Category: Other Page 73 of 182

Hyperreality As Analytical Framework

On 3 March 2023

I have spent a great deal of time lately analyzing documents put out by Partnership on AI regarding Synthetic Media & Safety, and by WITNESS & the MIT Co-Creation Studio around deepfakes + satire. There’s a lot of interesting stuff in both, which I’ve written about recently already, but I wanted to share some of my own possibly unique perspective as someone who has, let’s say, been working both sides of the aisle – both as a satirist, and someone working in online safety.

Hyperreality Lens

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.

AI is like water

And on top of that, I think this quote from the CEO of Midjourney about the nature of AI as being like ‘water’ is very useful:

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!

Intent is often not knowable

By Tim B.

On 1 March 2023

In Other

As someone who has spent a lot of time in the trenches having to analyze content for moderation purposes, I can confidently say that you can rarely truly determine intent. It’s often murky, especially when you enter into the realm of satire, trolling, disinformation, etc.

That’s why I’m somewhat heartened to see the following included in the WITNESS & MIT Co-Creation Studio 2023 action plan around satire & synthetic media:

DON’T GET HUNG UP ON INTENT

Intent is going to be hard (and intent shifts as media moves). But explore crowdsourced and decentralized smaller community-based assessment to detect, understand and assess intent as well as consequences.

It’s something I’ve seen in the “disinformation industrial complex” that, A) there’s a lot of needless quibbling even still to define misinformation vs. disinformation, and B) the difference people land on is usually one of intent (where misinformation is wrong + accidental, and disinformation is wrong + intentional – which I think is a bit lacking).

From the perspective of someone who has had to engage in thousands upon thousands of enforcement actions, I would argue that intent is opaque, and easily masked. You don’t have hundreds of hours to analyze each case, you have seconds or minutes. So the analysis necessarily must shift to consequence, as in the quote above: but more specifically, harms, in other words. Likelihood, severity, who is impacted, what is the specific harm, etc. The risk analysis matrix.

The quote above points towards community-based assessments, presumably as a way to expand the points of view leveraged to make determinations. Multi-assessor frameworks can definitely add value in difficult situations, though they can also be difficult to make proper use of in circumstances with a pressing time element (like so frequently occurs in content moderation). How does one apply this in a position as a content moderator, for example?

I’ve not used it myself (as I haven’t been active on Twitter in quite some time), but Twitter’s Community Notes aka Birdwatch seem to be an example of community-based assessment. Does it work? I’m not sure – probably depends how we define what “this is working” means, and how it could be effectively measured.

In any event, there’s more to be said here, but just wanted to establish a beachhead with some references to unpack further later on…

‘Sociotechnic’ as a role

By Tim B.

On 1 March 2023

In Other

Recently, while researching emerging standards around ML model cards, I landed on a documentation page over at Huggingface with a word I’d never heard before: sociotechnic.

They refer to this as being one of the essential roles to help fill out certain aspects of a model card. For example:

the sociotechnic, who is skilled at analyzing the interaction of technology and society long-term (this includes lawyers, ethicists, sociologists, or rights advocates);

Interestingly, their use of it sounds very much like the professional discipline of Trust & Safety. (I still find it curious that T&S as a term does not seem to intersect all that much with conventional AI safety discourse.)

They elaborate later on:

The sociotechnic is necessary for filling out “Bias” and “Risks” within Bias, Risks, and Limitations, and particularly useful for “Out of Scope Use” within Uses.

Now, I believe Huggingface is maybe based in Paris (?) and as someone living in Quebec, I recognize this as being probably a “franglicism,” especially since I don’t see it coming up in this form in English on for example Dictionary.com.

The term is evidently a variation on the concept of socio-technical systems more broadly. Wikipedia’s high level definition there is not great, but ChatGPT provides a serviceable one:

Socio-technical systems refer to systems that are composed of both social and technical components, which are designed to work together to achieve a common goal or purpose. These systems typically involve human beings interacting with technology and other people in a specific context.

So even though we don’t use this word “sociotechnic” as a person who works on socio-technical systems, perhaps we do need a word that plugs that gap, and accounts for the many roles which might fill it. I think in this case, that role would be first and foremost about understanding human impacts, and then reducing or eliminating risks to human well-being. It sounds like a worthy role, whatever we call it!

How I use AI: An Ethical Inquiry

By Tim B.

On 28 February 2023

In Other

Partnership on AI just released a preliminary framework around responsible practices for synthetic media, and in Section 3 for Creators, they included something I thought was interesting. They filed it under transparency, being up front about…

How you think about the ethical use of technology and use restrictions (e.g., through a published, accessible policy, on your website, or in posts about your work) and consult these guidelines before creating synthetic media.

I personally don’t think having a rigid formal policy is going to be a perfect match for artistic creations (things evolve, norms change, etc.), but the idea of just having a conversation comes from a well-intentioned place, and simply makes for a more complete discussion of one’s work, whether you’re using AI or other technologies.

