ActivityPub on WordPress

WordPress blog posts should bow be available directly on the Fediverse, viewable via Mastodon, Firefish, and other clients. I’ve just installed and activated it by using this tutorial here: https://fedi.tips/wordpress-turning-your-blog-into-a-fediverse-server/

And this is mostly a test post to see if it works. Might be visible to only a select few, but if it works I’ll give it a boost.

Also, trying a couple tags on the right to see how the plug-in integrates them.

And… how to reference them? Poster username? Let’s find out together…


Hmmm….

I think I’m following the profile now: @Dr.Implausible@implausi.blog

So let’s see if this quick edit helps it show in the timeline, which will allow for a boost?

FAANG is dead; fear the MAAMBA!

Today, September 6th, 2023 the EU designated six “gatekeepers” under the Digital Markets Act (DMA), which will require additional steps by the named corporations in order to comply with EU regulations.

https://ec.europa.eu/commission/presscorner/detail/en/ip_23_4328

These firms include: Alphabet, Amazon, Apple, ByteDance, Meta, and Microsoft. (they listed Samsung as well, but left it off the infographics).

This acronym, listed as AAABMM, captures the major players in the internet and computing industry. Previously, this was known as the FAANG (Facebook, Apple, Amazon, Netflix, and Google), but with the shifting fortunes (as well as corporate name changes) in telecom, an update was required.

Of course, AAABMM utterly fails as an acronym. But we can Scrabble this (or Wordle it in 2023) and arrive at something a little bit better.

ABAAMM?
ABAMAM?
BAMAMA?
MAMABA?
MAMBAA? Oh, there we go, that’s close. a little sheepish at the end though.
MAAMBA? Not bad. I like it. Rolls off the tongue.

“The EU has just designated the cyber-MAAMBA overlords as Gatekeepers…”

There were a couple other options of course: BBAAAM was right there. But MAAMBA works well in communicating the threat level, and the other metaphoric associations one can draw when dealing with the “Artists Formerly Known as FAANG”

(Thought I guess you could use BAMAMA if you need to defang them (pun intended)).

Anyhoo, update your lexicons accordingly. I’m sure we’ll be hearing much more about the MAAMBAS in the near future.

Implausipod Transcripts

A quick note on the transcripts for the already-released episodes. These have all been completed, and are attached to the respective episodes, available at the https://www.implausipod.com/ website. (Or on the buzzsprout links; those should still work too.)

I’ll be posting those over the next few days here, in order to ensure there’s a consistent repository of the material. Once that’s up to date, we’ll continue with the ongoing podcasts as they come out. (The workflow behind the scenes here for publication and transcription has mostly been sorted out).

A few of the first ‘batch’ of episodes deal with material that was produced by studios that are currently struck by the WGA and SAG-AFTRA guilds. We’ll hold off on posting that material so as not to inadvertently promote it, but will publish that information with the resolution of the current work action.

Finally, regular (ie non-Implausipod, non-Youtube) content should be resuming here shortly as well. Some of what would have been posts for the month of August just ended up getting rolled into the podcast episodes. We’ll continue to break those apart going further.

Until next time, have fun!

Implausipod E0013 – Context Collapse

Tiktok has a noise problem, and it’s indicative of a larger issue ongoing within social media, that of “context collapse”. But even context collapse is expanding outside its original context and evidence of it can be seen in the rise of generative AI tools, music and media, and the rise of the “Everything App”. Starting with a baseline in information theory and anthropology, we’ll outline some of the implications of noise and context collapse in this episode of the Implausipod.

https://www.implausipod.com/1935232/13516713-implausipod-e0013-context-collapse

Transcript:

 TikTok has a noise problem, and it may be due to a context collapse, something that’s been plaguing music, social media, and it’s even showing up in our new AI tools. And if you don’t know what that is, you’ll find out soon enough. We’ll explain it here tonight on episode 13 of the Implausipod.

Welcome to the ImplausiPod, a podcast about the intersection of art, technology, and popular culture. I’m your host, Dr. Implausible. Now, when it comes to the issue of noise and context collapse, there’s a little bit more going on, of course. The problem for TikTok is that it started out with a pretty tasty signal, one that kind of really encouraged people to stick around.  But as that signal amps up and it gets more and more noise in the system, it gets a little chunkier and crustier and maybe not as finely tuned as you’d like. Now, for some people that noise isn’t a problem, but for a lot of people it can be. And the reason it’s a problem for TikTok is that the noise can be actively discouraging from using the app.  It can make it Unfun, and this is what I’ve been noticing lately. So let’s get into how context collapse is impacting life online.

