Artists, illustrators and photographers have often led the way in embracing new technology. The concerns that creators such as Harry Woodgate have about AI programs ('It's the opposite of art': why illustrators are furious about AI, 23 January) that "rely entirely on the pirated intellectual property of countless working artists, photographers, illustrators and other rights holders" must be heeded. The UK's £116bn cultural and creative industries have an opportunity to be world leaders in developing and sustaining talent in emerging technologies, but the government must ensure that artists' rights are protected.
That was quick: Artificial intelligence has gone from science fiction to novelty to Thing We Are Sure Is the Future. One easy way to measure the change is via headlines -- like the ones announcing Microsoft's $10 billion investment in OpenAI, the company behind the dazzling ChatGPT text generator, followed by other AI startups looking for big money. Or the ones about school districts frantically trying to cope with students using ChatGPT to write their term papers. Or the ones about digital publishers like CNET and BuzzFeed admitting or bragging that they're using AI to make some of their content -- and investors rewarding them for it. "Up until very recently, these were science experiments nobody cared about," says Mathew Dryhurst, co-founder of the AI startup Spawning.ai.
Artificial Intelligence (AI) is the ability of machines to perform tasks that would normally require human intelligence. The rise in computer power, along with digital data, is what makes AI important today. Although forms of AI have existed since 1956, the next phase of artificial intelligence is only recently visible. A recent report by UNESCO estimates that artificial intelligence research grew 50% from 2015 to 2019. The combined research growth, along with an increase in spending, is creating a technological land rush in the field of artificial intelligence. The World Intellectual Property Organization has highlighted AI as one of the most rapidly growing areas of issued and filed patent applications.
GDL is a subfield of deep learning (Goodfellow et al., Reference Goodfellow, Bengio and Courville2016) with a focus on generation of new data. Following the definition provided by Foster (Reference Foster2019), a generative model describes how a dataset is generated (in terms of a probabilistic model); by sampling from this model, we are able to generate new data. Nowadays, machine-generated artworks have entered the market (Vernier et al., Reference Vernier, Caselles-Dupré and Fautrel2020), they are fully accessible online,Footnote 1 and they have the focus of major investments.Footnote 2 Ethical debates have, fortunately, found a place in the conversation (for an interesting summary of machine learning researches related to fairness, see Chouldechova and Roth (Reference Chouldechova and Roth2020)) because of biases and discrimination they may cause (as happened with AI Portrait Ars [O'Leary, Reference O'Leary2019], leading to some very remarkable attempts to overcome them, as in Xu et al. (Reference Xu, Yuan, Zhang and Wu2018) or Yu et al. (Reference Yu, Li, Zhou, Malik, Davis and Fritz2020)). In this context, it is possible to identify at least three problems: the use of protected works, which have to be stored in memory until the end of the training process (even if not for more time, in order to verify and reproduce the experiment); the use of protected works as training set, processed by deep learning techniques through the extraction of information and the creation of a model upon them; and the ownership of intellectual property (IP) rights (if a rightholder would exist) over the generated works. Although these arguments have already been extensively studied (e.g., Sobel (Reference Sobel2017) examines use as training set and Deltorn and Macrez (Reference Deltorn and Macrez2018) discuss authorship), this paper aims at analyzing all the problems jointly, creating a general overview useful for both the sides of the argument (developers and policymakers); aims at focusing only on GDL, which (as we will see) has its own peculiarities, and not on artificial intelligence (AI) in general (which contains too many different subfields that cannot be generalized as a whole); and is written by GDL researchers, which may help provide a new and practical perspective to the topic.
Shutterstock, one of the internet's biggest sources of stock photos and illustrations, is now offering its customers the option to generate their own AI images. In October, the company announced a partnership with OpenAI, the creator of the wildly popular and controversial DALL-E AI tool. Now, the results of that deal are in beta testing and available to all paying Shutterstock users. The new platform is available in "every language the site offers," and comes included with customers' existing licensing packages, according to a press statement from the company. And, according to Gizmodo's own test, every text prompt you feed Shutterstock's machine results in four images, ostensibly tailored to your request.
If you find a photo in an article, it probably came from Shutterstock, the largest source of online stock images. Recently, it opened access to AI-generated pictures. The new feature came after the company partnered with OpenAI, the creator of the popular AI image creator DALL-E. It is available to all users with paid Shutterstock subscriptions in "every language the site offers." Also, they do not have to worry about potential intellectual property issues.
I have a grayish dual position regarding generative art and, well, basically, generative creativity. One view is extremely cynical, and the other perspective is hopeful. I wrote earlier about this topic here (note: a bit gloomy). Let me start with the cynical view, hyperbolized for ease of communication. I see this as a big tech effort to lower tech wages, reduce negotiation positions of creative workers, push the commoditization of art, create a new scaleable consumer market, and more holistically drive society towards transhumanism.
Vivian Dye is a smart woman. And so it took less than six months of selling customized golf merchandise on Zazzle, the most prominent online marketplace with three Zs in its name, for her to figure out that she needed an "Elizabeth Taylor on the beach." "I call it'Elizabeth Taylor on the beach' because she used to attract men for a cousin of hers," said Dye, referring to a plotline from an old movie. Even if customers didn't fancy her Liz, a monogrammed golfball covered with rose-colored sparkles drew users to her store, where she offered them customized pickleball paddles, shower curtains, and other golf balls. "I'm the golf ball queen," says Dye, who started selling on Zazzle in 2021, soon after retiring from her job in the mortgage industry.
Microsoft today said that it's extending its partnership with OpenAI, the startup behind art- and text-generating AI systems like ChatGPT, DALL-E 2 and GPT-3, with a "multi-year, multi-billion-dollar" investment. OpenAI says that the infusion of new capital -- the exact amount of which wasn't disclosed -- will be used to continue its independent research and develop AI that's "safe, useful and powerful." The optics aren't the best for Microsoft, which just last week announced plans to lay off 10,000 employees as a part of broader cost-cutting measures. But they'd been telegraphed by the company earlier this month -- in an interview with The Wall Street Journal, Microsoft CEO Satya Nadella said that Microsoft planned to make OpenAI's foundational systems available as commercials platforms so that any entity in any industry can build on them. OpenAI will remain a capped-profit company as a part of the new investment deal with Microsoft.
Artificial intelligence (AI) is suddenly the darling of the tech world, thanks to ChatGPT, an AI chatbot that can do things such as carry on conversations and write essays and articles with what some people believe is human-like skill. In its first five days, more than a million people signed up to try it. The New York Times hails its "brilliance and weirdness" and says it inspires both awe and fear. For all the glitz and hype surrounding ChatGPT, what it's doing now are essentially stunts -- a way to get as much attention as possible. The future of AI isn't in writing articles about Beyoncé in the style of Charles Dickens, or any of the other oddball things people use ChatGPT for. Instead, AI will be primarily a business tool, reaping billions of dollars for companies that use it for tasks like improving internet searches, writing software code, discovering and fixing inefficiencies in a company's business, and extracting useful, actionable information from massive amounts of data.