Discover DALL-E, the Artificial Intelligence Artist That Lets You Create Surreal Artwork

DALL-E, an artificial intelligence system that generates viable-looking art in a variety of styles in response to user supplied text prompts, has been garnering a lot of interest since it debuted this spring.

It has yet to be released to the general public, but while we’re waiting, you could have a go at DALL-E Mini, an open source AI model that generates a grid of images inspired by any phrase you care to type into its search box.

Co-creator Boris Dayma explains how DALL-E Mini learns by viewing millions of captioned online images:

Some of the concepts are learnt (sic) from memory as it may have seen similar images. However, it can also learn how to create unique images that don’t exist such as “the Eiffel tower is landing on the moon” by combining multiple concepts together.

Several models are combined together to achieve these results:

• an image encoder that turns raw images into a sequence of numbers with its associated decoder

• a model that turns a text prompt into an encoded image

• a model that judges the quality of the images generated for better filtering 

My first attempt to generate some art using DALL-E mini failed to yield the hoped for weirdness.  I blame the blandness of my search term – “tomato soup.”

Perhaps I’d have better luck “Andy Warhol eating a bowl of tomato soup as a child in Pittsburgh.”

Ah, there we go!

I was curious to know how DALL-E Mini would riff on its namesake artist’s handle (an honor Dali shares with the titular AI hero of Pixar’s 2018 animated feature, WALL-E.)

Hmm… seems like we’re backsliding a bit.

Let me try “Andy Warhol eating a bowl of tomato soup as a child in Pittsburgh with Salvador Dali.”

Ye gods! That’s the stuff of nightmares, but it also strikes me as pretty legit modern art. Love the sparing use of red. Well done, DALL-E mini.

At this point, vanity got the better of me and I did the AI art-generating equivalent of googling my own name, adding “in a tutu” because who among us hasn’t dreamed of being a ballerina at some point?

Let that be a lesson to you, Pandora…

Hopefully we’re all planning to use this playful open AI tool for good, not evil.

Hyperallergic’s Sarah Rose Sharp raised some valid concerns in relation to the original, more sophisticated DALL-E:

It’s all fun and games when you’re generating “robot playing chess” in the style of Matisse, but dropping machine-generated imagery on a public that seems less capable than ever of distinguishing fact from fiction feels like a dangerous trend.

Additionally, DALL-E’s neural network can yield sexist and racist images, a recurring issue with AI technology. For instance, a reporter at Vice found that prompts including search terms like “CEO” exclusively generated images of White men in business attire. The company acknowledges that DALL-E “inherits various biases from its training data, and its outputs sometimes reinforce societal stereotypes.”

Co-creator Dayma does not duck the troubling implications and biases his baby could unleash:

While the capabilities of image generation models are impressive, they may also reinforce or exacerbate societal biases. While the extent and nature of the biases of the DALL·E mini model have yet to be fully documented, given the fact that the model was trained on unfiltered data from the Internet, it may generate images that contain stereotypes against minority groups. Work to analyze the nature and extent of these limitations is ongoing, and will be documented in more detail in the DALL·E mini model card.

The New Yorker cartoonists Ellis Rosen and Jason Adam Katzenstein conjure another way in which DALL-E mini could break with the social contract:

And a Twitter user who goes by St. Rev. Dr. Rev blows minds and opens multiple cans of worms, using panels from cartoonist Joshua Barkman’s beloved webcomic, False Knees:

Proceed with caution, and play around with DALL-E mini here.

Get on the waitlist for original flavor DALL-E access here.


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Ayun Halliday is the Chief Primatologist of the East Village Inky zine and author, most recently, of Creative, Not Famous: The Small Potato Manifesto.  Follow her @AyunHalliday.

