Generative Artificial Intelligence (AI) models such as OpenAI’s ChatGPT have the potential to revolutionize Statistical Process Control (SPC) practice, learning, and research. However, these tools are in the early stages of development and can be easily misused or misunderstood. In this paper, we give an overview of the development of Generative AI. Specifically, we explore ChatGPT’s ability to provide code, explain basic concepts, and create knowledge related to SPC practice, learning, and research. By investigating responses to structured prompts, we highlight the benefits and limitations of the results. Our study indicates that the current version of ChatGPT performs well for structured tasks, such as translating code from one language to another and explaining well-known concepts but struggles with more nuanced tasks, such as explaining less widely known terms and creating code from scratch. We find that using new AI tools may help practitioners, educators, and researchers to be more efficient and productive. However, in their current stages of development, some results are misleading and wrong. Overall, the use of generative AI models in SPC must be properly validated and used in conjunction with other methods to ensure accurate results.

Generative AI, in particular large language models (LLMs) such as ChatGPT, has the potential to revolutionize research. I describe dozens of use cases along six domains in which LLMs are starting to become useful as both research assistants and tutors: ideation and feedback, writing, background research, data analysis, coding, and mathematical derivations. I provide general instructions and demonstrate specific examples of how to take advantage of each of these, classifying the LLM capabilities from experimental to highly useful. I argue that economists can reap significant productivity gains by taking advantage of generative AI to automate micro tasks. Moreover, these gains will grow as the performance of AI systems across all of these domains will continue to improve. I also speculate on the longer-term implications of AI-powered cognitive automation for economic research.The resources provided here contain the prompts and code to reproduce the chats with GPT-3.5, GPT-4, ChatGPT and Claude 2 that are listed in the paper.

Generative AI Security Market Outlook

According to our latest research, the global Generative AI Security market size stood at USD 1.98 billion in 2024, reflecting robust momentum driven by the rapid integration of generative AI technologies across industries. The market is projected to expand at a CAGR of 28.1% from 2025 to 2033, reaching a forecasted value of USD 17.54 billion by 2033. This exceptional growth is underpinned by the escalating adoption of generative AI tools and the surging need for advanced security solutions to mitigate emerging AI-driven threats. As organizations increasingly leverage generative AI for innovation and automation, the imperative to secure these systems propels the market forward, making generative AI security a critical investment area for enterprises worldwide.

The primary growth driver for the generative AI security market is the exponential increase in the deployment of generative AI models across business processes and digital ecosystems. Organizations are leveraging generative AI for content creation, data analysis, and automation, but these advancements also introduce new vectors for cyber threats, such as data poisoning, model inversion, and adversarial attacks. The sophistication of these threats necessitates equally advanced security frameworks, prompting firms to invest in specialized generative AI security solutions. Moreover, the rising number of high-profile breaches involving AI-generated content and deepfakes has heightened awareness among both enterprises and regulators, further accelerating demand for robust generative AI security platforms.

Another significant factor fueling market growth is the tightening regulatory landscape surrounding AI and data security. Governments and industry bodies across North America, Europe, and Asia Pacific are introducing stringent compliance requirements to safeguard sensitive data processed by AI systems. These regulations mandate organizations to implement advanced security protocols, including real-time monitoring, threat detection, and automated response mechanisms specifically tailored for generative AI environments. Additionally, the growing emphasis on ethical AI usage and transparency compels organizations to adopt security solutions that not only protect data but also ensure the integrity and accountability of AI-generated outputs. This regulatory pressure, combined with increasing consumer expectations for privacy and trust, is a key catalyst for sustained market expansion.

The proliferation of cloud-based generative AI solutions is also reshaping the security landscape, creating both opportunities and challenges for market stakeholders. Cloud deployments offer scalability and flexibility, enabling organizations to rapidly experiment with and deploy generative AI models. However, this shift also exposes enterprises to new security risks, including multi-tenant vulnerabilities, data leakage, and unauthorized access to AI models and training data. As a result, there is a surge in demand for cloud-native generative AI security solutions that can provide end-to-end protection across distributed environments. Vendors are responding with innovations in secure model deployment, encryption, and access control, driving the evolution of the market and reinforcing the need for specialized expertise in generative AI security.

Regionally, North America continues to dominate the generative AI security market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The United States leads in both adoption and innovation, supported by a mature technology ecosystem and proactive regulatory initiatives. Europe is witnessing rapid growth due to the enforcement of GDPR and AI Act regulations, while Asia Pacific is emerging as a high-growth region driven by digital transformation initiatives in China, Japan, and India. Each region presents unique opportunities and challenges, with local market dynamics, regulatory frameworks, and industry verticals shaping the trajectory of generative AI security adoption.

