort may be one of the eventual
                    successes," Kanade said.



                    ----------------------------------------------------------------------
                    ----------
                  Your message has been successfully submitted and would be delivered to recipients shortly.