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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.

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?

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:

• Notes on the Lost Direction
• Notes on Conspiratopia

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.

• Notes on Das Machina
• Notes on Information Control

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.

• Why I don’t have stats, comments, or use social media
• Interrogate the technology
• How I use AI: An Ethical Inquiry
• Hyperreality As Analytical Framework
• RSS is a better homepage than any social media site
• Readers becoming writers
• Micro-Standards in AI
• AI needs to make room for non-STEM positions

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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!

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.

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.”

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!

When you move away from platforms as homepage, you’re saddled with the (good) problem of having to actively find things to follow, when those muscles have atrophied from too much time stuck in the existential trough of artificial recommender systems.

The method that always worked for me back in the day, and that is working swimmingly again now is to 1) start with a couple blogs to follow that are interesting, and 2) when those link out to other interesting blogs (they always do, its inherent in the nature of blogging), you simply follow those. And on and on.

Here is one follow I recommend, though I admit not knowing too much about the author; I just know they write good things. Robin Sloan, for example, in their latest newsletter writes about one of the scourges of the web, the newsletter sign-up popup (institutionalized by Substack, btw, but epidemic across the web):

The newsletter pop-up treats website visitors as means only — a flow of interactions to be optimized, rather than a parade of individuals having real experiences in the world.

This plugs right into my feelings about not having stats or comments.

Wanted to continue expanding on ideas from this post about the act of reading & writing becoming merged through the experience of generative AI tools.

While poking around on the topic, I discovered that dialogic reading is a term that is used (under the larger umbrella of dialogic learning) around a style of reading with children, where while looking through a book, you ask them questions and make observations about what’s happening in the narrative.

From the site linked above:

When most adults share a book with a preschooler, they read and the child listens. In dialogic reading, the adult helps the child become the teller of the story. The adult becomes the listener, the questioner, the audience for the child.

It’s interesting that they even use the word “prompts” in this context, where you prompt the child to say something about the book. Then you talk through together evaluating and expanding the response.

I’ve definitely used this method with developing storylines with AI partners; Character AI in particular springs to mind here, since it is literally a dialogue with a character. Each participant asks questions, adds, corrects, etc.

I was polling ChatGPT to see if it could come up with other possible names or correlated concepts to apply here.

It brought up both interactive fiction and choose your own adventure. But neither of those really captures for me the merging of reading & writing that happens with generative AI tools. Interactive fiction, at least in my mind, is still bound by the constraints set up by the original creator of the work or the narrative space or environment in which the action takes place. It might be that the reader/player can navigate in the order that they choose (also seen in open world games), but they don’t necessarily have the ability to invent whole cloth, and to determine (curate) if a generated element should be included as ‘canon’ in that world.

As Wikipedia states more plainly on the interactive storytelling page:

The author creates the setting, characters, and situation which the narrative must address, but the user (also reader or player) experiences a unique story based on their interactions with the story world.

That to me suggests there is a need for a new set of terms & concepts around these ideas being applied in a much more open-ended generative storytelling landscape.

My hunch is that AI technology is developing too rapidly, and in too many different ways, that trying to have a single set of industry standards is going to be extremely tough sell. Maybe that’s always been the case, but the problem feels amplified in AI.

There are too many different kinds of actors with different approaches, needs, and goals: for-profit enterprises, non-profits and educational institutions, governments, open source projects, end users, and others to be determined. A single one-size-fits-all set of standards may not accurately reflect the needs and priorities of all these actors.

Additionally, there’s always the possibility that a common standard may be wrong or insufficient, given the rapidly changing landscape of AI, and its impacts on people which are bound to compound or snowball over time. So, it seems important to have a diverse range of micro-standards that can adapt to the needs and trends of different actors and use cases.

One of the advantages of AI is that, when done well, one of its strengths is the ability to easily convert or translate between forms, styles, modes, etc. My hunch about having many micro-standards then is that once things are humming along smoothly, it should be relatively trivial to transfer compliance efforts between many different types of micro-standards, with varying degrees of fidelity and efficiency.

A single common industry standard might sound easier, but a more flexible and nuanced approach might prove to be healthier and more adaptable in the long run.