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.

The market size in the 'Autonomous & Sensor Technology' segment of the artificial intelligence market worldwide was modeled to amount to 57.3 billion U.S. dollars in 2025. Between 2020 and 2025, the market size rose by 24.52 billion U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The market size will steadily rise by 46.04 billion U.S. dollars over the period from 2025 to 2032, 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.

Complete dataset included in the full report. Detailed tables, regional splits, forecasts, and methodologies are available with purchase.

The artificial intelligence market is estimated to grow from $273.6 billion currently to $5,267 billion by 2035, at a CAGR of 30.84% during the forecast period.

Epoch’s AI models dataset is a collection of machine learning models useful for research about trends in the history and future of artificial intelligence.

Large-scale models are models that were trained with over 10^23 FLOP of compute, which is a static threshold that is used in some AI regulatory frameworks.

By 2035, the Artificial Intelligence (AI) Market is projected to reach USD 3520.9 Bn, up from USD 244.3 Bn in 2025, at a CAGR of 30.6%.

In 2025, 21 percent of AI leaders surveyed worldwide expected intelligent security systems and smart monitoring to have the greatest impact among physical AI types. Collaborative robotics and digital twins followed closely with a share of 20 percent.

The market size change in the 'Machine Learning' segment of the artificial intelligence market worldwide was modeled to amount to 203.79 percent in 2025. Between 2021 and 2025, the market size change rose by 154.04 percentage points, though the increase followed an uneven trajectory rather than a consistent upward trend. The market size change is expected to drop by 192.83 percentage points between 2025 and 2032, 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.

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.

Artificial Intelligence (AI) market size was valued at USD 629.97 Bn in 2026 and is forecast to reach USD 3,796.05 Bn by 2035, at a 21.3% CAGR from 2027 to 2035 .

According to a global survey conducted among 52 participating institutions in 2025, there were more participants who viewed AI's impact on education as an opportunity in every region than as a challenge. However, in the USA and Canada, 43 percent of faculty thought AI could be a challenge in education, higher than in every other region. On the other hand, Latin America had the highest share of respondents viewing AI's impact on education as an opportunity, at 78 percent.

Epoch’s AI models dataset is a collection of machine learning models useful for research about trends in the history and future of artificial intelligence.

The database covers AI models, especially models that are notable for advancing the state of the art, or a large impact on the world or the history of the field.

The data contain several subsets, which can be viewed using the interactive visualization. Notable AI models are those that meet our notability criteria. Frontier models are models that were in the top 5 of training compute as of the time of their release. Large-scale models are models trained with at least 10^23 floating-point operations. All models shows every model in the dataset, including models that do not qualify for the above categories, such as models found during our research on algorithmic progress, AI for biology, or other investigations.

The dataset was originally created for the report “Compute Trends Across Three Eras of Machine Learning” and has continually grown and expanded since then.

Where possible, we collect details such as training compute directly from publications. Otherwise, we estimate details from information such as model architecture and training data, or training hardware and duration.

The dataset is available on our website as a visualization or table, and is available for download in CSV format, updated daily.

AI statistics for B2B technology marketers — every figure traceable to the primary vendor or research-firm report. Coverage spans enterprise AI adoption, generative AI risk, AI-augmented threat detection, and AI infrastructure spending, reviewed by Nick Cavalancia.

Complete dataset included in the full report. Detailed tables, regional splits, forecasts, and methodologies are available with purchase.

The artificial intelligence market in the United States is expected to reach a projected revenue of US$ 483,598.5 million by 2033. A compound annual growth rate of 24% is expected of the United States artificial intelligence market from 2026 to 2033.

Epoch AI’s Biology AI models dataset is a collection of machine learning models trained on biological data, and key information about their training. This dataset is useful for research about trends in the application of artificial intelligence to biology.

The dataset focuses on biological ML models: those that are trained on biological data, including biological sequences, molecular structures or data about molecular properties, among others.

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.

The Dataset "AI Global index" includes The Global AI Index itself and seven indicators affecting the Index on 62 countries, as well as general information about the countries (region, cluster, income group and political regime).

The Global AI Index is the first index to benchmark nations on their level of investment, innovation and implementation of artificial intelligence.

Talent, Infrastructure and Operating Environment are the factors of AI Implementation group of indicators, which represents the application of artificial intelligence by professionals in various sectors, such as businesses, governments, and communities. - Talent indicator focuses on the availability of skilled practitioners for the provision of artificial intelligence solutions. - Infrastructure indicator focuses on the reliability and scale of access infrastructure, from electricity and internet, to super computing capabilities. - Operating Environment indicator focuses on the regulatory context, and public opinion surrounding artificial intelligence.

Research and Development are the factors of Innovation group of indicators, which reflects the progress made in technology and methodology, which signify the potential for artificial intelligence to evolve and improve. - Research indicator focuses on the extent of specialist research and researchers; investigating the amount of publications and citations in credible academic journals. - Development indicator focuses on the development of fundamental platforms and algorithms upon which innovative artificial intelligence projects rely.

Government Strategy and Commercial are the factors of Investment group of indicators, which reflects financial and procedural commitments to artificial intelligence. - Government Strategy indicator focuses on the depth of commitment from national government to artificial intelligence; investigating spending commitments and national strategies. - Commercial indicator focuses on the level of startup activity, investment and business initiatives based on artificial intelligence.

All these seven indicators were calculated by Tortoise Media via weighting and summarizing 143 other indicators.

The dataset can be used for practicing data cleaning, data visualization, finding correlations between the indexes, Machine Learning (classification, regression, clustering).

The data was used in the analytical article research Artificial Intelligence on the World Stage: Dominant Players and Aspiring Challengers

Artificial intelligence (AI) holds tremendous promise to benefit nearly all aspects of society, including the economy, healthcare, security, the law, transportation, even technology itself. On February 11, 2019, the President signed Executive Order 13859, Maintaining American Leadership in Artificial Intelligence. This order launched the American AI Initiative, a concerted effort to promote and protect AI technology and innovation in the United States. The Initiative implements a whole-of-government strategy in collaboration and engagement with the private sector, academia, the public, and like-minded international partners. Among other actions, key directives in the Initiative call for Federal agencies to prioritize AI research and development (R&emp;D) investments, enhance access to high-quality cyberinfrastructure and data, ensure that the Nation leads in the development of technical standards for AI, and provide education and training opportunities to prepare the American workforce for the new era of AI. In support of the American AI Initiative, this National AI R&emp;D Strategic Plan: 2019 Update defines the priority areas for Federal investments in AI R&emp;D. This 2019 update builds upon the first National AI R&emp;D Strategic Plan released in 2016, accounting for new research, technical innovations, and other considerations that have emerged over the past three years. This update has been developed by leading AI researchers and research administrators from across the Federal Government, with input from the broader civil society, including from many of America’s leading academic research institutions, nonprofit organizations, and private sector technology companies. Feedback from these key stakeholders affirmed the continued relevance of each part of the 2016 Strategic Plan while also calling for greater attention to making AI trustworthy, to partnering with the private sector, and other imperatives.

Integrated Systems Health Management includes as key elements fault detection, fault diagnostics, and failure prognostics. Whereas fault detection and diagnostics have been the subject of considerable emphasis in the Artificial Intelligence (AI) community in the past, prognostics has not enjoyed the same attention. The reason for this lack of attention is in part because prognostics as a discipline has only recently been recognized as a game-changing technology that can push the boundary of systems health management. This paper provides a survey of AI techniques applied to prognostics. The paper is an update to our previously published survey of data-driven prognostics.