kottke.org posts about artificial intelligence
Using AI image processing software, Hidreley Diao creates photorealistic portraits of familiar cartoon characters. The one of Moe from The Simpsons is kind of amazing — he’s got the look of a long-time character actor who’s developed so much depth over the years that he starts getting bigger roles and everyone’s like, this guy is actually kind of enigmatic and attractive and fantastic.
You can find more of his efforts on Instagram, where he also makes AI-assisted portraits of people we don’t have photos of (e.g. Michelangelo) and fictional people (e.g. Hercules and the Statue of Liberty).
See also the “Reverse Toonification” of Pixar Characters and What Homer Simpson Would Look Like in Real Life.
I know three things about Monopoly:
1. I do not like playing it.
2. No one plays by the actual rules.
3. A good strategy for Monopoly will anger the other players.
In the video above, a bunch of game-playing AI bots are pitted against each other in an attempt to find the best strategy for the game. No word on whether the bots had any fun playing the game.
Janelle Shane trained an AI on a few breakfast cereal names and it came up with some cool new cereal concepts on its own.
I mean, I would go to town on some Orb Crumpets. And don’t these sound delicious?!
Original Cool Ranch Cheese and Dried Cranberry Oatmeal — all the wholesome, cheesy oatmeal with a choice of mild, sweet or salty!
Ingredis Fiberwaste Cream Cheese Cheerios — kids grab a box and put them in their mouths, making fun flavors taste even better !!! !!! !!! !!!
Fibrewaste is probably an element in many American grocery items, so kudos for this brave truth in advertising on the part of our robot friend. (via waxy)
With nearly instant reaction times, superhuman button tapping frequency, and an inability to fatigue, an AI called StackRabbit can play Tetris better than any human player. But how much better? Well, it can play all the way to the end of the game, which…did you know Tetris ended? I didn’t. But before that happens, it plays flawlessly through hundreds of levels while the game itself is throwing up weirdo color schemes and scores from random places in its memory — the game’s creators didn’t imagine anyone or anything would get anywhere close to these levels. Also, I got surprisingly anxious watching this — it was just so fast with so much constant peril! (via waxy)
I have been trying not to read too much about Morgan Neville’s documentary Roadrunner: A Film About Anthony Bourdain before I have had a chance to watch it, but the few things I have read about it have given me some pause. From Helen Rosner’s piece about the film drawn from an interview with Neville:
There is a moment at the end of the film’s second act when the artist David Choe, a friend of Bourdain’s, is reading aloud an e-mail Bourdain had sent him: “Dude, this is a crazy thing to ask, but I’m curious” Choe begins reading, and then the voice fades into Bourdain’s own: “…and my life is sort of shit now. You are successful, and I am successful, and I’m wondering: Are you happy?” I asked Neville how on earth he’d found an audio recording of Bourdain reading his own e-mail. Throughout the film, Neville and his team used stitched-together clips of Bourdain’s narration pulled from TV, radio, podcasts, and audiobooks. “But there were three quotes there I wanted his voice for that there were no recordings of,” Neville explained. So he got in touch with a software company, gave it about a dozen hours of recordings, and, he said, “I created an A.I. model of his voice.” In a world of computer simulations and deepfakes, a dead man’s voice speaking his own words of despair is hardly the most dystopian application of the technology. But the seamlessness of the effect is eerie. “If you watch the film, other than that line you mentioned, you probably don’t know what the other lines are that were spoken by the A.I., and you’re not going to know,” Neville said. “We can have a documentary-ethics panel about it later.”
Per this GQ story, Neville got permission from Bourdain’s estate:
We fed more than ten hours of Tony’s voice into an AI model. The bigger the quantity, the better the result. We worked with four companies before settling on the best. We also had to figure out the best tone of Tony’s voice: His speaking voice versus his “narrator” voice, which itself changed dramatically of over the years. The narrator voice got very performative and sing-songy in the No Reservation years. I checked, you know, with his widow and his literary executor, just to make sure people were cool with that. And they were like, Tony would have been cool with that. I wasn’t putting words into his mouth. I was just trying to make them come alive.
As a post hoc ethics panel of one, I’m gonna say this doesn’t appeal to me, but I bet this sort of thing becomes common practice in the years to come, much like Errol Morris’s use of reenactment in The Thin Blue Line. A longer and more nuanced treatment of the issue can be found in Justin Hendrix’s interview of Sam Gregory, who is an “expert on synthetic media and ethics”.
There’s a set of norms that people are grappling with in regard to this statement from the director of the Bourdain documentary. They’re asking questions around consent, right? Who consents to someone taking your voice and using it? In this case, the voiceover of a private email. And what if that was something that, if the person was alive, they might not have wanted. You’ve seen that commentary online, and people saying, “This is the last thing Anthony Bourdain would have wanted for someone to do this with his voice.” So the consent issue is one of the things that is bubbling here. The second is a disclosure issue, which is, when do you know that something’s been manipulated? And again, here in this example, the director is saying, I didn’t tell people that I had created this voice saying the words and I perhaps would have not told people unless it had come up in the interview. So these are bubbling away here, these issues of consent and disclosure.