Component Analysis

The generative AI security market is segmented by component into software, hardware, and services, each playing a pivotal role in the overall security architecture. The software segment dominates the market, accounting for the highest revenue share in 2024, as organizations prioritize investment in advanced security platforms, threat detection tools, and AI-driven analytics. These software so

Snapshot img

Generative Artificial Intelligence (AI) Market Size 2025-2029

The generative artificial intelligence (AI) market size is forecast to increase by USD 185.82 billion at a CAGR of 59.4% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for AI-generated content. This trend is being driven by the accelerated deployment of large language models (LLMs), which are capable of generating human-like text, music, and visual content. However, the market faces a notable challenge: the lack of quality data. Despite the promising advancements in AI technology, the availability and quality of data remain a significant obstacle. To effectively train and improve AI models, high-quality, diverse, and representative data are essential. The scarcity and biases in existing data sets can limit the performance and generalizability of AI systems, posing challenges for businesses seeking to capitalize on the market opportunities presented by generative AI.
Companies must prioritize investing in data collection, curation, and ethics to address this challenge and ensure their AI solutions deliver accurate, unbiased, and valuable results. By focusing on data quality, businesses can navigate this challenge and unlock the full potential of generative AI in various industries, including content creation, customer service, and research and development.

What will be the Size of the Generative Artificial Intelligence (AI) Market during the forecast period?

Request Free Sample

The market continues to evolve, driven by advancements in foundation models and large language models. These models undergo constant refinement through prompt engineering and model safety measures, ensuring they deliver personalized experiences for various applications. Research and development in open-source models, language modeling, knowledge graph, product design, and audio generation propel innovation. Neural networks, machine learning, and deep learning techniques fuel data analysis, while model fine-tuning and predictive analytics optimize business intelligence. Ethical considerations, responsible AI, and model explainability are integral parts of the ongoing conversation.
Model bias, data privacy, and data security remain critical concerns. Transformer models and conversational AI are transforming customer service, while code generation, image generation, text generation, video generation, and topic modeling expand content creation possibilities. Ongoing research in natural language processing, sentiment analysis, and predictive analytics continues to shape the market landscape.

How is this Generative Artificial Intelligence (AI) Industry segmented?

The generative artificial intelligence (AI) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Services


Technology

  Transformers
  Generative adversarial networks (GANs)
  Variational autoencoder (VAE)
  Diffusion networks


Application

  Computer Vision
  NLP
  Robotics & Automation
  Content Generation
  Chatbots & Intelligent Virtual Assistants
  Predictive Analytics
  Others


End-Use

  Media & Entertainment
  BFSI
  IT & Telecommunication
  Healthcare
  Automotive & Transportation
  Gaming
  Others


Model

  Large Language Models
  Image & Video Generative Models
  Multi-modal Generative Models
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    Spain
    The Netherlands
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

Generative Artificial Intelligence (AI) is revolutionizing the tech landscape with its ability to create unique and personalized content. Foundation models, such as GPT-4, employ deep learning techniques to generate human-like text, while large language models fine-tune these models for specific applications. Prompt engineering and model safety are crucial in ensuring accurate and responsible AI usage. Businesses leverage these technologies for various purposes, including content creation, customer service, and product design. Research and development in generative AI is ongoing, with open-source models and transformer models leading the way. Neural networks and deep learning power these models, enabling advanced capabilities like audio generation, data analysis, and predictive analytics.

Natural language processing, sentiment analysis, and conversational AI are essential applications, enhancing business intelligence and customer experiences. Ethica

Argumentative skills are indispensable both personally and professionally to process complex information (CoI) relating to the critical reconstruction of meaning through critical thinking (CT). This remains a particularly relevant priority, especially in the age of social media and artificial intelligence-mediated information. Recently, the public dissemination of what has been called generative artificial intelligence (GenAI), with the particular example of ChatGPT (OpenAI, 2022), has made it even easier today to access and disseminate information, written or not, true or not. New tools are needed to critically address post-digital information abundance.

In this context, argumentative maps (AMs), which are already used to develop argumentative skills and critical thinking, are studied for multimodal and dynamic information visualization, comprehension, and reprocessing. In this regard, the entry of generative AI into university classrooms proposes a novel scenario of multimodality and technological dynamism.

Building on the Vygotskian idea of mediation and the theory of "dual stimulation" as applied to the use of learning technologies, the idea was to complement AMs with the introduction of a second set of stimuli that would support and enhance individual activity: AI-mediated tools. With AMs, an attempt has been made to create a space for understanding, fixing, and reconstructing information, which is important for the development of argumentative skills. On the other hand, by arranging forms of critical and functional interaction with ChatGPT as an ally in understanding, reformulating, and rethinking one's argumentative perspectives, a new and comprehensive argumentative learning process has been arranged, while also cultivating a deeper understanding of the artificial agents themselves.

Our study was based on a two-group quasi-experiment with 27 students of the “Research Methods in Education” course, to explore the role of AMs in fixing and supporting multimodal information reprocessing. In addition, by predicting the use of the intelligent chatbot ChatGPT, one of the most widely used GenAI technologies, we investigated the evolution of students' perceptions of its potential role as a “study companion” in information comprehension and reprocessing activities with a path to build a good prompt.