Remembering Dave Smith (RIP), the Father of MIDI & the Creator of the 80s’ Most Beloved Synthesizer, the Prophet-5

Some founders rest on their laurels, build industries around themselves like a cocoon, and never escape or outgrow the big achievement that made their name. Some, like Dave Smith — the so-called “father of MIDI,” and one of the most innovative synthesizer pioneers of the last several decades – don’t stop creating for long enough to collect dust. You may never have heard of Smith, but you’ve heard his technology. Before pioneering MIDI (Musical Instrument Digital Interface), the digital standard that allows hundreds of electronic instruments to play nicely with each other across computer and software makers, Smith founded Sequential Circuits and built one of the most revered synthesizers ever made, the Prophet-5, invented in 1977 and essential to the sound of the 1980s and beyond.

Smith’s keyboards made appearances on stage, video, and albums throughout the decade. Duran Duran’s Nick Rhodes used the Prophet-5 on the band’s first album and “virtually every record I have made since then,” he said in a statement. “Without Dave’s vision and ingenuity,” Rhodes went on, “the sound of the 1980s would have been very different, he truly changed the sonic soundscape of a generation.”

Sequential synths appeared on albums by bands as disparate as The Cure and Daryl Hall & John Oates, who demonstrate the dream-like, ethereal capabilities of the Prophet-5 — the first fully programmable polyphonic analog synth — in “I Can’t Go for That (No Can Do).” The Prophet-5 also drove the sound of Radiohead’s Kid A, and indie dance darlings Hot Chip wrote they would be “nothing without what [Smith] created.” Few vintage synths are as desirable as the Prophet-5.

The original Prophet is “not immune to the dark side of vintage synths,” writes Vintage Synth Explorer, including problems such as unstable tuning and a lack of MIDI. Smith fixed that issue himself with new iterations of the Prophet and other synths featuring his most famous post-Prophet-5 technology. “Like so many brilliant and creative people,” the MIDI Association writes, Smith “always focused on the future.” He was “not actually a big fan of being called the ‘Father of MIDI.'” Many people contributed to the development of the technology, especially Roland founder Ikutaro Kakehashi, who won a technical Grammy with Smith in 2013 for the protocol that made its debut as a new standard in 1983.

Smith preferred making hardware instruments and “almost begrudgingly accepted interviews about his contributions to MIDI….. He was also not a big fan of organizations, committees and meetings.” He was a synth lover’s synth maker, a designer and engineer with a “deep understanding of what musicians wanted,” says Rhodes. Collaborations with Yamaha and Korg produced more software innovations in the 90s, but in the 2000s, Smith returned to Sequential Circuits and debuted the Prophet X, Prophet-6, and OB-6 with Tom Oberheim. The two designers collaborated in 2021 on the Oberheim OB-X8 and Smith introduced it just weeks before his death.

He had traveled a long way from inventing the Prophet-5 in 1977 and presenting a paper in 1981 to the Audio Engineering Society on what he then called a Universal Synthesizer Interface. Smith himself never seemed to stop and look back, but lovers of his famous instruments are happy we still can, and that electronic instruments and computers can talk to each other easily thanks to MIDI. Few of those instruments sound as good as the original, however. See a demonstration of the Prophet-5’s range of sounds in the video just above and hear more tracks that show off the synth in the list here.

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Josh Jones is a writer and musician based in Durham, NC. Follow him at @jdmagness

The Animated Map of Quantum Computing: A Visual Introduction to the Future of Computing

If you listen to the hype surrounding quantum computing, you might think the near future shown in Alex Garland’s sci-fi series Devs is upon us — that we have computers complex enough to recreate time and space and reconstruct the human mind. Far from it. At this still-early stage, quantum computers promise much more than they can deliver, but the technology is “poised,” writes IBM “to transform the way you work in research.” The company does have — as do most other other big makers of what are now called “classical computers” — a “roadmap” for implementing quantum computing and a lot of cool new technology (such as the quantum runtime environment Quiskit) built around the qubit, the quantum computer version of the classical bit.

