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

[Greg Thompson]

Michael G,
Yes, This seems a very important point. But I’m wondering why you think
CHAT would be particularly good at making this point. Any further
explanation?
Greg

On Sun, Jul 15, 2018 at 6:25 PM Glassman, Michael <glassman.13@osu.edu>
wrote:

> I wonder if where CHAT might be most interesting in addressing AI are on
> topics of bias and oppression.  I believe that there is a real danger that
> AI can be used as a tool for oppression, especially from some of its early
> uses.  One of the things people discussing the possibilities of AI don’t
> discuss near enough is that it picks up and integrates biases from the
> information it receives.  Sometimes this can be interesting such as the
> program Libratus that beat world class poker players at Texas Hold ‘em.
> One of the less discussed aspects is that one of the reasons it was capable
> of doing this is it picks up on the playing biases of the players it is
> competing with and integrates them into its decision making process.  This
> I think is one of the reasons that it has to play only one player at a time
> to be successful.
>
>
>
> The danger is when it integrates these biases into a larger decision
> making process.  There is an AI program called Northpointe used by the
> justice department that uses a combination of big data and deep learning to
> make decisions about whether people convicted of crimes will wind up back
> in jail.  This should have implications for sentencing.  The program,
> surprise, tends to be much harsher with Black individuals than white
> individuals.  Even if you keep ethnicity outside of the equation it has
> enough other information to create a natural bias.  There are also some of
> the more advanced translation programs which tend to incorporate the biases
> of the languages (e.g. mysoginistic) into the translations without those
> getting the translations realizing it.  AI , especially machine learning,
> is in many ways a prisoner to the information it receives.  Who decides
> what information it receives? Much like the intelligence tests of an
> earlier age people will use AI decision making as being neutral or
> objective when it actually mirrors back (almost perfectly) those who are
> feeding it information.
>
>
>
> Like I said I don’t see this point raised nearly enough.  Perhaps CHAT is
> one of the fields in a position to constantly point this out, explore the
> ways that AI is culturally biases, and those that dominate information flow
> can easily use it as a tool for oppression.
>
>
>
> Michael
>
>
>
> *From:* xmca-l-bounces@mailman.ucsd.edu <xmca-l-bounces@mailman.ucsd.edu> *On
> Behalf Of *Greg Thompson
> *Sent:* Sunday, July 15, 2018 12:12 PM
>
>
> *To:* eXtended Mind, Culture, Activity <xmca-l@mailman.ucsd.edu>
> *Subject:* [Xmca-l] Re: Interesting article on robots and social learning
>
>
>
> And I'm still curious if any others out there might have anything to
> contribute to Doug's query regarding what CHAT theory (particularly
> developmental theories) might have to offer thinking about AI?
>
>
>
> It seems an interesting question to think through even if you aren't on
> board with the larger AI project...
>
>
>
> -greg
>
> On Sun, Jul 15, 2018 at 10:55 AM, Andy Blunden <andyb@marxists.org> wrote:
>
> 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>
> <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> <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>
> <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>
> <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 <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
> *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 <xmca-l-bounces@mailman.ucsd.edu>
> <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>
> <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://byu.academia.edu/GregoryThompson
> ------------------------------
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> Gregory A. Thompson, Ph.D. <http://www.plymouth.ac.uk/worldclass>
>
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>
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-- 
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://byu.academia.edu/GregoryThompson
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