Hi Lance, I may have seen a weird creepy crawly robot that this guy Rodney
created. Thanks for bringing it up. Yes, it seems to me too: emergent is
the way to go. I need to look into that more, for sure.

On Sat, Feb 26, 2022 at 8:19 PM Lance White <[email protected]> wrote:

> Hi Dave and Mark,
>
> Yes, I do feel that the best path is emergent.  When I first saw Rodney
> Brooks work on Atilla I felt that this showed the path forward!
>
>
> https://books.google.com.au/books?id=EfBdq9DU2FMC&pg=PA16&lpg=PA16&dq=rodney+brooks+attila&source=bl&ots=MVGsW089aH&sig=ACfU3U1NvnOiXWduINViFWsI-gwlb_nQnA&hl=en&sa=X&ved=2ahUKEwj7vt6ygZ_2AhUySmwGHX60C98Q6AF6BAhBEAM#v=onepage&q=rodney%20brooks%20attila&f=false
>
> Goal driven with sensory input giving the feedback.  From there, the trial
> and error produces emergent behaviour.  Of course there is a world of
> opportunity to do that efficiently.  Along with the benefit of compute
> power steadily making it something that could be achieved in less than
> glacial times.
>
> It does seem that being humans we gravitate to the engineering approach to
> be solution focused rather than be the architects of the goal and the
> system.  I guess it's inerrant to those that generally succeed in
> software development.
>
> Regards,
>
> Lnce
>
>
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>
> On Sun, Feb 27, 2022 at 11:15 AM Mark Wigzell <[email protected]>
> wrote:
>
>> Hi Dave, I like the "servos randomly twitching" vision!
>> It does sound like we have a similar view on how general intelligence
>> should start. You have a cradle ready to go. I was trying to get this one
>> in opencog/docker to run, but there is a ways to go. Assuming I can get it
>> going, at least it will have the hookups to the atomspace. Then what? I am
>> lacking any clear vision on how this all can go down. Seems to me,
>> consciousness, like the big bang, the fine tuning problem, and DNA
>> information encoding, is a miracle.
>> Are you planning on adding the atomspace into your "infant AGI" ?
>>
>> On Sat, Feb 26, 2022 at 1:36 PM xanatos xanatos.com <[email protected]>
>> wrote:
>>
>>> Hi Mark,
>>>
>>>
>>>
>>> I am in full agreement with you on genuine AGI.  An architecture needs
>>> to be created that has no "if-thens" as I call them – it has to start from
>>> scratch and build its world model in a way that allows it to record
>>> "successful" interactions with the world (such as receiving food, aka power
>>> recharges), and it needs to start from the "servos just randomly twitching"
>>> phase.
>>>
>>>
>>>
>>> That said, my expertise has always been in tying many different
>>> functions together in python.  I've managed to tie together the visual,
>>> auditory and movement functions on my robots successfully, but definitely
>>> NOT in the "neonatal" AGI framework I would have liked.  I was after
>>> immediate results and short-term successes, which I achieved, but I regret
>>> not having put forth the effort to produce an "infant AGI".
>>>
>>>
>>>
>>> That said, I have created the frameworks for such an AGI to express
>>> within.  The visual acquisition system is robust, with a few hardwired
>>> functions pertaining to recognizing faces and objects; the auditory
>>> processing functions are robust and in some ways do still, despite my
>>> lagging in SO many other areas, excel beyond much else out there (with the
>>> exception perhaps of GPT-3, but my model is about 1% of the size of GPT-3
>>> and works well at very specific parts of identifying what is being
>>> presented to it.)  The auditory output is quite robust, although devoid of
>>> nonverbal utterances, which I have created in code to allow a sort of
>>> analog movement-based expression of unverbalized "statements".  And I have
>>> a rudimentary facial/head/body movement function running, but nothing
>>> anywhere near close to the amazing stuff I'm seeing with Ameca (
>>> https://www.cnn.com/videos/business/2021/12/08/humanoid-robot-ameca-lon-orig-tp.cnn
>>> )
>>>
>>>
>>>
>>> I consider having this framework in place a perfect "cradle" in which to
>>> embed the type of AGI we are both interested in, but I am still myself in
>>> "AGI Infancy".  My goal was to create robots that I could interact with in
>>> a useful and significant manner (for laughs, one of my goals is to have
>>> robots that can stack my annual firewood deliveries lol)
>>>
>>>
>>>
>>> I have other systems functioning in test-bed environments that are
>>> non-human in structure, but with very sophisticated visual systems that are
>>> – apologies – intended to drive intelligent wagons with grippers that will
>>> autonomously pick up pinecones on my property every spring.  We literally
>>> get hundreds of thousands from the pine groves that  line our property lol.
