This one is a “ripped from the headlines” re-imagining of actual real work being done to use mushrooms in computing. I thought this was too good of a topic to pass up, and anyway “lawn computing” has been a staple in the Early Clues Universe (the “Cluesiverse”) since time immemorial.
I reimagined this work within the context of the AI Takeover, and thought that probably the reason people turn to mushroom computing is that it cannot be traced by the AIs, which have their hands in everything electronic. There are many twists and turns and incredible discoveries they make as a result…
In case you’re curious, here are all the other ripped from the headlines volumes in this series:
Found this Dana Boyd quote via a Substack. She is ostensibly talking about QAnon, but speaks more broadly about the phenomenon known as apophenia.
“Apophenia” refers to the idea of making connections between previously unconnected ideas. Unlike the concept of learning, apophenia suggests a cognitive disorder because the connections made are not real. They are imaginary. People see patterns that don’t exist and devise elaborate internally coherent explanations for non-sensical notions.
Like the cognitive process of apophenia, the social mechanisms of conspiratorial thinking are rooted in reality. It’s the pattern that’s non-existent.
Have to respectfully disagree here. The pattern is very much existent in the mind of the experiencer. That is, it is experientially really, regardless of its outward reality. In other words, it is fundamental to the hyperreal.
Boyd herself later seems to admit this in the post:
From the outside, it looks completely unreal, but on the inside, it feels quite real.
It’s not that QAnon is not inherently dangerous (it is very much so). It’s that things that are experientially real to the person(s) experiencing them have equal or often greater impact to those peoples’ lives and behavior than things that are outwardly real, but don’t necessarily correlate with their lived experience.
I tried (but probably failed) to cover this in my hyperreality framework. It’s hard to talk about these kinds of fine-grained but essential distinctions, especially when the knee-jerk reaction is to call them cognitive disorders and ignore their core reality in terms of the human experience.
Can people take it way too far and it becomes problematic? Absolutely. But that’s part and parcel of hyperreality. It’s the deep disorientation and confusion that sets in when authority and context get flattened, and everything gets blended together and re-organized based on “likes” instead of prior notions of validity. It’s not a prescription to cure what’s happening; it’s a diagnosis of the condition. I’m not sure there is a cure, and if there was, it would most likely have to be a cure for the entire human condition.
I will admit that since somewhere in the high 60’s, I have found my pace of production slowing down significantly. It’s not that I’m running out of stories – not by a long shot. But I have experienced a kind of repetitiveness from using the tools so heavily for such a long period of time (approximately a book every three days since early August). So it is taking a bit more nowadays to put something out than it once did before I laid so much track down in the back country of the latent space, so to speak. I’m also aware that there’s a limited number of creative hours in a day/week/month, and have been burning up a good bit of those lately on standards and related work. It’s been fruitful, but it will be fun now to get back into the fiction stuff more seriously again for a while.
This book continues the Dalton Trask storyline, who is a sort of ripped-from-the-headlines agglomeration of a tech magnate plus aspects of Palmer Eldritch. This book focuses on his global network of satellites, and chronicles briefly the First Satellite War which results from them.
The title of this one is a play on both the “Beyond…” and “Mysterious…” title formats I’ve been using.
For the next 31 volumes (as I continue the race to 100), I am going to have to take a bunch of left turns and get more experimental in order to keep this interesting.
The aide said that guys like me were ‘in what we call the reality-based community,’ which he defined as people who ‘believe that solutions emerge from your judicious study of discernible reality.’ […] ‘That’s not the way the world really works anymore,’ he continued. ‘We’re an empire now, and when we act, we create our own reality. And while you’re studying that reality—judiciously, as you will—we’ll act again, creating other new realities, which you can study too, and that’s how things will sort out. We’re history’s actors…and you, all of you, will be left to just study what we do’.
2004 feels like literally a million years ago. But this still feels prescient even today as consensus reality has gone down the toilet, and is destined to get even more fractured through synthetic realities and histories.
The supposedly senior White House official who is supposed to have made the above quote was talking in the context of “empire,” but what is an empire but a kind of hegemonic metaverse, if you will? Forced interoperability at gunpoint.
I guess what I want to say here is that, yes, being based in reality is a good thing. We shouldn’t abandon that. But the internet is a place that is not based on reality. Or if it is, it only is in the sense that a mostly invented movie might be “based on a true story.” There are things that it references which may be real things, but it blends them with the manufactured unreal.
In the case of the internet, this leads to a massive flattening of information that is nothing if not postmodern – the hyperreal. Massive distrust in the grand organizing narratives. Flattening of authority ad infinitum.
