[Xmca-l] Re: Interesting article on robots and social learning

Andy Blunden andyb@marxists.org
Sat Jul 14 18:55:09 PDT 2018


I think we go back to Martin's earlier ironic comment here,
Michael.

Andy

------------------------------------------------------------
Andy Blunden
http://www.ethicalpolitics.org/ablunden/index.htm
On 15/07/2018 9:44 AM, Glassman, Michael wrote:
>
> The Turing test, at least the test he wrote in his
> article, is actually a big more complicated than this, and
> especially poignant today.  Turing’s test of whether
> computers are acting as human was based on an old English
> game show called The Lying Game (I suppose one of the
> reasons for the title of the movie on Turing, though of
> course it had multiple meanings.  But for some reason they
> never mentioned the origin of the phrase in the movie). 
> Anyway in the lying game the contestant had to listen to
> two individuals, one of whom was telling the truth about
> the situation and one of whom was lying. The way Turing
> describes it, it sounds quite brutal.  The contestant had
> to figure out who the liar was (there was a similar much
> milder version years later in the US). Anyway Turing’s
> proposal, if I remember correctly, was that a computer
> could be considered thinking like a human if the comp the
> contestant was listening to was lying and he or she
> couldn’t tell. In essence the computer would successfully
> lie.  Everybody think Turing believed that computers would
> eventually think like humans but my reading of the article
> was that he had no idea, but as the computer stood at the
> time there was no chance.
>
>  
>
> The reason this is so poignant is the Mueller indictments
> that came down yesterday.  For those outside the U.S. or
> not following the news the indictments were against
> Russian military leading a scheme to convince individuals
> of lies about various actor in the 2016 election (also
> times release of information and breaking in to voting
> systems).  But it is the propagation of lies by robots and
> people believing them that interests me.  I feel like we
> aren’t putting enough thought into that.  Many of the
> people receiving the information could not tell it was no
> from humans and believed it even though in many cases it
> was generated by robots, passing it seems to me Turing’s
> test.  How and why did this happen? Of course Turing died
> before the Internet so he couldn’t have known about it. 
> But I wonder if part of the reason the robots were
> successful is that they have the ability to mine, collect
> and aggregate people’s biases and then reflect them back
> to us.  We tend to engage, believe things in the contexts
> of our own biases.  They say in salesmanship that the
> trick is figuring out what people want to here and then
> couching whatever you want to see in that.  Trump is a
> master of reading what a group of people want to hear at
> the moment, their biases, and then mirroring it back to them
>
>  
>
> If we went back to the Chinese room and the person inside
> was able to read our biases from our messages would they
> then be human. 
>
>  
>
> We live in a strange age.
>
>  
>
> *From:*xmca-l-bounces@mailman.ucsd.edu
> <xmca-l-bounces@mailman.ucsd.edu> *On Behalf Of *Andy Blunden
> *Sent:* Saturday, July 14, 2018 8:58 AM
> *To:* xmca-l@mailman.ucsd.edu
> *Subject:* [Xmca-l] Re: Interesting article on robots and
> social learning
>
>  
>
> I understand that the Turing Test is one which AI people
> can use to measure the success of their AI - if you can't
> tell the difference between a computer and a human
> interaction then the computer has passed the Turing test.
> I tend to rely on a kind of anti-Turing Test, that is,
> that if you can tell the difference between the computer
> and the human interaction, then you have passed the
> anti-Turing test, that is, you know something about humans.
>
> Andy
>
> ------------------------------------------------------------
>
> Andy Blunden
> http://www.ethicalpolitics.org/ablunden/index.htm
>
> On 14/07/2018 1:12 PM, Douglas Williams wrote:
>
>     Hi--
>
>     I think I'll come out of lurking for this one.
>     Actually, what you're talking about with this pain
>     algorithm system sounds like a modeling system that
>     someone might need to develop what Alan Turing
>     described as a P-type computing device. A P-type
>     computer would receive its programming from inputs of
>     pleasure and pain. It was probably derived from
>     reading some of the behavioralist models of mind at
>     the time. Turing thought that he was probably pretty
>     close to being able to develop such a computing
>     device, which, because its input was similar, could
>     model human thought. The Eliza Rogersian analysis
>     computer program was another early idea in which the
>     goal was to model the patterns of human interaction,
>     and gradually approach closer to human thought and
>     interaction that way. And by the 2000's, the idea of
>     the "singularity" was afloat, in which one could model
>     human minds so well as to enable a human to be
>     uploaded into a computer, and live forever as software
>     (Kurzweil, 2005). But given that we barely had a
>     sufficient model of mind to say Boo with at the time
>     (what is consciousness? where does intention come
>     from? What is the balance of nature/nurture in
>     motivation? Speech utterances? and so on), and you're
>     right, AI doesn't have much of a theory of emotion,
>     either--the goal of computer software modeling human
>     thought seemed very far away to me.