>>>
>>>
>>>
>>> So no, Mark, I don't believe you're being impractical.  What I believe
>>> is being impractical is the trend towards creating AGI as an "instant
>>> adult".  That is what I attempted to do and as a result I have systems that
>>> are very impressive in a very narrow range of functions.  So long as they
>>> are here, on my property, in the environments I have trained them on, on
>>> the tasks I have trained them on, they're pretty cool.  But they'd fail
>>> horribly outside of this environment.  Your vision is what will create
>>> robust AGI.
>>>
>>>
>>>
>>> I just hope we survive as a species long enough to see these goal
>>> realized….
>>>
>>>
>>>
>>> Let me know if any of this is useful to you,
>>>
>>>
>>>
>>> Dave
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> *From:* [email protected] <[email protected]> *On Behalf
>>> Of *Mark Wigzell
>>> *Sent:* Saturday, February 26, 2022 3:24 PM
>>> *To:* opencog <[email protected]>
>>> *Subject:* Re: [opencog-dev] Vision for pi_vision and AGI/atomspace
>>>
>>>
>>>
>>> Hi Dave, if you have the impulse to help out, I'm happy to work with you.
>>>
>>>
>>>
>>> On the subject of this thread, I was actually hoping to hear more about
>>> how low level "AGI" type of perception works. I have no idea. I would be
>>> happy to work with someone who wants to try hooking up the visual input to
>>> something that is or could become "intelligent". I see the issue of true
>>> artificial vision as being one that tries to avoid clever but
>>> non-intelligent algorithms in favour of something more organic. Surely a
>>> vision system must be trained much as a baby is trained. Indeed, surely AGI
>>> must start off like a baby? (everything is hooked up, but there is no
>>> control. movement is wild, emitted sounds are non-sensical, experienced
>>> input blends with the general awareness stemming from all feedback systems,
>>> but no "sense" is being made initially. Intelligence is present hopefully,
>>> but not manifesting rationally at this point. Or am I being completely
>>> impractical?
>>>
>>>
>>>
>>> On Fri, Feb 25, 2022 at 5:45 PM Linas Vepstas <[email protected]>
>>> wrote:
>>>
>>>
>>>
>>>
>>>
>>> On Fri, Feb 25, 2022 at 6:41 PM xanatos xanatos.com <[email protected]>
>>> wrote:
>>>
>>>  But again, I haven't really done much in about a year, partially
>>> because I am seeing tech SOAR past what I can do on my own.  It's a little
>>> disheartening.
>>>
>>>
>>>
>>> search the term "soft programming". Basically, its about how to harness
>>> some of that whiz-bang-ness into a framework that you can treat as
>>> tinker-toy building blocks.
>>>
>>>
>>>
>>> But yeah, individuals cannot compete against either sharply focused
>>> startups, or against the giant corporations. It takes money, time,
>>> coordination. and a vision for how to do things. When something becomes
>>> economically important, the tinkerers are left behind.
>>>
>>>
>>>
>>> I'm tinkering with stuff that the start-ups and the big compass don't
>>> yet know how to do. I'm interested in common-sense reasoning. This leaves
>>> me in calm, placid backwaters where no one is paying attention, and the
>>> stress levels are low.