Gregory also said he appreciated that Posobiec didn’t use the video to warn about the dangers of deepfakes, which he described as “an over-used technique” that “seems to contribute to undermining trust in real media,” but to focus on a political hypothetical.
As well as Cory Doctorow’s observation that the reason people are losing trust in (some) institutions is because we’re seeing how often unworthy they are of our trust.
What is “real media” now, anyway, when so much of what passes for journalism is just clickbait or re-reporting things that happened on Twitter? And further, what are the institutions that deserve our uncritical trust?
I’m not saying there’s no objective ground truth and we should just ignore reality; I’m saying that these are not the things upon which information is based online, and that clinging to them exclusively during the rise of generative AI will make our lives very difficult in a world where anyone – not just empires (but especially empires) – can make their own “reality” that is anything but. Applying hyperreality as a lens, for me, then is a way to recognize the essential blending that happens online. And to perhaps realize that this is now our default state…
One of the most over-used of all terms within the world of content moderation, disinfo, online abuse, etc. etc. has got to be “bad actor.”
I’m sure I’ve used it myself in the past, because it is a quick convenient shorthand to collapse a potentially complex thing into. But I’m going to go out of my way to avoid using it anymore, because I’ve been feeling sort of triggered by it when I see it come up, like in that Atlantic article about conspiracy theories & generative AI (two things I happen to know something about):
These are powerful and easy-to-use programs that produce synthetic text, images, video, and audio, all of which can be used by bad actors to fabricate events, people, speeches, and news reports to sow disinformation.
I’m trying to unpack why I’ve come to hate this term, apart from mere overuse. I think it has to do also with the inherent judgement included in it, which seems to go something like: actor A did “bad” thing B, and therefore actor A is “bad.” I just think that’s an overly simplistic way to look at things.
Plenty of times it occurs that basically “innocent” actors engage in an activity online which to them in the moment might not seem all that bad, per se, but might later prove to have unintended negative consequences. Does that make them “bad”? Or does that require actual malice? (as difficult to detect as any kind of intent)
It seems to me like it might be more fruitful to put away the notion of bad actors, and even perhaps the idea of “bad actions,” because analytically it’s just not that precise . I think it would be better to instead analyze the consequences of a given action, rather than decide if the perpetrator is ontologically “bad” (which is basically unknowable). Further, even “good” actions can end up having negative consequences. So, instead of getting stuck on the action, look at what happens as a result of it. Look at the actual harms caused by things, and focus on mitigating those, instead of passing moral judgements without any specific outcome arising from it.
I like that in security contexts, there are alternative more neutral terms in use, like “threat actor.” It helps to refocus the conversation toward the specific threat —> which is generally linked to the risk of a specific negative outcome(s), instead of an unnecessarily judgemental decision about the moral character of participants.
I know the quote below is supposed to sound bad & scary (like everything online), but to me it just sounds like storytelling. In fact, it sounds like exactly the kind of hyperreal storytelling that I’ve been doing (I’m up to 69 books).
The power of AI-generated histories, Horvitz told me, lies in “deepfakes on a timeline intermixed with real events to build a story.”
The quote is from Eric Horvitz, Microsoft’s chief scientific officer. You can find Horvitz’ paper here, which I haven’t read yet. From the abstract:
Compositional deepfakes leverage synthetic content in larger disinformation plans that integrate sets of deepfakes over time with observed, expected, and engineered world events to create persuasive synthetic histories. Synthetic histories can be constructed manually but may one day be guided by adversarial generative explanation (AGE) techniques. In the absence of mitigations, interactive and compositional deepfakes threaten to move us closer to a post-epistemic world, where fact cannot be distinguished from fiction.
“Post-epistemic” seems to be here a synonym for hyperreality.
For some reason, these scenarios don’t scare me all that much – perhaps because I’m already living them from the inside out… I think it would be a mistake here to only focus on the threats and ignore the opportunities to reinvent storytelling.
I generally like Enrique Dans’ writing, but found this line to be a little bit much:
Anyone who lets a search assistant do their thinking for them deserves what they get.
It seems at this point kinda comparable to saying anyone who uses a search engine deserves what they get. I won’t say there are no issues here, obviously. But this probably isn’t the right direction to point fingers…
I went back and fished this graphic out of an old external hard drive from circa 2019. It speaks about how disinformation actors do not generally fall into neat boxes or categories, but exist along a “hybrid threat continuum.”
It occurs to me that the current work I’ve been exploring around analyzing hyperreal artifacts is really an extension of the ideas I was playing with back then. The emerging generative AI landscape has a great deal in common with disinformation, though it is also full of new threats and opportunities.