The computer bit, as we know, is a binary entity: either 1 or 0 and nothing in-between. The qubit, on the other hand, mimics quantum phenomena by remaining in a state of superposition of all possible states between 1 and 0 until users interact with it, like a spinning coin that only lands on one face if it’s physically engaged. And like quantum particles, qubits can become entangled with each other. Thus, “Quantum computers work exceptionally well for modeling other quantum systems,” writes Microsoft, “because they use quantum phenomena in their computation.” The possibilities are thrilling, and a little unsettling, but no one’s modeling the universe, or even a part of it, just quite yet.

“Use cases are largely experimental and hypothetical at this early stage,” McKinsey Digital writes in a report for businesses, while also noting that usable quantum systems may be on the market as early as 2030. If the roadmaps serve, that’s just around the corner, especially given how quickly quantum computers have evolved in relation to their (exponentially slower) classical forebears. “From the first idea of a quantum computer in 1980 [an idea attributed to Nobel prize-winning physicist Richard Feynman] to today, there has been a huge growth in the quantum computing industry, especially in the last ten years,” says Dominic Walliman in the video above, “with dozens of companies and startups spending hundreds of millions of dollars in a race to build the world’s best quantum computers.”

Walliman offers not only a (non-hyped) map of the possible future, but also a map of quantum computing’s past. He promises to clear up misconceptions we might have about the “different kinds of quantum computing, how they work, and why so many people are investing in the quantum computing industry.” We’ve previously seen Walliman’s Domain of Science channel do the same for such huge fields of scientific study as physics, chemistry, math, and classical computer science. Here, he presents cutting-edge science on the cusp of realization, explaining three essential ideas — superposition, entanglement, and interference — that govern quantum computing. The primary difference between quantum and classical computing from the point of view of non-specialists is algorithmic speed: while classical computers could theoretically perform the same complex functions as their quantum cousins, they would take ages to do so, or would halt and fizzle out in the attempt.

Will quantum computers be able to simulate nature down to the subatomic level in the future? McKinsey cautions, “experts are still debating the most foundational topics for the field.” Despite the industry’s rapid growth, “it’s not yet clear,” Walliman says, “which approach” among the many he surveys “will win out in the long run.” But if the roadmaps serve, we may not have to wait long to find out.

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Josh Jones is a writer and musician based in Durham, NC. Follow him at @jdmagness

M.I.T. Computer Program Predicts in 1973 That Civilization Will End by 2040

In 1704, Isaac Newton predicted the end of the world sometime around (or after, “but not before”) the year 2060, using a strange series of mathematical calculations. Rather than study what he called the “book of nature,” he took as his source the supposed prophecies of the book of Revelation. While such predictions have always been central to Christianity, it is startling for modern people to look back and see the famed astronomer and physicist indulging them. For Newton, however, as Matthew Stanley writes at Science, “laying the foundation of modern physics and astronomy was a bit of a sideshow. He believed that his truly important work was deciphering ancient scriptures and uncovering the nature of the Christian religion.”

Over three hundred years later, we still have plenty of religious doomsayers predicting the end of the world with Bible codes. But in recent times, their ranks have seemingly been joined by scientists whose only professed aim is interpreting data from climate research and sustainability estimates given population growth and dwindling resources. The scientific predictions do not draw on ancient texts or theology, nor involve final battles between good and evil. Though there may be plagues and other horrible reckonings, these are predictably causal outcomes of over-production and consumption rather than divine wrath. Yet by some strange fluke, the science has arrived at the same apocalyptic date as Newton, plus or minus a decade or two.

The “end of the world” in these scenarios means the end of modern life as we know it: the collapse of industrialized societies, large-scale agricultural production, supply chains, stable climates, nation states…. Since the late sixties, an elite society of wealthy industrialists and scientists known as the Club of Rome (a frequent player in many conspiracy theories) has foreseen these disasters in the early 21st century. One of the sources of their vision is a computer program developed at MIT by computing pioneer and systems theorist Jay Forrester, whose model of global sustainability, one of the first of its kind, predicted civilizational collapse in 2040. “What the computer envisioned in the 1970s has by and large been coming true,” claims Paul Ratner at Big Think.

