Conditional generative models such as DALL-E and Stable Diffusion generate images based on a user-defined text, the prompt. Finding and refining prompts that produce a desired image has become the art of prompt engineering. Generative models do not provide a built-in retrieval model for a user's information need expressed through prompts. In light of an extensive literature review, we reframe prompt engineering for generative models as interactive text-based retrieval on a novel kind of "infinite index". We apply these insights for the first time in a case study on image generation for game design with an expert. Finally, we envision how active learning may help to guide the retrieval of generated images.

The term "generative AI" is used to describe AI systems that can create new information from scratch, as opposed to merely evaluating or acting on preexisting data. Avatars on social media sites and text-to-image converters have both made generative AI more accessible to the general public in recent weeks. The widespread implementation of AI will have far-reaching consequences for the future of business, affecting everything from daily operations to product development to worldwide expansion. Generative AI has impressive capabilities and a wide range of possible implementations. Blog entries, code, poetry, FAQ responses, sentiment analysis, artwork, and even films are just some of the textual and visual outputs of generative AI models.

We can easily drown in water. But it has no intent. And the challenge that humans face when it comes to water is learning to swim, building boats and dams, and finding ways to wield its power. "You can make two images, and it's cool, but you make 100,000 images, and you have an actual physical sensation of drowning," says Holz in an interview with Fortune. "So we are trying to figure out how do you teach people to swim? And how do you build these boats that let them navigate and be empowered and sort of sail the ocean of imagination, instead of just drowning?"

Artificial Intelligence (AI) surged in popularity last year, as both businesses and the public saw first-hand examples of its potential applications. Companies like OpenAI released a wave of public demos, such as the advanced chatbot ChatGPT that has drawn the attention of Microsoft. Text-to-image generators such as Dall-E 2, Midjourney and Stable Diffusion took the limelight, as millions of users began to create their own AI-generated art, to the anger of artists and companies such as Getty Images. In its tech predictions for 2023, Dell Technologies Ireland said AI could become the "main engine of innovation" for the year, as more organisations adopt the technology to harness the full potential of data and support teams across a business. The sector has shown no sign of slowing down so far this year, with OpenAI reportedly in talks to raise funds at a $29bn valuation, Apple rolling out an AI audio narration tool and Microsoft researching an AI model that can simulate anyone's voice from only three seconds of audio.

Faculty from the Stanford Accelerator for Learning are already thinking about the ways in which ChatGPT and other generative artificial intelligence will change and contribute to education in particular. Victor Lee, associate professor of education and the faculty lead for the accelerator initiative on generative AI in education, stresses the importance of educators in harnessing this technology. "If we want generative AI to meaningfully improve education," he says, "there is the obvious step we need to take of listening to the existing expertise in education -- from educators, parents, students, and scholars who have spent years studying education -- and using what we learn to find the most pertinent and valuable use cases for generative AI in a very complicated educational system." Over the next several weeks, the Stanford Accelerator for Learning will launch listening sessions and gatherings with educators to strategize a path for generative AI. Says Lee, "We need the use of this technology to be ethical, equitable, and accountable."

Search 10M+ prompts, create and generate AI Art and AI Avatar via Stable Diffusion, DALL·E 2.

Sometimes we stumble upon insight in unexpected places. Late last year, for example, I read perhaps the most precise description ever written about AI-generated art in The New York Times comments section. The article described what happened when a man named Jason Allen submitted an image generated by the AI program Midjourney to an art contest and won. While the story focused on the debate over the ethics of AI image generators, the comment had nothing to do with thorny moral considerations. Instead, it described how the winning work looked.

If you've ever uploaded photos or art, written a review, "liked" content, answered a question on Reddit, contributed to open source code, or done any number of other activities online, you've done free work for tech companies, because downloading all this content from the web is how their AI systems learn about the world. Tech companies know this, but they mask your contributions to their products with technical terms like "training data," "unsupervised learning," and "data exhaust" (and, of course, impenetrable "Terms of Use" documents). In fact, much of the innovation in AI over the past few years has been in ways to use more and more of your content for free. This is true for search engines like Google, social media sites like Instagram, AI research startups like OpenAI, and many other providers of intelligent technologies. This exploitative dynamic is particularly damaging when it comes to the new wave of generative AI programs like Dall-E and ChatGPT.

A new AI-generated chatbot named ChatGPT is now raising ethical concerns regarding copywriting and plagiarism. AI-generated profile pictures from apps like Lensa AI have also become a viral sensation, and they, too, have faced criticism; artists are accusing the tech of forgery and theft, one of many conversations about how AI art has the potential to devalue work made by humans. No matter your stance on generative AI, one thing is clear: It will only continue to evolve, and as it does, its impact across business sectors is likely to be huge. Generative AI, as the name suggests, leverages artificial intelligence models to create various types of content, including images, code and text. The technology draws from existing data and content, as well as machine learning to predict the next word based on previous word sequences or to create an image based on words describing other images.

Attempts to ban generative AI such as ChatGPT are not all they are cracked up to be. To ban, or not to ban, that is the question. I would guess that if Shakespeare were around nowadays, he might have said something like that about the recent efforts to ban the use of a type of AI known as Generative AI, which is especially exemplified and popularized due to an AI app called ChatGPT. Some high-profile entities have been attempting to ban the use of ChatGPT. For example, the New York City (NYC) Department of Education recently announced that they were proceeding to block access to ChatGPT on its various networks and connected devices. The reported rationale for the ban consisted of indications that this AI app and the overall use of generative AI seemingly portend negative consequences for student learning. Students that opt to use ChatGPT are said to be undercutting the development of their crucial critical-thinking skills and undermining the growth of their problem-solving abilities. On top of those rather stoutly worrisome qualms, there is the undisputed fact that such AI can produce inaccurate outputs that contain errors and other factual maladies. The dangerous icing on the cake is the imagined possibility that the outputs could potentially be used in an unsafe manner by students that unknowingly rely upon said falsehoods. No such documented harms have yet surfaced that I've seen, so we'll need to just take at face value that this could potentially happen (I have discussed the range of possibilities in my postings; for example, some have posited that generative AI essays could tell someone to take medicines that they should not be taking or provide mental health advice that ought to be proffered by human mental health professionals, etc.).