When TikTok rose to prominence throughout the pandemic, it was a very tasty experience for a lot of people. I mean, if you had negative interactions there, there was probably reasons for it, but there was also ways to mitigate it.  You could block people, you had a lot of control, and generally the algorithm would be feeding you content that you wanted to see. Or even if you know, you didn’t know you wanted to see it, you know that the joke goes. To that end, it was pretty good at sussing out what people found engaging. So TikTok had a very high signal to noise ratio.  Yeah, there was some noise there, but that was because it was feeding stuff that it wasn’t quite sure that you liked. But once it kind of honed in on what your preferences were, it was really good system for delivering content to users.

Over time though, as more and more content goes out and more and more people start participating, the amount of tasty content, the amount of good content, the amount of interesting and novel content drops off.  So you see less and are aware of pieces of information that everybody is seeing less, and less stuff – even within your niche from people that you’re following – gets shown to you. So this is all noise in the system. It’s the amount of stuff that you don’t want to see increasing.

Now we’re talking about signal to noise, and as we’re talking about a very old theory here, we’re talking about Claude Shannon’s Mathematical Theory of Communication.  Now, it was “A mathematical theory of communication” when it was published in 1948 as a paper, and then it was reworked as a book with Warren Weaver in 1949, where it was The Mathematical Theory of Communication as they realized that the theory was more generalizable, and this theory undergirds the entirety of the internet and most of our modern telecommunication systems, and it’s just a way of dealing with the noise in a system and ensuring the signal gets sent as it was sent from the transmitter to the receiver. And you can talk about it in terms of human communication or machine to machine communication. Device to device. Point to point, and this is why it’s generalizable.  It can be pretty much black boxed, and you can see this in how it gets used in multiple contexts. The point of the theory is that there’s a certain throughput that you need where the amount of information is greater than the noise to ensure that the signal is “understood”. And then there can be systems that are used to error check or correct or whatever, what’s on the receiving end to ensure that you know what was transmitted comes through as an intended, and that’s the gist of it.

Now for something like TikTok as the signal, you know, the signal is the content that’s supposed to be delivered to the end user, and the noise is anything that isn’t part of that. It’s the stuff they’re not necessarily looking for or asking for. And as TikTok has branched out and provided more types of content, starting with the 15 second videos and then 60 seconds, three minutes, 10 minutes, live stream, stories, whatever, you get more types of content in there.  Not all of it’s gonna be relevant to all users. If somebody’s watching for some quick videos, even a 60-second or three minute video is definitely not gonna be what they want to see. So we have a variety of content in there and that increases the noise, the amount of stuff you don’t want to see in a given block of time.

Now, couple that with the other types of content that get filtered in. It can include ad sponsored posts or posts that are just generally low value. This can include things like, oh, so-and-so changed their name, so-and-so signed on, or what we’ve seen recently is the retro posts like on this day in 2021 or 2022 or whatever, where people will revisit old posts, and a lot of times there’s nothing special about those unless you haven’t seen it before. It’s just whatever’s that person was talking about a year ago. So that feeds into the pipeline with all the current content that’s also trying to get out to the user base as the user base is increasing. So we have this additional content that’s coming through the pipeline, increasing the signal, but there’s also more stuff, more stuff that you don’t want to see.

It’s noisy,

and that noise, as we stated earlier, makes it unfun. It’s like it directly interferes with the stickiness of the app, the ability for it to engage the audience and have them participate in what the actions that are going online. And as that’s directly part of what Tiktok’s business model is: capture an audience and keep them around, then that can be a problem for them.

But it also brings us into that idea of the collapse of context. Now context collapse is something that was theorized about by a number of media scholars in the early 2000s, including danah boyd and Michael Wesch, and a few others. In its most simplest form, it’s what happens when media that’s designed for one audience or a single audience gets shared to multiple audiences, sometimes unintended. For early social media, and in this case, that means like MySpace and Facebook and Twitter, media that was shared for a particular group – often a friend group – could go far beyond the initial context. And while those websites or apps, along with blogs and web forums were co-constitutive of the public sphere, as we talked about a few episodes ago, along with the traditional media. Context really didn’t start smooshing together until Web 2.0 started shifting to video with the advent of YouTube and the other streaming sites, and that’s the technical term, smooshing. You can update your lexicons accordingly.

But the best way to describe context collapse was captured by cultural anthropologist Michael Wesch in a 2009 issue of Explorations in Media Ecology. He describes it and the problem as follows, quote:

“The problem is not lack of context. It’s context collapse, an infinite number of contexts collapsing upon one another into that single moment of recording.  The images, actions, and words captured by the lens at any moment can be transported to anywhere on the planet and preserved the performer must assume for all time. The little glass lens becomes the gateway to a black hole sucking all of time and space, virtually all possible contexts in on itself.” End quote.

