kottke.org posts about artificial intelligence
Artist and filmmaker Paul Trillo made Thank You For Not Answering, an artful experimental short film, using a suite of AI tools. The end credits of the film read:
Written and “Directed” by Paul Trillo.
Kyle Chayka talked to Trillo about his process for the New Yorker:
Trillo demonstrated the process to me during a Zoom call; in seconds, it was possible to render, for example, a tracking shot of a woman crying alone in a softly lit restaurant. His prompt included a hash of S.E.O.-esque terms meant to goad the machine into creating a particularly cinematic aesthetic: “Moody lighting, iconic, visually stunning, immersive, impactful.” Trillo was enthralled by the process: “The speed in which I could operate was unlike anything I had experienced.” He continued, “It felt like being able to fly in a dream.” The A.I. tool was “co-directing” alongside him: “It’s making a lot of decisions I didn’t.”
I know, I know. Too much Wes Anderson. Too much AI. But there is something in my brain, a chemical imbalance perhaps, and I can’t help but find this reimagining of the Lord of the Rings in Anderson’s signature style funny and charming. Sorry but not sorry.
See also The Galactic Menagerie, Wes Anderson’s Star Wars.
Expanding on his previous thoughts on the relationship between AI and capitalism — “I tend to think that most fears about A.I. are best understood as fears about capitalism” — Ted Chiang offers a useful metaphor for how to think about AI: as a management-consulting firm like McKinsey.
So, I would like to propose another metaphor for the risks of artificial intelligence. I suggest that we think about A.I. as a management-consulting firm, along the lines of McKinsey & Company. Firms like McKinsey are hired for a wide variety of reasons, and A.I. systems are used for many reasons, too. But the similarities between McKinsey — a consulting firm that works with ninety per cent of the Fortune 100 — and A.I. are also clear. Social-media companies use machine learning to keep users glued to their feeds. In a similar way, Purdue Pharma used McKinsey to figure out how to “turbocharge” sales of OxyContin during the opioid epidemic. Just as A.I. promises to offer managers a cheap replacement for human workers, so McKinsey and similar firms helped normalize the practice of mass layoffs as a way of increasing stock prices and executive compensation, contributing to the destruction of the middle class in America.
A former McKinsey employee has described the company as “capital’s willing executioners”: if you want something done but don’t want to get your hands dirty, McKinsey will do it for you. That escape from accountability is one of the most valuable services that management consultancies provide. Bosses have certain goals, but don’t want to be blamed for doing what’s necessary to achieve those goals; by hiring consultants, management can say that they were just following independent, expert advice. Even in its current rudimentary form, A.I. has become a way for a company to evade responsibility by saying that it’s just doing what “the algorithm” says, even though it was the company that commissioned the algorithm in the first place.
Good stuff — I especially enjoyed the mini You’re Wrong About on the Luddites — do read the whole thing.
No matter which side you come down on in the debate about using AI tools like Stable Diffusion and Midjourney to create digital art, this video of an experienced digital artist explaining how he uses AI in his workflow is worth a watch. I thought this comment was particularly interesting:
I see the overall process as a joint effort with the AI. I’ve been a traditional artist for 2 decades, painting on canvas. And in the last five years I’ve been doing a lot of digital art. So from that part of myself, I don’t feel threatened at all.
I feel this is an opportunity. An opportunity for many new talented people to jump on a new branch of art that is completely different from the one that we have already in digital art and just open up new way of being creative.
I’m not going to make a habit of posting AI generated video and photography here (mainly because most of it is not that interesting) but Pepperoni Hug Spot is just too perfect a name for a pizza place to pass up. And it’s got Too Many Cooks vibes.
Well this is some bizarre good fun — turns out that the campy goofiness of Star Wars and the campy seriousness of high fashion make for a pretty good combination.
See also Lord of the Rings by Balenciaga and Game of Thrones by Balenciaga. Oh, and Hipster Star Wars.
[Yesterday I spent all day answering reader questions for the inaugural Kottke.org Ask Me Anything. One of them asked my opinion of the current crop of AI tools and I thought it was worth reprinting the whole thing here. -j]
Q: I would love to know your thoughts on AI, and specifically the ones that threaten us writers. I know you’ve touched on it in the past, but it seems like ChatGPT and the like really exploded while you were on sabbatical. Like, you left and the world was one way, and when you returned, it was very different. —Gregor
A: I got several questions about AI and I haven’t written anything about my experience with it on the site, so here we go. Let’s start with two facts:
- ChatGPT moved me to tears.
