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.

Medical AI Research Foundations is a repository of open-source medical foundation models. With this collection of non-diagnostic models, APIs, and resources like code and data, researchers and developers can accelerate their medical AI research. This is a clear unmet need as currently there is no central resource today that developers and researchers can leverage to build medical AI and as such, this has slowed down both research and translation efforts. Our goal is to democratize access to foundational medical AI models, and help researchers and medical AI developers rapidly build new solutions. To this end, we open-sourced REMEDIS code-base and we are currently hosting REMEDIS models for chest x-ray and pathology. We expect to add more models and resources for training medical foundation models such as datasets and benchmarks in the future. We also welcome the medical AI research community to contribute to this.

Israel's academic institutions are making significant strides in artificial intelligence (AI) research, with the Israeli Institute of Technology (Technion) leading the pack. As of November 2024, researchers from the Technion released an average of **** AI-related publications, ranking **** globally in this measure. The institute was followed by Tel Aviv University and the Hebrew University of Jerusalem. This robust academic output and high concentration of AI-related talent underscores the country’s prominence in the AI field. Is AI academic research in decline? While Israeli universities continue to produce notable AI research, the overall the number of AI publications has declined. In 2023, the country saw a decline in AI academic articles across various subfields, including computer vision, artificial neural networks, natural language processing, and robotics. Specifically, computer vision papers decreased by almost ** percent from the previous year. Still, in terms of quality, publications from Israel were among the most cited articles in 2023. Investment and industry dynamics The AI industry in Israel remains robust, with almost ***** active AI companies as of 2023. However, recent years have seen a slowdown in growth and investment. In 2023, investment in AI startups in Israel reached *** billion U.S. dollars, marking a significant decrease from previous years. Despite this, international tech giants continue to show interest in Israeli AI companies, with notable acquisitions made by major corporations like NVIDIA, Google, and Salesforce in 2023.

This document includes relevant text from the 2016 and 2019 national AI R&D strategic plans, along with updates prepared in 2023 based on Administration and interagency evaluation of the National AI R&D Strategic Plan: 2019 Update as well as community responses to a Request for Information on updating the Plan. The 2019 strategies were broadly determined to be valid going forward. The 2023 update adds a new Strategy 9, which establishes a principled and coordinated approach to international collaboration in AI research.

During a January 2025 survey among consumers in selected countries worldwide, 51 percent of respondents in Canada were likely to use artificial intelligence (AI) tools to research purchases. In comparison, 43 percent of respondents in Australia were likely to use AI tools for the same reason.

In 2023, a total of 7185 research publications on artificial intelligence (AI) were recorded in Singapore, indicating a significant increase in the number of research publications on this topic from the year before. In comparison, the number of publications on AI in the country reached 3,659 in 2014.

Dimensions is the largest database of research insight in the world. It represents the most comprehensive collection of linked data related to the global research and innovation ecosystem available in a single platform. Because Dimensions maps the entire research lifecycle, you can follow academic and industry research from early stage funding, through to output and on to social and economic impact. Businesses, governments, universities, investors, funders and researchers around the world use Dimensions to inform their research strategy and make evidence-based decisions on the R&D and innovation landscape. With Dimensions on Google BigQuery, you can seamlessly combine Dimensions data with your own private and external datasets; integrate with Business Intelligence and data visualization tools; and analyze billions of data points in seconds to create the actionable insights your organization needs. Examples of usage: Competitive intelligence Horizon-scanning & emerging trends Innovation landscape mapping Academic & industry partnerships and collaboration networks Key Opinion Leader (KOL) identification Recruitment & talent Performance & benchmarking Tracking funding dollar flows and citation patterns Literature gap analysis Marketing and communication strategy Social and economic impact of research About the data: Dimensions is updated daily and constantly growing. It contains over 112m linked research publications, 1.3bn+ citations, 5.6m+ grants worth $1.7trillion+ in funding, 41m+ patents, 600k+ clinical trials, 100k+ organizations, 65m+ disambiguated researchers and more. The data is normalized, linked, and ready for analysis. Dimensions is available as a subscription offering. For more information, please visit www.dimensions.ai/bigquery and a member of our team will be in touch shortly. If you would like to try our data for free, please select "try sample" to see our openly available Covid-19 data.Learn more