Those predictions include population growth and pollution levels, “worsening quality of life,” and “dwindling natural resources.” In the video at the top, see Australia’s ABC explain the computer’s calculations, “an electronic guided tour of our global behavior since 1900, and where that behavior will lead us,” says the presenter. The graph spans the years 1900 to 2060. “Quality of life” begins to sharply decline after 1940, and by 2020, the model predicts, the metric contracts to turn-of-the-century levels, meeting the sharp increase of the “Zed Curve” that charts pollution levels. (ABC revisited this reporting in 1999 with Club of Rome member Keith Suter.)

You can probably guess the rest—or you can read all about it in the 1972 Club of Rome-published report Limits to Growth, which drew wide popular attention to Jay Forrester’s books Urban Dynamics (1969) and World Dynamics (1971). Forrester, a figure of Newtonian stature in the worlds of computer science and management and systems theory—though not, like Newton, a Biblical prophecy enthusiast—more or less endorsed his conclusions to the end of his life in 2016. In one of his last interviews, at the age of 98, he told the MIT Technology Review, “I think the books stand all right.” But he also cautioned against acting without systematic thinking in the face of the globally interrelated issues the Club of Rome ominously calls “the problematic”:

Time after time … you’ll find people are reacting to a problem, they think they know what to do, and they don’t realize that what they’re doing is making a problem. This is a vicious [cycle], because as things get worse, there is more incentive to do things, and it gets worse and worse.

Where this vague warning is supposed to leave us is uncertain. If the current course is dire, “unsystematic” solutions may be worse? This theory also seems to leave powerfully vested human agents (like Exxon’s executives) wholly unaccountable for the coming collapse. Limits to Growth—scoffed at and disparagingly called “neo-Malthusian” by a host of libertarian critics—stands on far surer evidentiary footing than Newton’s weird predictions, and its climate forecasts, notes Christian Parenti, “were alarmingly prescient.” But for all this doom and gloom it’s worth bearing in mind that models of the future are not, in fact, the future. There are hard times ahead, but no theory, no matter how sophisticated, can account for every variable.

Note: An earlier version of this post appeared on our site in 2018.

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Josh Jones is a writer and musician based in Durham, NC. Follow him at @jdmagness

Take an Intellectual Odyssey with a Free MIT Course on Douglas Hofstadter’s Pulitzer Prize-Winning Book Gödel, Escher, Bach: An Eternal Golden Braid

In 1979, mathematician Kurt Gödel, artist M.C. Escher, and composer J.S. Bach walked into a book title, and you may well know the rest. Douglas R. Hofstadter won a Pulitzer Prize for Gödel, Escher, Bach: an Eternal Golden Braid, his first book, thenceforth (and henceforth) known as GEB. The extraordinary work is not a treatise on mathematics, art, or music, but an essay on cognition through an exploration of all three — and of formal systems, recursion, self-reference, artificial intelligence, etc. Its publisher settled on the pithy description, “a metaphorical fugue on minds and machines in the spirit of Lewis Carroll.”

GEB attempted to reveal the mind at work; the minds of extraordinary individuals, for sure, but also all human minds, which behave in similarly unfathomable ways. One might also describe the book as operating in the spirit — and the practice — of Herman Hesse’s Glass Bead Game, a novel Hesse wrote in response to the data-driven machinations of fascism and their threat to an intellectual tradition he held particularly dear. An alternate title (and key phrase in the book) Magister Ludi, puns on both “game” and “school,” and alludes to the importance of play and free association in the life of the mind.

Hesse’s esoteric game, writes his biographer Ralph Freedman, consists of “contemplation, the secrets of the Chinese I Ching and Western mathematics and music” and seems similar enough to Hofstadter’s approach and that of the instructors of MIT’s open course, Gödel, Escher, Bach: A Mental Space Odyssey. Offered through the High School Studies Program as a non-credit enrichment course, it promises “an intellectual vacation” through “Zen Buddhism, Logic, Metamathematics, Computer Science, Artificial Intelligence, Recursion, Complex Systems, Consciousness, Music and Art.”

