https://heliontechnologies.com/wp-content/uploads/2024/04/AI.jpeg" alt="Ai"> The dataset, "The Rise of Artificial Intelligence," contains 8 entries and 16 columns, providing various insights on AI adoption, market trends, and job impact from 2018 to 2025.

Year: The year of data (2018–2025). AI Software Revenue: Annual revenue generated from AI software (e.g., "$10.1 billion"). Global AI Market Value: The global market value of AI (e.g., "$29.5 billion"). AI Adoption (%): Percentage of organizations adopting AI. Organizations Using AI: Percentage of organizations currently using AI. Organizations Planning to Implement AI: Percentage of organizations planning to adopt AI. Global Expectation for AI Adoption: Global expectations for AI adoption. Net Job Loss in the US: The estimated job loss in the U.S. due to AI. Organizations Believing AI Provides Competitive Edge: Percentage of organizations that think AI gives them an edge. Companies Prioritizing AI in Strategy: Percentage of companies prioritizing AI in their strategy. Marketers Believing AI Improves Email Revenue: Percentage of marketers who believe AI enhances email revenue. Americans Using Voice Assistants: The percentage of Americans using voice assistants (e.g., "Over 50%"). Medical Professionals Using AI for Diagnosis: Percentage of medical professionals using AI for diagnosis. Jobs at High Risk of Automation - Transportation & Storage: Percentage of jobs at high risk in this sector. Jobs at High Risk of Automation - Wholesale & Retail Trade: Percentage of jobs at high risk in this sector. Jobs at High Risk of Automation - Manufacturing: Percentage of jobs at high risk in manufacturing.

In 2024, the market size change in the 'Machine Learning' segment of the artificial intelligence market worldwide was modeled to stand at 44.66 percent. Between 2021 and 2024, the market size change dropped by 99.08 percentage points. The market size change is expected to drop by 15.3 percentage points between 2024 and 2031, showing a continuous downward movement throughout the period.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Machine Learning.

In 2024, the market size change in the 'Autonomous & Sensor Technology' segment of the artificial intelligence market worldwide was modeled to amount to 30.92 percent. Between 2021 and 2024, the market size change dropped by 69.03 percentage points. The market size change is expected to drop by 25.49 percentage points between 2024 and 2031, showing a continuous downward movement throughout the period.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Autonomous & Sensor Technology.

Coordinate responsible and trustworthy artificial intelligence (AI) governance and capabilities. AITO is the connective tissue for all things AI at the Department of Energy.

This dataset was created to measure the impact of artificial intelligence on society. The data was collected in Turkey for a Public Opinion Research course, using snowball and convenience sampling methods.

The dataset consists of 205 entries and 20 variables.

The last three variables were designed to assess the participants' knowledge level of artificial intelligence. The intention was to create a new variable by scoring correct answers as '1' and incorrect answers as '0', and then to analyze the relationship between this new score and the other variables.

The majority of the variables were conceived as Likert scale questions. The task of assigning numerical values has been left to the person who will analyze the data.

The dataset is composed entirely of real data.

***Here are the detailed descriptions for each column in the dataset.

• ID: A unique identifier for each survey respondent.

• What is your age range?: The age group of the participant.

• What is your gender?: The gender of the participant.

• What is your education level?: The highest education level completed by the participant.

• What is your employment status?: The current employment status of the participant.

• What is your occupation? (optional): The self-reported occupation of the participant. This field is optional.

• How often do you use technological devices?: The participant's self-reported frequency of daily technology use.

• How much knowledge do you have about artificial intelligence (AI) technologies?: The participant's self-assessed level of knowledge about AI technologies.

• Do you generally trust artificial intelligence (AI)?: The participant's general level of trust in artificial intelligence.

• Do you think artificial intelligence (AI) will be generally beneficial or harmful to humanity?: The participant's opinion on whether AI will ultimately be beneficial or harmful to humanity.

• Please rate how actively you use AI-powered products in your daily life on a scale from 1 to 5.: A numerical rating provided by the participant, from 1 (not active at all) to 5 (very active), on their usage of AI products.

• Would you like to use more AI products in the future?: The participant's stated desire to use more AI-powered products in the future.

• I think artificial intelligence (AI) could threaten individual freedoms.: The participant's level of agreement with the stat- ement that AI could pose a threat to individual freedoms.

• Could artificial intelligence (AI) completely eliminate some professions?: The participant's opinion on the possibility of AI completely replacing some job professions.

• Do you think your own job could be affected by artificial intelligence (AI)?: The participant's belief on whether their own job could be impacted by AI.