>>>
>>>
>>>
>>>
>>>
>>> If you ever think I can help – let me know…
>>>
>>>
>>>
>>> This is between you and Mark.  He's taken an interest in modernizing the
>>> old infrastructure, and that is definitely a worthwhile task.  If you think
>>> some parts can be swapped out for better parts, go for it.  I'm busy with
>>> my project(s) above, and so can't really do any coding.  I can act as a
>>> question-answering machine, though, and explain how it all used to work.
>>>
>>>
>>>
>>> For the future, I'd like to see something that is modular and documented
>>> and is a collection of "tinker-toy" parts that people can assemble and
>>> re-assemble for personal projects. Despite Ameca and gpt-3 and boston
>>> dynamics, I still think there's plenty of space for tinkerers. What's
>>> missing are the tinker-tools. For example, if lego mindstorms had been
>>> open-source, wow, things could be, could have been different. Lego
>>> mindstorms was one of the great missed opportunities.  Capitalism seems to
>>> fail whenever a broader common-good is needed. That's why I'm into open
>>> source.
>>>
>>>
>>>
>>> So, then, you understand the general architecture, the general
>>> requirements. How cann all this be packaged as a kit, with a set of
>>> instructions, put-it-together and it will work type system. I think that's
>>> the goal.
>>>
>>>
>>>
>>> I mean, its easy enough to make things work in a narrow sense. Just
>>> hard-wire everything together. It's a lot harder to make it modular, so it
>>> can be adapted for different uses.
>>>
>>>
>>>
>>> --linas
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> Dave
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> *From:* [email protected] <[email protected]> *On Behalf
>>> Of *Linas Vepstas
>>> *Sent:* Friday, February 25, 2022 7:07 PM
>>> *To:* opencog <[email protected]>
>>> *Subject:* Re: [opencog-dev] Vision for pi_vision and AGI/atomspace
>>>
>>>
>>>
>>> Hi Dave,
>>>
>>>
>>>
>>> Thank you for that nice note!  I want to splice in some comments with my
>>> own real-world experience ....
>>>
>>>
>>>
>>> On Fri, Feb 25, 2022 at 3:42 PM xanatos xanatos.com <[email protected]>
>>> wrote:
>>>
>>> Not sure if this is cogent since my application is autonomous robots in
>>> actual hardware, but maybe useful…
>>>
>>>
>>>
>>> I used OpenCV with a carrier board ("StereoPi") for the Raspberry Pi
>>> Compute Module that breaks out both camera ports on the Pi.  I automated
>>> face recognition with code that leveraged OpenCV that I came to find from
>>> one Adrian Rosebock (pyimagesearch.com) that employed Haar Cascades to
>>> determine there was a face present.
>>>
>>>
>>>
>>> The code we used also rested on a Haar cascade. It "worked great" if you
>>> were in conventional office lighting, and faced the camera squarely.  It
>>> failed if you turned quarter-face, or showed a profile. It failed if your
>>> office had windows, and the shade wasn't drawn. It failed in direct
>>> sunlight. Outdoors. In stage demo and trade-show lighting conditions.  We
>>> considered a medical-training robot application, where the first responder
>>> would be kneeling over the robot-dummy, and so their face would be at
>>> right-angles to the camera.  The Haar cascade can't do that. (We never did
>>> find a better solution, either, at least while I was there.)
>>>
>>>
>>>
>>> The Haar cascade was able to measure the distance between the eyes, and
>>> thus able to estimate the distance to the face, and thus able to get the
>>> parallax right when steering the robot eyes to focus on the right spot.
>>> (The two eyes in blender move automatically, so in principle, you could
>>> have a cross-eyed animation, or a roll-your-eyes animation, but we never
>>> did that.) The depth was noisey. We used an alpha-beta filter to smooth out
>>> the jitter.
>>>
>>>
>>>
>>> I've heard vague intimations that neural nets can do better, but if so,
>>> I suspect all available systems are proprietary and expensive (and wouldn't
>>> run on a Pi, anyway)  I do have some general ideas on how to improve on
>>> this situation, but it would blow up this email.