>
>      
>
>     At someone's request, I wrote a rather whimsical paper
>     called "What is Artificial Intelligence?" back in 2006
>     about such things. My argument was that statistical
>     modeling of human interaction and capturing thought
>     was not too easy after all, precisely because of the
>     parts of mind we don't think of, and the social
>     interactions that, at the time, were not a primary
>     focus. I mused about that in the context of my trying
>     to write a computer program by applying Chomsky's
>     syntactic structures to interpret intention of a few
>     simple questions--without, alas, in my case, a
>     corpus-supported Markov chain logic to do it.
>     Generative grammar would take care of it, right? Wrong.
>
>
>     So as someone who had done a little primitive,
>     incompetent attempt at speech modeling myself, and in
>     the light of my later-acquired knowledge of CHAT,
>     Burke, Bakhtin, Mead, and various other people in
>     different fields, and of the tendency of people to
>     interact through the world through cognitive biases,
>     complexes, and embodied perceptions that were not
>     readily available to artificial systems, I didn't
>     think the singularity was so near.
>
>     The terrible thing about computer programs is that
>     they do just what you tell them to do, and no more.
>     They have no drive to improve, except as programmed.
>     When they do improve, their creativity is limited. And
>     the approach now still substantially is
>     pattern-recognition based. The current paradigm is
>     something called Convolutional Neural Network Long
>     Short-Term Memory Networks (CNN/LSTM) for speech
>     recognition, in which the convolutional neural
>     networks reduce the variants of speech input into
>     manageable patterns, and temporal processing (temporal
>     patterns of the real wold phenomena to which the AI
>     system is responding). But while such systems combined
>     with natural language processing can increasingly
>     mimic human response, and "learn" on their own, and
>     while they are approaching the "weak" form of
>     artificial general intelligence (AGI), the
>     intelligence needed for a machine to perform any
>     intellectual task that a human being can, they are an
>     awfully long way from "strong" AGI--that is, something
>     approaching human consciousness. I think that's
>     because they are a long way from capturing the kind of
>     social embeddedness of almost all animal behavior, and
>     the sense in which human cognition is embedded in the
>     messy things, like emotion. A computer algorithm can
>     recognize the patterns of emotion, but that's it. An
>     AGI system that can experience emotions, or have
>     motivation, is quite another thing entirely.
>
>     I can tell you that AI confidence is still there. In
>     raising questions about cultural and physical
>     embodiment in artficial intelligence interations with
>     someone in the field recently, he dismissed the idea
>     as being that relevant. His thought was that "what I
>     find essential is that we acknowledge that there's no
>     obvious evidence  supporting that the current paradigm
>     of CNN/LSTM under various reinforcement algorithms
>     isn't enough for A AGI and in particular for broad
>     animal-like intelligence like that of ravens and dogs."
>
>     But ravens and dogs are embedded in social
>     interaction, in intentionality, in
>     consciousness--qualitatively different than ours,
>     maybe, but there. Dogs don't do what you ask them to,
>     always. When they do things, they do them for their
>     own intentionality, which may be to please you, or may
>     be to do something you never asked the dog to do,
>     which is either inherent in its nature, or an
>     expression of social interactions with you or others,
>     many of which you and they may not be consciously
>     aware of. The deep structure of metaphor, the
>     spatiotemporal relations of language that Langacker
>     describes as being necessary for construal, the worlds
>     of narrativized experience, are mostly outside of the
>     reckoning, so far as I know (though I'm not an
>     expert--I could be at least partly wrong) of the
>     current CNN/LSTM paradigm.
>
>     My old interlocutor in thinking about my language
>     program, Noam Chomsky, has been a pretty sharp critic
>     of the pattern recognition approach to artificial
>     intelligence.
>
>     Here's Chomsky's take on the idea:
>
>     http://languagelog.ldc.upenn.edu/myl/PinkerChomskyMIT.html
>
>     And here's Peter Norvig's response; he's a director of
>     research at Google, where Kurzweil is, and where, I
>     assume, they are as close to the strong version of
>     artificial general intelligence as anyone out there...
>
>     http://norvig.com/chomsky.html
>
>     Frankly, I would be quite interested in what you think
>     of these things. I'm merely an Isaiah Berlin fox,
>     chasing to and fro at all the pretty ideas out there.
>     But you, many of you, are, I suspect, the untapped
>     hedgehogs whose ideas on these things would see more
>     readily what I dimly grasp must be required, not just
>     for achieving a strong AGI, but for achieving
>     something that we would see as an ethical, reasonable
>     artificial mind that expands human experience, rather
>     than becomes a prison that reduces human interactions
>     to its own level.
