<p>Creativity is not only for humans. AI-generated artwork has captured the public and even a few collectors’ imaginations. But is this truly the future of contemporary art?</p>

Is AI art any good?

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<p>Last fall, Christie’s became the first auction house to put an AI-generated artwork under the hammer. Created using a generative adversarial network (GAN) trained to make new images based on a dataset of 15,000 portraits painted between the 14th and the 20th centuries, the piece sold for $432,500 - over 40 times its $10,000 estimate - prompting widespread speculation about whether AI could be behind the next great art movement.</p>  <div>Although there’s a 30-year history of artists employing algorithms and computation within their practices, this spike in contemporary AI art, led by artist/software engineers such as Robbie Barrat and Google’s Mike Tyka, has occurred in the last five years or so, made possible by recent breakthroughs in machine learning like GANs, which, by pitting two neural networks (machine networks that approximate how the brain works) against each other, can be used to create original pieces that are increasingly difficult to tell apart from those made by humans. Also vital to this development is Google’s DeepDream, invented in 2014, which uses a convolutional neural network to find and enhance patterns in images.</div>  <div>But while technically impressive, are these artworks actually any good? Well, it depends on who you talk to. At this year’s AI-themed Christie’s Art + Tech Summit, Jason Bailey, founder of art analytics website Artnome, argued that if what separates our generation from its predecessors is the invention of computers, then software or computational art is necessarily the most important art of our generation. In theory, his statement makes a lot of sense, but faced with paintings of blurry-faced, GAN-enabled portraits or the psychedelic landscapes produced with DeepDream, I can’t help thinking that we’re not there yet.&nbsp;</div>

<p>Mike Pepi, a critic who has written extensively on the intersections between art, culture and technology, is similarly critical of the bluster around AI art. ‘I guess I’m just very frustrated with these tech people coming in and willy-nilly trying to use these interesting GAN networks to spit out something that just sort of looks surrealist or abstract,’ he says. ‘I definitely feel like there are some artists who do that, and the results just aren’t very good.’</p>  <div>Instead, Pepi points to artists like Agnieszka Kurant and Ian Cheng, whom he sees as successfully using AI as a tool to talk about the cultural impact of this technology without fetishizing the end results. Of all the writers, artists and curators I spoke to in writing this piece, Cheng’s name is the one that came up the most frequently as an example of an artist who has enjoyed mainstream success through projects that meaningfully engage with algorithmic processes. <em>BOB (Bag of Beliefs) </em>(2018-2019), for example, comprises, in Cheng’s words, ‘an AI creature whose personality, body and life script evolve across exhibitions.’ The work’s open-ended narrative, which Cheng has likened to computer games like&nbsp;‘The Sims’&nbsp;and the reality TV franchise&nbsp;‘The Real Housewives’, also has an interactive element - during exhibitions viewers are not only able to watch BOB on a 12-foot-high screen but can alter its reality by making offerings via an app.&nbsp;</div>

<p>Alongside Cheng, another artist who has received widespread attention for his use of AI technology is Trevor Paglen. In September this year, <em>ImageNet Roulette</em> (2019), an AI tool developed by Paglen and AI researcher Kate Crawford that labeled user-uploaded selfies with often bizarre categories (newsreader, pipe smoker, femme fatale) went viral when it revealed the racial and gendered bias of the data set on which it had been trained. ImageNet Roulette wasn’t Paglen’s first work that looked to reveal how machines ‘see’&mdash;his 2017 film <em>Sight Machine</em> trod similar ground&mdash;but it was arguably the most successful. As well as making visible the usually unseen act of AI classification to thousands of people who might otherwise have been unaware of the process, ImageNet ultimately decided to remove 600,000 images from its online data bank due to the accusations that came to light through Paglen and Crawford’s tool’s use.</p>  <div>One of the things that makes this piece so successful is that it points to the current gap between the possibilities of AI technology and its limits. ‘Treating AI with exceptionality is in some ways a mistake. We need to be realistic about what it can do and what it can’t do,’ says Lachlan Kermode, software development lead for the research collective Forensic Architecture. For one of the collective’s recent investigations, Kermode and his colleagues trained a ‘computer vision’ classifier to recognize Safariland-manufactured tear gas canisters (the project was commissioned by the Whitney in response to the controversy surrounding Warren B. Kanders’ association with the institution) among the thousands of images of tear gas shared online, and in doing so revealed the company’s complicity in violence committed by the Israeli military against Palestinians in Gaza. Although the campaign was ultimately successful, Kermode is careful to point out that machine learning can only be used as a ‘prediction,’ but never to ‘short-circuit the due process of doing ‘manual labor’ - the nuanced and highly sophisticated labor of working out what an image actually means.’</div>

<p><strong></strong></p><p>Although just a handful of examples, these works suggest that artists may be perfectly positioned to slice through the hype and mysticism that still surrounds AI technology, but that in order to do so, they can’t just demonstrate what a tool can do but must critically comment on the cultural implications of that tool’s use. Women and ethnic minorities are currently underrepresented in the tech industry, meaning that, at present, it is mostly white men who are building and choosing data sets, training algorithms and ultimately curating the results. As algorithms increasingly affect how we view the world, a key to building strong AI ethics in the future will be wrestling some of that control from programmers in Silicon Valley and encouraging an interdisciplinary and nuanced conversation that includes artists and other creative practitioners. ‘It’s crucial that we get new technology in the hands of artists,’ agrees Kelani Nichole, a design strategist and the founder of the L.A.-based experimental media art gallery TRANSFER. ‘They have an ability to present the ideas of their time in a way that can help shape culture in a way that is hopefully more magical, ethical, equitable, subversive or challenging.’</p><p>This article was originally published in Art Basel Miami Beach Magazine, available in select locations in the US.</p><p>Top image:&nbsp;A rendering of <em>Sight Machine</em> by Trevor Paglen, courtesy of Paglen Studio</p>

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Creativity is not only for humans. AI-generated artwork has captured the public and even a few collectors’ imaginations. But is this truly the future of contemporary art? 

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Creativity is not only for humans. AI-generated artwork has captured the public and even a few collectors’ imaginations. But is this truly the future of contemporary art?