As generative AI (GenAI) increasingly permeates design workflows, its impact on design outcomes and designers' creative capabilities warrants investigation. We conducted a within-subjects experiment where we asked participants to design advertisements both with and without GenAI support. Our results show that expert evaluators rated GenAI-supported designs as more creative and unconventional ("weird") despite no significant differences in visual appeal, brand alignment, or usefulness, which highlights the decoupling of novelty from usefulness-traditional dual components of creativity-in the context of GenAI usage. Moreover, while GenAI does not significantly enhance designers' overall creative thinking abilities, users were affected differently based on native language and prior AI exposure. Native English speakers experienced reduced relaxation when using AI, whereas designers new to GenAI exhibited gains in divergent thinking, such as idea fluency and flexibility. These findings underscore the variable impact of GenAI on different user groups, suggesting the potential for customized AI tools.

With the exponential growth in the number of papers and the trend of AI research, the use of Generative AI for information retrieval and question-answering has become popular for conducting research surveys. However, novice researchers unfamiliar with a particular field may not significantly improve their efficiency in interacting with Generative AI because they have not developed divergent thinking in that field. This study aims to develop an in-depth Survey Forest Diagram that guides novice researchers in divergent thinking about the research topic by indicating the citation clues among multiple papers, to help expand the survey perspective for novice researchers.

As LLMs become increasingly prevalent, it is interesting to consider how ``creative'' these models can be. From cognitive science, creativity consists of at least two key characteristics: \emph{convergent} thinking (purposefulness to achieve a given goal) and \emph{divergent} thinking (adaptability to new environments or constraints) \citep{runco2003critical}. In this work, we introduce a framework for quantifying LLM creativity that incorporates the two characteristics. This is achieved by (1) Denial Prompting pushes LLMs to come up with more creative solutions to a given problem by incrementally imposing new constraints on the previous solution, compelling LLMs to adopt new strategies, and (2) defining and computing the NeoGauge metric which examines both convergent and divergent thinking in the generated creative responses by LLMs. We apply the proposed framework on Codeforces problems, a natural data source for collecting human coding solutions. We quantify NeoGauge for various proprietary and open-source models and find that even the most creative model, GPT-4, still falls short of demonstrating human-like creativity. We also experiment with advanced reasoning strategies (MCTS, self-correction, etc.) and observe no significant improvement in creativity. As a by-product of our analysis, we release NeoCoder dataset for reproducing our results on future models.

What constitutes human creativity, and is it possible for computers to exhibit genuine creativity? We argue that achieving human-level intelligence in computers, or so-called Artificial General Intelligence, necessitates attaining also human-level creativity. We contribute to this discussion by developing a statistical representation of human creativity, incorporating prior insights from stochastic theory, psychology, philosophy, neuroscience, and chaos theory. This highlights the stochastic nature of the human creative process, which includes both a bias guided, random proposal step, and an evaluation step depending on a flexible or transformable bias structure. The acquired representation of human creativity is subsequently used to assess the creativity levels of various contemporary AI systems. Our analysis includes modern AI algorithms such as reinforcement learning, diffusion models, and large language models, addressing to what extent they measure up to human level creativity. We conclude that these technologies currently lack the capability for autonomous creative action at a human level.

The arrival of ChatGPT, however, feels different. Most new technologies have transformed the products of creative agencies, but ChatGPT is a product that could transform the strategic and creative process itself. Agencies and consultancies should therefore be seriously considering the potential value that this tool can bring to their businesses. One of the helpful ways to go about this is to frame ChatGPT as a thinking partner and to explore the two basic thinking styles that underpin the practice of innovation and strategy formation: divergent and convergent thinking. This approach can help us to understand the benefits of ChatGPT, and how we can start capitalizing on them.

The Artificial Intelligence revolution is here. That might sound like hyperbole. After all, the world looks the same. The revolution didn't arrive with Skynet and robots or with Blade Running cyborgs. A small chat at the bottom right hand corner. If you're imagining Siri or Alexa or even Clippy (Rest in Peace, Clippy), it's so much more than that.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Creative cognitive processes, such as divergent thinking (DT), abstraction, and improvisation, are based on different novelty-based processes. The neural substrates for novelty-based creative thinking are described in the image1. The neuralnetwork model architecture and learning rules to simulate creative processes based on novelty, including divergent thinking (DT), abstraction, and improvisation. It illustrates the neural network architecture and the relevant model functions based on computational models for each brain area. The neural substrates include four brain areas modulated by the DA system and the sensory association cortex, which provides input.

"Is Artificial Intelligence better than Human Intelligence?" is a popular question. Artificial intelligence's negative reputation stems from its apparent overmatching of human intellect. Compared to a brilliant individual, a computer is quicker in various fields. What would take years for a person to complete will only take minutes for AI, and that is the case. In other words, comparing the two types of intelligence may prove that they are significantly different from one another. But how distinct are they? Artificial Intelligence has advanced a long way from being a science fiction component to reality. We have self-driving cars, smart virtual assistants, chatbots, and surgical robots, among other intelligent machines these days.

Almost every business, of every size, across sectors, employs creativity training, from whiteboard brainstorming sessions to design thinking. It's a billion-dollar industry, and with good reason: Creativity is the main engine of innovation and entrepreneurship, and a major driver of resilience. Instead, it perpetuates expert bias and pseudo-innovation, and although it can temporarily boost morale, it does little over the long haul to reduce burnout. On the whole, research has shown it to be at best inadequate and at worst counterproductive. To understand what's broken, and how to fix it, my lab partnered with teams at a variety of organizations, among them Silicon Valley startups, U.S. Special Operations, the University of Chicago Booth School of Business, and Fortune 50 companies.

I pour a cup of coffee, sharpen my pencil, and get ready to create. I've dusted off a half-conceived novel outline I abandoned three years ago, but this time I'm not waiting for my muse to intervene. Instead I hit the play button on the Creative Thinker's Toolkit, an audio lecture series from The Great Courses that I've downloaded on my computer. Gerard Puccio, a psychologist who heads the International Center for Studies in Creativity at SUNY Buffalo State, and the voice of the toolkit, tells me to engage in "forced relationships." Choose a random object, he instructs. I scan my office and settle on a bag of Skittles left over from Halloween. Next, he says, describe the object's attributes. "Sweet, round, colorful, chewy," I write. I start to draw more fruitful connections.