>
>     My own thinking is that lately, Cognitive Metaphor
>     Theory (CMT), which I knew more of in its earlier (now
>     "standard model') days, is getting even more
>     interesting than it was. I'd done a transfer term to
>     UC Berkeley to study with George Lakoff, but we didn't
>     hit it off well, perhaps I kept asking him questions
>     about social embeddedness, and similarities to
>     Vygotsky's theory of complex thought, and was too
>     expressive about my interest in linking out from his
>     approach than folding in. It seems that the idea I was
>     rather woolily suggesting to Lakoff back then has
>     caught on: namely, that utterances could be explored
>     for cultural variation and historical embeddedness, a
>     form ofsocial context to the narratives and metaphors
>     and blended spaces that underlay speech utterances and
>     thought; that there was a degree of social embodiment
>     as well as physiological embodiment through which
>     language operated. I thought then, and it looks like
>     some other people now, are thinking that someone
>     seeking to understand utterances (as a strong AGI
>     system would need to do) really, would need to engage
>     in internalizing and ventriloqusing a form of Geertz's
>     thick description of interactions. In such forms,
>     words do not mean what they say, and can have
>     different affect that is a bit more complex than I
>     think temporal processing currently addresses.
>
>     I think these are the kind of things that artificial
>     intelligence would need truly to advance, and that
>     Bakhtin and Vygotsky and Leont'ev and in the visual
>     world, Eisenstein were addressing all along...
>
>     And, of course, you guys.
>
>      
>
>     Regards,
>
>     Douglas Willams
>
>      
>
>      
>
>      
>
>     On Tuesday, July 3, 2018, 10:35:45 AM PDT, David H
>     Kirshner <dkirsh@lsu.edu> <mailto:dkirsh@lsu.edu> wrote:
>
>      
>
>      
>
>     The other side of the coin is that ineffable human
>     experience is becoming more effable.
>
>     Computers can now look at a human brain scan and
>     determine the degree of subjectively experienced pain:
>
>      
>
>     In 2013, Tor Wager, a neuroscientist at the University
>     of Colorado, Boulder, took the logical next step by
>     creating an algorithm that could recognize pain’s
>     distinctive patterns; today, it can pick out brains in
>     pain with more than ninety-five-per-cent accuracy.
>     When the algorithm is asked to sort activation maps by
>     apparent intensity, its ranking matches participants’
>     subjective pain ratings. By analyzing neural activity,
>     it can tell not just whether someone is in pain but
>     also how intense the experience is.
>
>      
>
>     So, perhaps the computer can’t “feel our pain,” but it
>     can sure “sense our pain!”
>
>      
>
>     Here’s the full article:
>
>     https://www.newyorker.com/magazine/2018/07/02/the-neuroscience-of-pain
>
>      
>
>     David
>
>      
>
>     *From:*xmca-l-bounces@mailman.ucsd.edu
>     <mailto:xmca-l-bounces@mailman.ucsd.edu>
>     <xmca-l-bounces@mailman.ucsd.edu>
>     <mailto:xmca-l-bounces@mailman.ucsd.edu> *On Behalf Of
>     *Glassman, Michael
>     *Sent:* Tuesday, July 3, 2018 8:16 AM
>     *To:* eXtended Mind, Culture, Activity
>     <xmca-l@mailman.ucsd.edu> <mailto:xmca-l@mailman.ucsd.edu>
>     *Subject:* [Xmca-l] Re: Interesting article on robots
>     and social learning
>
>      
>
>     / /
>
>      
>
>     It seems like we are still having the same argument as
>     when robots first came on the scene.  In response to
>     John McCarthy, who was claiming that eventually robots
>     can have belief systems and motivations similar to
>     humans through AI John Searle wrote the Chinese room. 
>     There have been a lot of responses to the Chinese room
>     over the years and a number of digital philosopher
>     claim it is no longer salient, but I don’t think
>     anybody has ever effectively answered his central
>     question.
>
>      
>
>     Just a quick recap.  You come to a closed door and
>     know there is a person on the other side. To
>     communicate you decide the teacher the person on the
>     other side Chinese. You do this by continuously
>     exchanging rules systems under the door.  After a
>     while you are able to have a conversation with the
>     individual in perfect Chinese. But does that person
>     actually know Chinese just from the rule systems.  I
>     think Searle’s major point is are you really learning
>     if you don’t know why you’re learning, or are you just
>     repeating. Learning is embedded in the human condition
>     and the reason it works so well and is adaptable is
>     because we understand it when we use what we learn in
>     the world in response to others.  To put it in
>     response to the post, does a bomb defusion robot
>     really learn how to defuse a bomb if it does not know
>     why it is doing it.  It might cut the right wires at
>     the right time but it doesn’t understand why and
>     therefore is not doing the task just a series of steps
>     it has been able to absorb.  Is that the opposite of
>     human learning?