• Do you believe that artificial intelligence (AI) should be limited by ethical rules?: The participant's level of agreement with the statement that AI development and use should be constrained by ethical rules.

• Could artificial intelligence (AI) one day become conscious like humans?: The participant's opinion on the possibility of AI achieving human-like consciousness.

• Which of the following do you think is NOT an artificial intelligence (AI) application?: A multiple-choice question to test the participant's knowledge of what constitutes an AI application.

• Which of the following is a machine learning algorithm used in the field of artificial intelligence?: A multiple-choice question to test the participant's knowledge of common machine learning algorithms.

• The artificial intelligence application called 'ChatGPT' is an example of which type of AI system?: A multiple-choice question to test the participant's knowledge about the classification of specific AI systems like ChatGPT.***

The market size in the 'Natural Language Processing' segment of the artificial intelligence market worldwide was modeled to be 39.79 billion U.S. dollars in 2024. Between 2020 and 2024, the market size rose by 26.41 billion U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The market size will steadily rise by 161.7 billion U.S. dollars over the period from 2024 to 2031, reflecting a clear upward trend.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Natural Language Processing.

Artificial Intelligence (AI) Training Dataset Market Outlook

According to our latest research, the global Artificial Intelligence (AI) Training Dataset market size reached USD 3.15 billion in 2024, reflecting robust industry momentum. The market is expanding at a notable CAGR of 20.8% and is forecasted to attain USD 20.92 billion by 2033. This impressive growth is primarily attributed to the surging demand for high-quality, annotated datasets to fuel machine learning and deep learning models across diverse industry verticals. The proliferation of AI-driven applications, coupled with rapid advancements in data labeling technologies, is further accelerating the adoption and expansion of the AI training dataset market globally.

One of the most significant growth factors propelling the AI training dataset market is the exponential rise in data-driven AI applications across industries such as healthcare, automotive, retail, and finance. As organizations increasingly rely on AI-powered solutions for automation, predictive analytics, and personalized customer experiences, the need for large, diverse, and accurately labeled datasets has become critical. Enhanced data annotation techniques, including manual, semi-automated, and fully automated methods, are enabling organizations to generate high-quality datasets at scale, which is essential for training sophisticated AI models. The integration of AI in edge devices, smart sensors, and IoT platforms is further amplifying the demand for specialized datasets tailored for unique use cases, thereby fueling market growth.

Another key driver is the ongoing innovation in machine learning and deep learning algorithms, which require vast and varied training data to achieve optimal performance. The increasing complexity of AI models, especially in areas such as computer vision, natural language processing, and autonomous systems, necessitates the availability of comprehensive datasets that accurately represent real-world scenarios. Companies are investing heavily in data collection, annotation, and curation services to ensure their AI solutions can generalize effectively and deliver reliable outcomes. Additionally, the rise of synthetic data generation and data augmentation techniques is helping address challenges related to data scarcity, privacy, and bias, further supporting the expansion of the AI training dataset market.

The market is also benefiting from the growing emphasis on ethical AI and regulatory compliance, particularly in data-sensitive sectors like healthcare, finance, and government. Organizations are prioritizing the use of high-quality, unbiased, and diverse datasets to mitigate algorithmic bias and ensure transparency in AI decision-making processes. This focus on responsible AI development is driving demand for curated datasets that adhere to strict quality and privacy standards. Moreover, the emergence of data marketplaces and collaborative data-sharing initiatives is making it easier for organizations to access and exchange valuable training data, fostering innovation and accelerating AI adoption across multiple domains.

As the AI training dataset market continues to evolve, the role of Perception Dataset Management Platforms is becoming increasingly crucial. These platforms are designed to handle the complexities of managing large-scale datasets, ensuring that data is not only collected and stored efficiently but also annotated and curated to meet the specific needs of AI models. By providing tools for data organization, quality control, and collaboration, these platforms enable organizations to streamline their data management processes and enhance the overall quality of their AI training datasets. This is particularly important as the demand for diverse and high-quality datasets grows, driven by the expanding scope of AI applications across various industries.

From a regional perspective, North America currently dominates the AI training dataset market, accounting for the largest revenue share in 2024, driven by significant investments in AI research, a mature technology ecosystem, and the presence of leading AI companies and data annotation service providers. Europe and Asia Pacific are also witnessing rapid growth, with increasing government support for AI initiatives, expanding digital infrastructure, and a rising number of AI startups. While North America sets the pace in terms of technological

There are the underlying data for our report "Artificial Intelligence: How knowledge is created, transferred, and used", published 2018. Data can be used to construct the graphs used in the report.

