Generative AI enables users to generate new content based on various inputs (including text, images, sounds, animation, 3D models, or other data types). By using neural network technology to identify the patterns and structures within existing data, generative AI creates new and original content. The technology can be a powerful tool for streamlining the workflow of creatives, engineers, researchers, scientists, and others—across all industries.
According to a recent McKinsey report, generative AI is being used by 79% of workers—22% use it regularly to complete their day-to-day work tasks. 40% of organizations employ generative AI to speed up repetitive tasks so workers can focus on more creative endeavors. Generative AI has the potential to impact a wide range of applications and is an essential area of AI research and development.
The IEEE Xplore digital library brings you access to the latest advancements and breakthroughs in Generative AI. We have highlighted several recent developments below:
Expressions of AI Competence
The emerging social status of generative AI shapes the legitimacy of AI use. There is a comprehensive and nuanced spectrum of discursive positions, from AI as a competent and creative partner in work to AI as a recombinant yet dumb tool. This debate reshapes public understanding of what it means to be creative and what is distinctive about humanity.
Researchers examine public perception of generative AI actors in a paper presented at the 2023 International Conference on Control Systems and Computer Science (CSCS). The four analyzed news sources highlighted conflicting vocabularies of (in)competence, portraying AI as either creative or imitative, with some nuances of partial competence in between. The vocabularies of AI creativity also describe AI-based forms of cognition as quasi-neurodiversity that needs social awareness and acceptance. On the contrary, vocabularies of AI incompetence portray AI as polluting human culture, replacing and degrading human skills and knowledge. The emergence of generative AI raises considerable challenges for social organizations, opening a horizon of opportunities and risks beyond previous discussions of AI-based decisions.
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AI-Based Hybrid Spaces for Indoor and Outdoor Activities
Artificial intelligence has been endorsed through daily activities reflecting our use-pattern from navigated webpages, visited locations, or even elected songs. Our daily activities witness machine-learned applications on cellular mobile devices, tablets, and electronic gadgets. A futuristic approach can incorporate our basic personal chores and architectural space design.
In a paper presented at the 2023 Learning and Technology Conference (L&T), researchers outline a study investigating futuristic AI-based lifestyles and their impact on architectural spaces and clusters agglomeration. This research explores machine-learned use patterns of human activities in outdoor and indoor areas. It proposes scenarios to be used through a Generative Adversarial Network for AI-based use-pattern generative spaces. This hybrid space can be a virtual and real space that accommodates various functionalities with flexibilities relying on IOT, AI-based, and XR technologies.
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Limits of AI in Art
Artificial intelligence is already making significant progress, especially when it comes to the production of artistic works. Even though artificial intelligence has developed numerous algorithms and approaches for producing creative works, it still lacks originality. Image generation technology using artificial intelligence and Generative Adversarial Networks is currently reaching the peak of its performance. While it is true that some of the images generated are good enough to be used, it is still being determined whether the capabilities of AI image generation can outperform their creative human counterparts.
In a literature study presented at the 2023 International Conference on Computer Science, Information Technology, and Engineering, researchers explore the basics of AI image generation, how they work, and what factors contribute to creating art. The review shows how much there is to Generative Adversarial Networks application in making creative art, the massive advantage of GANs, their versatility, increasing data, and how different data types can lead to different results. The majority of artificially created images may still be recognized from real ones by their differences in composition. The actual application for future work is to advance in speculative fields to handle issues like training challenges, non-unionization, and model breakdown.
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The Way Forward Concerning Generative AI
Generative AI has become increasingly popular—however, there is much ambiguity from the providers about the technology, the methodology, and the way it works. Unfortunately, despite the importance of these issues to the scientific community, they are hardly discussed. A recent article in IEEE Intelligent Systems addresses some of the issues to stimulate further studies of these critical topics regarding generative AI.
According to the author, we are only beginning to understand how AI will change the way we work and live. What the world looks like regarding AI will depend heavily on how we handle the next five to 10 years. Undoubtedly, there are risks and the potential for misuse as generative AI technologies become more widespread—and safeguards and human-in-the-loop systems must be established to mitigate them.
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These are just a few examples of the hundreds of articles in the IEEE Xplore Digital Library related to Generative AI.
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