Students will not study directly the work of Gödel, Escher, and Bach but rather “find their spirits aboard our mental ship,” the course description notes, through contemplations of canons, fugues, strange loops, and tangled hierarchies. How do meaning and form arise in systems like math and music? What is the relationship of figure to ground in art? “Can recursion explain creativity,” as one of the course notes asks. Hofstadter himself has pursued the question beyond the entrenchment of AI research in big data and brute force machine learning. For all his daunting erudition and challenging syntheses, we must remember that he is playing a highly intellectual game, one that replicates his own experience of thinking.

Hofstadter suggests that before we can understand intelligence, we must first understand creativity. It may reveal its secrets in comparative analyses of the highest forms of intellectual play, where we see the clever formal rules that govern the mind’s operations; the blind alleys that explain its failures and limitations; and the possibility of ever actually reproducing workings in a machine. Watch the lectures above, grab a copy of Hofstadter’s book, and find course notes, readings, and other resources for the fascinating course Gödel, Escher, Bach: A Mental Space Odyssey archived here. The course will be added to our list, 1,700 Free Online Courses from Top Universities.

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Josh Jones is a writer and musician based in Durham, NC. Follow him at @jdmagness

AI Software Creates “New” Nirvana, Jimi Hendrix, Doors & Amy Winehouse Songs: Hear Tracks from the “Lost Tapes of the 27 Club”

What would pop music sound like now if the musicians of the 27 club had lived into maturity? Can we know where Amy Winehouse would have gone, musically, if she had taken another path? What if Hendrix’s influence over guitar heroics (and less obvious styles) came not only from his sixties playing but from an unimaginable late-career cosmic blues? Whether questions like these can ever be given real flesh and blood, so to speak, by artificial intelligence may still be very much undecided.

Of course, it may not be for us to decide. “The charts of 2046,” Mark Beaumont predicts at NME, “will  be full of 12G code-pop songs, baffling to the human brain, written by banks of composerbots purely for the Spotify algorithm to recommend to its colonies of ÆPhone listening farms.” Seems as likely as any other future music scenario at this point. In the meantime, we still get to judge the successes, such as they are, of AI songwriters on human merits.

The Beatles-esque “Daddy’s Car,” the most notable computer-generated tribute song to date, was “composed by AI… capable of learning to mimic a band’s style from its entire database of songs.” The program produced a competent pastiche that nonetheless sounds like “cold computer psychedelia — eerie stuff.” What do we, as humans, make of Lost Tapes of the 27 Club, a compilation of songs composed in the style of musicians who infamously perished by suicide or overdose at the tender age of 27?

The “tapes” include four tracks designed to sound like lost songs from Hendrix, Winehouse, Nirvana, and the Doors. Highlighting a handful of artists who left us too soon in order to address “music’s mental health crisis,” the project used Magenta, the same Google AI as “Daddy’s Car,” to analyze the artists’ repertoires, as Rolling Stone explains:

For the Lost Tapes project, Magenta analyzed the artists’ songs as MIDI files, which works similarly to a player-piano scroll by translating pitch and rhythm into a digital code that can be fed through a synthesizer to recreate a song. After examining each artist’s note choices, rhythmic quirks, and preferences for harmony in the MIDI file, the computer creates new music that the staff could pore over to pick the best moments.

There is significant human input, such as the curation of 20 or 30 songs fed to the computer, broken down separately into different parts of the arrangement. Things did not always go smoothly. Kurt Cobain’s “loose and aggressive guitar playing gave Magenta some trouble,” writes Endgadget, “with the AI mostly outputting a wall of distortion instead of something akin to his signature melodies.”

Judge the end results for yourself in “Drowned by the Sun,” above. The music for all four songs is synthesized with MIDI files. “An artificial neural network was then used to generate the lyrics,” Eddie Fu writes at Consequence of Sound, “while the vocals were recorded by Eric Hogan, frontman of an Atlanta Nirvana tribute band.” Other songs feature different sound-alike vocalists (more or less). In no ways does the project claim that MIDI-generated computer files can replace actual musicians.