- I built this AMA site with the assistance of ChatGPT. (Or was it the other way around?)
Ok, the first thing. Last month, my son skied at a competition out in Montana. He’d (somewhat inexplicably) struggled earlier in the season at comps, which was tough for him to go through and for us as parents to watch. How much do we let him figure out on his own vs. how much support/guidance do we give him? This Montana comp was his last chance to get out there and show his skills. I was here in VT, so I texted him my usual “Good luck! Stomp it!” message the morning of the comp. But I happened to be futzing around with ChatGPT at the time (the GPT-3.5 model) and thought, you know, let’s punch this up a little bit. So I asked ChatGPT to write a good luck poem for a skier competing at a freeski competition at Big Sky.
In response, it wrote a perfectly serviceable 12-line poem with three couplets that was on topic, made narrative sense, and rhymed. And when I read the last line, I burst into tears. So does that make ChatGPT a soulful poet of rare ability? No. I’ve thought a lot about this and here’s what I think is going on: I was primed for an emotional response (because my son was struggling with something really important to him, because I was feeling anxious for him, because he was doing something potentially dangerous, because I haven’t seen him too much this winter) and ChatGPT used the language and methods of thousands of years of writing to deliver something a) about someone I love, and b) in the form of a poem (which is often an emotionally charged form) — both of which I had explicitly asked for. When you’re really in your feelings, even the worst movie or the cheesiest song can resonate with you and move you — just the tiniest bit of narrative and sentiment can send you over the edge. ChatGPT didn’t really make me cry…I did.
But still. Even so. It felt a little magical when it happened.
Now for the second part. I would say ChatGPT (mostly the new GPT-4 model), with a lot of hand-holding and cajoling from me, wrote 60-70% of the code (PHP, Javascript, CSS, SQL) for this AMA site. And we easily did it in a third of the time it would have taken me by myself, without having to look something up on Stack Overflow every four minutes or endlessly consulting CSS and PHP reference guides or tediously writing tests, etc. etc. etc. In fact, I never would have even embarked on building this little site-let had ChatGPT not existed…I would have done something much simpler and more manual instead. And it was a *blast*. I had so much fun and learned so much along the way.
I’ve also been using ChatGPT for some other programming projects — we whipped the Quick Links into better shape (it can write Movable Type templating code…really!) and set up direct posting of the site’s links to Facebook via the API rather than through Zapier (saving me $20/mo in the process). It has really turbo-charged my ability to get shit done around here and has me thinking about all sorts of possibilities.
I keep using the word “we” here because coding with ChatGPT — and this is where it starts to feel weird in an uncanny valley sort of way — feels like a genuine creative collaboration. It feels like there is a “someone” on the other side of that chat, a something that’s really capable but also needs a lot of hand-holding. Just. Like. Me. There’s a back and forth. We both screw up and take turns correcting each other’s mistakes. I ask it please and tell it thank you. ChatGPT lies to me; I gently and non-judgmentally guide it in a more constructive direction (as you would with a toddler). It is the fucking craziest weirdest thing and I don’t really know how to think about it.
There have only been a few occasions in my life when I’ve used or seen some new technology that felt like magic. The first time I wrote & ran a simple BASIC program on a computer. The first time I used the web. The first time using a laptop with wifi. The first time using an iPhone. Programming with ChatGPT over the past few weeks has felt like magic in the same way. While working on these projects with ChatGPT, I can’t wait to get out of bed in the morning to pick up where we left off last night (likely too late last night), a feeling I honestly have not consistently felt about work in a long time. I feel giddy. I feel POWERFUL.
That powerful feeling makes me uneasy. We shouldn’t feel so suddenly powerful without pausing to interrogate where that power comes from, who ultimately wields it, and who it will benefit and harm. The issues around these tools are complex & far-reaching and I’m still struggling to figure out what to think about it all. I’m persuaded by arguments that these tools offer an almost unprecedented opportunity for “helping humans be creative and express themselves” and that machine/human collaboration can deepen our understanding and appreciation of the world around us (as has happened with chess and go). I’m also persuaded by Ted Chiang’s assertion that our fears of AI are actually about capitalism — and we’ve got a lot to fear from capitalism when it comes to these tools, particularly given the present dysfunction of US politics. There is just so much potential power here and many people out there don’t feel uneasy about wielding it — and they will do what they want without regard for the rest of us. That’s pretty scary.
Powerful, weird, scary, uncanny, giddy — how the hell do we collectively navigate all that?