>>>
>>>
>>>
>>> Once a face is detected, it sends the center-of-face data to another Pi
>>> (the robots have three Pis in them – "cores" – a vision acquisition "core",
>>> a language "core" and vision processing core).  The vision processing core
>>> (depending on the state the robot is in) takes this face positioning data,
>>> chews on it and sends the corresponding servo signals to the motor core
>>> that controls the head and eyes, and the robot follows you with its gaze
>>> and head movements.  So in theory, face **detection** and tracking are
>>> always functionally available, but may be overridden/ignored by other
>>> behavioral commands/statuses.
>>>
>>>
>>>
>>> The language processing side of things is always listening (I use python
>>> speech recognition with PocketSphinx as the recognizer which works
>>> surprisingly well)
>>>
>>>
>>>
>>> I never experimented directly with this, but everyone turned up their
>>> noses at this, and opted for a real-time internet connection to google
>>> speech.  In retrospect, I'm wondering if this is because all the developers
>>> either had a heavy foreign accent, or had a habit of slurring their speech
>>> and/or mumbling. At any rate, trade-show floors are problematic, what with
>>> the sonic assault of neighboring booths. Questions from the audience via
>>> microphones are also a problem, although there, you could get a direct
>>> audio cable from the mixing  board that the stage techs were running.
>>>
>>>
>>>
>>> The point here is that in natural settings, audio quality is an issue.
>>> I'm not aware of the current state-of-the-art with regards to neural nets.
>>> I suspect that, again, the solutions are proprietary, expensive, and don't
>>> run on Pi. But I dunno, i'm pretty much 100% totally unplugged from that
>>> world.
>>>
>>>
>>>
>>> and now has several hundred routines it can engage depending on what it
>>> hears, and some conflict resolution and buffering code in case responses to
>>> one phrase would interfere with ongoing responses playing out).
>>>
>>>
>>>
>>> The system is set up so that if I use a phrase like "my name is", or
>>> "I'd like to introduce you to"
>>>
>>>
>>>
>>> We had three versions. One was to feed text into AIML. There's an
>>> AIML-to-AtomSpace converter. It worked as well as "native" AIML chatbots,
>>> except that it took several minutes on startup, to load the database.  That
>>> was almost fatal.
>>>
>>>
>>>
>>> Its easy, "trivial", to write custom response rules in AIML. If I recall
>>> the syntax, it would be something like "PATTERN:my name is *"
>>> "RESPONSE:pleased to meet you $star-1"
>>>
>>>
>>>
>>> The second was ChatScript. That bypassed the atomspace entirely.
>>>
>>>
>>>
>>> The third was a chat-script-inspired domain-specific language called
>>> "ghost".  The intent was that authors would be able to write rules such as
>>> "RESPONSE: please to meet you $star-1 BLINK GAZE-AT $star-1 BLINK SMILE"  I
>>> guess it worked. I never saw a working demo.  The actual authors were drama
>>> students, with no software experience: they felt it was "difficult
>>> programming", they were used to type-written scripts for TV shows and if it
>>> wasn't done on a word-processor, it was "programming".  This was tough.
>>> Only one person was good at this, Audrey LeeAnn Brown, and she had a
>>> background in C++.  And I don't think she liked ghost. I think there were
>>> some PhD students who did manage to get something going for LovingAI. But I
>>> think they too side-stepped the complexity.
>>>
>>>
>>>
>>> I later saw a demo from a game company. It was actually fairly
>>> impressive: they had developed a GUI that allowed game designers to
>>> drag-n-drop their way through directed NPC interactions.  Basically, the
>>> NPC is trying to tell the player to go to this-n-such spaceport and meet
>>> some sketchy space-pirate to get gold, weapons, etc.  The dialog tree
>>> automated a lot of the low-level interaction, yet allowed fine-grained
>>> control.  In this sense, the GUI's that have been developed for games are
>>> light-years beyond what you can do with AIML or ChatScript; the main
>>> problem is that they're expensive, proprietary, and have lots of core
>>> issues that would need to be fixed to apply them to robots.