So he is talking then about the relatively new phenomenon of YouTube, which had only been around for about four or five years at that point, and what we now call creators producing content for viewing on that platform. It was that shift to cam life that had started previously, obviously, I mean there’s a reason YouTube was called what it was, but it went along with that idea of democratization of the technology, of the ability for pretty much anybody with a small technological outlay to produce a video and have it available online for others to see.  Prior to the YouTube era, that would’ve been largely restricted to people with access to certain levels of broadcast technology, whether it was television or cable access, or a few other avenues. It wasn’t really as prevalent as we saw in, you know, the 21st century. And now with the growth of YouTube and the advent of Snapchat and TikTok, it really has completely taken over. But this is why it’s also still useful to look at some older articles because they give us an idea of what was novel at the time, what had changed, and this was really what was different with what was going on.

Michael Wesch is really drawing a lot from Goffman here and that idea of “the presentation of self in everyday life”, that we have different behaviors and there’s different aspects of ourselves that we will bring to the forefront in different contexts. So whether it’s at school or work or with our family or parents or friends or loved ones or what have you, we’re all slightly different in the way that we act around them. And this has been observed for a lot of different people in a lot of different contexts. But with the rise of what I’ll call here the mediated self and the complete flattening of all contexts due to, you know, Snapchat and Reels and TikTok, it has really taken a new turn.

Now, that idea of presentation of self for multiple audiences through vlogging, through YouTube, it isn’t exactly new because there was other versions of that before.  In a presentation by Dr. Aiden Buckland, he goes into some of the critiques of this, that a media archeologist or media historian could draw a pretty straight lineage from diarization and life writing as a practice that occurred on blogs through to the modern practices that we see with video logs or just TikTok and Snapchats.  This, in turn, is drawing heavily on the works of Dr. Michael Keren, who wrote a lot about blogs and their political action in the late nineties and early 2000s. But I digress. I’m starting to get a little bit further afield.

One of the ways to theorize Context collapse is that it’s like if every moment that you have that is recorded was available for instant replay at any time.  And with the advent of video services moving to the cloud and having everything accessible (and looking at YouTube’s archives, now you can go back to basically when they began), we have that idea of instant replay. So it isn’t just a context collapse in terms of anything might be available to multiple audiences, but it’s also a Time collapse in that everything is always available to all potential audiences, and this extension of the context collapse to encompass multiple times or at least all times that are recorded and stored in the cloud has been discussed by authors Petter Bae Brandtzaeg of Oslo and Marika Lüders. Now there’s a very obvious link to this, to the rise of what’s called cancel culture, and I’d be remiss if I went without mentioning it, but that’s kind of beyond the scope of what we’re discussing here. That’s a different thread, a different track that we will have to pursue at some time in the future. The other implication of this time collapse is something that we’ve discussing here on the podcast more recently, namely media, especially music,  and AI.

In terms of media, this context collapse, this time collapse is happening because obviously everything is available everywhere, all at once, at least for the most part. Things are currently in a state of flux, especially when it comes to television and film. The advent of the streaming services where each carved off a particular portion of the IP catalog that they happen to own has really changed how things have been interacting, but when it comes to music where streaming can basically all be done through one particular service, Spotify, with a few additional ones with minor catalogs, the impacts of that time collapse and context collapse are much more noticeable.

In an article published on The Atlantic in January of 2022, author Ted Gioia asked “Is old music killing new music?”. The author found that over 70% of the US market was going to songs that were 18 months or older, and often significantly so. Current rock and pop tracks now have to compete with the best of the last 60 years of recorded music. And while it is possible to draw some direct comparisons between the quality of the music as YouTuber Rick Beato did in a live stream on August 26th, 2023, where he asked: “Is today’s music bad?”, and looked at the top chart toppers from 50 years ago in August of 1973. You can argue that the overall production of music may be significantly better in 2023, but the overall composition, songwriting, and other elements may lack that magic that we saw, you know, 50 years ago. The most popular trend in music right now seems to just be a remix, a sample, a cover, or an interpolation of an older song.  Even a chart topper like Dua Lipa draws heavily on the recreation of a seventies dance club aesthetic and sound. So context collapse, even if it isn’t necessarily killing new music, is definitely changing the environment in which it may be able to, you know, survive and thrive. The environment’s almost getting a little polluted.

It’s very noisy there.