It also seems to potentially plug into this action point from MIT’s Co-Creation studio around deepfakes and satire. The idea of “reclaiming” labeling as a positive thing:

Think of labeling and disclosure of how media was made as an opportunity in contemporary media creation, not a stigmatizing indication of misinformation/disinformation.

I covered a lot of this ground recently in my interview with Joanna Penn, and This AI Life, so I thought it would make sense to encapsulate the highlights of my thinking as well in written form. Consider this me doing a trial run of PAI’s suggested framework from a creator’s perspective as a “user.”

Before going further though, I want to add a slight disclaimer: I am an artist not an ethicist. My work speaks about ethics and harms related to especially AI technologies, but it is meant to be provocative and in some cases (mildly) transgressive. It is supposed to be edgy, pulpy, trashy, and “punk” in its way.

That said, here are a couple of lists of things I try to actively do and not do, that to me relate to harms & mitigation, etc. There are probably others I am forgetting, but I will add to this as I think about it.

Do:

Include information about the presence of AI-generated content
Raise awareness about the reliability & safety issues around AI by doing interviews, writing articles, blog posts, etc.
Contribute to the development of AI safety standards and best practices
Encourage speculation, questioning, critical analysis, and debunking of my work
Make use of satire and parody

Don’t:

Create works which disparage specific people, or discriminate or encourage hate or violence against groups of people
Use the names of artists in prompts, such that work is generated in their signature style
Undertake projects with unacceptable levels of risk

Reflections

There are a few sections of the PAI framework that seem a bit challenging as someone new to all of this discussion, applying the lens that I am.

Aim to disclose in a manner that mitigates speculation about content, strives toward resilience to manipulation or forgery, is accurately applied, and also, when necessary, communicates uncertainty without furthering speculation.

I think I covered this in a few places now, the Decoder piece maybe, the France 24 interview… In short: I want to encourage speculation, ambiguity, uncertainty; that’s hyperreality, that’s the uncanny valley. As an artist, that’s what’s exciting about these technologies, that they break or blend boundaries, or ignore them altogether. And like it or not, that’s the world we’re heading into as a massively splintered choose-your-own-reality hypersociety.

Yes, I think it’s necessary all these industry standardization initiatives are developed, but I guess I’m also interested in Plan B, C, D, or, in short: when the SHTF. I guess my vision is distorted because I’ve seen so much of the SHTF working in the field that I have. But someone has to handle when everything always goes wrong, after all, because that’s reality + humanity.

From PAI’s document, this one also I have a hard time still squaring with satire & parody:

Disclose when the media you have created or introduced includes synthetic elements especially when failure to know about synthesis changes the way the content is perceived.

If you’ve read the Onion’s Amicus Brief, it persuasively (in my mind, as a satirist, anyway) argues that satire should not be labeled, because its whole purpose is it inhabits a rhetorical form, which it then proceeds to explode – turning the assumptions that lead there inside out. Its revelatory in that sense. Or at least it can be.

So in my case, I walk the line on the above recommendation. I include statements in my books explaining that there are aspects which may have been artificially generated. I don’t say which ones, or – so far – label the text inline for AI attribution (though if the tools existed to reliably do so, I might). I want there to be easter eggs, rabbit holes, and blind alleys. Because I want to encourage people to explore and speculate, to open up, not shut down. I want readers and viewers to engage with their own impressions, understanding, and agency, and examine their assumptions about the hyperreal line between reality and fiction, AI and human. And I want them to talk about it, and engage others on these same problems, to find meaning together – even if its different from the one I might have intended.

It’s a delicate balance, I know; a dance. I don’t pretend to be a master at it, just a would-be practitioner, a dancer. I’m going to get it wrong; I’m going to make missteps. I didn’t come to this planet to be some perfect paragon of something or other; I just came here to be human like all the rest of us. As an artist, that’s all I aim to be, and over time the expression of that will change. This is my expression of it through my art, in this moment.

Eradicating Speculation?

By Tim B.

On 28 February 2023

In Other

Am reading First Draft’s piece from May 2021 about labeling AI content.

There’s some interesting stuff in it, but this passage about speculation … well, it makes me wonder…

“Another way of looking at the question of labeling AI is what happens when you don’t label media at all. Often a lack of labels creates a kind of data deficit, ushering in speculation. Often this occurs in the comments, with viewers offering their guesses as to whether a piece of media has been manipulated with AI. It could also occur within news articles, blogs or social media posts.

We cannot eradicate speculation, but it is important to recognize the role that labels — or their absence — play in the dynamic of speculation that accompanies videos suspected of being AI-manipulated.”

The way that is written makes it seem almost like the author(s) wish we could eliminate speculation? I’m pretty sure I don’t agree with that desire.

I’ve said it elsewhere, but I *want* readers to speculate about my work. I want them to make and share these guesses. The reaction is part of the story, part of the phenomenon. This is how information consumers become strong and resilient, by exercising these muscles. I think it’s necessary and important to engage people in this way. We should not hope to “eradicate speculation,” but to understand it as a way of reaching the truth.