>>>
>>>
>>>
>>> Open source is great for operating systems, compilers and databases. Not
>>> so much for everything else.
>>>
>>>
>>>
>>> (and several similar phrases that are recognized by a fuzzy-logic kind
>>> of similarity finder I wrote), **AND** it can tell a face is present,
>>> It can filter out the name given, if any.  Then a few things happen –
>>> first, the language processor confirms the name by speaking "Hello <name> -
>>> did I get that right?" and listens for a variety of words that are either
>>> affirming or denying.
>>>
>>>
>>>
>>> If you're walking that path, .. well, this is what AIML is really good
>>> at.  Or, I guess, ghost?
>>>
>>>
>>>
>>> On affirmation, the system immediately begins taking snapshots every 10
>>> frames and stores them in a folder (the new faces dataset) of the person's
>>> name plus the date and time as a numeric string (Dave-202202251623 for
>>> example).  Once either the person exits the view for more than 100 frames
>>> (would-be 10 snapshots) or the system gains 100 actual face snapshots, it
>>> hands off those images to another of the scripts from Adrian Rosebock
>>> (encode_faces.py) that encodes the faces and turns the whole bunch into a
>>> pickle, which is then appended to the bigger pickle that all the other
>>> known faces are in…  The name and data are also written to the database of
>>> "people known", where additional data is written over time as interactions
>>> with that person accrue.
>>>
>>>
>>>
>>> So I'm not sure if this answers your question about integrating it into
>>> the speech subsystem – I basically have the audio input and processing,
>>> audio output and visual input and processing all running in parallel on
>>> separate physical SBCs, which all talk to each other via ZeroMQ (or PyZMQ
>>> specifically).
>>>
>>>
>>>
>>> The point of using ROS was that it allowed everything to be "modular",
>>> at least in theory. That you could replace one subsystem by another.  Much
>>> easier said than done.
>>>
>>>
>>>
>>> ROS uses UDP to "talk". For ROS2, they thought about using ZMQ but
>>> rejected it in favor of something else.  I forget what.
>>>
>>>
>>>
>>> It works very well, reasonably fast (especially given it only runs on Pi
>>> 4/8gig SBCs) and provides people interacting with the unmistakable feeling
>>> that the robot sees them, responds to their movements and speech, etc., and
>>> remembers them.
>>>
>>>
>>>
>>> Moore's law.
>>>
>>>
>>>
>>> So, ahh, one person who should have known better ordered the best,
>>> highest-resolution webcams they could find. 1280x1024 or something. You
>>> could only plug two of them into a USB hub before the USB hub was
>>> overwhelmed. And the CPU attached to that could barely keep up with the
>>> frame rate. Despite this obvious hardware-fail, there was tremendous
>>> resistance to down-scaling to a far more practical 640x480.  Add to that a
>>> power, heat and cooling budget. Ugh.
>>>
>>>
>>>
>>> Managing engineers is like herding cats.  Or pushing rope. Something
>>> like that.
>>>
>>>
>>>
>>>
>>>
>>> The drawback that I haven't done anything with in the past year or so,
>>> but has a relatively easy fix – is that the pickle data for a given person
>>> ages (my grandkids are no longer reliably recognized since they were 3 and
>>> 5 when I first implemented that build, and they are 6 and 8 now) – so I
>>> need to add a routine that occasionally updates the images silently in the
>>> background in the recognition pickle to keep up with changes…  but I've not
>>> had the time I wanted to to do these things…
>>>
>>>
>>>
>>> For more-or-less all of the performances, there was a robot operator who
>>> sat in the audience, monitoring the system in case it went haywire,
>>> over-riding any responses that were inappropriate.  Putting together a good
>>> GUI that allowed the robot operator to do this, running on a tablet, is
>>> non-trivial. (It was a website, with assorted javascript attached to
>>> various bits and pieces of the processing pipeline.)