They’re affectionate tributes, made by players without hearts, but they don’t really tell us anything about what, say, Jim Morrison would have done if he hadn’t died at 27. Yet the cause is a noble one: a rejection of the romantic idea at the heart of the “27 Club” narrative — that mental illness, substance abuse, etc. should be glamorized in any way. “Lost Tapes of the 27 Club is the work of Over the Bridge,” notes Fu, “a Toronto organization that helps members of the music industry struggling with mental illness.” Learn more about the project here and about Over the Bridge’s programs here.

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Josh Jones is a writer and musician based in Durham, NC. Follow him at @jdmagness

MIT’s Introduction to Deep Learning: A Free Online Course

MIT has posted online its introductory course on deep learning, which covers applications to computer vision, natural language processing, biology, and more. Students “will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow.” Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication). Experience in Python is helpful but not necessary. The first lecture appears above. The rest of the course materials (videos & slides) can be found here.

Introduction to Deep Learning will be added to our list of Free Computer Science Courses, a subset of our larger meta collection, 1,700 Free Online Courses from Top Universities.  You can also find Deep Learning courses on Coursera.

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Discovered: The User Manual for the Oldest Surviving Computer in the World

Image by Clemens Pfeiffer via Wikimedia Commons

The first computer I ever sat before, the 1983 Apple IIe, had a manual the size of a textbook, which included a primer on programming languages and a chapter entitled “Getting Down to Business and Pleasure.” By “pleasure,” Apple mostly meant “electronic worksheets,” “word processors,” and “database management.” (They hadn’t fully established themselves as the fun one yet.) Getting these programs running took real effort and patience, especially compared to the MacBook Air on which I’m typing now.

All those old tedious processes are automated, and no more do we need manuals—we’ve got the internet, which also happens to be the only way I could operate an Apple IIe, whether that means tracking down a manual on eBay or finding a scanned copy somewhere online. Luckily, for vintage Apple enthusiasts, this isn’t difficult, and someone with rudimentary knowledge of Apple DOS could muddle through without one.

When we go further back into computer history, we find machines that became incomprehensible over time without their operating instructions. Such was the case with the Zuse Z4, “considered the oldest preserved digital computer in the world,” notes Vice. “The Z4 is one of those machines that takes up a whole room, runs on magnetic tapes, and needs multiple people to operate. Today it sits in the Deutsches Museum in Munich, unused. Until now, historians and curators only had a limited knowledge of its secrets because the manual was lost long ago.”

The computer’s inventor, Konrad Zuse, first began building it for the Nazis in 1942, then refused its use in the VI and V2 rocket program. Instead, he fled to a small town in Bavaria and stowed the computer in a barn until the end of the war. It wouldn’t see operation until 1950. The Z4 proved to be “a very reliable and impressive computer for its time,” Sarah Felice writes. “With its large instruction set it was able to calculate complicated scientific programs and was able to work during the night without supervision, which was unheard of for this time.”

These qualities made the Zuse Z4 particularly useful to the Institute of Applied Mathematics at the Swiss Federal Institute of Technology (ETH), where the computer performed advanced calculations for Swiss engineers in the early 50s. “Around 100 jobs were carried out with the Z4 between 1950 and 1955,” writes Herbert Bruderer, retired ETH lecturer. “These included calculations on the trajectory of rockets… on aircraft wings…” and “on flutter vibrations,” an operation requiring “800 hours machine time.”

René Boesch, one of the airplane researchers working on the Z4 in the 50s kept a copy of the manual among his papers, and it was there that his daughter, Evelyn Boesch, also an ETH researcher, discovered it. (View it online here.) Bruderer tells the full story of the computer’s development, operation, and the rediscovery of its only known copy of operating instructions here.

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Josh Jones is a writer and musician based in Durham, NC. Follow him at @jdmagness

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