Large language models (LLMs) are widely applied across diverse domains, raising concerns about their limitations and potential risks. In this study, we investigate two types of bias that LLMs may display: stereotype bias and deviation bias. Stereotype bias refers to when LLMs consistently associate specific traits with a particular demographic group. Deviation bias reflects the disparity between the demographic distributions extracted from LLM-generated content and real-world demographic distributions. By asking four advanced LLMs to generate profiles of individuals, we examine the associations between each demographic group and attributes such as political affiliation, religion, and sexual orientation. Our experimental results show that all examined LLMs exhibit both significant stereotype bias and deviation bias towards multiple groups. Our findings uncover the biases that occur when LLMs infer user attributes and shed light on the potential harms of LLM-generated outputs.

I've been a freelance journalist for 10 years, usually writing for magazines and websites about cinema. I presented a morning show on Radio Kraków twice a week for about two years. It was only one part of my work, but I really enjoyed it. It was about culture and cinema, and featured a range of people, from artists to activists. I remember interviewing Ukrainians about the Russian invasion for the first programme I presented, back in 2022. I was let go in August 2024, alongside a dozen co-workers who were also part-time. We were told the radio station was having financial problems.

As modern Large Language Models (LLMs) shatter many state-of-the-art benchmarks in a variety of domains, this paper investigates their behavior in the domains of ethics and fairness, focusing on protected group bias. We conduct a two-part study: first, we solicit sentence continuations describing the occupations of individuals from different protected groups, including gender, sexuality, religion, and race. Second, we have the model generate stories about individuals who hold different types of occupations. We collect >10k sentence completions made by a publicly available LLM, which we subject to human annotation. We find bias across minoritized groups, but in particular in the domains of gender and sexuality, as well as Western bias, in model generations. The model not only reflects societal biases, but appears to amplify them. The model is additionally overly cautious in replies to queries relating to minoritized groups, providing responses that strongly emphasize diversity and equity to an extent that other group characteristics are overshadowed. This suggests that artificially constraining potentially harmful outputs may itself lead to harm, and should be applied in a careful and controlled manner.

"From one day to the next, our profession was wiped out. We woke up and discovered our skills were redundant." This is what two successful graphic designers told me about the impact of AI. The old promise – creative workers would be better protected than others from mechanisation – imploded overnight. If visual artists can be replaced by machines, who is safe?

There's this odd feeling that starts at the back of the neck. It feels like the hairs are raising up slightly. The first time I felt it was in the mid-70s. I was in high school. I was sitting in front of an ASR-33 teletype machine and I hit something, probably the RE-TURN key. That's how it was spelled.

Generative AI based art has proliferated in the past year, with increasingly impressive use cases from generating fake human faces to the creation of systems that can generate thousands of artistic images from text prompts - some of these images have even been "good" enough to win accolades from qualified judges. In this paper, we explore how Generative Models have impacted artistry, not only from a qualitative point of view, but also from an angle of exploitation of artists -- both via plagiarism, where models are trained on their artwork without permission, and via profit shifting, where profits in the art market have shifted from art creators to model owners. However, we posit that if deployed responsibly, AI generative models have the possibility of being a positive, new modality in art that does not displace or harm existing artists.

You likely see the power of artificial intelligence (AI) daily when you log in to social media or order something online. Many companies use AI to improve business operations and automate more stages of the customer experience. The goal is for AI to allow machines to replace people so that you don't need a human touch for basic and even some complex tasks. With many companies investing in artificial intelligence, it's no surprise that there are AI-powered graphic generators that can create original works of art. We're going to look at the DALL·E 2, DALL·E Mini, and what the future of AI painting looks like. Before we analyze the world of AI-generated artwork, we're going to look at the two most popular tools you may have heard of, including DALL·E 2 and DALL·E Mini.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Designer was announced this week.

Want to see a picture of Jesus Christ laughing at a meme on his phone, Donald Trump as the Nevermind baby, or Karl Marx being slimed at the Nikelodeon Kid's Choice awards? If you've been on Twitter or Instagram in the past couple of weeks, it's been hard to miss odd-looking formulations of these kinds of scenarios in the form of AI art. DALL-E (and DALL-E mini), the creator of these artworks, is a neural network that can take a text phrase and transform it an image. It was trained by looking at millions of images on the internet along with accompanying text and it learned to create pictures of things you'd never expect to be combined, such as an avocado armchair. Text to image technology is proceeding at a rapid pace, and the full DALL-E model is able to produce scarily clear images based on the input you provide, while the mini version is still clunky enough to capture the weird internet style that makes them instantly meme-able.

Whether you're creating a sleek new logo for your company or a magazine cover that's popping with bright colors and interesting shapes, graphic designers need the right kind of laptop to get the job done. The most important thing is powerful hardware. For tasks like 3D modeling, you're going to need a powerful CPU and a good amount of RAM. Depending on the size and complexity of the project, you may need a processor with multiple cores. Another essential piece of hardware is the graphics card.