>>>
>>>
>>>
>>> For pretty much anything non-trivial running in the atomspace, one needs
>>> some kind of visualization GUI to see what's going on.  We do not have
>>> one.  I personally use printf for everything, because I can. But its not,
>>> umm, usable by anyone else.
>>>
>>>
>>>
>>> If any of this gives you anything useful to pick from, I can get you
>>> code, original source and my custom stuff.  It's all Python, so I'm
>>> guessing you should be good with that.
>>>
>>>
>>>
>>> Dave
>>>
>>>
>>>
>>>
>>>
>>> *From:* [email protected] <[email protected]> *On Behalf
>>> Of *Linas Vepstas
>>> *Sent:* Friday, February 25, 2022 3:33 PM
>>> *To:* opencog <[email protected]>
>>> *Subject:* Re: [opencog-dev] Vision for pi_vision and AGI/atomspace
>>>
>>>
>>>
>>> Hi Mark,
>>>
>>>
>>>
>>> Preface for anyone else reading this: Mark is dusting off the old Hanson
>>> Robotics code for Eva.  One of the subsystems was face-tracking. When your
>>> webcam was calibrated correctly, then Eva had this uncanny ability to look
>>> at you from out of the screen: her eyes would track your position. It was
>>> really pretty cool, as you really got the sense she was looking at you.
>>>
>>>
>>>
>>> Anyway, it seems that Mark has this code working again, or almost
>>> working? A related gotcha is some of the camera-transforms in Blender
>>> needed to be adjusted, to accurately reflect that you sit about an
>>> arms-length away from your computer screen, which is small on laptops but
>>> big on desktops, etc. so eye tracking didn't work right if all these
>>> dimensions weren't accounted for. It was kind of tricky to get it all
>>> right.  But when it worked, it was really cool and even spine-tingling.
>>>
>>>
>>>
>>> What about face recognition? This too worked, in a limited setting: she
>>> could recognize a handful of faces, and pull out the names of those people
>>> from a database.  There are then three questions; how did this work, back
>>> then, how can it be made to work in the short term, and what is the correct
>>> long-term architecture?
>>>
>>>
>>>
>>> First part: "how did it work back then"? See
>>> https://github.com/opencog/ros-behavior-scripting The code might be
>>> bit-rotted, but it worked. (There was some radical meatball surgery towards
>>> the end; this might need to be revisited.)  The general philosophy, back
>>> then, was that:
>>>
>>> * The 3D locations of objects (such as faces) would be stored in the
>>> opencog "spacetime server".
>>>
>>> * The only reason to do this was so that there could be an API for
>>> verbal propositions: near, far, next to, behind, in front of, to the left
>>> of, etc. that the language subsystem could use. That API was never built.
>>>
>>> * The AtomSpace would hold all information about everything, e.g face
>>> #135 is actually Ben who is NN years old, lives in YY, loves robots, and is
>>> standing "next to" David (as reported by the space-server)
>>>
>>> * Why the AtomSpace? Because its the obvious place where current sensory
>>> info: sight & sound, can be integrated in with long-term knowledge and
>>> memories, as well as the dialog/language subsystem, as well as controlling
>>> movement and behaviour (turn left, right, blink and smile..)
>>>
>>> * Unfortunately, integrating the senses together with the background
>>> knowledge is hard. It was done in an ad hoc manner, it was
>>> under-documented, hard to use, hard to understand.  An adequate framework
>>> was never developed. This is not something one college student can knock
>>> out in a few weeks. The foundation for that framework is in the
>>> ros-behavior-scripting git repo. Fragments are in other places, I'd have to
>>> dig them up.