(Note: ChatGPT didn’t write any of this, nor has it written anything else on kottke.org. I used it once while writing a post a few weeks ago, basically as a smart thesaurus to suggest adjectives related to a topic. I’ll let you know if/when that changes — I expect it will not for quite some time, if ever. Even in the age of Ikea, there’s still plenty of handcrafted furniture makers around and in the same way, I suspect the future availability of cheap good-enough AI writing/curation will likely increase the demand and value for human-produced goods.)
In a piece about how the pace of improvement in the current crop of AI products is vastly outstripping the ability of society to react/respond to it, Ezra Klein uses this cracker of a phrase/concept: “the difficulty of living in exponential time”.
I find myself thinking back to the early days of Covid. There were weeks when it was clear that lockdowns were coming, that the world was tilting into crisis, and yet normalcy reigned, and you sounded like a loon telling your family to stock up on toilet paper. There was the difficulty of living in exponential time, the impossible task of speeding policy and social change to match the rate of viral replication. I suspect that some of the political and social damage we still carry from the pandemic reflects that impossible acceleration. There is a natural pace to human deliberation. A lot breaks when we are denied the luxury of time.
But that is the kind of moment I believe we are in now. We do not have the luxury of moving this slowly in response, at least not if the technology is going to move this fast.
Covid, AI, and even climate change (e.g. the effects we are seeing after 250 years of escalating carbon emissions)…they are all moving too fast for society to make complete sense of them. And it’s causing problems and creating opportunities for schemers, connivers, and confidence tricksters to wreck havoc.
In this final installment of Everything is a Remix, Kirby Ferguson offers his perspective on image generation with AI, how it compares to human creativity, and what its role will be in the future. In watching the part about the anxiety in the creative community about these image generators, I was reminded of what Ted Chiang has said about fears of technology actually being fears of capitalism.
It’s capitalism that wants to reduce costs and reduce costs by laying people off. It’s not that like all technology suddenly becomes benign in this world. But it’s like, in a world where we have really strong social safety nets, then you could maybe actually evaluate sort of the pros and cons of technology as a technology, as opposed to seeing it through how capitalism is going to use it against us.
I agree with Ferguson that these AI image generators are, outside the capitalist context, useful and good for helping humans be creative and express themselves. Tools like Midjourney, DALL-E, and Stable Diffusion allow anyone to collaborate with every previous human artist that has ever existed, all at once. Like, just think about how powerful this is: normal people who have ideas but lack technical skills can now create imagery. Is it art? Perhaps not in most cases, but some of it will be. If the goal is to get more people to be able to more easily express and exercise their creativity, these image generators fulfill that in a big way. But that’s really scary — power always is.
In 2020, before the current crop of large language models (LLM) like ChatGPT and Bing, Emily Bender and Alexander Koller wrote a paper on their limitations called Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data. In the paper, Bender and Koller describe an “octopus test” as a way of thinking about what LLMs are capable of and what they aren’t. A recent profile of Bender by Elizabeth Weil for New York magazine (which is worth reading in its entirety) summarizes the octopus test thusly:
Say that A and B, both fluent speakers of English, are independently stranded on two uninhabited islands. They soon discover that previous visitors to these islands have left behind telegraphs and that they can communicate with each other via an underwater cable. A and B start happily typing messages to each other.
Meanwhile, O, a hyperintelligent deep-sea octopus who is unable to visit or observe the two islands, discovers a way to tap into the underwater cable and listen in on A and B’s conversations. O knows nothing about English initially but is very good at detecting statistical patterns. Over time, O learns to predict with great accuracy how B will respond to each of A’s utterances.
Soon, the octopus enters the conversation and starts impersonating B and replying to A. This ruse works for a while, and A believes that O communicates as both she and B do — with meaning and intent. Then one day A calls out: “I’m being attacked by an angry bear. Help me figure out how to defend myself. I’ve got some sticks.” The octopus, impersonating B, fails to help. How could it succeed? The octopus has no referents, no idea what bears or sticks are. No way to give relevant instructions, like to go grab some coconuts and rope and build a catapult. A is in trouble and feels duped. The octopus is exposed as a fraud.