Dataset Description This dataset contains the metadata for 10,000 research papers in the field of artificial intelligence (AI) that were published on arXiv.org. The dataset includes the following columns:

authors: The authors of the paper. categories: The categories of the paper. comment: Any additional comments about the paper. doi: The Digital Object Identifier (DOI) for the paper. entry_id: The arXiv entry ID for the paper. journal_ref: The journal reference for the paper, if applicable. pdf_url: The URL to the PDF of the paper. primary_category: The primary category of the paper. published: The date the paper was published. summary : abstract of the paper

Summary This dataset is a valuable resource for researchers and practitioners in the field of AI. It can be used to track the latest research trends, identify emerging areas of research, and find relevant papers. The dataset can also be used to train machine learning models that can be used to analyze AI research papers.

Data License The dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This means that you are free to use, share, and adapt the dataset, as long as you give credit to the original authors.

How to Use the Dataset The dataset can be downloaded from the Kaggle website. Once you have downloaded the dataset, you can use it with any software that can read CSV files. You can also use the dataset with machine learning libraries such as scikit-learn and TensorFlow.

The market size in the 'Autonomous & Sensor Technology' segment of the artificial intelligence market worldwide was modeled to stand at 26.46 billion U.S. dollars in 2024. Between 2020 and 2024, the market size rose by 10.73 billion U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The market size will steadily rise by 42.29 billion U.S. dollars over the period from 2024 to 2031, reflecting a clear upward trend.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Autonomous & Sensor Technology.

Contains Artificial Intelligence Patent Landscape data classifying 13,244,037 granted patents and PGPubs published from 1976 through 2023 in eight AI component technologies using state-of-the art machine learning based models.

The United States Artificial Intelligence Data Center Market Report is Segmented by Data Center Type (Cloud Service Providers, Colocation Data Centers, and More), Component (Hardware, Software Technology, and Services), Tier Standard (Tier III and Tier IV), and End-User Industry (IT and IT Services, Internet and Digital Media, and More). The Market Forecasts are Provided in Terms of Value (USD).

The AI Data Services market is booming, projected to reach $100 billion by 2033 with a 20% CAGR. Discover key trends, growth drivers, and leading companies shaping this dynamic sector. Learn more about data annotation, AI data labeling, and market segmentation.

The market size in the 'Computer Vision' segment of the artificial intelligence market worldwide was modeled to amount to 25.92 billion U.S. dollars in 2024. Between 2020 and 2024, the market size rose by 16.3 billion U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The market size will steadily rise by 46.74 billion U.S. dollars over the period from 2024 to 2031, reflecting a clear upward trend.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Computer Vision.

Dataset related to surveys on the use of artificial intelligence in higher education

In 2024, the market size change in the 'Natural Language Processing' segment of the artificial intelligence market worldwide was modeled to amount to 32.43 percent. Between 2021 and 2024, the market size change dropped by 17.57 percentage points. The market size change is forecast to decline by 14.27 percentage points from 2024 to 2031, fluctuating as it trends downward.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Natural Language Processing.

The North America Artificial Intelligence Data Center Market Report is Segmented by Data Center Type (Cloud Service Providers, Colocation Data Centers, and More), Component (Hardware, Software Technology, and Services), Tier Standard (Tier III and Tier IV), End-User Industry (IT and IT Services, Internet and Digital Media, and More). The Market Forecasts are Provided in Terms of Value (USD).

This is a collection of information and resources relating to artificial intelligence.

This dataset captures insights into the use of Artificial Intelligence (AI) among 535 students in Indonesian higher education, focusing on their expectations, challenges, attitudes, perceptions, and motivations regarding AI-based learning tools. Collected through a structured survey, the dataset includes demographic variables such as university type, field of study, and educational level, along with students' self-reported experiences with AI in academic settings. The dataset serves as a valuable resource for understanding AI adoption trends in higher education, identifying barriers to AI integration, and evaluating its impact on student engagement and learning outcomes. It enables comparative analysis across different academic disciplines and institutional contexts, offering opportunities for policymakers and educators to design AI-informed curricula. Additionally, this dataset is structured for reproducibility and reuse, allowing researchers to extend its findings, apply alternative analytical approaches, and conduct cross-regional or longitudinal studies on AI integration in higher education.

📊 About the Dataset

This dataset explores the influence of AI-generated content across various industries, including journalism, social media, entertainment, and marketing. It provides insights into public sentiment, engagement trends, economic impact, and regulatory responses over time.

With AI-generated content becoming increasingly prevalent, this dataset serves as a valuable resource for data analysts, business strategists, and machine learning researchers to study trends, detect biases, and predict future AI adoption patterns.

💡 This dataset is perfect for AI adoption analysis, industry forecasting, and ethical AI research!