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It was clear that OpenAI was on to something. In late 2021, a small team of researchers was playing around with an idea at the company’s San Francisco office. They’d built a new version of OpenAI’s text-to-image model, DALL-E, an AI that converts short written descriptions into pictures: a fox painted by Van Gogh, perhaps, or a corgi made of pizza. Now they just had to figure out what to do with it.

“Almost always, we build something and then we all have to use it for a while,” Sam Altman, OpenAI’s cofounder and CEO, tells MIT Technology Review. “We try to figure out what it’s going to be, what it’s going to be used for.”

Not this time. As they tinkered with the model, everyone involved realized this was something special. “It was very clear that this was it—this was the product,” says Altman. “There was no debate. We never even had a meeting about it.”

But nobody—not Altman, not the DALL-E team—could have predicted just how big a splash this product was going to make. “This is the first AI technology that has caught fire with regular people,” says Altman.

DALL-E 2 dropped in April 2022. In May, Google announced (but did not release) two text-to-image models of its own, Imagen and Parti. Then came Midjourney, a text-to-image model made for artists. And August brought Stable Diffusion, an open-source model that the UK-based startup Stability AI has released to the public for free.

The doors were off their hinges. OpenAI signed up a million users in just 2.5 months. More than a million people started using Stable Diffusion via its paid-for service Dream Studio in less than half that time; many more used Stable Diffusion through third-party apps or installed the free version on their own computers. (Emad Mostaque, Stability AI’s founder, says he’s aiming for a billion users.)

And then in October we had Round Two: a spate of text-to-video models from Google, Meta, and others. Instead of just generating still images, these can create short video clips, animations, and 3D pictures.

The pace of development has been breathtaking. In just a few months, the technology has inspired hundreds of newspaper headlines and magazine covers, filled social media with memes, kicked a hype machine into overdrive—and set off an intense backlash.

This story is part of our upcoming 10 Breakthrough Technologies 2023 series. Sign up for The Download to get the full list in January.

“The shock and awe of this technology is amazing—and it’s fun, it’s what new technology should be,” says Mike Cook, an AI researcher at King’s College London who studies computational creativity. “But it’s moved so fast that your initial impressions are being updated before you even get used to the idea. I think we’re going to spend a while digesting it as a society.”

Artists are caught in the middle of one of the biggest upheavals in a generation. Some will lose work; some will find new opportunities. A few are headed to the courts to fight legal battles over what they view as the misappropriation of images to train models that could replace them.

Creators were caught off guard, says Don Allen Stevenson III, a digital artist based in California who has worked at visual-effects studios such as DreamWorks. “For technically trained folks like myself, it’s very scary. You’re like, ‘Oh my god—that’s my whole job,’” he says. “I went into an existential crisis for the first month of using DALL-E.”

The image above is based on a variation of the prompt that created the final art. “After landing on an image I was happy with, I went in and made adjustments to clean up any AI artifacts and make it look more ‘real.’ I’m a big fan of sci-fi from that era,” explains Erik Carter.ERIK CARTER VIA DALL-E 2

But while some are still reeling from the shock, many—including Stevenson—are finding ways to work with these tools and anticipate what comes next.

The exciting truth is, we don’t really know. For while creative

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By: Will Douglas Heaven
Title: Generative AI is changing everything. But what’s left when the hype is gone?
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Published Date: Fri, 16 Dec 2022 09:12:05 +0000

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Giddy up: Colt Wrangler’s prizewinning Harley Sportster



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Custom Harley-Davidson Sportster by Colt Wrangler
With a name like Colt Wrangler, it’s safe to assume that the man is from Texas and probably a bit of a cowboy. Hot dang if you aren’t right on both counts—Colt has been riding broncos and bulls competitively since he was a kid. But what you’re less likely to guess, is that this young cowpoke is also one of the most impressive motorcycle builders to emerge from the US custom scene in recent years.

Since Colt Wrangler Motorcycles was founded in 2015, Colt has established a distinct design language of his own. Recognizable by their high-level sheet-metal work, his builds exist in perfect proportion and hold high-performance details for those that know what they’re looking at—or even better, for those lucky enough to ride them.

Custom Harley-Davidson Sportster by Colt Wrangler

We have featured Cole’s bikes before. But with more time to work on his latest project—a 1999 Harley-Davidson Sportster—he’s taken his personal style to new heights.

Colt had just started the Harley Sportster project, working in collaboration with local Texas truck builders Vintage Vendetta Garage, when Roland Sands Design announced the Dream Build-Off. This was a competition for local shops and backyard builders, with new motorcycles from BMW, Indian, and Royal Enfield as prizes. The Sportster was originally supposed to be a street-ready scrambler, but with this added motivation, Colt went

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By: Morgan Gales
Title: Giddy up: Colt Wrangler’s prizewinning Harley Sportster
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Published Date: Fri, 19 May 2023 17:01:46 +0000

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LISTEN: Kaytranda & Aminé Unleash Anticipated Collaborative Album, “KAYTRAMINÉ”



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Kaytranada and Aminé have joined forces to deliver on their captivating new collab album, KAYTRAMINÉ. As you’ll hear below, the highly-anticipated project showcases the unique and genre-blending abilities of both artists; from Kaytranada’s signature production style and Aminé’s charismatic flow, the EP is a sonic journey that arrives just in time for summer. The duo effortlessly combines elements of hip-hop, R&B, and electronic music, pushing boundaries and creating a refreshing sound that defies categorization. Hear what we mean by streaming the album below and let us know your thoughts in the comments section as well.