Preliminary analyses showed that in both groups, AMs supported the increase in mean CoI and CT levels for analog and digital information. However, the group with analog texts showed more complete reprocessing.The interaction with the chatbot was analyzed quantitatively and qualitatively, and there emerged an initial positive reflection on the potential of ChatGPT and increased confidence in interacting with intelligent agents after learning the rules for constructing good prompts.

This Zenodo record follows the full analysis process with R (https://cran.r-project.org/bin/windows/base/ ) and Nvivo (https://lumivero.com/products/nvivo/) composed of the following datasets, script and results:

1. Comprehension of Text and AMs Results - Arg_G1.xlsx & Arg_G2.xlsx

2. Opinion and Critical Thinking level - Opi_G1.xlsx & Opi_G2.xlsx

3. Data for Correlation and Regression - CorRegr_G1.xlsx & CorRegr_G2.xlsx

4. Interaction with ChatGPT - GPT_G1.xlsx & GPT_G2.xlsx

5. Descriptive and Inferential Statistics Comprehension and AMs Building - Analysis_RES_Comprehension.R

6. Descriptive and Inferential Statistics Opinion and Critical Thinking level - Analysis_RES_Opinion.R

7. Correlation and Regression - Analysis_RES_CorRegr.R

8. Descriptive and Inferential Statistics Interaction with ChatGPT - Analysis_RES_ChatGPT.R

9. Sentiment Analysis - Sentiment Analysis_G1.R & Sentiment Analysis_G2.R

10. Vocabulary Frequent words - Vocabulary.csv

11. Codebook qualitative Analysis with Nvivo (Codebook.xlsx)

12. Results Nvivo Analysis G1 - Codebook - ChatGPT2 G1.docx

13. Results Nvivo Analysis G2 - Codebook - ChatGPT2 G2.docx

Any comments or improvements are welcome!

The AI Image Generator Market size was valued at USD 356.1 USD Million in 2023 and is projected to reach USD 1094.58 USD Million by 2032, exhibiting a CAGR of 17.4 % during the forecast period. AI image generator refers to a software application for generating image data by means of artificial intelligence, utilizing such models as deep learning, neural networks, and others. Some of them are GANs which stand for Generative Adversarial Networks, VAEs which stand for Variational Autoencoders, and diffusion models. Essential characteristics include crystal clear display of the resultant image, conversion of the source image to another style, and image improvement. It makes use for the generation of art, designing, virtual fitting, and even in-game design . These generators facilitate the quickly and cheaply generated visualization and image modifications depending on certain parameters or styles, hence changing the creative landscapes of various industries by improving efficiency and creativity. Recent developments include: September 2023 - OpenAI, a company specializing in the generative AI industry, introduced DALL-E 3, the latest version of its image generator. This upgrade, powered by the ChatGPT controller, produces high-quality images based on natural-language prompts and incorporates ethical safeguards., May 2023 - Stability AI introduced StableStudio, an open-source version of its DreamStudio AI application, specializing in converting text into images. This open-source release enabled developers and creators to access and utilize the technology, creating a wide range of applications for text-to-image generation., April 2023 - VanceAI launched an AI text-to-image generator called VanceAI Art Generator, powered by Stable Diffusion. This tool could interpret text descriptions and generate corresponding artworks. Users could combine image types, styles, artists, and adjust sizes to transform their creative ideas into visual art., March 2023 - Adobe unveiled Adobe Firefly, a generative AI tool in beta, catering to users without graphic design skills, helping them to create images and text effects. This announcement coincided with Microsoft’s launch of Copilot, offering automatic content generation for 365 and Dynamics 365 users. These advancements in generative AI provided valuable support and opportunities for individuals facing challenges related to writing, design, or organization., March 2023 - Runway AI introduced Gen-2, a combination of AI models capable of producing short video clips from text prompts. Gen-2, an advancement over its predecessor Gen-1, would generate higher-quality clips and provide users with increased customization options.. Key drivers for this market are: Growing Adoption of Augmented Reality (AR) and Virtual Reality (VR) to Fuel the Market Growth. Potential restraints include: Concerns related to Data Privacy and Creation of Malicious Content to Hamper the Market. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