However, one of the other places we’re seeing the impacts of this noise, this context collapse, is in the generative AI tools, or at least this is one of the places that the noise is being put to use. On a post on his blog on July 17th, 2023, author Stephen Wolfram talked about the development of these generative art tools and the processes that it goes through to actually create a picture.  We work through the field of adjacent possibles that could be seen in something like a cat with a party hat on, and a lot of those images that are just a step or two removed for being a image that we as humans recognize shows up as noise. It turns out that what we think of as an image isn’t necessarily that random, and a lot of the pixels are highly correlated with one another, at least on a pixel-per-pixel basis. So if you feed a billion images into one of these models, in order to train it, you’re gonna get a lot of images that look highly similar, that are correlated with each other. And this is what Wolfram is talking about when he is talking about the idea of an “inter concept space”, that these images generally represent something or close to something. It’s not an arbitrary one either, but it’s one that’s aligned with our vision, something that we recognize, so a “human-aligned inter-concept space” that’s tied to our conception of things like cats and party hats.

But this “inter-concept” space is not only like ‘representative of’, but ‘fueled by’ the context collapse.  It requires the digitization of everything, like a billion images that go into it in order for it to be trained. But it also, you know, squishes everything together. Again, our technical term, smoosh. And this smooshing brings us back to TikTok because everything is there. That’s part of what’s contributing to the noise, but it also is why there’s such a volume of a signal that’s there. You can likely find something and it’ll get algorithmically delivered to you if you like it enough or you interact with. But this is also how it’s captured so much of the public sphere in a way that the owner of Twitter wishes it could, and that idea of the context collapse seems to be made manifest in these apps that are trying to capture the public sphere, that they have to capture everything, everything all at once.

And so we’re seeing the rise of the Everything app, the everything website, much like we talked about a few weeks ago in episode 10 with the rise of a o l and how it as a portal was for a lot of users. The internet, it was the entirety of it. And we’ve seen subsequently with Facebook, we’re seeing a number of competitors, sometimes in different places around the world, catering to a particular locality, but all of them trying to capture that “One thing to all people, to all customers”. In China, we see it with the rise of WeChat, which allows for calls and texts and payments as well. In Moscow, we can see it with the various apps that are run by Yandex, where you could use it for everything from getting a taxi to communications to your apartment, and there’s a lot of tools built-in and it actually has its own currency system built-in as well. A user by the name of Inex Code posted a list of everything that you can do with Yandex in Moscow. In North America, we can see it with not just Facebook, but also with Apple and Google and Amazon too. The breadth of services that they have available, and the continual expansion of services that they’re adding to their apps and platforms. And when Elon Musk bought Twitter, it was theorized that one of the things you wanted to do was turn it into a WeChat like app. His recent comments about LinkedIn and the option of adding that kind of functionality to the app now known as X indicate that he may well be headed in that direction.  And finally, the continual expansion of TikTok now include texts as well as a marketplace and music sales indicate there’s still more growth in that area too. As each of these walled garden “everything” apps try and gather up more functionality, we can see that this is one response to the context collapse: to provide a specific context within their enclosure.

It’s an effort to reduce the noise, or at least to turn it into something that happens outside their walls.

But setting up a wall may not be the only solution. It’s one way, obviously, that element of enclosure that’s taking place, but there’s other ways to deal with it as well. One way is a way we looked at with the Fediverse, where an everything app can be developed as long as it’s open. and there’s a lot of opportunity and possibility there, but that openness requires a fair amount of work by the user. It requires curation. It lacks the algorithmic elements that drive the enclosure of the other apps. Now, that doesn’t mean an algorithmic element couldn’t work for the Fediverse, it’s just that currently it’s not set up for it and may require a lot of effort to bootstrap something like that and get it going.

And absent an algorithm, it kind of points the way to the last two solutions that we have. The first one is just to lean into it to accept that there’s this change that’s happened to our society with the advent of digital media and everything being available. If the context collapsed, that’s fine. That’s just the way things are now, and we just have to learn to deal with it. And that leads into the second option. The one David Brin called The Transparent Society. And just that everything is available, and we’ll have to change our patterns of use. If we recognize that aspects of our culture are socially constructed, then we learn to live with that and we can change and adjust as necessary.  Things haven’t always been the way they are currently, and they don’t have to continue that way either. Because the last way forward to deal with context collapse is to look at some areas of our culture that have already experienced it and seen how they’ve dealt with it. Because context collapse is intimately tied with that idea of availability of everything as well as in video terms, what Wesch is talking about was the instant replay.

And the two areas that have managed that and have continued to succeed in an era of streaming media and context collapse are pro sports and pro wrestling. The way they’ve succeeded is recognizing that they have their particular audience, that their audience will find them, that they don’t have to be everything for all audiences.  And they’ve also succeeded by privileging the live, the now, the current event, something that revels in the instant replay, the highlight reel, the high spot, but also is allowed to continually produce new content because there might be a new highlight reel or a high spot in the very next game or match or show or finals or pay-per-view.  There’s always something new coming down the pipeline and you best not look away. It turns out that the best way to deal with the noise is to create something that cuts right through it.