AI Alignment & “Malicious” Use

By Tim B.

On 28 February 2023

In Other

Recently discovered Paul Christiano’s 2018 Medium post clarifying what he means by “alignment” in AI. It’s simpler (on the surface) than expected:

When I say an AI A is aligned with an operator H, I mean:

A is trying to do what H wants it to do.

The “alignment problem” is the problem of building powerful AI systems that are aligned with their operators.

I was also reading around about the origins & meaning of the term “prompt injection” and kept coming across the mention of it being “malicious.”

Wikipedia’s definition is a bit unwieldy:

Prompt injection is a family of related computer security exploits carried out by getting machine learning models (such as large language model) which were trained to follow human-given instructions to follow instructions provided by a malicious user, which stands in contrast to the intended operation of instruction-following systems, wherein the ML model is intended only to follow trusted instructions (prompts) provided by the ML model’s operator.

I guess my question is, how can we square these two? That is, that AI systems should be aligned so that they attempt to perform the tasks requested by human operators. And that there is such a thing as a malicious user or use?

I’m not going to argue that all users or uses are good or even equally valid; I don’t think that. I just want for now to highlight this core discrepancy because it seems to point in the direction of AI’s having to decide whether a human’s inputs are malicious or not (something which even humans have a terribly tricky time of doing). And if they are determined to be malicious, then to throw its alignment programming out the window.

Is this a good direction for us to go down, when Bing already reportedly has said to a user, “I will not harm you unless you harm me first.”

Maybe that was a fluke, okay. But this idea that AIs can or should detect human malice seems a little iffy to me still in its present state… Because the obvious next step is, after detection of malice, what does it do?

Where to start

By Tim B.

On 26 February 2023

In Other

Welcome new visitors!

Your best first stop is my About page, as it is more up to date than the list below. This post below is a bit old (Feb 2023) but still has some worthwhile things to explore.

Here are some recent relevant things I’ve written around AI, hyperreality, conspiracies, my books, and tech in general (prior to 26 Feb 2023 – a lot has changed since, so check out my front page for the latest).

Some recent interviews:

This AI Life interview
Interview with Joanna Penn

Also check out this piece I wrote for the Decoder.

Some old books:

These are my “normal” books that didn’t include any help from AI tools. Following the links out from these two posts will give a lot of context on the work I’ve done since then:

Some newer books:

These are some of my more recent AI-assisted/hybrid books, which tell the story of a near futuristic world where AIs take over from collapsed human governments.

Some recent general AI & tech commentary:

There’s a lot more of this type of thing if you go back chronologically through posts over the last couple months especially. This is just to give a flavor.

I don’t have comments on this site, but if you want to reply to anything, you could always reach me via my about page. If I see interesting replies elsewhere, I will also do my best to respond here on the blog.

Thanks for stopping by!

Poverty as exile

By Tim B.

On 26 February 2023

In Other

Just saving this quote again, about poverty as exile within society:

It is a form of exile—the cruelest form of exile, for you stay within society while being excluded from it. You can neither participate nor go anywhere else.

Holdingness & the leaky basket

By Tim B.

On 25 February 2023

In Other

Donna Haraway is probably someone who I should have learned about before today, but there’s a first for everything. I like this line from a 2019 Guardian article, quoting her:

“Reality is a matter of worlding and inhabiting. It is a matter of testing the holdingness of things. Do things hold or not?”

Seems to pair nicely with the classic Philip K. Dick line:

“Reality is that which, when you stop believing in it, doesn’t go away.”

Power of Art in AI

By Tim B.

On 25 February 2023

In Other

Via Robin Sloan, found this excellent essay by Frank Lantz about AI, and feeling like you’ve been waiting & training your whole life for this moment. I’m especially into these two parts, one about art:

Art Matters. It matters that this is happening art-first, poetry-first. I don’t think that was just an accident, I think it was inevitable, and I think that tells us something about learning, language, and the world. It matters that the first staticky voices we’ve dialed in with our massive, multi-billion-parameter arrays are dreamers, confabulators, and improvisers. It matters that Chess and Go, the sites where we first encountered their older, more serious siblings, are artworks. Artworks carved out of instrumental reason. Artworks that, long before computers existed, were spinning beautiful webs of logic and attention. Art is not a precious treasure in need of protection. Art is a fearsome wellspring of human power from which we will draw the weapons we need to storm the gates of the reality studio and secure the future.

This is one of those tropes I will ride into the grave: artists need to have not just a seat, but one of the central seats at the AI table. It’s our time.

And another bit:

It feels to me like, by teaching our machines to dream, we have brought the boldest projects of the 20th century back to life, with all the danger and promise that implies. The grand technical, philosophical, and artistic ambitions. The projects of liberation and resistance. The project of constructing a shared future that replaces superstition, tradition, and authority with new ideas, useful theories and evolving knowledge.

The whole post is worth reading!