The size of the Europe Generative AI Market market was valued at USD 3.13 billion in 2023 and is projected to reach USD 26.66 billion by 2032, with an expected CAGR of 35.8 % during the forecast period. The Europe generative AI market is primarily centered on applying Artificial Intelligence for the generation of content, designs or solutions in different fields. Generative AI involves the application of sophisticated logic to create new information elements based on the input data which resembles the real-world data, for example words, images, or sounds. Some of the important uses include business promotion through creating content such as articles, blogging, and creating designs and arts, customized suggestions, and enriching datasets. The current trends within the market include more utilization of AI in improving the customer experiences, enhancements in the natural language processing and even the use and development of deep learning for integration of the AI in business processes for efficiency and innovation. The market is being influenced by prospects associated with automation, creativity, and constructing data-focused insights in addition to pending interest in acquiring AI studies and development. Recent developments include: In February 2024, Capgemini partnered with Mistral AI, an artificial intelligence company, to focus on accelerating the evolution towards more versatile, accessible, and cost-effective generative AI implementation at scale. Capgemini aims to support its numerous global clients in maximizing long-term value and expediting the implementation of their generative AI initiatives by integrating Mistral AI's exceptionally efficient foundational models into their comprehensive generative AI framework. , In February 2024, IBM and Natwest announced upgrades to the bank's virtual assistant, Cora, leveraging generative AI technology to offer customers access to a broader spectrum of information through conversational interactions. This initiative positions the bank as one of the adopters of generative AI within the UK, enhancing the safety, intuitiveness, and accessibility of its digital services through the virtual assistant. , In July 2023, OYO launched ChatGPT-powered self-check-in in the UK. The virtual solution powered by ChatGPT aims to minimize wait times for customers of partner hotels by providing a streamlined check-in process that takes just five minutes. .

Snapshot img

AI API Market Size 2025-2029

The AI API market size is forecast to increase by USD 78.45 billion at a CAGR of 22% between 2024 and 2029.

The market is experiencing significant growth as the democratization of advanced AI capabilities through API models gains momentum. This shift enables businesses of all sizes to integrate AI functionality into their operations without requiring extensive expertise or resources. A key trend in the market is the move towards multimodality and native integration, allowing for seamless interaction between various AI technologies and applications. The emergence of generative AI at the edge is a significant driver, enabling advanced analytics and decision-making capabilities in industries such as manufacturing, healthcare, and transportation.
However, the market faces challenges as concerns over data privacy, model governance, and ethical risks continue to mount. Companies must navigate these obstacles effectively to capitalize on the opportunities presented by the market and maintain customer trust. By focusing on transparency, security, and ethical AI implementation, businesses can differentiate themselves and thrive in this dynamic landscape. Data security protocols are crucial in the edge infrastructure landscape, with predictive maintenance and thermal management systems ensuring operational efficiency and reliability.

What will be the Size of the AI API Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

In the dynamic API market, reliability metrics and performance optimization are crucial factors for businesses seeking to deliver high-quality digital experiences. API response tracing and request validation help identify and address performance issues, ensuring optimal API runtime environments. Error handling and change management are essential for maintaining API uptime and minimizing downtime. API deployment pipelines and testing methodologies streamline the development process, enabling continuous integration and delivery. Monitoring dashboards provide real-time insights into API usage, while data modeling and schema validation ensure data accuracy and consistency.

Security compliance is a top priority, with event handling and API infrastructure costs shaping the market landscape. API documentation standards facilitate developer onboarding and adoption, while caching techniques and scalability strategies optimize API performance. API management platforms offer comprehensive solutions, integrating testing automation, traffic analysis, and response formatting capabilities. Ultimately, the API market is driven by the evolving needs of businesses, requiring adaptable solutions that prioritize reliability, performance, and security.

How is this AI API Industry segmented?

The AI API industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Usage

  Generative AI API
  Text and NLP API
  Image and vision API
  Speech and voice API
  Predictive analytics API


Deployment

  Cloud-based API
  Edge and on-premises API


End-user

  Healthcare and life sciences
  BFSI
  Retail and e-commerce
  Media and entertainment
  Telecom and government sector


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Usage Insights

The Generative AI API segment is estimated to witness significant growth during the forecast period. The API market is witnessing significant advancements with the integration of Generative AI APIs, a transformative function that creates new, synthetic, and original content based on user prompts and underlying patterns learned from extensive training datasets. This functionality spans various intellectual tasks, such as generating human-like text, computer code, novel images, music, and spoken audio. Businesses are capitalizing on this capability to automate and scale content creation, accelerate research and development, and build innovative applications. The API landscape encompasses several key components, including data transformation, microservice architecture, data validation, traffic routing, governance policies, performance testing, documentation generator, AI-powered testing, mocking frameworks, and security auditing.

Generative AI APIs represent the most transformative and rapidly advancing functionality within the AI API market. Their core function is not to analyze existing data but to create new, synthetic, and original content based on user prompts and underlying patterns learned from vast

This supplementary dataset provides full transparency and reproducibility for the empirical study titled "Accessible Web Content Generation Using LLMs: An Empirical Study on Prompting Strategies and Template-Guided Remediation". It contains all experimental data used to analyze the impact of different prompting strategies and input configurations in the automated remediation of web accessibility issues.

The spreadsheet is organized into multiple sheets that document both manual and automated evaluations:

Manual evaluation sheets report the results of expert assessments using the Barrier Walkthrough (BW) method, including severity levels (Minor, Significant, Critical) per variant and barrier. Summary graphs (bar and pie charts) aggregate this data for visualization and interpretation.

Automated evaluation sheets include metrics obtained from accessibility tools such as WAVE and Lighthouse, reporting the number of errors, alerts, and compliance indicators across variants.

Correlation sheets compare automated and manual results to assess alignment between tool-based evaluations and expert heuristics.

Reference materials document the barrier definitions, scoring scales, and variant descriptions used in the study, ensuring methodological transparency.

This dataset enables reproducibility, supports comparative analyses of prompt effectiveness, and provides a structured foundation for future research in AI-driven accessibility remediation.

Global Generative AI In Creative Industries market size is expected to reach $12.61 billion by 2029 at 32.5%, segmented as by text-to-image generation, ai-powered image generation from text prompts, text-to-image synthesis for art and design

GenAIPABench is a specialized dataset designed to evaluate Generative AI-based Privacy Assistants (GenAIPAs). These assistants aim to simplify complex privacy policies and data protection regulations, making them more accessible and understandable to users. The dataset provides a comprehensive framework for assessing the performance of AI models in interpreting and explaining privacy-related documents.

Components of the Dataset:

Privacy Documents:

Privacy Policies: The dataset includes five privacy policies from various organizations or services. These policies are selected to represent a range of industries and complexity levels. Data Protection Regulations: It also contains two major data protection regulations (such as the EU's GDPR and California's CCPA), providing a legal context for evaluation. Question Corpus:

Privacy Policy Questions: Contains 32 questions related to the privacy policies. These questions address key topics like data collection practices, data sharing, user rights, data security, and retention policies. Regulation Questions: Includes 6 questions about data protection regulations, focusing on compliance requirements, user rights under the law, and organizational obligations. Question Variations: Each question comes with paraphrased versions and variations to test the AI's ability to handle different phrasings and nuances. Annotated Answers: Expert-Curated Responses: Each question is accompanied by meticulously crafted answers provided by privacy experts. Cross-Verification: Answers are cross-verified for accuracy and completeness, ensuring they align precisely with the source documents. Purpose and Objectives:

Benchmarking GenAIPAs: Provides a standardized dataset for evaluating and comparing the effectiveness of different AI-based privacy assistants. Improving AI Understanding of Privacy: Helps identify strengths and weaknesses in AI models regarding comprehension of privacy policies and regulations. Enhancing User Experience: Aims to improve how AI assistants communicate complex privacy information to users, making it more accessible and actionable. Usage Scenarios:

Academic Research: Researchers can use the dataset to study how AI models interpret and summarize legal and policy documents. AI Development: Developers can train and fine-tune AI models to better handle privacy-related queries. Policy Analysis Tools: Organizations can leverage the dataset to create tools that help users understand and navigate privacy policies. Key Features:

Diverse Content: Covers a range of privacy documents and questions to ensure a comprehensive evaluation. Expert Validation: Responses are verified by privacy experts, ensuring high-quality benchmarks. Robust Testing Framework: The evaluator tool allows systematic testing under different scenarios and prompts. Focus on Real-world Applicability: Questions are derived from user inquiries, FAQs, and online forums to reflect genuine user concerns. Benefits:

Enhances Trustworthiness: The dataset helps improve user trust in AI assistants by promoting accuracy and clarity. Supports Regulatory Compliance: Helps organizations ensure their AI tools provide information consistent with legal requirements. Facilitates Transparency: Encourages AI models to provide transparent and reference-backed responses.

Snapshot img

Personal AI Assistant Market Size 2025-2029

The personal AI assistant market size is forecast to increase by USD 9.31 billion at a CAGR of 33.9% between 2024 and 2029.

The market is experiencing significant growth, driven by the transformative impact of advanced generative AI and large language models. These technologies enable assistants to understand and respond to complex queries, learn from user interactions, and even generate human-like text and speech. This enhances the user experience, making personal assistants increasingly indispensable in both personal and professional settings. However, the mainstreaming of these advanced technologies also brings challenges.
Ensuring robust data protection measures and transparent user consent practices are crucial for companies to build trust and maintain user loyalty. Companies must navigate these challenges while capitalizing on the market's potential by focusing on user experience, innovation, and ethical data handling. Data security and privacy vulnerabilities intensify as AI assistants collect and process vast amounts of personal information.

What will be the Size of the Personal AI Assistant Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

The market is experiencing significant advancements, with key areas of focus including prompt engineering, platform compatibility, and data governance policies. Reinforcement learning and cross-platform support are driving innovation, enabling seamless interaction across various devices. Performance benchmarking and cost optimization strategies are essential for ensuring efficiency and competitiveness. Knowledge base management and feedback loop mechanisms facilitate continuous learning, while scalability challenges are being addressed through transfer learning and algorithm efficiency.

Localization strategies and deployment strategies cater to diverse markets, with internationalization support and compliance regulations ensuring accessibility and legal compliance. API integration methods, language model training, system integration, and model evaluation metrics are crucial for enhancing functionality and user experience. Error handling mechanisms and zero-shot learning are also gaining importance in the development of advanced AI assistants. Artificial intelligence (AI) and machine learning (ML) are powering personalized learning paths, enabling learners to progress at their own pace.

