An image generated by DALL-E 2 from information submitted by artist Rebecca Brewer.Rebecca Brewer/Handout The Vancouver artist Rebecca Brewer is a painter; they apply oils to wood panels to create dreamscapes that hover between the abstract and the representational, offering a low viewpoint or hallucinogenic take on tangled images that might evoke the forest floor or the ocean depths but can't be pinned down. Inspired by the 17th-century tradition of sottobosco, still life paintings of undergrowth, Brewer brings attention to the overlooked or hidden, and lets the viewer glimpse images in their figures the way one might see shapes in clouds. To make work for their recent show at the Catriona Jeffries Gallery in Vancouver, they wondered if some artificial intelligence might help conjure up these surreal images. "I started to fool around with the Open AI tool DALL-E, developing ideas for the show," they said in a recent interview, explaining how they fed descriptions of the effects they had wished to achieve in previous paintings into the program. I could get to something quite similar to what I had in mind." Brewer resubmitted versions of the best AI-generated images along with new prompts into the program and eventually incorporated a few examples into their new works, projecting the computer-generated imagery onto panels and then painting them. Sabrina Ratté's exploration of the blurred line between tech and humanity is making her an art world star "I felt I was very creatively involved.

The AI industry is going wild and proof of that is that we're constantly being bombarded with news about new algorithms and features data scientists are developing. Adobe didn't want to be left behind and decided to use AI to provide what they've called Adobe Firefly: a family of creative generative AI models coming to Adobe products. It was just yesterday that Adobe presented Firefly to the public and there's a lot of hype around it. I can understand that and I'm sure you'll do after this story because what they've created looks amazing. In this post, I'll focus on explaining what Adobe Firefly is, some of its cool features, and how we can profit from it as data scientists.

Existing foundation models are trained on copyrighted material. Deploying these models can pose both legal and ethical risks when data creators fail to receive appropriate attribution or compensation. In the United States and several other countries, copyrighted content may be used to build foundation models without incurring liability due to the fair use doctrine. However, there is a caveat: If the model produces output that is similar to copyrighted data, particularly in scenarios that affect the market of that data, fair use may no longer apply to the output of the model. In this work, we emphasize that fair use is not guaranteed, and additional work may be necessary to keep model development and deployment squarely in the realm of fair use. First, we survey the potential risks of developing and deploying foundation models based on copyrighted content. We review relevant U.S. case law, drawing parallels to existing and potential applications for generating text, source code, and visual art. Experiments confirm that popular foundation models can generate content considerably similar to copyrighted material. Second, we discuss technical mitigations that can help foundation models stay in line with fair use. We argue that more research is needed to align mitigation strategies with the current state of the law. Lastly, we suggest that the law and technical mitigations should co-evolve. For example, coupled with other policy mechanisms, the law could more explicitly consider safe harbors when strong technical tools are used to mitigate infringement harms. This co-evolution may help strike a balance between intellectual property and innovation, which speaks to the original goal of fair use. But we emphasize that the strategies we describe here are not a panacea and more work is needed to develop policies that address the potential harms of foundation models.

Recent progress in diffusion models has revolutionized the popular technology of text-to-image generation. While existing approaches could produce photorealistic high-resolution images with text conditions, there are still several open problems to be solved, which limits the further improvement of image fidelity and text relevancy. In this paper, we propose ERNIE-ViLG 2.0, a large-scale Chinese text-to-image diffusion model, to progressively upgrade the quality of generated images by: (1) incorporating fine-grained textual and visual knowledge of key elements in the scene, and (2) utilizing different denoising experts at different denoising stages. With the proposed mechanisms, ERNIE-ViLG 2.0 not only achieves a new state-of-the-art on MS-COCO with zero-shot FID score of 6.75, but also significantly outperforms recent models in terms of image fidelity and image-text alignment, with side-by-side human evaluation on the bilingual prompt set ViLG-300.

Last week, I had the pleasure of interviewing Kevin Novak, founder of Rackhouse Venture Capital and Uber's first head of AI, and Alex Lee, founder and CEO, of Truewind, in front of a crowd of investors and LPs. The panel was titled, "AI and the battle to capture its value chain: base layer accrual vs the fine tuners." Here's a sample of the questions and topics we addressed. How has AI evolved since you started working in the field, and what is different about this current hype cycle compared to previous ones? According to the Economist, over 500 generative AI startups have collectively raised over $11B, not including OpenAI.

The recent surge in interest in new AI applications in 2023 has been nothing short of extraordinary. From ChatGPT to a growing list of other new apps, our technology and business worlds are rapidly evolving before our eyes in many exciting ways. As a curious technologist, I am fascinated by these new trends, and I wrote this primer on the topic back in January: "ChatGPT: Hopes, Dreams, Cheating and Cybersecurity." I have received many questions about the use of ChatGPT to generate content, and this YouTube video addressed the question: "Is It Plagiarism to Use ChatGPT in Your Published Works?" But as an author, blogger and creator of original content, I have other concerns that are growing just as fast as the new technology is being deployed.

Half of college students are likely already using ChatGPT to cheat, experts have estimated. They warn the revolutionary AI has created a cheating epidemic that poses a huge threat to the integrity of academia. 'At present, well over half of students are likely using AI tools to cheat the education system in exams or essays, but it wouldn't surprise me if that number were already higher.' Could educators resort to written tests to deal with AI cheating? He added: 'If educators make the mistake of ignoring the threat of AI-based cheating, I can honestly see more than 90 percent of students cheating in this way [in future].' OpenAI's new GPT-4 update (GPT-3 and GPT-4 are the models which underlie ChatGPT) is able to get 90 percent on a huge number of exams, including the American bar exam.

What are the use cases for embedding generative AI in your enterprise? How can it help ease burden of repetitive admin? Research firm Gartner reports that venture capital companies have invested over $1.7bn in generative AI solutions over the last three years. With AI-enhanced chatbots taking media by storm, this is only going to sharply increase. As it gains popularity among millions of users worldwide, there is no denying the power of generative AI for enterprise.

The wheel, electricity and the computer are among some two dozen general purpose technologies, aka GPTs, that have greatly transformed human economies and societies. Is it just a coincidence that OpenAI's GPT shares this initialism? In the new paper GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models, a research team from OpenAI, OpenResearch, and the University of Pennsylvania investigates the potential impact of LLMs like GPT on the US labour market, shedding light on the economic, social, and policy implications. The "GPT" in ChatGPT stands for generative pretrained transformer, an LLM architecture with game-changing abilities across a variety of generative tasks. Amid the recent public fascination with ChatGPT, however, concerns are emerging -- as people wonder how such models could impact their workplaces and to what extent they might replace human workers.

The popular Dall-E 2, created by OpenAI and named after painter Salvador Dali and Disney Pixar's WALL-E, shook the internet when it launched last July. In August, the start-up Stable Diffusion released its own version, essentially an anti-DALL-E with fewer restrictions on how it could be used. Research lab Midjourney debuted its own version during the summer, which created the picture that sparked a controversy in August when it won an art competition at the Colorado State Fair.