The data represents the comparison of Elicit: The AI Research Assistant to PubMed and CINAHL Complete. A descriptive study design was used for graduate nursing students at a large southern Alabama university college of nursing to compare the effectiveness of a GenAI literature search tool, Elicit: The AI Research Assistant, to PubMed and CINAHL. A two-phase framework was utilized to organize complex information and justify a preference. A rubric was designed to promote and assess critical thinking through inquiry-based learning in educating graduate nursing students on information literacy and structuring a literature search. Discovering a relationship between the search tools, students identified the strengths (pros) and weaknesses (cons) of each tool and determined which tool was more effective in terms of accuracy, relevance and efficiency.

Please refer to the following source for the original datasets:

GSM8K: https://huggingface.co/datasets/openai/gsm8k MATH: https://huggingface.co/datasets/hendrycks/competition_math math-resample: In this section we subsample the 1,000 subsample only (yes it's balance)

HumanEval+: https://huggingface.co/datasets/evalplus/humanevalplus MBPP: https://huggingface.co/datasets/google-research-datasets/mbpp MBPP+: https://huggingface.co/datasets/evalplus/mbppplus ARC Challenge:… See the full description on the dataset page: https://huggingface.co/datasets/appier-ai-research/robust-finetuning.

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.

This submission consists of 12 data sets containing Twitter IDs pertaining to 6 AI controversies identified by UK-based experts in AI and Society as especially significant during the period 2012-2021. The data sets were collected by researchers at the University of Warwick as part of the 3-year international project “Shaping AI” which mapped controversies about “Artificial Intelligence” (AI) during 2012-2022. Research teams in the UK, France, Germany and Canada analysed controversies about AI in their countries across different spheres: research, policy and the media during this 10-year period. The UK team at the University of Warwick designed and undertook an analysis of research controversies about AI in the relevant period following a standpoint methodology. Our study began with an online consultation that took place in the Autumn of 2021, in which we asked UK-based experts in AI from across disciplines to identify what are the most important concerns, disputes and problematics that have arisen in the last 10 years in relation to AI as a strategic area of research.

Based on the responses to this expert consultation—described in detail in Marres et al (2024) and Poletti et al (forthcoming)—we identified a broad range of relevant controversy topics, objects and problems. To select controversies for further analysis, we considered their research intensity, in the form of a frequency count of research publications mentioned by respondents in relation to controversy topics.

On this basis, we selected 6 AI research controversies for further research: COMPAS; NHS+Deepmind; Gaydar; Facial recognition; Stochastic Parrots (LLMs) & Deeplearning as a solution for AI. For each of these controversies, we collected Twitter data by submitting queries to Twitter's academic API using TWARC between January 2022 and June 2022. Further details of the methods of data collection and curation can be found in the methods file with further detail of the queries in the ReadMe file.

Artificial Intelligence Market Outlook

According to our latest research, the global Artificial Intelligence (AI) market size reached USD 215.8 billion in 2024, demonstrating robust expansion driven by rapid digital transformation across key sectors. The market is projected to grow at a CAGR of 36.6% between 2025 and 2033, reaching a forecasted value of USD 2,870.1 billion by 2033. This remarkable growth trajectory is fueled by increasing adoption of AI-powered solutions in industries such as healthcare, finance, manufacturing, and retail, as well as advancements in machine learning, deep learning, and natural language processing technologies.

The primary growth factor for the Artificial Intelligence market is the accelerating integration of AI technologies into business operations to enhance productivity, automate repetitive tasks, and enable data-driven decision-making. Organizations are increasingly leveraging AI-based tools to streamline workflows, reduce operational costs, and improve customer experiences. The proliferation of big data and the need for advanced analytics have further amplified the demand for AI solutions, as businesses seek to extract actionable insights from massive volumes of structured and unstructured data. Additionally, the growing availability of affordable computing power and cloud-based AI platforms has democratized access to advanced AI capabilities, enabling companies of all sizes to deploy intelligent solutions at scale.

Another significant driver propelling the AI market is the rapid evolution of AI technologies themselves. Innovations in areas such as machine learning, computer vision, and natural language processing are paving the way for more sophisticated and versatile AI applications across industries. For instance, AI-powered diagnostic tools are revolutionizing healthcare by enabling earlier and more accurate disease detection, while intelligent automation is transforming manufacturing processes through predictive maintenance and quality assurance. The rise of AI-powered virtual assistants and chatbots has also enhanced customer engagement in sectors like retail and banking, providing personalized and efficient service around the clock. The convergence of AI with other emerging technologies, such as the Internet of Things (IoT) and edge computing, is further expanding the potential use cases for AI, driving deeper market penetration.

Strategic investments and supportive government initiatives are playing a pivotal role in fostering the growth of the AI market. Governments across the globe are recognizing the transformative potential of AI and are investing heavily in research and development, talent development, and digital infrastructure. Public-private partnerships, favorable regulatory frameworks, and targeted funding programs are accelerating AI innovation and adoption, particularly in regions like North America, Europe, and Asia Pacific. Moreover, the emergence of AI startups and the increasing collaborations between technology giants and industry players are catalyzing the creation of new AI-driven products and services, further stimulating market expansion.

From a regional perspective, North America continues to dominate the global Artificial Intelligence market, accounting for the largest share in 2024. The region's leadership is attributed to its advanced digital ecosystem, concentration of leading AI technology providers, and strong investment climate. However, Asia Pacific is emerging as a high-growth market, driven by rapid digitalization, expanding internet penetration, and significant investments in AI research and development by countries such as China, Japan, and South Korea. Europe is also witnessing substantial growth, supported by robust regulatory frameworks, government initiatives, and a thriving innovation ecosystem. Meanwhile, Latin America and the Middle East & Africa are gradually embracing AI technologies, with increasing adoption in sectors such as banking, healthcare, and government services.

Component Analysis

The global Artificial Intelligence market size was USD 223.7 Billion in 2024 and is expected to reach USD 1,359.7 Billion by 2034 and register a CAGR of 19.6%. AI industry report classifies global market by share, trend, and based on offering, technology, end-user industry, and region | Artificial I...

Dataset contains 4 csv files containing 1. Total AI publications over the years, 2. AI publication by field and 3. AI skill penetration

Public Data and Tools:

The AI Index 2022 Report is supplemented by raw data and an interactive tool. Where readers are invited to use the data and the tool in a way most relevant to their work and interests. • Raw data and charts: The public data and high-resolution images of all the charts in the report are available on Google Drive .

"The AI Index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI). We welcome feedback and new ideas for next year. Contact us at AI-Index-Report@stanford.edu. The AI Index was conceived within the One Hundred Year Study on AI (AI100)."