How is this Personal AI Assistant Industry segmented?

The personal AI assistant industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Chatbot
  Smart speaker
  Others


Technology

  Natural language processing
  Machine learning
  Text-based


Deployment

  Cloud-based
  On-premises


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    Japan
    South Korea


  Rest of World (ROW)

By Product Insights

The Chatbot segment is estimated to witness significant growth during the forecast period. The market is witnessing significant advancements, with the chatbot sub-segment leading the way. This evolution from rule-based scripts to conversational agents powered by generative artificial intelligence is a major technology shift. The driving force behind this transformation is the rapid progress and expanding availability of large language models (LLMs), enabling the creation of chatbots that exhibit contextual understanding, human-like text generation, and complex reasoning capabilities. This democratization of chatbot development fosters an innovative ecosystem. Engaging virtual reality (VR) and augmented reality (AR) language learning videos are gaining traction, providing users with authentic language experiences.

Major technology corporations are intensely competing to dominate the market by continually enhancing their foundational models and integrating them into their product offerings. Advanced features such as bias mitigation, user experience optimization, dialogue management, personalization techniques, deep learning algorithms, knowledge graph integration, common sense reasoning, text summarization, conversational AI, named entity recognition, machine learning models, and speech recognition are shaping the market's dynamics. However, the advent of advanced LLMs has revolutionized these assistants, enabling them to comprehend nuance, context, and complex intent. Key technologies driving this market include personalization techniques, deep learning algorithms, knowledge graph integration, text summarization, conversational AI,

DiffusionDB is the first large-scale text-to-image prompt dataset. It contains 2 million images generated by Stable Diffusion using prompts and hyperparameters specified by real users. The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models.

Snapshot img

AI Creativity And Art Generation Market Size 2025-2029

The AI creativity and art generation market size is forecast to increase by USD 9.01 billion at a CAGR of 11.6% between 2024 and 2029.

The market is experiencing significant growth, driven by the democratization of content creation and increased accessibility to advanced AI technologies. This trend is enabling a wider range of individuals and organizations to generate creative content, leading to new opportunities and applications in various industries. Mobility solutions and quantum computing are also expected to provide new growth opportunities. Furthermore, the ascendancy of multimodal and video generation is transforming the creative landscape, offering innovative solutions for marketing, entertainment, and education.
Additionally, ethical considerations surrounding the use of AI in art generation, such as authenticity and human creativity, necessitate ongoing dialogue and industry standards. Companies seeking to capitalize on market opportunities and navigate these challenges effectively must stay informed of emerging trends and engage in open discussions with stakeholders. However, the market faces challenges, including pervasive intellectual property and ethical dilemmas. Machine learning and 3D object detection are emerging trends in the market.

What will be the Size of the AI Creativity And Art Generation Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

In the dynamic market, digital art platforms are leveraging transformer-based models and image upscaling algorithms to enhance art creation techniques. Computer vision, a subset of artificial intelligence (AI), is revolutionizing various industries by enabling machines to identify and interpret visual information. Computer vision algorithms and image editing software enable image-to-image transformation, while stylegan2 architecture and GAN image generation push the boundaries of image synthesis. Convolutional neural networks and autoencoder compression optimize the image pipeline, and latent space manipulation, diffusion model sampling, and self-attention mechanisms fuel creative AI pipelines.

Recurrent image generation, image inpainting methods, text-guided image generation, and neural style transfer are also trending, as the AI art community explores new ways to manipulate and generate captivating visuals. The integration of these advanced techniques into art generation workflows is revolutionizing the way businesses approach AI image manipulation. As AI-generated content becomes more sophisticated, it raises questions about ownership and authorship, requiring clear guidelines and regulations. Machine learning and deep learning models are powering cloud and edge computing technologies, enhancing autonomous driving solutions in the automotive sector.

How is this AI Creativity And Art Generation Industry segmented?

The AI creativity and art generation industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Generative AI tools
  AI design tools
  AI music tools
  AI video and animation tools
  Others


Application

  Visual arts
  Music and sound design
  Film and animation
  Digital media and advertising
  Others


End-user

  Entertainment and media
  Marketing and advertising
  Gaming and VR
  Education and training
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    Singapore
    South Korea


  Rest of World (ROW)

By Type Insights

The Generative AI tools segment is estimated to witness significant growth during the forecast period. The text-to-image synthesis segment in the AI creativity market is experiencing significant advancements, driven by innovative technologies such as variational autoencoders, transformer networks, and generative adversarial networks. Edge computing is another crucial aspect of AI-driven predictive maintenance, enabling data processing at the source for quicker response times and improved efficiency. These tools enable digital art creation by generating novel images from user-defined prompts. Notable entities include computer vision techniques, attention mechanisms, super-resolution models, and recurrent neural networks. Model training efficiency and image generation pipelines are crucial factors, with diffusion models and data augmentation strategies employed to enhance performance. Loss functions optimization and backpropagation algorithms facilitate the refinement of these models.

