![]() ![]() Where did these images come from and can we credit Miller with a “real” creative act. The concern about realness comes from two places. What are we even talking about? It’s too basic but I can’t help it. I ask Lebowitz a clumsy question, something like, ‘Isn’t the labor of trying to make something worth something?” She says of course. The slippery slope tack: if we’ve accepted that cameras do not make the photographs, but that photographers do, why should any succeeding technology that the human mind directs for its purpose not be judged similarly? That is, as a genuine, human act of creation. “Are the only real photographs the ones made on film, not the digital ones? My friend Peter Hujar would say so.” “These are not real photographs, but what are real photographs?” Lebowtiz begins. She repeats an apology to me several times: she doesn’t know what this means, the exhibition, the fact of its genesis. It turns out Miller also interviewed Lebowitz for his documentary, though it doesn’t seem clear why. She’s thumbing through the exhibition text that was produced for the show by author Benjamin Labatut partially using ChatGPT, an AI text generator also produced by OpenAI. ![]() Blunt, coarse bob, big coat, tortoiseshell glasses perched on her nose and another set in her welt pocket. Beguilingly simple, pointing back to nothing. A disk, just a flat circle of some substance, held in the hands of a woman. ![]() Here is a mushroom cloud, as if from an explosion, but flattened in a way that, perhaps, Nature wouldn’t allow. Here is a profile that looks Native American, extending an arm that could be a wing, that could be cultural dress. Some of them seem to represent momentous or historical moments in the past. Often extremely out of focus and piled on with grain, there is just enough form to suggest a subject or a landscape. Sure, many of Miller’s works look like they could be photographs, but many are heavily stylized. Don’t Appear to Be Focused on Artists’ Artificial Intelligence Concerns Naturally, that means GPT-3 can generate music itself after being given a few chords to start.Policymakers in D.C. Guitar tabs are shared on the web using ASCII text files, so you can bet they comprise part of GPT-3’s training dataset. In an example on Twitter, a user input text in “plain language” and asked GPT-3 to change it to “legal language.” This transforms inputs from “my landlord didn’t maintain the property” to “The Defendants have permitted the real property to fall into disrepair and have failed to comply with state and local health and safety codes and regulations.” Input text written in a certain style and GPT-3 can change it to another. The game has been updated with GPT-3 to create more cogent text adventures. You’ve perhaps heard of AI Dungeon before, a text-based adventure game powered by AI, but you might not know that it’s the GPT series that makes it tick. The program not only gave the right answer but correctly explained the underlying biological mechanism. A medical student from the UK used GPT-3 to answer health care questions. ![]() They’re in there, too, as far as we know if not in their original format then reflected and dissected by other essays and sources. Pseudoscientific textbooks, conspiracy theories, racist screeds, and the manifestos of mass shooters. And, yes, that includes the bad stuff as well. Any type of text that’s been uploaded to the internet has likely become grist to GPT-3’s mighty pattern-matching mill. That means GPT-3’s training data includes not only things like news articles, recipes, and poetry, but also coding manuals, fanfiction, religious prophecy, guides to the songbirds of Bolivia, and whatever else you can imagine. (Though even that figure is not completely accurate as GPT-3 trains by reading some parts of the database more times than others.) The rest comes from digitized books and various web links. It’s hard to estimate the total size, but we know that the entirety of the English Wikipedia, spanning some 6 million articles, makes up only 0.6 percent of its training data. The dataset GPT-3 was trained on is similarly mammoth. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |