The light and dark sides of AI have been in the spotlight for years. Think of facial recognition, algorithms that make loan and sentencing recommendations and medical image analysis. But the impressive — and sometimes terrifying — capabilities of ChatGPT, DALL-E 2, and other conversational and image-generating artificial intelligence programs feel like a turning point.
The most significant change has been the emergence in the last year of powerful generative AI, software that not only learns from massive amounts of data, but also produces things – convincingly written documentsinteresting conversation, photorealistic images And celebrity voice clones.
Generative AI already exists for almost a decadeas long-standing worry about deepfake videos can testify. Now, however, the AI models have grown so large and consumed such large swaths of the internet that people have become uncertain about 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 that measure 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 the technology will replace you or make you more productive.
University of Tennessee computer scientist Lynn Parker wrote that while generative AI has significant benefits, such as making creativity and knowledge work more accessible, the new tools also have drawbacks. Specifically, they could lead to an erosion of skills such as writing, and could raise questions about intellectual property protection since the models are trained on human creations.
Computer scientist from the University of Colorado Boulder Daniel Acuna has found the tools useful in his own creative endeavours, but has concerns about inaccuracy, bias, and plagiarism.
University of Michigan computer scientist Kentarō Toyama wrote that human skill is likely to become precious and strange in some areas. “If history is any guide, it is almost certain that advances in AI will cause more jobs to disappear, people of the creative class with only human skills will become richer but fewer in number, and those who own creative technology the new mega empire.”
Computer scientist from Florida International University Mark Finlayson wrote that some jobs are likely to disappear, but new skills in working with these AI tools are likely to be appreciated. By analogy, he noted that the advent of word processing software largely removed the need for typists, but enabled almost anyone with access to a computer to produce typeset documents, leading to a new class of skills to list on a resume.
University of Colorado Anschutz biomedical informatics researcher Casey Greene wrote that just as Google has pushed people to develop skills to find information on the web, AI language models will push people to develop skills to get the best results from the tools. “As with many technological developments, the way people interact with the world will change in the era of widely accessible AI models. The question is whether society will use this moment to promote equality or increase inequalities.”
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 out of words
Generative AI can seem like magic. It’s hard to imagine how image-generating AIs could take a few words of text and produce an image that matches the words.
Hany Farid, a University of California, Berkeley computer scientist who specializes in image forensics, explained the process. The software is trained on a huge set of images, each of which contains a short text description.
“The model gradually corrupts each image until only visual noise remains, then trains a neural network to undo this corruption. By repeating this process hundreds of millions of times, the model learns to transform pure noise into a coherent image of each caption ”, he wrote.
Read more: Text-to-image AI: powerful, easy-to-use technology for creating art – and forgeries
3. Machine marking
Many of the images produced by generative AI are difficult to distinguish from photos, and AI-generated video is quickly improving. This increases the commitment to combating fraud and disinformation. Fake videos of corporate executives can be used to manipulate stock prices, and fake videos of political leaders can be used to spread dangerous misinformation.
Farid explained how it is possible to create AI-generated photos and videos that contain watermarks that verify that they are synthetic. The trick is to create digital watermarks that cannot be altered or removed. “These watermarks can be baked into the generative AI systems by watermarking all training data, after which the generated content will contain the same watermark,” he wrote.
Read more: Watermarking ChatGPT, DALL-E and other generative AIs can help protect against fraud and misinformation
4. Abundance of ideas
Despite all the legitimate concerns about the drawbacks of generative AI, the tools are proving to be useful for some artists, designers, and writers. Those in creative fields can use the image generators to quickly sketch out ideas, including unexpected off-the-wall material.
Rochester Institute of Technology industrial designer and professor Juan Noguera and his students use tools like DALL-E or Midjourney to create thousands of images of abstract ideas – a kind of sketchbook on steroids.
“Enter any phrase – no matter how crazy – and you will receive a series of unique images created just for you. Do you want to design a teapot? Here, have 1000,” he wrote. “Although only a small subset of them are useful as a teapot, they provide a source of inspiration for the designer to 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
But using AI to produce finished works of art is another matter, he says Nir Eisikovits And Alex Stubbsphilosophers at the Center for Applied Ethics at the University of Massachusetts Boston. They note that the process of making art is more than just coming up with ideas.
The practical process of producing something, repeating the process and making refinements—often in the moment in response to audience reactions—are indispensable aspects of making art, they wrote.
“It’s the work of making something real and working out the details of it that has value, not just the moment you imagine it,” they wrote. “Artistic works are praised not only for the finished product, but also for the struggle, the playful interaction and the skilful involvement in the artistic task, all of which carry the artist from the beginning 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 archives of The Conversation.