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Enterprise AI Analysis: Artificial Intelligence in Creative Industries: Advances Prior to 2025

Enterprise AI Analysis

Artificial Intelligence in Creative Industries: Advances Prior to 2025

Nantheera Anantrasirichai, Fan Zhang, and David Bull

MyWorld, University of Bristol

The rapid advancements in artificial intelligence (AI), particularly in generative AI and large language models (LLMs), have profoundly impacted the creative industries, enabling more innovative content creation, enhancing workflows, and democratizing access to creative tools. This paper explores these technological shifts, with particular focus on how those that have emerged since our previous review in 2022 have expanded creative opportunities and improved efficiency. These technological advancements have enhanced the capabilities of text-to-image, text-to-video, and multimodal generation technologies. In particular, key breakthroughs in LLMs have established new benchmarks in conversational AI, while advancements in image generators have revolutionized content creation. We also discuss the integration of AI into post-production workflows, which has significantly accelerated and improved traditional processes. Once content has been created, it must be delivered to its audiences; the media industry is now facing the demands of increased communication traffic due to creative content. We therefore include a discussion of how AI is beginning to transform the way we represent and compress media content. We highlight the trend toward unified AI frameworks capable of addressing and integrating multiple creative tasks, and we underscore the importance of human insight to drive the creative process and oversight to mitigate AI-generated inaccuracies. Finally, we explore AI's future potential in the creative sector, stressing the need to navigate emerging challenges and to maximize its benefits while addressing the associated risks.

Executive Impact & Key Metrics

Generative AI and Large Language Models (LLMs) have dramatically transformed creative industries by enabling innovative content creation, streamlining workflows, and democratizing access to powerful tools, with significant advancements since 2022 across various applications from content generation to data compression and quality assessment.

0 Increase in Spatial Audio Plays
0 Recommendation Accuracy with GenAI
0 GPT-4 Parameters

Deep Analysis & Enterprise Applications

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This section provides a high-level overview of key AI technologies that have gained prominence since 2022, including Transformers, Large Language Models (LLMs), Diffusion Models (DMs), and Implicit Neural Representations (INRs). These foundation models underpin many recent advancements across creative applications.

Explores the application of current AI technologies across various creative domains, such as content creation (text, audio, image, video, 3D), information analysis (text categorization, film analysis, content retrieval), content enhancement (restoration, super-resolution, style transfer), information extraction (segmentation, detection, tracking), and data compression. It highlights how AI is transforming traditional creative workflows and opening new opportunities.

Discusses the future potential of AI in creative applications, addressing challenges like consistency, control, ethical issues (fakes, bias, copyright), and job displacement. It emphasizes the need for human oversight, robust regulatory frameworks, and continued adaptation to emerging technologies.

Key Insight: GPT-4 Parameter Scale

1.8T Trillion Parameters

GPT-4, unveiled in 2023, is a significantly larger multimodal LLM with an estimated 1.8 trillion parameters, demonstrating improved performance over its predecessors.

Key Insight: Sora's Video Generation Capability

60s Second Realistic Videos

OpenAI's Sora model, previewed in early 2024, is capable of generating impressive, realistic videos up to one minute long, outperforming other text-to-video models.

Comparison: Diffusion Models vs. GANs in Generative AI

Feature Diffusion Models GANs
Sample Diversity
  • Higher diversity samples
  • Potentially less diverse (mode collapse)
Training Stability
  • Much more stable training process
  • Can suffer from mode collapse
Computational Intensity
  • Computationally intensive, longer training
  • Faster training times
Realism
  • Excellent realism, especially with Latent Diffusion Models
  • Excels at producing realistic images

Case Study: AI in Post-Production & Marketing

Generative AI is not only transforming content creation but also significantly accelerating post-production workflows. The AI-generated Christmas commercial by Coca-Cola serves as a recent example, demonstrating how short videos with rapid scene transitions can mitigate current technological limitations, such as unnatural artifacts. Furthermore, the media industry faces increased communication traffic, making AI-driven media representation and compression crucial. Technologies are beginning to transform how media content is represented and compressed, ensuring efficient delivery to audiences.

Enterprise Process Flow

Content Creation (Text, Audio, Image, Video)
Information Analysis (Categorization, Retrieval)
Content Enhancement (Restoration, SR, Style Transfer)
Data Compression (Image, Video, Audio)
Visual Quality Assessment (Perceptual Metrics)

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Phase 4: Future-Proofing & Innovation

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