Update: From Anthony’s ex-wife Ottavia Bourdain about the statement that “Tony would have been cool with that”:
I certainly was NOT the one who said Tony would have been cool with that.
(via @drawnonglass)
I have an unusually good memory, especially for symbols, words, and text, but since I don’t use regular expressions (ahem) regularly, they’re one of those parts of computer programming and HTML/EPUB editing that I find myself relearning over and over each time I need it. How did something this arcane but powerful even get started? Naturally, its creators were trying to discover (or model) artificial intelligence.
That’s the crux of this short history of “regex” by Buzz Andersen over at “Why is this interesting?”
The term itself originated with mathematician Stephen Kleene. In 1943, neuroscientist Warren McCulloch and logician Walter Pitts had just described the first mathematical model of an artificial neuron, and Kleene, who specialized in theories of computation, wanted to investigate what networks of these artificial neurons could, well, theoretically compute.
In a 1951 paper for the RAND Corporation, Kleene reasoned about the types of patterns neural networks were able to detect by applying them to very simple toy languages—so-called “regular languages.” For example: given a language whose “grammar” allows only the letters “A” and “B”, is there a neural network that can detect whether an arbitrary string of letters is valid within the “A/B” grammar or not? Kleene developed an algebraic notation for encapsulating these “regular grammars” (for example, a*b* in the case of our “A/B” language), and the regular expression was born.
Kleene’s work was later expanded upon by such luminaries as linguist Noam Chomsky and AI researcher Marvin Minsky, who formally established the relationship between regular expressions, neural networks, and a class of theoretical computing abstraction called “finite state machines.”
This whole line of inquiry soon falls apart, for reasons both structural and interpersonal: Pitts, McCullough, and Jerome Lettvin (another early AI researcher) have a big falling out with Norbert Wiener (of cybernetics fame), Minsky writes a book (Perceptrons) that throws cold water on the whole simple neural network as model of the human mind thing, and Pitts drinks himself to death. Minsky later gets mixed up with Jeffrey Epstein’s philanthropy/sex trafficking ring. The world of early theoretical AI is just weird.
But! Ken Thompson, one of the creators of UNIX at Bell Labs comes along and starts using regexes for text editor searches in 1968. And renewed takes on neural networks come along in the 21st century that give some of that older research new life for machine learning and other algorithms. So, until Skynet/global warming kills us all, it all kind of works out? At least, intellectually speaking.
(Via Jim Ray)
A new iOS app called Brickit has been developed to breathe new life into your old Lego pile. Just dump your bricks out into a pile and the app will analyze what Lego bricks you have, what new creations you can build with them, and provide you with detailed build instructions. It can even guide you to find individual pieces in the pile. View a short demo — I’m assuming they’re using some sort of AI/machine learning to do this?
My kids have approximately a billion Legos at my house, so I downloaded Brickit to try it out. The process is a little slow and you need to do a little bit of pre-sorting (by taking out the big pieces and spreading your pile out evenly), but watching the app do its thing is kinda magical. When I have more time later, I’m definitely going to go back and try to build some of the ideas it found for me. (via @marcprecipice)
In 1715, a significant chunk of Rembrandt’s masterpiece The Night Watch, including a 2-foot-wide swath from the left side of the painting, was lopped off in order to fit the painting in a smaller space. (WTF?!) Using a contemporary copy of the full scene painted by Gerrit Lundens and an AI program for getting the colors and angles right, the Rijksmuseum has “restored” The Night Watch, augmenting the painting with digital printouts of the missing bits. The uncropped Rembrandt is shown above and here is Lundens’s version:
I’m not an expert on art, but the 1715 crop and the shift of the principal characters from right-of-center to the center appears to have radically altered the whole feel of the painting.
With the addition especially on the left and the bottom, an empty space is created in the painting where they march towards. When the painting was cut [the lieutenants] were in the centre, but Rembrandt intended them to be off-centre marching towards that empty space, and that is the genius that Rembrandt understands: you create movement, a dynamic of the troops marching towards the left of the painting.
(via @john_overholt)
Writer Ted Chiang (author of the fantastic Exhalation) was recently a guest on the Ezra Klein Show. The conversation ranged widely — I enjoyed his thoughts on superheroes — but his comments on capitalism and technology seem particularly relevant right now. From the transcript:
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. Most of the things that we worry about under the mode of capitalism that the U.S practices, that is going to put people out of work, that is going to make people’s lives harder, because corporations will see it as a way to increase their profits and reduce their costs. It’s not intrinsic to that technology. It’s not that technology fundamentally is about putting people out of work.
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. How are giant corporations going to use this to increase their profits at our expense?