>
>      
>
>     What the researcher did really isn’t that special at
>     this point.  Well I definitely couldn’t do it and it
>     is amazing, but it is in essence a miniature version
>     of Libratus (which beat experts at Texas Hold em) and
>     Alphago (which beat the second best Go player in the
>     world).  My guess it is the same use of deep learning
>     in which the program integrates new information into
>     what it is already capable of.  If machines can learn
>     from interacting with other humans then they can learn
>     from interacting with other machines.  It is the same
>     principle (though much, much simpler in this case). 
>     The question is what does it mean.  As we defining
>     learning down because of the zeitgeist.  Greg started
>     his post saying a socio-cultural theorist be
>     interested in this research.  I wonder if they might
>     more likely to be the ones putting on the brakes,
>     asking questions about it.
>
>      
>
>     Michael
>
>      
>
>     *From:*xmca-l-bounces@mailman.ucsd.edu
>     <mailto:xmca-l-bounces@mailman.ucsd.edu>
>     <xmca-l-bounces@mailman.ucsd.edu
>     <mailto:xmca-l-bounces@mailman.ucsd.edu>> *On Behalf
>     Of *Andy Blunden
>     *Sent:* Tuesday, July 03, 2018 7:04 AM
>     *To:* xmca-l@mailman.ucsd.edu
>     <mailto:xmca-l@mailman.ucsd.edu>
>     *Subject:* [Xmca-l] Re: Interesting article on robots
>     and social learning
>
>      
>
>     Does a robot have "motivation"?
>
>     andy
>
>     ------------------------------------------------------------
>
>     Andy Blunden
>     http://www.ethicalpolitics.org/ablunden/index.htm
>
>     On 3/07/2018 5:28 PM, Rod Parker-Rees wrote:
>
>         Hi Greg,
>
>          
>
>         What is most interesting to me about the
>         understanding of learning which informs most AI
>         projects is that it seems to assume that affect is
>         irrelevant. The role of caring, liking, worrying
>         etc. in social learning seems to be almost
>         universally overlooked because information is seen
>         as something that can be ‘got’ and ‘given’ more
>         than something that is distributed in relationships.
>
>          
>
>         Does anyone know about any AI projects which
>         consider how machines might feel about what they
>         learn?
>
>          
>
>         All the best,
>
>
>         Rod
>
>          
>
>         *From:*xmca-l-bounces@mailman.ucsd.edu
>         <mailto:xmca-l-bounces@mailman.ucsd.edu>
>         <xmca-l-bounces@mailman.ucsd.edu>
>         <mailto:xmca-l-bounces@mailman.ucsd.edu> *On
>         Behalf Of *Greg Thompson
>         *Sent:* 03 July 2018 02:50
>         *To:* eXtended Mind, Culture, Activity
>         <xmca-l@mailman.ucsd.edu>
>         <mailto:xmca-l@mailman.ucsd.edu>
>         *Subject:* [Xmca-l] Interesting article on robots
>         and social learning
>
>          
>
>         I’m ambivalent about this project but I suspect
>         that some young CHAT scholar out there could have
>         a lot to contribute to a project like this one:
>
>         https://www.sapiens.org/column/machinations/artificial-intelligence-culture/
>
>          
>
>         -Greg 
>
>         -- 
>
>         Gregory A. Thompson, Ph.D.
>
>         Assistant Professor
>
>         Department of Anthropology
>
>         880 Spencer W. Kimball Tower
>
>         Brigham Young University
>
>         Provo, UT 84602
>
>         WEBSITE: greg.a.thompson.byu.edu
>         <http://greg.a.thompson.byu.edu> 
>         http://byu.academia.edu/GregoryThompson
>
>         ------------------------------------------------------------
>
>         Image removed by sender.
>         <http://www.plymouth.ac.uk/worldclass>
>
>         This email and any files with it are confidential
>         and intended solely for the use of the recipient
>         to whom it is addressed. If you are not the
>         intended recipient then copying, distribution or
>         other use of the information contained is strictly
>         prohibited and you should not rely on it. If you
>         have received this email in error please let the
>         sender know immediately and delete it from your
>         system(s). Internet emails are not necessarily
>         secure. While we take every care, University of
>         Plymouth accepts no responsibility for viruses and
>         it is your responsibility to scan emails and their
>         attachments. University of Plymouth does not
>         accept responsibility for any changes made after
>         it was sent. Nothing in this email or its
>         attachments constitutes an order for goods or
>         services unless accompanied by an official order
>         form.
>
>      
>
>  
>

-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mailman.ucsd.edu/pipermail/xmca-l/attachments/20180715/36fe97b0/attachment.html 


More information about the xmca-l mailing list