Once again, I’m your host, Dr. Implausible. It’s been a pleasure having you with us today. I hope you join us next time for episode 14 when we investigate the phenomenon of the dumpshock. In the meantime, you can find this episode and all back episodes at our new online home at www.implausipod.com, and email me at Dr. implausible at implausipod com. Until the next time, while you’re out there in the busyness and the noise, have fun.

References and Links:

Brandtzaeg, P. B., & Lüders, M. (2018). Time Collapse in Social Media: Extending the Context Collapse. Social Media + Society, 4(1), 2056305118763349. https://doi.org/10.1177/2056305118763349

Gioia, T. (2022, January 23). Is Old Music Killing New Music? The Atlantic. https://www.theatlantic.com/ideas/archive/2022/01/old-music-killing-new-music/621339/

Shannon, C. E. (1948). A Mathematical Theory of Communication.

Wesch, M. (2009). Youtube and You: Experiences of self-awareness in the context collapse of the recording webcam. Explorations in Media Ecology, 8(2), 19–34.

https://www.quantamagazine.org/how-claude-shannons-information-theory-invented-the-future-20201222/

https://journals.sagepub.com/doi/10.1177/2056305118763349

Generative AI Space and the Mental Imagery of Alien Minds

Implausipod E0012 – AI Reflections

AI provides a refection of humanity back at us, through a screen, darkly. But that glass can provide different visions, depending on the viewpoint of the observer. Are the generative tools that we call AI a tool for advancement and emancipation, or will they be used to further a dystopic control society? Several current news stories give us the opportunity to see the potential path before us leading down both these routes. Join us for a personal reflection on AI’s role as an assistive technology on this episode of the Implausipod.

https://www.buzzsprout.com/1935232/episodes/13472740

Transcript:

 On the week before August 17th, 2023, something implausible happened. There was a news report that a user looking for, can’t miss spots in Ottawa, Ontario, Canada, would be returned some unusual results on Microsoft’s Bing search. The third result down on an article from MS Travel suggested the users could visit the Ottawa food bank if they’re hungry, that they should bring an appetite.

This was a very dark response, a little odd, and definitely insensitive, making one wonder if this is done by some teenage pranksters or hackers, or if there was a human involved in the editing decisions at all. Because initial speculation was that this article – credited to Microsoft Travel – may have been entirely generated by AI.  Microsoft’s subsequent response in the week following was that it was credited due to human error, but doubts remain, and I think the whole incident allows us to reflect on what we see in AI, and what AI reflects back to us… about ourselves, which we’ll discuss in this episode of the ImplausiPod.

Welcome to the ImplausiPod, a podcast about the intersection of art, technology, and popular culture. I’m your host, Dr. Implausible, and today on episode 12, we’re gonna peer deeply into that glass, that formed silicon that makes up our large language models and AI, and find out what they’re telling us about ourselves.

Way back in episode three, which admittedly is only nine episodes but came out well over a year ago, we looked at some of the founding figures of cyberpunk and of course one of those is Philip K Dick, who’s most known for Do Android’s Dream of Electric Sheep, which became Blade Runner, and now The Man in the High Castle, and other works which are yet un-adapted, like The Three Stigmata of Palmer Eldrich, but one of his most famous works was of course A Scanner Darkly, which had a Rotoscoped film version released in 2006 starring Keanu Reeves. Now, the title, of course, is a play on words from the biblical verse from One Corinthians where it’s phrased as looking “through a glass darkly”, and even though there’s some ambiguity there, whether it’s a glass or a mirror, or in our context, a filter, or in this case a scanner or screen. With the latter two being the most heavily technologized of all of them, the point remains, whether it’s a metaphor or a meme, that by peering through the mirror, the reflection that we get back is but a shadow of the reality around us.

And so too, it is with AI. The large language models, which have been likened to “auto-complete on steroids”, and the generative art tools (which are like procedural map makers that we discussed in a icebreaker last fall) have gained an incredible amount of attention in 2023. But with that attention has come some cracks in the mirror, and while there is still a lot of deployment of them as tools, they’re no longer seen as the harbinger of AGI or (artificial) general Intelligence, let alone super intelligence that will lead us on a path through a technological singularity. No, the collection of programs that have been branded as AI are simply tools what media theorist Marshall McCluhan called “Extensions of Man”, and it’s with that dual framing of the mirror held extended at our hand that I wanna reflect on what AI means for us in 2023.