And so, I feel like that is kind of the unexamined assumption in a lot of discussions about the inevitability of technological change and technologically-induced unemployment. Those are fundamentally about capitalism and the fact that we are sort of unable to question capitalism. We take it as an assumption that it will always exist and that we will never escape it. And that’s sort of the background radiation that we are all having to live with. But yeah, I’d like us to be able to separate an evaluation of the merits and drawbacks of technology from the framework of capitalism.
Echoing some of his other thoughts during the podcast, Chiang also wrote a piece for the New Yorker the other day about how the singularity will probably never come.
In the latest episode of the Vox series Glad You Asked, host Joss Fong looks at how racial and other kinds of bias are introduced into massive computer systems and algorithms, particularly those that work through machine learning, that we use every day.
Many of us assume that tech is neutral, and we have turned to tech as a way to root out racism, sexism, or other “isms” plaguing human decision-making. But as data-driven systems become a bigger and bigger part of our lives, we also notice more and more when they fail, and, more importantly, that they don’t fail on everyone equally. Glad You Asked host Joss Fong wants to know: Why do we think tech is neutral? How do algorithms become biased? And how can we fix these algorithms before they cause harm?
How is Tesla’s full-self driving system coming along? Perhaps not so good. YouTuber AI Addict took the company’s FSD Beta 8.2 for a drive through downtown Oakland recently and encountered all sorts of difficulties. The video’s chapter names should give you some idea: Crosses Solid Lines, Acting Drunk, Right Turn In Wrong Lane, Wrong Way!!!, Near Collision (1), and Near Collision (2). They did videos of drives in SF and San Jose as well.
I realize this is a beta, but it’s a beta being tested by consumers on actual public roads. While I’m sure it works great on their immaculate test track, when irregularities in your beta can easily result in the death or grave injury of a pedestrian, cyclist, or other motorist several times over the course of 30 minutes, how can you consider it safe to release to the public in any way? It seems like Level 5 autonomy is going to be difficult to manage under certain road conditions. (via @TaylorOgan)
Colorado State University and the Cornell Lab of Ornithology have developed a system called BirdCast that uses machine learning & two decades of historical bird movement data to develop daily bird migration forecasts for the United States.
Bird migration forecasts show predicted nocturnal migration 3 hours after local sunset and are updated every 6 hours. These forecasts come from models trained on the last 23 years of bird movements in the atmosphere as detected by the US NEXRAD weather surveillance radar network. In these models we use the Global Forecasting System (GFS) to predict suitable conditions for migration occurring three hours after local sunset.
The map above is the migration forecast for tonight — overall, warmer temperatures and increased bird movement are predicted for the next week or two. They also maintain up-to-the hour records of migration activity detected by the US weather surveillance radar network; this was the activity early this morning at 3:10am ET:
If the current & predicted bird radar maps were a part of the weather report on the local news, I might start watching again.
GANksy is an AI program trained on Banksy’s street art.
GANksy was born into the cloud in September 2020, then underwent a strenuous A.I. training regime using hundreds of street art photos for thousands of iterations to become the fully-formed artist we see today. All of GANksy’s works are original creations derived from its understanding of shape, form and texture. GANksy wants to be put into a robot body so it can spraypaint the entire planet.
The results are cool but not super coherent — these look more like abstract NIN and Radiohead album covers than the sly & whimsical works Banksy stencils up around the world. With GANksy, you get the feel of Banksy’s art and the surfaces he chooses to put it on but little of the meaning, which is about what you would expect from training using a neural network based on style.
I missed this back in March (I think there was a lot going on back then?) but the feature-length documentary AlphaGo is now available to stream for free on YouTube. The movie documents the development by DeepMind/Google of the AlphaGo computer program designed to play Go and the competition between AlphaGo and Lee Sedol, a Go master.
With more board configurations than there are atoms in the universe, the ancient Chinese game of Go has long been considered a grand challenge for artificial intelligence. On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined The DeepMind Challenge Match. Hundreds of millions of people around the world watched as a legendary Go master took on an unproven AI challenger for the first time in history.
During the competition back in 2016, I wrote a post that rounded up some of the commentary about the matches.
Move after move was exchanged and it became apparent that Lee wasn’t gaining enough profit from his attack.
By move 32, it was unclear who was attacking whom, and by 48 Lee was desperately fending off White’s powerful counter-attack.
I can only speak for myself here, but as I watched the game unfold and the realization of what was happening dawned on me, I felt physically unwell.
Second-tier Scottish football club Inverness Caledonian Thistle doesn’t have a camera operator for matches at their stadium so the club uses an AI-controlled camera that’s programmed to follow the ball for their broadcasts. But in a recent match against Ayr United, the AI controller kept moving the camera off the ball to focus on the bald head of the linesman, making the match all but unwatchable. No fans allowed in the stadium either, so the broadcast was the only way to watch.
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