Among creative professionals, Generative Artificial Intelligence (GenAI) has sparked excitement over its capabilities and fear over unanticipated consequences. How does GenAI impact User Experience Design (UXD) practice, and are fears warranted? We interviewed 20 UX Designers, with diverse experience and across companies (startups to large enterprises). We probed them to characterize their practices, and sample their attitudes, concerns, and expectations. We found that experienced designers are confident in their originality, creativity, and empathic skills, and find GenAI's role as assistive. They emphasized the unique human factors of "enjoyment" and "agency", where humans remain the arbiters of "AI alignment". However, skill degradation, job replacement, and creativity exhaustion can adversely impact junior designers. We discuss implications for human-GenAI collaboration, specifically copyright and ownership, human creativity and agency, and AI literacy and access. Through the lens of responsible and participatory AI, we contribute a deeper understanding of GenAI fears and opportunities for UXD.

The use of question-based activities (QBAs) is wide-spread in education, traditionally forming an integral part of the learning and assessment process. In this paper, we design and evaluate an automated question generation tool for formative and summative assessment in schools. We present an expert survey of one hundred and four teachers, demonstrating the need for automated generation of QBAs, as a tool that can significantly reduce the workload of teachers and facilitate personalized learning experiences. Leveraging the recent advancements in generative AI, we then present a modular framework employing transformer based language models for automatic generation of multiple-choice questions (MCQs) from textual content. The presented solution, with distinct modules for question generation, correct answer prediction, and distractor formulation, enables us to evaluate different language models and generation techniques. Finally, we perform an extensive quantitative and qualitative evaluation, demonstrating trade-offs in the use of different techniques and models.

Artificial Intelligence (AI) systems, especially generative AI technologies are becoming more relevant in our society. Tools like ChatGPT are being used by members of the disabled community e.g., Autistic people may use it to help compose emails. The growing impact and popularity of generative AI tools have prompted us to examine their relevance within the disabled community. The design and development phases often neglect this marginalized group, leading to inaccurate predictions and unfair discrimination directed towards them. This could result from bias in data sets, algorithms, and systems at various phases of creation and implementation. This workshop paper proposes a platform to involve the disabled community while building generative AI systems. With this platform, our aim is to gain insight into the factors that contribute to bias in the outputs generated by generative AI when used by the disabled community. Furthermore, we expect to comprehend which algorithmic factors are the main contributors to the output's incorrectness or irrelevancy. The proposed platform calls on both disabled and non-disabled people from various geographical and cultural backgrounds to collaborate asynchronously and remotely in a democratic approach to decision-making.

Earlier this year, the stock-photo service provider Getty Images sued Stability AI over what Getty said was the misuse of more than 12 million Getty photos in training Stability's AI photo-generation tool, Stable Diffusion. Now Getty Images is releasing its own AI photo-generation tool, which will be available to its commercial customers. And it's bringing in the big dog to do it: Nvidia. Called simply Generative AI by Getty Images, the tool is paywalled on the Getty.com It will also be available through an API, so Getty customers can plug it into other apps.

As part of the deal, Amazon, the world's largest provider of cloud infrastructure services through its AWS unit, will become the primary provider of computational processing power, also called compute, for Anthropic. The process of training and running state-of-the-art AI models requires vast amounts of compute, and many analysts expect future AI models to require increasing amounts of compute. In return, Amazon will acquire a minority ownership position in Anthropic, and Amazon's engineers will be able to incorporate Anthropic's AI models into their products and services such as Amazon's personal assistant, Alexa. Anthropic has also committed to offering its models via Bedrock, Amazon's online platform on which it hosts foundation models--broadly capable AI models that can be adapted for different tasks. Anthropic was founded in 2021, after a group of OpenAI employees left over differences in their approach to AI safety.

OpenAI's ChatGPT is getting a major update that will enable the viral chatbot to have voice conversations with users and interact using images, moving it closer to popular artificial intelligence (AI) assistants like Apple's Siri. The voice feature "opens doors to many creative and accessibility-focused applications", OpenAI said in a blog post on Monday. Similar AI services like Siri, Google voice assistant and Amazon's Alexa are integrated with the devices they run on and are often used to set alarms and reminders, and deliver information off the internet. Since its debut last year, ChatGPT has been adopted by companies for a wide range of tasks from summarizing documents to writing computer code, setting off a race among big tech companies to launch their own offerings based on generative AI. Google has imminent plans to launch its answer to ChatGPT, called Gemini, which is reportedly already being tested by a small group of companies.

Up to now, people could ask ChatGPT questions by speaking them out loud on its mobile app, but the bot would respond with text. OpenAI also said people can now upload images as part of their questions to the bot, such as showing a photo of the ingredients in a fridge and asking ChatGPT to come up with recipe suggestions. Adding voice and image capabilities also puts ChatGPT further along the line toward becoming a true "multimodal" model -- a chatbot that can "see" and "hear" the world, and respond with voice and images, in addition to being fed text-only prompts. AI researchers and analysts say multimodal models are the next stage of competition in the industry, and companies are racing to create the most capable one.

Editor's note: This article is part of The Atlantic's series on Books3. You can search the database for yourself here, and read about its origins here. This summer, I reported on a data set of more than 191,000 books that were used without permission to train generative-AI systems by Meta, Bloomberg, and others. "Books3," as it's called, was based on a collection of pirated ebooks that includes travel guides, self-published erotic fiction, novels by Stephen King and Margaret Atwood, and a lot more. Books play a crucial role in the training of generative-AI systems.

Editor's note: This searchable database is part of The Atlantic's series on Books3. You can read about the origins of the database here, and an analysis of what's in it here. This summer, I acquired a data set of more than 191,000 books that were used without permission to train generative-AI systems by Meta, Bloomberg, and others. I wrote in The Atlantic about how the data set, known as "Books3," was based on a collection of pirated ebooks, most of them published in the past 20 years. Since my article appeared, I've heard from several authors wanting to know if their work is in Books3.

Anthropic's chief executive Dario Amodei met with UK Prime Minister Rishi Sunak and the heads of DeepMind and OpenAI in May to discuss the potential risks from AI - from disinformation to national security and even "existential threats" - and the voluntary actions and regulation required to manage them.