The light and dark sides of AI have been in the public spotlight for many years. Think facial recognition, algorithms making loan and sentencing recommendations, and medical image analysis. But the impressive – and sometimes frightening – capabilities of ChatGPT, DALL-E 2 and other conversational, image-evoking AI programs seem like a turning point.
The key change has been the emergence over the past year of powerful generative AI, software that not only learns from large amounts of data, but also produces things – compellingly written documents, conversations engaging, photorealistic images and celebrity voice clones.
Generative AI has been around for nearly a decade, as evidenced by longstanding concerns over deepfake videos. Now, however, AI models have become so vast and have digested such vast swaths of the internet that people no longer know what AI means for the future of knowledge work, the nature of creativity and the origins and veracity of content on the Internet. .
Here are five articles from our archives to take the measure of this new generation of artificial intelligence.
1. Generative AI and work
A panel of five AI experts discussed the implications of generative AI for artists and knowledge workers. It’s not just about whether technology will replace you or make you more productive.
University of Tennessee computer scientist Lynne Parker has written that while generative AI has significant benefits, such as making creativity and knowledge work more accessible, the new tools also have downsides. Specifically, they could lead to an erosion of skills like writing, and they raise issues of intellectual property protection given that the models are trained on human creations.
Daniel Acuña, a computer scientist at the University of Colorado at Boulder, has found the tools useful in his own creative endeavors, but worries about inaccuracy, bias, and plagiarism.
Kentaro Toyama, a computer scientist from the University of Michigan, wrote that human skills would likely become expensive and superfluous in some areas. “If history is any guide, it is almost certain that advances in AI will kill more jobs, that people in the creative class with uniquely human skills will become wealthier but fewer in number, and that those who possess creative technology will become the new mega-rich.
Florida International University computer scientist Mark Finlayson wrote that some jobs are likely to disappear, but new skills in using these AI tools are likely to be valued. By analogy, he noted that the rise of word processing software largely eliminated the need for typists, but enabled almost anyone with access to a computer to produce typed documents and led to a new class of skills to list on a CV.
Casey Greene, a biomedical informatics researcher at the University of Colorado at Anschutz, wrote that just as Google caused people to develop skills for finding information on the Internet, AI language models will cause people to develop skills to get the best result from the tools. “As with many technological advancements, the way people interact with the world will change in the era of widely available AI models. The question is whether society will use this moment to advance fairness or exacerbate disparities.
Read more: AI and the future of work: 5 experts on what ChatGPT, DALL-E and other AI tools mean for artists and knowledge workers
2. Conjure Images from Words
Generative AI can seem like magic. It’s hard to imagine how image-generating AIs can take a few words of text and produce an image that matches the words.
Richard A. Brooks/AFP via Getty Images
Hany Farid, a University of California, Berkeley computer scientist who specializes in forensic imaging, explained the process. The software is trained on a massive set of images, each including a short textual description.
“The model gradually corrupts each frame until only visual noise remains, then trains a neural network to reverse that corruption. By repeating this process hundreds of millions of times, the model learns to convert pure noise into a consistent image from any legend,” he wrote.
Read more: Text-to-image AI: Powerful, easy-to-use technology for creating art – and fakes
3. Machine marking
Many images produced by generative AI are difficult to distinguish from photographs, and AI-generated video is improving rapidly. This raises the stakes in the fight against fraud and misinformation. Fake videos of corporate executives could be used to manipulate stock prices, and fake videos of political leaders could be used to spread dangerous misinformation.
Farid explained how it is possible to produce AI-generated photos and videos that contain watermarks verifying that they are synthetic. The trick is to produce digital watermarks that cannot be changed or removed. “These watermarks can be integrated into generative AI systems by watermarking all training data, after which the generated content will contain the same watermark,” he wrote.
Read more: ChatGPT watermark, DALL-E and other generative AIs could help protect against fraud and misinformation
4. Deluge of ideas
Despite all the legitimate concerns about the downsides of generative AI, the tools are proving useful for some artists, designers, and writers. People working in creative fields can use the image generators to quickly sketch out ideas, including original and unexpected material.
Juan Noguera, industrial designer and professor at the Rochester Institute of Technology, and his students use tools like DALL-E or Midjourney to produce thousands of images from abstract ideas – a kind of sketchbook on steroids.
“Enter any phrase – no matter how crazy – and you’ll receive a set of unique images generated just for you. Want to design a teapot? Here have 1,000,” he wrote. “While only a small subset of these may be usable as a teapot, they provide a seed of inspiration that the designer can nurture and refine into a finished product.”
Read more: DALL-E 2 and Midjourney can be a boon for industrial designers
5. Shorten the creative process
However, using AI to produce finished works of art is another matter, according to Nir Eisikovits and Alec Stubbs, philosophers at the Applied Ethics Center at the University of Massachusetts in Boston. They note that the process of creating art is about more than coming up with ideas.
The hands-on process of producing something, iterating the process, and making improvements—often in the moment in response to audience feedback—are indispensable aspects of creating art, they wrote.
“It’s the work of making something real and working out its details that has value, not just that moment of imagining it,” they wrote. “Artistic works are praised not just for the finished product, but for the struggle, playful interaction, and skillful engagement with the artistic task, all of which carry the artist from the moment of creation to the end result.”
Read more: ChatGPT, DALL-E 2 and the collapse of the creative process
Editor’s note: This story is a summary of articles from The Conversation archives.