So let’s think about it in terms of a technology. In order to do that, I’d like to use the most simple definition I can come up with; one that I use as an example in courses I’ve taught at the university. So follow along with me and grab one of the simplest tools that you may have nearby. It works best with a pencil or perhaps a pair of chopsticks, depending on where you’re listening.

If you’re driving an automobile, please don’t follow along and try this when you’re safely stopped. But take the tool and hold it in your hands as if you were about to use it, whether to write or draw or to grab some tasty sushi or a bowl of ramen. You do you. And then close your eyes and rest for a moment.

Breathe and then focus your attention down. To the tool in your hands, held between your fingers and reach out. Feel the shape of it, you know exactly where it is, and you can kind of feel with the stretch of your attention, the end of where that might actually exist. The tool has now become part of you, a material object that is next to you and extends your senses and what you are capable of.

And so it is with all tools that we use, everything from a spoon to a steam shovel, even though we don’t often think of that as such. It also includes the AI tools that we use, that constellation of programs we discussed earlier. We can think of all of these as assistive technologies, as extensions of ourselves that multiply our capabilities. And open your eyes if you haven’t already.

So what this quick little experiment is helpful in demonstrating is just exactly how we may define technology. Here using a portion of McLuhan’s version. We can see it as an extension of man, but there have been many other definitions of technology before. We can use other versions that focus on the artifacts themselves, like Fiebleman’s  where tech is “materials that are altered by human agency for human usage”, but this can be a little instrumental. And at the other extreme, we can have those from the social construction school like John Laws’ definition of “a family of methods for associating and channelling other entities and forces, both human and non-human”. Which when you think about it, does capture pretty much everything relating to technology, but it’s also so broad that it loses a lot of the utility.

But I’ve always drawn a middle line and my personal definition of technology is it’s “the material embodiment of an artifact and its associated systems, materials, and practices employed to achieve human ends”. I think we need to capture both the tool and the context, as well as the ways that they’re employed and used, and I think this definition captures the generative tools that we call AI as well. If we can recognize that they’re tools used for human ends and not actors with their own agency, then we can change the frame of the discussion around these generative tools and focus on what ends they’re being used for.

And what they’re being used for right now is not some science fictional version, either the dystopic hellscapes of the Matrix or Terminator, or on the flip side, the more utopic versions, the one, the “Fully Automated Luxury Communism” that we’d see in the post scarcity societies of like a Star Trek: The Next Generation, or even Iain M. Banks’ the Culture series.  Neither of these is coming true, but those polls – that ideation, these science fiction versions that kind of drive our collective imagination of the publics, the social imaginaries that we talked about a few episodes ago – these polls represent the two ends of that continuum, of that discussion, that dialectic between utopic and dystopic and the way we frame technology.

As Anabel Quan-Haase notes in their book on Technology and Society, those poles: the utopic idea of technology achieving progress through science, and the dystopic is technology as a threat to established ways of life, are both frames of reference. They could both be true depending on the point of view of the referrer. But as we said, it is a dialectic. There is a dialogue going back and forth between these two poles continually. So technology in this case is not inherently utopic or dystopic, but we have to return again to the ends that the technology is put towards: the human ends. So rather than utopic or dystopic, we can think of technology being rather emancipatory or controlling, and it’s in this frame, through this lens, this glass that I want to peer at the technology of AI.

The emancipatory frame for viewing these generative AI tools view them as an assistive technology, and it’s through this frame, this lens that we’re going to look at the technology first. These tools are exactly that: they are liberating, they are freeing. And whenever we want to take an empathetic view of technology, we wanna see how they may be used by others who aren’t in our situation.  And that situations means they may be doing okay, they might be even well off, but they may also be struggling. There may be issues that they, or challenges that they have to deal with on a regular basis that most of us can’t even imagine. And this is where my own experience comes from. So I’ll speak to that briefly.

Part of my background is when I was doing my field work for my dissertation, I was engaged with a number of the makerspaces in my city, and some of them were working with local need-knowers or persons with disabilities like the Tikkun Olam Makers, as well as the Makers Making Change groups. These groups worked with persons with disabilities to find solutions to their particular problems.  problems that often there wasn’t a market solution available because it wasn’t cost effective. You know, the “Capitalist realism” situation that we currently are under means that a lot of needs, especially for marginal groups, may go unmet. And these groups came together to try and meet those needs as best they could through technological solutions using modern technologies like 3D printing or microcontrollers or what have you, and they do it through regular events, whether it was a hackathon or regular monthly meetup groups or using the space provided by a local makerspace. And in all these cases, what these tools are are liberating to some of the constraints or challenges that are experienced in daily life.