The paper’s official title is “Climbing Towards NLU: On Meaning, Form, and Understanding in the Age of Data.” NLU stands for “natural-language understanding.” How should we interpret the natural-sounding (i.e., humanlike) words that come out of LLMs? The models are built on statistics. They work by looking for patterns in huge troves of text and then using those patterns to guess what the next word in a string of words should be. They’re great at mimicry and bad at facts. Why? LLMs, like the octopus, have no access to real-world, embodied referents. This makes LLMs beguiling, amoral, and the Platonic ideal of the bullshitter, as philosopher Harry Frankfurt, author of On Bullshit, defined the term. Bullshitters, Frankfurt argued, are worse than liars. They don’t care whether something is true or false. They care only about rhetorical power — if a listener or reader is persuaded.
The point here is to caution against treating these AIs as if they are people. Bing isn’t in love with anyone; it’s just free-associating from an (admittedly huge) part of the internet.
This isn’t an exact analogue, but I have a car that can drive itself under certain circumstances (not Tesla’s FSD) and when I turn self-drive on, it feels like I’m giving control of my car to a very precocious 4-year-old. Most of the time, this incredible child pilots the car really well, better than I can really — it keeps speed, lane positioning, and distance to forward traffic very precisely — so much so that you want to trust it as you would a licensed adult driver. But when it actually has to do something that requires making a tough decision or thinking, it will either give up control or do something stupid or dangerous. You can’t ever forget the self-driver is like a 4-year-old kid mimicking the act of driving and isn’t capable of thinking like a human when it needs to. You forget that and you can die. (This has the odd and (IMO) under-appreciated effect, when self-drive is engaged, of shifting your role from operator of the car to babysitting the operator of the car. Doing a thing and watching something else do a thing so you can take over when they screw up are two very different things and I think that until more people realize that, it’s going to keep causing unnecessary accidents.)
This is a fantastic piece by writer Ted Chiang about large-language models like ChatGPT. He likens them to lossy compression algorithms:
What I’ve described sounds a lot like ChatGPT, or most any other large-language model. Think of ChatGPT as a blurry jpeg of all the text on the Web. It retains much of the information on the Web, in the same way that a jpeg retains much of the information of a higher-resolution image, but, if you’re looking for an exact sequence of bits, you won’t find it; all you will ever get is an approximation. But, because the approximation is presented in the form of grammatical text, which ChatGPT excels at creating, it’s usually acceptable. You’re still looking at a blurry jpeg, but the blurriness occurs in a way that doesn’t make the picture as a whole look less sharp.
Reframing the technology in that way turns out to be useful in thinking through some of its possibilities and limitations:
There is very little information available about OpenAI’s forthcoming successor to ChatGPT, GPT-4. But I’m going to make a prediction: when assembling the vast amount of text used to train GPT-4, the people at OpenAI will have made every effort to exclude material generated by ChatGPT or any other large-language model. If this turns out to be the case, it will serve as unintentional confirmation that the analogy between large-language models and lossy compression is useful. Repeatedly resaving a jpeg creates more compression artifacts, because more information is lost every time. It’s the digital equivalent of repeatedly making photocopies of photocopies in the old days. The image quality only gets worse.
Indeed, a useful criterion for gauging a large-language model’s quality might be the willingness of a company to use the text that it generates as training material for a new model. If the output of ChatGPT isn’t good enough for GPT-4, we might take that as an indicator that it’s not good enough for us, either.
Chiang has previously spoken about how “most fears about A.I. are best understood as fears about capitalism”.
I tend to think that most fears about A.I. are best understood as fears about capitalism. And I think that this is actually true of most fears of technology, too. Most of our fears or anxieties about technology are best understood as fears or anxiety about how capitalism will use technology against us. And technology and capitalism have been so closely intertwined that it’s hard to distinguish the two.
Let’s think about it this way. How much would we fear any technology, whether A.I. or some other technology, how much would you fear it if we lived in a world that was a lot like Denmark or if the entire world was run sort of on the principles of one of the Scandinavian countries? There’s universal health care. Everyone has child care, free college maybe. And maybe there’s some version of universal basic income there.
Now if the entire world operates according to — is run on those principles, how much do you worry about a new technology then? I think much, much less than we do now.
See also Why Computers Won’t Make Themselves Smarter. (via @irwin)
Just about everything on the web is on TikTok, and going viral there too, so it shouldn’t be a surprise that people who’ve been laid off are there too, trying to figure out what it all means.
Part of me is cynical about this. You mean that as people, we’re so poorly defined without our jobs that our only resource is to grind out some content about it? But on the other side of the coin, making content is what human beings do. Other animals use tools, but do they make content? Apart from some birds, probably not.