Hyperparameter tuning and inpainting algorithms are essential fo

This dataset contains a subset of the ANES 2020. It contains the prompts used to generate persona's for the human voters using llama 2 and GPT3.5 and parsed outputs.

SolutionsChat: AI-powered chatbots can be used to answer questions, provide information, and facilitate discussion during meetings.AI Writer: AI-powered writing assistants can be used to generate meeting summaries, action items, and other documents.Real Time Transcription and Tagging: AI-powered transcription and tagging tools can be used to provide real-time transcriptions and tagging of meeting recordings.Post-meeting Solution: AI-powered post-meeting solutions can be used to analyze meeting recordings, identify key insights, and generate action items.Insight Management: AI-powered insight management tools can be used to track and manage insights from meetings.Others: Other solutions in the market include meeting scheduling, room booking, and video conferencing.Pricing ModelFree Plan: Some AI meeting assistants offer a free plan with limited features and functionality.Chargeable: Most AI meeting assistants offer a chargeable plan with more advanced features and functionality.ApplicationSales: AI meeting assistants can be used to improve sales productivity by automating tasks, providing insights, and facilitating communication.Business Development: AI meeting assistants can be used to support business development activities by providing insights, generating leads, and automating tasks.Content Marketing: AI meeting assistants can be used to support content marketing activities by generating content ideas, writing copy, and promoting content.Product and Market Research: AI meeting assistants can be used to support product and market research activities by gathering insights from meetings and analyzing data.Customer Success: AI meeting assistants can be used to support customer success activities by providing insights, resolving issues, and automating tasks.Consulting & Professional Services: AI meeting assistants can be used to support consulting and professional services activities by providing insights, generating reports, and automating tasks.IndustryHealthcare: AI meeting assistants can be used to support healthcare activities by providing insights, automating tasks, and improving communication.Legal: AI meeting assistants can be used to support legal activities by providing insights, generating documents, and automating tasks.Financial: AI meeting assistants can be used to support financial activities by providing insights, generating reports, and automating tasks.Education: AI meeting assistants can be used to support educational activities by providing insights, generating content, and automating tasks.Others: AI meeting assistants can be used to support other industries such as government, non-profits, and retail. Recent developments include: August 2023, Duet AI from Google is now generally available, adding the generative AI assistant to its Workplace productivity tools. The Duet AI features will cost USD 30 per user each month, placing the technology on par with competitor Microsoft's planned Copilot. Duet AI use genAI to aid consumers in gaining access to various Workspace products. It can, for example, produce email responses in Gmail and organize data in the Sheets spreadsheet program based on user prompts in Google Docs. In the Meet videoconferencing tool, it can also take notes and summarize conversations..

This dataset is a collection of over 15,000 records generated by Databricks employees, specifically designed to enable large language models to exhibit the interactive qualities of conversational AI. It serves as an open-source, human-generated instruction corpus, invaluable for fine-tuning large language models. The contributors created prompt and response pairs across eight distinct instruction categories, carefully avoiding external web sources (with the exception of Wikipedia for certain subsets) and generative AI in their formulations. This dataset holds significant value for instruction fine-tuning, synthetic data generation, and data augmentation, and is openly available for any purpose, including academic and commercial applications.

Columns

• instruction: Represents the prompt or question provided.
• context: Serves as reference material relevant to the instruction.
• response: Contains the generated response to the instruction.
• category: Indicates the annotator behavioural category, derived from the InstructGPT paper.

Distribution

The dataset is provided as a CSV file, containing fields for instruction, context, response, and category. It comprises over 15,000 records, with 14,781 unique values for 'instruction' and 14,944 unique values for 'category'.

Usage

This dataset is ideal for several applications, including: * Instruction fine-tuning of large language models to enhance their interactive capabilities. * Generating synthetic data by using the human-generated prompts as few-shot examples for large open language models. * Data augmentation techniques, such as paraphrasing prompts or short responses to regularise the dataset and improve model robustness.

Coverage

The dataset has a global reach. It was listed on 11/06/2025. The data is human-generated by Databricks employees. While the language used is American English, it is noted that some annotators may not be native English speakers. The demographic profile and subject matter of the data may reflect the composition of Databricks employees. It is important to note that as Wikipedia was consulted for certain categories, the dataset may reflect biases, factual errors, or topical focuses present in Wikipedia.

License

CC-BY-SA

Who Can Use It

This dataset is intended for a wide range of users, including: * Data Scientists and Machine Learning Engineers: For fine-tuning and developing large language models. * Researchers: For studies on instruction-following, synthetic data generation, and data augmentation in natural language processing. * Developers: Building applications that require interactive or instruction-based language model capabilities. * Organisations: For commercial product development involving custom language models.

