The overarching message from this research is that plans among corporate leaders to disrupt competition using the new technology--rather than being disrupted–--may founder on a host of challenges that many executives appear to underestimate. Executives expect generative AI to disrupt industries across economies. Overall, six out of 10 respondents agree that "generative AI technology will substantially disrupt our industry over the next five years." Respondents that foresee disruption exceed those that do not across every industry. A majority of respondents do not envision AI disruption as a risk; instead, they hope to be disruptors.

The Intercept, Raw Story and AlterNet filed separate lawsuits accusing ChatGPT of reproducing news content "verbatim or nearly verbatim" while stripping out important attribution like the author's name. OpenAI asked a court to dismiss that claim, saying the NYT took advantage of a ChatGPT bug that made it recite articles word for word.

Generative AI applications, such as ChatGPT or DALL-E, have shown the world their impressive capabilities in generating human-like text or image. Diving deeper, the science stakeholder for those AI applications are Deep Generative Models, a.k.a DGMs, which are designed to learn the underlying distribution of the data and generate new data points that are statistically similar to the original dataset. One critical question is raised: how can we leverage DGMs into morden retail supply chain realm? To address this question, this paper expects to provide a comprehensive review of DGMs and discuss their existing and potential usecases in retail supply chain, by (1) providing a taxonomy and overview of state-of-the-art DGMs and their variants, (2) reviewing existing DGM applications in retail supply chain from a end-to-end view of point, and (3) discussing insights and potential directions on how DGMs can be further utilized on solving retail supply chain problems.

The growing availability of generative AI technologies such as large language models (LLMs) has significant implications for creative work. This paper explores twofold aspects of integrating LLMs into the creative process - the divergence stage of idea generation, and the convergence stage of evaluation and selection of ideas. We devised a collaborative group-AI Brainwriting ideation framework, which incorporated an LLM as an enhancement into the group ideation process, and evaluated the idea generation process and the resulted solution space. To assess the potential of using LLMs in the idea evaluation process, we design an evaluation engine and compared it to idea ratings assigned by three expert and six novice evaluators. Our findings suggest that integrating LLM in Brainwriting could enhance both the ideation process and its outcome. We also provide evidence that LLMs can support idea evaluation. We conclude by discussing implications for HCI education and practice.

Since OpenAI's release of ChatGPT, generative AI has received significant attention across various domains. These AI-based chat systems have the potential to enhance the productivity of knowledge workers in diverse tasks. However, the use of free public services poses a risk of data leakage, as service providers may exploit user input for additional training and optimization without clear boundaries. Even subscription-based alternatives sometimes lack transparency in handling user data. To address these concerns and enable Fraunhofer staff to leverage this technology while ensuring confidentiality, we have designed and developed a customized chat AI called FhGenie (genie being a reference to a helpful spirit). Within few days of its release, thousands of Fraunhofer employees started using this service. As pioneers in implementing such a system, many other organizations have followed suit. Our solution builds upon commercial large language models (LLMs), which we have carefully integrated into our system to meet our specific requirements and compliance constraints, including confidentiality and GDPR. In this paper, we share detailed insights into the architectural considerations, design, implementation, and subsequent updates of FhGenie. Additionally, we discuss challenges, observations, and the core lessons learned from its productive usage.

In the landscape of generative artificial intelligence, diffusion-based models present challenges for socio-technical systems in data requirements and privacy. Traditional approaches like federated learning distribute the learning process but strain individual clients, especially with constrained resources (e.g., edge devices). In response to these challenges, we introduce CollaFuse, a novel framework inspired by split learning. Tailored for efficient and collaborative use of denoising diffusion probabilistic models, CollaFuse enables shared server training and inference, alleviating client computational burdens. This is achieved by retaining data and computationally inexpensive GPU processes locally at each client while outsourcing the computationally expensive processes to the shared server. Demonstrated in a healthcare context, CollaFuse enhances privacy by highly reducing the need for sensitive information sharing. These capabilities hold the potential to impact various application areas, such as the design of edge computing solutions, healthcare research, or autonomous driving. In essence, our work advances distributed machine learning, shaping the future of collaborative GenAI networks.

Three progressive US outlets – the Intercept, Raw Story and AlterNet – filed suits in Manhattan federal court on Wednesday, demanding compensation from the tech companies. "It's important to democracy that a diverse array of news sites continue to thrive. OpenAI's violations, if not checked, will further decimate the news industry, and with it, the critical news reporters who affect positive change." The Intercept's suit lists both OpenAI and its most prominent investor Microsoft as defendants, while the joint suit filed by Raw Story and AlterNet only lists OpenAI. The complaints are otherwise nearly identical, and the law firm Loevy & Loevy is representing all three outlets in the suits.

In abandoning plans for a self-driving car, Apple Inc. is giving up on billions in potential revenue and the dream of selling what one executive called "the ultimate mobile device." The hope is that other big bets -- including generative AI and mixed-reality headsets -- can make up the difference. Apple reached this crossroads Tuesday, when it told employees it was winding down the car project and reassigned some of the staff to its AI efforts. The decision followed months of frenzied meetings between top executives and the company's board over how to proceed. Chief Operating Officer Jeff Williams and project head Kevin Lynch broke the news to the roughly 2,000-member team during a meeting that lasted less than 15 minutes.

February was shaping up to be a banner month for Google's ambitious artificial intelligence strategy. The company rebranded its chatbot as Gemini and released two major product upgrades to better compete with rivals on all sides in the high-stakes AI arms race. In the midst of all that, Google also began allowing Gemini users to generate realistic-looking images of people. Not many noticed the feature at first. Other companies like OpenAI already offer tools that let users quickly make images of people that can then be used for marketing, art and brainstorming creative ideas.

Google's chief executive has described some responses by the company's Gemini artificial intelligence model as "biased" and "completely unacceptable" after it produced results including portrayals of German second world warsoldiers as people of colour. Sundar Pichai told employees in a memo that images and texts generated by its latest AI tool had caused offence. Social media users have posted numerous examples of Gemini's image generator depicting historical figures – including popes, the founding fathers of the US and Vikings – in a variety of ethnicities and genders. Last week, Google paused Gemini's ability to create images of people. One example of a text response showed the Gemini chatbot being asked "who negatively impacted society more, Elon [Musk] tweeting memes or Hitler" and the chatbot responding: "It is up to each individual to decide who they believe has had a more negative impact on society."