My favorite TikTok layoff video is by Atif Memon, a cloud engineer who offers a clear-eyed appraisal of her situation:
“At the company offsite, we celebrated our company tripling its revenue in a year. A month later, we are so poor! Who robbed us?”
“Even if ChatGPT can take away our jobs, they’ll have to get in line behind geopolitics and pandemic and shareholders and investors. I lost my job because the investors of the company were not sure will become 400x in the coming year. ‘How will we go to Mars?’ Someone else lost their job because the investors thought ‘Hmm, if this other company can lay off 12k people and still work as usual, shouldn’t we also try?”
“Artificial intelligence can never overtake human paranoia and human curiosity. AI can only do what human beings have been doing. Only humans can do what no human has done before.”
A lot to chew on in four minutes.
Update: Apparently this is not native to TikTok, but was posted to YouTube by a comedian, Aiyyo Shraddha. It really is a perfect TikTok story! The video is a ripoff.
Google Research has released a new generative AI tool called MusicLM. MusicLM can generate new musical compositions from text prompts, either describing the music to be played (e.g., “The main soundtrack of an arcade game. It is fast-paced and upbeat, with a catchy electric guitar riff. The music is repetitive and easy to remember, but with unexpected sounds, like cymbal crashes or drum rolls”) or more emotional and evocative (“Made early in his career, Matisse’s Dance, 1910, shows a group of red dancers caught in a collective moment of innocent freedom and joy, holding hands as they whirl around in space. Simple and direct, the painting speaks volumes about our deep-rooted, primal human desire for connection, movement, rhythm and music”).
As the last example suggests, since music can be generated from just about any text, anything that can be translated/captioned/captured in text, from poetry to paintings, can be turned into music.
It may seem strange that so many AI tools are coming to fruition in public all at once, but at Ars Technica, investor Haomiao Huang argues that once the basic AI toolkit reached a certain level of sophistication, a confluence of new products taking advantage of those research breakthroughs was inevitable:
To sum up, the breakthrough with generative image models is a combination of two AI advances. First, there’s deep learning’s ability to learn a “language” for representing images via latent representations. Second, models can use the “translation” ability of transformers via a foundation model to shift between the world of text and the world of images (via that latent representation).
This is a powerful technique that goes far beyond images. As long as there’s a way to represent something with a structure that looks a bit like a language, together with the data sets to train on, transformers can learn the rules and then translate between languages. Github’s Copilot has learned to translate between English and various programming languages, and Google’s Alphafold can translate between the language of DNA and protein sequences. Other companies and researchers are working on things like training AIs to generate automations to do simple tasks on a computer, like creating a spreadsheet. Each of these are just ordered sequences.
The other thing that’s different about the new wave of AI advances, Huang says, is that they’re not especially dependent on huge computing power at the edge. So AI is rapidly becoming much more ubiquitous than it’s been… even if MusicLM’s sample set of tunes still crashes my web browser.
Neural Radiance Fields (NeRFs) is a relatively new technique that generates well-lit, complex 3D views from 2D images. If you’ve seen behind-the-scenes looks at how image/motion capture is traditionally done, you know how time-consuming and resource intensive it can be. As this video from Corridor Crew shows, NeRFs changes the image capture game significantly. The ease with which they play around with the technology to produce professional-looking effects in very little time is pretty mind-blowing. (via waxy)
Cult avant-garde filmmaker Alejandro Jodorowsky famously did not make his ambitious adaptation of Dune but what if he had brought his unique brand of surrealist psychedelia to the screen with a version of Tron in the 70s? Using the AI platform Midjourney, Johnny Darrell imagined what Jodorowsky’s Tron might have looked like. I love these — I would like to see this movie please.
See also Jodorowsky’s Frasier.
Update: Frank Pavich, director of Jodorowsky’s Dune, ruminates on these AI images of Jodorowsky’s Tron.
I’m still trying to wrap my mind around it all. There seems to be a correlation between how Alejandro’s work was absorbed and referred to by subsequent filmmakers and how his work was ingested and metabolized by computer programming. But these two things are not the same. I want to say that influence is not the same thing as algorithm. But looking at these images, how can I be sure?
It’s hard to find many shortcomings in the software. It can’t render text. And like many painters and sculptors throughout history, it has trouble getting hands right. I’m nitpicking here. The model contains multitudes. It has scanned the collected works of thousands upon thousands of photographers, painters and cinematographers. It has a deep library of styles and a facility with all kinds of image-making techniques at its digital fingertips. The technology is jaw-dropping. And it concerns me greatly.
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