>>>
>>>
>>>
>>> So ... back to the question: face recognition:  Sure. Whatever. If you
>>> have a module that can recognize faces, then sure, whatever, have it
>>> forward that info to the AtomSpace.  That's the easy part.  The hard part
>>> is to integrate it into the speech subsystem.  So, when a new person
>>> appears in front of the camera, and says "Hi, my name is Mark", something
>>> has to extract the word "Mark", realize that "Mark" is someone's name,
>>> understand that there is probably a real-time correlation between that name
>>> and what the camera is seeing, take a snapshot of what the camera is
>>> seeing, and permanently tag that image with the name "Mark". To remember
>>> it. So that, minutes later, when Mark leaves the room and comes back, or
>>> months later, after a reboot, Eva still remembers what Mark looks like, as
>>> well as his favorite color, sports-team, childhood hero, mother's maiden
>>> name, last four digits of his soc sec and bank account #.
>>>
>>>
>>>
>>> I think all that is doable, and there are many different ways of doing
>>> the above, from quick short hacks to complicated theoretically-correct
>>> approaches ... but .. this email is too long, so, let me leave it at that.
>>>
>>>
>>>
>>> -- Linas
>>>
>>>
>>>
>>> On Fri, Feb 25, 2022 at 8:16 AM Mark Wigzell <[email protected]>
>>> wrote:
>>>
>>> Hi folks, my subject stems from having recently done a deep-dive into
>>> the pi_vision implementation. The original face detection and tracking was
>>> rusted, so I revamped it. In doing so I added in a hook for eventually
>>> augmenting the "new_face" message with some face recognition. I was
>>> informed that rather than splicing in some face detection algorithm at the
>>> pi_vision level, the "vision" would be to have the image elements reach the
>>> atomspace, and thus allow recognition to occur at a more basic level.
>>>
>>>
>>>
>>> Therefore, pursuant to the above, I'm asking for a high level
>>> description of how AGI vision could be accomplished. Perhaps we can also
>>> address the question of why face detection and tracking are "ok" but face
>>> recognition is not? Maybe all processing should be done at a lower level?
>>>
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>>>
>>>
>>>
>>> --
>>>
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>>>
>>> Sponge Bob: No, Patrick, they are laughing next to us.
>>>
>>>
>>>
>>>
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>>> <https://groups.google.com/d/msgid/opencog/CAHrUA37f2-KYNQG8oj4QQdoLNXLib4%2BeJB1hKrf5H9r5qPy%3Dgw%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>> .
>>>
>>> --
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>>> .
>>>
>>>
>>>
>>> --
>>>
>>> Patrick: Are they laughing at us?
>>>
>>> Sponge Bob: No, Patrick, they are laughing next to us.
>>>
>>>
>>>
>>>
>>>
>>> --
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>>> <https://groups.google.com/d/msgid/opencog/CAHrUA35x4vEj_GJvFdOYNc7-4vfnjipW%2BdFv1LQDc%3DcSpTOTOA%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>> .
>>>
>>> --
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>>> .
>>>
>>>
>>>
>>> --
>>>
>>> Patrick: Are they laughing at us?
>>>
>>> Sponge Bob: No, Patrick, they are laughing next to us.
>>>
>>>
>>>
>>>
>>>
>>> --
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>>> .
>>>
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>>> .
>>>
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>>> .
>>>
>> --
>> You received this message because you are subscribed to the Google Groups
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>> <https://groups.google.com/d/msgid/opencog/CA%2Ba9A7C1M8eh4tQFauLjXSe3b_U3Nt9bQP0o6cqFsM6Ly6SMRw%40mail.gmail.com?utm_medium=email&utm_source=footer>
>> .
>>
>
>
> --
>
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>
> --
> You received this message because you are subscribed to the Google Groups
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> email to [email protected].
> To view this discussion on the web visit
> https://groups.google.com/d/msgid/opencog/CAB%2BUbUgwMFa1ObNHTAE%3DbJsQDBWnaexJs1NtAPu0m4gicz%2BzdA%40mail.gmail.com
> <https://groups.google.com/d/msgid/opencog/CAB%2BUbUgwMFa1ObNHTAE%3DbJsQDBWnaexJs1NtAPu0m4gicz%2BzdA%40mail.gmail.com?utm_medium=email&utm_source=footer>
> .
>

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