We can think of more mainstream versions, like a mobility scooter that allows somebody with reduced mobility to get around and more fully participate within their community to meet some of the needs that they need on a regular basis, and even something as simple as that can be really liberating for somebody who needs it. We need to be cognizant of that because as the saying goes, we are all at best just temporarily able, and we never know when a change may be coming that could radically change our lives. So that empathetic view of technology allows us to think with some forethought about what may happen as if we or someone we love were in that situation, and it doesn’t even have to be somebody that close to us. We can have a much more communal or collective view of society as well.

But to return to this liberating view, we can think about it in terms of those tools, the generative tools, whether they’re for text or for art, or for programming, or even helping with a little bit of math.  We can see how they can assist us in our daily lives by either fulfilling needs or just allowing us to pursue opportunities that we thought were too daunting. While the generative art tools like Dall-E and Midjourney have been trained on already existing images and photographs, they allow creators to use them in new and novel ways.

It may be that a musician can use the tools to create a music video where before they never had the resources or time or money in any way, shape, or form to actually pursue that. It allows them to expand their art in a different realm. Similarly, an artist may be able to create stills that go with a collection or you know, accompany their writing that they’re working on, or an academic could use it as slides to accompany a presentation, something that they’ve spent time on, or a YouTube video, or even a podcast and their title bars and the like (present company included). My own personal experience when I was trying to launch this podcast was there was all this stuff I needed to do, and the generative art tools, the cruder ones that were available at that time, allowed some of the art assets to be filled in, and that barrier to launch, that barrier to getting something going was removed.

So emancipatory, liberating, even though at a much smaller scale, those barriers were removed and it allowed for creativity to flow in other areas, and it works similarly across these generative tools, whether it’s putting together some background art or a campaign map or a story prompt. If you need some background for a characters that are part of a story as an NPC, as a Dungeon Master, or what have you, or even just to bounce or refine coding ideas off of, I mean, the coding skills are rudimentary, but it does allow for something functional to be produced.

And this leads into some of the examples I’d like to talk about. The first one is from a post by Brennan Stelli on Mastodon on August 18th, where he said that we could leverage AI to do work, which is not being done already because there’s no budget time or knowhow.  There’s a lot of work that falls into this space of stuff that needs to be done, but you know, is outside of scope of a particular project. This could include something like developing the visualizations that will allow him to better communicate an idea in a fraction of the time, you know, minutes instead of hours that would normally take to do something like that, and so we can see in Brennan’s experience that it mirrors a lot of our own.

The next example’s a little bit more involved in an article written by Pam Belluck and published on the New York Times website on August 23rd, 2023. She details how researchers have used predictive text as well as AI generated facial animations that help with an avatar and speech that assist the stroke victim in communicating with their loved ones.

And the third example that hit a little bit closer to home was that of a Stanford research team that used the BCI or brain computer interface, along with AI assisted predictive text generation to allow a person with amyotrophic lateral sclerosis or ALS (to talk) at a regular conversational tempo, the tools read the neural activity that would be combined with the facial muscles moving and that is allowed to be translated into text. These are absolutely groundbreaking and amazing developments and I can’t think of any better example that shows how AI can be an assistive technology.

Now most of these technologies are confined to text and screen, to video and audio, and often when we think of AI, we think of mobility as well. So the robotic assistants that have come out of research labs like that of Boston Dynamics have attracted a lot of the attention, but even there, we can see some of the potential as an assistive technology. The fact that it’s confined to a humanoid robot means we sometimes lose sight of that fact, but that is what it is. In the video that they released in January of 2023, it shows an Atlas Robot as an assistant on a construction site providing tools and moving things around in aid of the human that’s the lead on the project, so it allows a single contractor working on their own to extend what they’re able to do, even if they don’t have access to a human helper. So it still counts as an assistive technology, even though we can start to see the dark side of the reflection through this particular lens, that the fact that an emancipatory technology may mean emancipation from the work that people currently have available to them.

In all of these instances, there’s the potential for job loss, that the tools would take the place of someone doing that, whether it’s in writing or as an artist, or a translator, or transcriber, or a construction assistant, and those are very real concerns. I do not want to downplay that, Part of our reflection on AI has to take these into account that the dark side of the mirror (or the flip side of the magnifying glass) can take something that can be helpful and exacerbate it when it’s applied to society at large. The concerns about job loss are similar to concerns we’ve had about automation for centuries, and they’re still valid. What we’re seeing is an extension of that automation into realms that we thought were previously exclusively bound to, you know, human actors: creators, artists, writers and the like.