KAYTRAMINÉ (Self Titled) | Stream

‘LISTEN: Kaytranda & Aminé Unleash Anticipated Collaborative Album, “KAYTRAMINÉ”

The post LISTEN: Kaytranda & Aminé Unleash Anticipated Collaborative Album, “KAYTRAMINÉ” appeared first on Run The Trap: The Best EDM, Hip Hop & Trap Music.


By: Max Chung
Title: LISTEN: Kaytranda & Aminé Unleash Anticipated Collaborative Album, “KAYTRAMINÉ”
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Published Date: Sat, 20 May 2023 19:49:30 +0000

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How AI is helping historians better understand our past



It’s an evening in 1531, in the city of Venice. In a printer’s workshop, an apprentice labors over the layout of a page that’s destined for an astronomy textbook—a dense line of type and a woodblock illustration of a cherubic head observing shapes moving through the cosmos, representing a lunar eclipse.

Like all aspects of book production in the 16th century, it’s a time-consuming process, but one that allows knowledge to spread with unprecedented speed.

Five hundred years later, the production of information is a different beast entirely: terabytes of images, video, and text in torrents of digital data that circulate almost instantly and have to be analyzed nearly as quickly, allowing—and requiring—the training of machine-learning models to sort through the flow. This shift in the production of information has implications for the future of everything from art creation to drug development.

But those advances are also making it possible to look differently at data from the past. Historians have started using machine learning—deep neural networks in particular—to examine historical documents, including astronomical tables like those produced in Venice and other early modern cities, smudged by centuries spent in mildewed archives or distorted by the slip of a printer’s hand.

Historians say the application of modern computer science to the distant past helps draw connections across a broader swath of the historical record than would otherwise be possible, correcting distortions that come from analyzing history one document at a time. But it introduces distortions of its own, including the risk that machine learning will slip bias or outright falsifications into the historical record. All this adds up to a question for historians and others who, it’s often argued, understand the present by examining history: With machines set to play a greater role in the future, how much should we cede to them of the past?

Parsing complexity

Big data has come to the humanities throughinitiatives to digitize increasing numbers of historical documents, like the Library of Congress’s collection of millions of newspaper pages and the Finnish Archives’ court records dating back to the 19th century. For researchers, this is at once a problem and an opportunity: there is much more information, and often there has been no existing way to sift through it.

That challenge has been met with the development of computational tools that help scholars parse complexity. In 2009, Johannes Preiser-Kapeller, a professor at the Austrian Academy of Sciences, was examining a registry of decisions from the 14th-century Byzantine Church. Realizing that making sense of hundreds of documents would require a systematic digital survey of bishops’ relationships, Preiser-Kapeller built a database of individuals and used network analysis software to reconstruct their connections.

This reconstruction revealed hidden patterns of influence, leading Preiser-Kapeller to argue that the bishops who spoke the most in meetings weren’t the most influential; he’s since applied the technique to other networks, including the 14th-century Byzantian elite, uncovering ways in which its social fabric was sustained through the hidden contributions of women. “We were able to identify, to a certain extent, what was going on outside the official narrative,” he says.

Preiser-Kapeller’s work is but one example of this trend in scholarship. But until recently, machine learning has often been unable to draw conclusions from ever larger collections of text—not least because certain aspects of historical documents (in Preiser-Kapeller’s case, poorly handwritten Greek) made them indecipherable to machines. Now advances in deep learning have begun to address these limitations, using networks that mimic the human brain to pick out patterns in large and complicated data sets.

Nearly 800 years ago, the 13th-century astronomer Johannes de Sacrobosco published the Tractatus de sphaera, an introductory treatise on the geocentric cosmos. That treatise became required reading for early modern university students. It was the most widely distributed textbook on geocentric cosmology, enduring even after the Copernican revolution upended the geocentric view of the cosmos in the 16th century.

The treatise is also the star player in a digitized collection of 359 astronomy textbooks published between 1472 and 1650—76,000 pages, including tens of thousands of scientific illustrations and astronomical tables. In that comprehensive data set, Matteo Valleriani, a professor with the Max Planck Institute for the History of Science, saw an opportunity to trace the evolution of European knowledge toward a shared scientific worldview. But he realized that discerning the pattern required more than human capabilities. So Valleriani and a team of researchers at the Berlin Institute for the

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By: Moira Donovan
Title: How AI is helping historians better understand our past
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Published Date: Tue, 11 Apr 2023 09:00:00 +0000

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