One issue I saw when I was working on related problems back then was that precisely because these actors didn’t fall into neat little boxes (and were usually hopelessly mixed together), a lot of important data was getting put by the wayside, because it didn’t quite fall into anyone’s jurisdiction in a clear cut manner. Showing hybrid threats as a continuum here was an effort to bridge those gaps, and make things actionable which might not have been before.
I wasn’t really aware of spider/radar graphs at the time, but the above would be a good candidate for using them to visualize incidents and artifacts as well.
There’s a fair amount of cross-over with studying misinformation & disinformation, to be sure. But if we join that with the broader field of hyperreality, we see it’s not only that. There’s also a heavy cross-over with synthetic media, and deepfakes, but if you map the phenomenon to a more multi-dimensional chart, it’s easy to see that there’s more to the picture.
Here’s two sample graphs applying my provisional criteria (which I think need to be fine-tuned still). They use a simple 0 = none and 1 = some rating system to create imaginary profiles of two different pieces of content within this space.
I tried to make the one on the left match sort of the general outlines of a single piece of intentionally malicious disinformation. In this case, you could create that kind of content without using AI. So it might not be appropriate to lump it in with deepfakes or synthetic media, depending on your analytical objectives.
The one on the right is intended to just describe a set of AI-generated images, shared on social media. In this case, there’s no specific harm, and it’s not really expressing any specific opinion, etc.
This experiment of working up hypothetical profiles of content types is a useful one, though it makes it clear I need to tweak my data labels/dimensions/features under analysis probably.
In any event, it ends up being helpful to be able to quickly compare two different content type profiles. I can see that, okay, categories x,y,z probably are common to the vast majority of social media artifacts. And I probably am missing some other ones (a,b,c) that might help further distinguish meaningful differences… But it’s a quick V1 so I encourage others to take the best parts of this, and improve on it to fit better.
Circling back though, it’s clear to see that having a broader common framework lets us compare and contrast things across older categories in a possibly more holistic way… But I’m no closer to having a name for the approach as a whole: hyperreality studies? That doesn’t seem quite right, but at least it’s pretty neutral and doesn’t suggest any specific judgement about the object under study being good or bad, or whatever…
First, I’m applying hyperreality as my lens. You.com/chat gave me a serviceable definition of hyperreality, which is mostly paraphrased from the Wikipedia article, it seems:
Hyperreality is a concept used to describe a state in which what is real and what is simulated or projected is indistinguishable. It is a state in which reality and fantasy have become so blended together that it is impossible to tell them apart. Hyperreality is often used to describe the world of digital media, virtual reality, and augmented reality, where the boundaries between what is real and what is simulated have become blurred.
Maybe it’s just me, but this feels like a useful starting point because it speaks to shades of grey (and endless blending) as being the natural state of things nowadays. It’s now a ‘feature not a bug’ of our information ecosystems. And even though Truth might still be singular, its faces now are many. We need new ways to talk about and understand it.
Right now, people totally misunderstand what AI is. They see it as a tiger. A tiger is dangerous. It might eat me. It’s an adversary. And there’s danger in water, too — you can drown in it — but the danger of a flowing river of water is very different to the danger of a tiger. Water is dangerous, yes, but you can also swim in it, you can make boats, you can dam it and make electricity. Water is dangerous, but it’s also a driver of civilization, and we are better off as humans who know how to live with and work with water. It’s an opportunity. It has no will, it has no spite, and yes, you can drown in it, but that doesn’t mean we should ban water.
Water is also for the most part ubiquitous (except I guess during droughts & in deserts, etc.) as AI soon will be. It will be included in or able to be plugged into everything in the coming years.
Lingua Franca
Thinking of it that way, we need a new language to talk about these phenomena which will, as Jack Clark aptly pointed out, lead to “reality collapse.” That is, we need a new lingua franca, and I suspect that we have that in the concept of hyperreality; we just need to draw it out a little into a more comprehensive analytical framework.
Dimensionality
One thing I’ve observed in other analyses of the conversations emerging around AI generated content and allied phenomena is that there is a bit of reduction happening. Possibly too much. It appears to me that most discussions usually center around a very limited set of axes to describe what’s happening:
Real vs. fake
Serious vs. satire
Harmful vs. responsible
Labeled vs. unlabeled
Certainly those form the core of the conversation for a reason; they are important. But alone they give an incomplete picture of a complex thing.
Speaking as someone who has had to do the dirty work of a lot of practical detection and enforcement around questionable content, I think what we need is what might be called in machine learning a “higher-dimensional” space to do our analysis. That is, we need more axes on our graphs, because applying low-dimensional frameworks appears to be throwing out too much important information, and risks collapsing together items which are fundamentally different and require different responses.