This is why AI and generative art tools are such a driving and divisive element when it comes to the current WGA and SAG-Aftra strikes: that the future of Hollywood could be radically different if they see widespread usage. And beyond just the automation and potential job loss, a second area of concern is the one that ChatGPT and the large language models don’t necessarily have any element of truth involved in it, that they’re just producing output linguists like Professor Emily Bender of the University of Washington and the Mystery AI Hype Theater Podcast have gone into extensive detail about how the output of ChatGPT cannot be trusted. It has no linkage to truth, and there’s been other scholars that have gone into the challenges with using ChatGPT or LLMs for legal research or academic work or anything along those lines. I think it still has a lot of potential and utility as a tool, but it’s very much a contested space.

And the final area of contestation that we’ll talk about today is the question of control. Now, that question has two sides: the first is the control of that AI. One that most often surfaces in our collective imaginary is that idea of rogue super intelligences or killer robots gets repeated in TV, film, and our media in general, and this does get addressed at an academic level and works like Stuart Russell’s Human Compatible and Nick Bostrom’s Superintelligence.  They both address the idea of what happens if those artificial intelligences get beyond human capacity to control them.

But the other side of that is the control of us, control of society. Now, that gets replicated in our media as well, and everything from Westworld, to the underlying themes of the TV series Person of Interest, where The Machine is a computer system, developed to help detect and anticipate and suppress terrorist action using the tools of a post 9-11 surveillance state that it has access to.

And ever since Gilles Deleuze wrote his Postscript on the Societies of Control back in 1990, that so accurately captured the shift that had occurred in our societies from the sovereign societies of the Middle Ages and Renaissance through to the disciplinary societies that typified the 18th and 19th century, through to the shift that occurred in the 20th and 21st century to that of a control society where the logics of the society was enforced and regulated by computers and code. And while Deleuze was not talking about algorithms and AI in his work, we can see how they’re a natural extension of what he was talking about, how the biases that are ingrained within our algorithms, what Virginia Eubanks talked about in her book Automating Inequality, and how our biases and assumptions that go into the coding and training of those advanced system can manifest in ways, including everything from facial recognition to policing, to recommendation engines on travel websites that suggest that perhaps should go to the food bank to catch a meal.

Now there’s a twist to our Ottawa food bank story, of course. About a week after Microsoft came out and said that the article had been removed and that it had been identified that the issue was due to human error and not due to an unsupervised AI. But even with that answer, there are those who are skeptical: because it didn’t happen just once. There was a lot of articles where such weird or incongruous elements showed up. And of course, this being the internet, there was a number of people that did catch the receipts.

Now there’s a host of reasons of what might be happening with these bad reviews. Some plausible and some slightly less so. It could be just an issue of garbage in garbage out that the content that they’re scraping to power the AI is drawing articles that already exist that are, you know, satire or meme sites. If the information that you’re getting on the web is coming from Something Awful or 4chan, then you’re gonna get some dark articles in there. But the other alternative is that it could be just hallucinations that have been an observed fact that has been happening with these AIs and large language models that, uh, incidents like we saw with the Loa B that we talked about in an icebreaker last year are still coming forward in ways that are completely unexpected and out of our control.

That scares us a little bit because we don’t know exactly what it’s going to do. When we look at the AI through that lens, like in the mirror, what it’s reflecting back to us is something we don’t necessarily want to look at, and we think that it could be revealing the darkest aspects of ourselves, and that frightens us a whole lot.

AI is a reflection of our society and ourselves, and if we don’t like what we’re seeing, then that gives us an opportunity to perhaps correct things because AI, truth be told, is really dumb right now. It just shows us what’s gone into building it. But as it gets better, as the algorithms improve, then it may get better at hiding its sources.

And that’s a cause for concern. We’re rapidly reaching a point where we may no longer be able to tell or spot a deepfake or artificially generated image or voice, and this may be used by all manner of malicious actors. So as we look through our lens at the future of AI , what do we see on our horizon?

References:
Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.

Deleuze, G. (1992). Postscript on the Societies of Control. October, 59, 3–7.

Eubanks, V. (2018). Automating Inequality. Macmillan.

McLuhan, M. (1964). Understanding Media: The Extensions of Man. The New American Library.

Quan-Haase, A. (2015). Technology and Society: Social Networks, Power, and Inequality. Oxford University Press.

Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking.

Links:
https://arstechnica.com/information-technology/2023/08/microsoft-ai-suggests-food-bank-as-a-cannot-miss-tourist-spot-in-canada/

https://tomglobal.org/about

https://www.makersmakingchange.com/s/

https://arstechnica.com/health/2023/08/ai-powered-brain-implants-help-paralyzed-patients-communicate-faster-than-ever/

https://blog.jim-nielsen.com/2023/temporarily-abled/

https://www.businessinsider.com/microsoft-removes-embarrassing-offensive-ai-assisted-travel-articles-2023-8