A suppressed kiss, unwelcome or badly timed. These were some of the interpretations that reverberated in my brain after I viewed a weird digital-art trifle by the Emoji Mashup Bot, a popular but defunct Twitter account that combined the parts of two emoji into new, surprising, and astonishingly resonant compositions. The bot had taken the hand and eyes from the yawning emoji and mashed them together with the mouth from the kissing-heart emoji. Compare that simple method with supposedly more sophisticated machine-learning-based generative tools that have become popular in the past year or so. When I asked Midjourney, an AI-based art generator, to create a new emoji based on those same two, it produced compositions that were certainly emojiform but possessed none of the style or significance of the simple mashup: a series of yellow, heart-shaped bodies with tongues sticking out.

An anonymous reader shares a report: ChatGPT's artificial intelligence generated dialogue has gotten pretty sophisticated -- to the point where it can write convincing sounding essays. So Edward Tian, a computer science student at Princeton, built an app called GPTZero that can "quickly and efficiently" label whether an essay was written by a person or ChatGPT. In a series of recent tweets, Tian provided examples of GPTZero in progress; the app determined John McPhee's New Yorker essay "Frame of Reference" to be written by a person, and a LinkedIn post to be created by a bot. On Twitter, he said he created the app over the holidays, and was motivated by the increasing possibility of AI plagiarism. Further reading: 1. OpenAI is developing a watermark to identify work from its GPT text AI; 2. OpenAI's attempts to watermark AI text hit limits; 3. A metadata'watermark' could be the solution to ChatGPT plagiarism fears.

More than 450 start-ups are now working on generative A.I., by one venture capital firm's count. Five weeks ago, OpenAI, a San Francisco artificial intelligence lab, released ChatGPT, a chatbot that answers questions in clear, concise prose. The A.I.-powered tool immediately caused a sensation, with more than a million people using it to create everything from poetry to high school term papers to rewrites of Queen songs. Now OpenAI is in the midst of a new gold rush. The lab is in talks to complete a deal that would value it at around $29 billion, more than twice its valuation in 2021, two people with knowledge of the discussions said.

When ChatGPT--the ingenious, garrulous, and occasionally unhinged chatbot from OpenAI--was asked this week how much the company behind it is worth, its responses included: "It is likely that its worth is in the hundreds of millions of dollars, if not more." Microsoft, which is rumored to be weighing a $10 billion investment in OpenAI on top of an earlier $1 billion commitment, is betting that the company is worth a lot more--despite the fact neither ChatGPT nor other AI models made by OpenAI are yet raking in huge amounts of cash. OpenAI has built several impressive and attention-grabbing demos and powers a popular autocomplete function for coders offered by Microsoft's GitHub. But despite the hype swirling around its technology, the startup hasn't created a breakout, highly lucrative product or business. "We don't really know what ChatGPT is going to be great at," says James Cham, a partner at Bloomberg Beta, an investment firm.

Recently it seems everybody is talking about generative AI. Deep learning-powered large language and text-to-image models like ChatGPT, Stable Diffusion, DALL-E and Midjourney have caused much fuss in the tech world, and beyond. Many include them among the most significant recent developments in AI. Whether or not you agree, the general sentiment seems to be that something very all-powerful has appeared. In 2023 we'll hear about models that can help you draw or create videos.

Generative AI, also known as Generative Artificial Intelligence, is a branch of AI that focuses on creating new and unique data, such as images, videos, text, and audio.

From medtech to fintech, Artificial Intelligence (AI) is rapidly transforming our world, with the potential to revolutionise industries and fundamentally alter how we live our lives. Whether it be self-driving cars or smart homes, AI has infiltrated nearly every aspect of modern life – and its influence is growing every day. In recent years, AI tools have become more sophisticated and are now being used to help individuals and organisations in various ways. One such tool that has developed a buzz in recent times on the Internet is none other than ChatGPT, developed by OpenAI. The tool has quickly grabbed attention for its detailed responses and articulate answers across many knowledge domains.

This blog post is an excerpt from my doctoral dissertation, "'What makes a great story?': Multidisciplinary and international perspectives on digital stories created by youth formerly in foster care in Canada" (York University, 11 Apr. The debate about whether or not to work with Artificial Intelligence (AI) technologies because they are "good," or "bad" is overly simplistic and lacks a critical lens. As history conveys, one can infer that artists and technologists are going to try working with AI despite, or because of these challenges. AI systems like "Dall:E" that translate words and sentences into images, have solidified concerns about representation, specifically of gender and race raised early in the advent of AI-based data visualization (Nicholas, 2022). Nevertheless, I believe that it is necessary for women and non-binary people -- especially racialized women -- to begin to explore these technologies as a way of claiming this space and defining the ethical implications of AI.

Using a pretrained AI model from NVIDIA, startup Evozyne created two proteins with significant potential in healthcare and clean energy. A joint paper released today describes the process and the biological building blocks it produced. One aims to cure a congenital disease, another is designed to consume carbon dioxide to reduce global warming. Initial results show a new way to accelerate drug discovery and more. "It's been really encouraging that even in this first round the AI model has produced synthetic proteins as good as naturally occurring ones," said Andrew Ferguson, Evozyne's co-founder and a co-author of the paper.

Generative AI technologies are growing in power, utility, and use. As generative technologies are being incorporated into mainstream applications, there is a need for guidance on how to design those applications to foster productive and safe use. Based on recent research on human-AI co-creation within the HCI and AI communities, we present a set of seven principles for the design of generative AI applications. These principles are grounded in an environment of generative variability. Six principles are focused on designing for characteristics of generative AI: multiple outcomes & imperfection; exploration & control; and mental models & explanations. In addition, we urge designers to design against potential harms that may be caused by a generative model's hazardous output, misuse, or potential for human displacement. We anticipate these principles to usefully inform design decisions made in the creation of novel human-AI applications, and we invite the community to apply, revise, and extend these principles to their own work.