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

[Andy Blunden]

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> 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
> <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>
> *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
>
>     ------------------------------------------------------------
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