It’s interesting once we open up this can of worms, that a more dimensional approach actually corresponds quite closely to the so-called “latent space” which is so fundamental to machine learning. Simple definition:
Formally, a latent space is defined as an abstract multi-dimensional space that encodes a meaningful internal representation of externally observed events. Samples that are similar in the external world are positioned close to each other in the latent space.
In ML, according to my understanding (I had to ask ChatGPT a lot of ELI5 questions to get this straight): for items in a dataset, we have characteristics, each of which is a feature. Then a set of features that describes an item is the feature vector. Each feature corresponds to a dimension, which is sort of a measurement of the presence and quantity of a given feature. So I higher-dimensional space uses more dimensions (to measure features of items), and a low or lower dimensional space attempts to translate down to fewer dimensions while still remaining adequately descriptive for the task at hand.
In my mind, anyway, it seems altogether appropriate to adopt the language and concepts of machine learning to analyze phenomena which include generative AI – which is really usually just machine learning. It seems to fit more completely than applying other older models, but maybe that’s just me…
Higher-dimensional analysis of hyperreality artifacts
So, what does any of that mean? To me, it means we simply need more features, more dimensions that we are measuring for. More axes in our graph. I spent some time today trying to come up with more comprehensive characteristics of hyperreality artifacts, and maxed out at around 23 or so pairs of antonyms which we might try to map to any given item under analysis.
However, when I was trying to depict that many visually, it quickly became apparent that having that many items was quite difficult to show clearly in pictorial form. So I ended up reducing it to 12 pairs of antonyms, or basically 24 features, each of which corresponds to a dimension, which may itself have a range of values.
Here is my provisional visualization:
And the pairs or axes that I applied in the above goes like this:
Fiction / Non-fiction
Fact (Objective) / Opinion (Subjective)
Cohesive / Fragmentary
Clear / Ambiguous
Singular / Multiple
Static / Dynamic
Ephemeral / Persistent
Physical / Virtual
Harmful / Beneficial
Human-made / AI generated
Shared / Private
Serious / Comedic
From my exercise in coming up with this list, I realize that the items included above as axes are not the end-all be-all here. It’s not meant to be comprehensive & other items may become useful for specific types of analysis. In fact, in coming up with even this list, I realized how fraught this kind of list is, and how many holes and how much wiggle room there is in it. But I wanted to come up with something that was broadly descriptive above and beyond what I’ve seen anywhere else.
Graphing Values
What’s the benefit of visualizing it like this? Well, having a chart helps us situate artifacts within the landscape of hyperreality; it lets us make maps. I wasn’t familiar with them before trying to understand how to represent high-dimensional sets visually, but there’s something called a radar graph or spider graph which is useful in this context.
I found a pretty handy site for making radar graphs here, and plugged my data labels (features) into it. Then, for each one, I invented a value between 0-4, which would correspond to the range of the dimension. Here’s how two different sets of values look, mapped to my graphic:
Now, these are just random values I entered to give a flavor of what two different theoretical artifacts might look like. I’m not really a “math” guy, per se, but it becomes clear right away once you start visualizing these with dummy values that you could start to make useful and meaningful comparisons between artifacts under analysis – provided you have a common criteria you’re applying to generate scores.
Criteria & Scoring
So the way you would generate real scores would be – first decide on your features/dimensions you want to study within your dataset. Then, come up with criteria that are observable in the data, and are as objective as possible. You should not have to guess for things like this, and if you are guessing a lot, your scores are probably not going to be especially meaningful. You want scoring to be repeatable and consistent, so that you can make accurate comparisons across diverse kinds of artifacts, and group them accordingly. A simple way to score would just be with a 0 for “none” and a 1 for “some.” Beyond that, you could have higher numbers for degrees or amount of which a given feature is observable in an artifact. So in the examples above, 1 could represent “a little” and 4 would be “a whole lot.”
Taking Action
Within an enforcement context – or any kind of active response, really (like for example, fact checking) – once you’ve got objective, measurable criteria that allow you to sort artifacts into groups, you can then assign each group a treatment, mitigation, or intervention – in other words, an action to take. This is usually done based on risk: likelihood, severity of harm, etc.
Anyway, I hope this gives some useful tools and mental models for people who are working in this space to apply in actual practice. Hopefully, it opens the conversation up significantly more than just trying to decide narrowly if something is real or fake, serious or satire, and getting stuck in the narrower outcomes those labels seem to point us towards.
Hyperreality is here to stay – we might as well make it work for us!