Dataset Name Suggestions

• Dolly 15K Instruction Corpus
• Databricks Human Instruction Data
• LLM Fine-tuning Prompt Dataset
• Opendatabay Dolly 15K
• Interactive AI Training Data

Attribute

Original Data Source: Databricks Dolly 15K Dataset

This dataset is related to the publication "Developing an institutional AI digital assistant in an age of Industry 5.0". As stated in the abstract "using Technology Acceptance Model (TAM) and following a Design-Based Research (DBR)approach we explored the perspectives and experiences of a beta-test of an institutionally developed AIDA (i-AIDA) with 18 UK students using multiple methods and data sources (including pre-post-test, interviews, think-aloud, and prompt analysis). Our research underscores the potential benefits and limitations of in-house i-AIDA in enhancing learning experiences without compromising academic integrity or privacy, and how higher education institutions can prepare themselves for Industry 5.0." This dataset consists of the raw data of "RQ1 After engaging with a beta-version of an institutional AI digital assistant (i-AIDA) what are the main perceptions of participants, and do they change their perceptions?". Given the sensitive nature of the comments related to RQ2 and RQ3 the qualitative data is only available upon request (email bart.rienties@open.ac.uk)In the legend worksheet the detailed descriptions per variable are provided.Rienties, B., Ullmann, T., Tessarolo, F., Kwarteng, J., Domingue, J., Coughlan, T., Coughlan, E., Bektik, D. (2025). Developing an institutional AI digital assistant in an age of Industry 5.0. Applied Sciences.

This repository contains the supplementary materials for the study "Simplifying healthcare communication: Evaluating AI-driven plain language editing of informed consent forms", presented at the [AI4PL Workshop of the MT Summit Conference, 2025].

The study investigates how generative AI can be used to simplify cancer-related informed consent forms (ICFs) using Plain Language (PL) strategies. Two prompt engineering approaches were tested—Simple AI Edit and Complex AI Edit—and their output was evaluated using standard readability metrics.

The materials provided allow full replication of the study and support further research on readability, health literacy, and AI for accessible communication.

Contents:
📁 Corpus - Informed Consent Forms.zip:

• Original ICFs (in TXT format)

• AI-edited versions using the Simple AI Edit and Complex AI Edit strategies

📄 data_analysis.xlsx:

• Readability scores (Flesch Reading Ease, Gunning Fog Index, and SMOG Index) for each version of each ICF

📓 AI4PL_Paper_in_MT_summit.ipynb:

• Google Colab / Jupyter Notebook to reproduce the readability evaluation and generate visualisations

• The notebook is accessible here: https://colab.research.google.com/drive/1YMVUk7yDbTMjea3eCseUacp6EWI5MbHn?usp=sharing

How to cite:
Briva-Iglesias, V., & Peñuelas Gil, I. (2025). Simplifying healthcare communication: Evaluating AI-driven plain language editing of informed consent forms. MT Summit 2025.

This dataset contains synthetic time series data generated to simulate the operation of a road tunnel ventilation system subject to vehicle induced disturbances. The data were created as part of a study on generative modeling for industrial equipment condition monitoring. The primary goal is to illustrate how latent input effects (specifically, the "piston effect" induced by passing vehicles) can be accounted for in a model that estimates the relationship between the power supplied to the ventilation fans and the measured wind speed in the tunnel.

Context and Application: In the simulated tunnel, ventilation fans are used to renew the air continuously. The fans can operate at different speeds; under normal conditions, a higher power input produces a faster wind speed. However, as the fans degrade over time, the same power input results in a lower wind speed. A major challenge in monitoring system performance is that passing vehicles generate transient disturbances (the piston effect) that temporarily alter the measured wind speed. These synthetic data mimic the operational scenario where measurements of wind speed (from anemometers placed at the tunnel entrance and exit) are corrupted by such disturbances.

File Descriptions: The dataset comprises six CSV files. Each file contains a sequence of 600 measurements and represents one of two vehicle separation scenarios combined with three noise (disturbance) conditions. The naming convention is as follows:

Filename Convention: The files follow the format 25_3_SYN_{separation}_G{gain}.csv, where: {separation} indicates vehicle separation (20 = low rate, 5 = high rate) {gain} indicates the noise level (1.5 = high noise, 1.0 = medium noise, 0.5 = low noise).

Data Format and Variables: Each CSV file includes the following columns:

time: Sequential time steps (in seconds). u1, u2: The observable input representing the fan setpoint (power supplied to the fans). y1, y2: The measured output, corresponding to the wind speed recorded by the anemometers. y1clean, y2clean: The theoretical wind speed in absence of vehicles. ySSA1, ySSA2: The estimation of y1clean and y2clean from u1, u2, y1, y2 in the accompanying paper.

Note: The latent variable representing vehicle entries that cause the piston effect is not directly observable in the files; instead, its impact is embedded in the measured output.

Usage: Researchers can use these data files to:

• Reproduce the experiments described in the accompanying paper.
• Test and benchmark alternative methods for filtering or denoising signals affected by transient disturbances.
• Explore generative modeling techniques and inverse problem formulations in the context of equipment condition monitoring.

Citation: If you use these data in your research, please cite the accompanying paper as well as this dataset.