Executive Summary: Artificial intelligence (AI) is a transformative technology that holds promise for tremendous societal and economic benefit. AI has the potential to revolutionize how we live, work, learn, discover, and communicate. AI research can further our national priorities, including increased economic prosperity, improved educational opportunities and quality of life, and enhanced national and homeland security. Because of these potential benefits, the U.S. government has invested in AI research for many years. Yet, as with any significant technology in which the Federal government has interest, there are not only tremendous opportunities but also a number of considerations that must be taken into account in guiding the overall direction of Federally-funded R&D in AI. On May 3, 2016,the Administration announced the formation of a new NSTC Subcommittee on Machine Learning and Artificial intelligence, to help coordinate Federal activity in AI.1 This Subcommittee, on June 15, 2016, directed the Subcommittee on Networking and Information Technology Research and Development (NITRD) to create a National Artificial Intelligence Research and Development Strategic Plan. A NITRD Task Force on Artificial Intelligence was then formed to define the Federal strategic priorities for AI R&D, with particular attention on areas that industry is unlikely to address. This National Artificial Intelligence R&D Strategic Plan establishes a set of objectives for Federallyfunded AI research, both research occurring within the government as well as Federally-funded research occurring outside of government, such as in academia. The ultimate goal of this research is to produce new AI knowledge and technologies that provide a range of positive benefits to society, while minimizing the negative impacts. To achieve this goal, this AI R&D Strategic Plan identifies the following priorities for Federally-funded AI research: Strategy 1: Make long-term investments in AI research. Prioritize investments in the next generation of AI that will drive discovery and insight and enable the United States to remain a world leader in AI. Strategy 2: Develop effective methods for human-AI collaboration. Rather than replace humans, most AI systems will collaborate with humans to achieve optimal performance. Research is needed to create effective interactions between humans and AI systems. Strategy 3: Understand and address the ethical, legal, and societal implications of AI. We expect AI technologies to behave according to the formal and informal norms to which we hold our fellow humans. Research is needed to understand the ethical, legal, and social implications of AI, and to develop methods for designing AI systems that align with ethical, legal, and societal goals. Strategy 4: Ensure the safety and security of AI systems. Before AI systems are in widespread use, assurance is needed that the systems will operate safely and securely, in a controlled, well-defined, and well-understood manner. Further progress in research is needed to address this challenge of creating AI systems that are reliable, dependable, and trustworthy. Strategy 5: Develop shared public datasets and environments for AI training and testing. The depth, quality, and accuracy of training datasets and resources significantly affect AI performance. Researchers need to develop high quality datasets and environments and enable responsible access to high-quality datasets as well as to testing and training resources. Strategy 6: Measure and evaluate AI technologies through standards and benchmarks. . Essential to advancements in AI are standards, benchmarks, testbeds, and community engagement that guide and evaluate progress in AI. Additional research is needed to develop a broad spectrum of evaluative techniques. Strategy 7: Better understand the national AI R&D workforce needs. Advances in AI will require a strong community of AI researchers. An improved understanding of current and future R&D workforce demands in AI is needed to help ensure that sufficient AI experts are available to address the strategic R&D areas outlined in this plan. The AI R&D Strategic Plan closes with two recommendations: Recommendation 1: Develop an AI R&D implementation framework to identify S&T opportunities and support effective coordination of AI R&D investments, consistent with Strategies 1-6 of this plan. Recommendation 2: Study the national landscape for creating and sustaining a healthy AI R&D workforce, consistent with Strategy 7 of this plan.

In 2023, around 4,141 researches on Artificial Intelligence (AI) were published in Vietnam, indicating a slight decrease in the number of research publications on this topic from the year before. In the last decade, the number of publications on AI in the country had been growing year after year.

The statistic shows the top ten countries with the highest number of artificial intelligence (AI) research institutions with publications as of 2017. The United States accounted for 43 percent of AI research institutions with publications worldwide.

Executive Summary: Artificial intelligence (AI) is a transformative technology that holds promise for tremendous societal and economic benefit. AI has the potential to revolutionize how we live, work, learn, discover, and communicate. AI research can further our national priorities, including increased economic prosperity, improved educational opportunities and quality of life, and enhanced national and homeland security. Because of these potential benefits, the U.S. government has invested in AI research for many years. Yet, as with any significant technology in which the Federal government has interest, there are not only tremendous opportunities but also a number of considerations that must be taken into account in guiding the overall direction of Federally-funded R&D in AI. On May 3, 2016,the Administration announced the formation of a new NSTC Subcommittee on Machine Learning and Artificial intelligence, to help coordinate Federal activity in AI.1 This Subcommittee, on June 15, 2016, directed the Subcommittee on Networking and Information Technology Research and Development (NITRD) to create a National Artificial Intelligence Research and Development Strategic Plan. A NITRD Task Force on Artificial Intelligence was then formed to define the Federal strategic priorities for AI R&D, with particular attention on areas that industry is unlikely to address. This National Artificial Intelligence R&D Strategic Plan establishes a set of objectives for Federallyfunded AI research, both research occurring within the government as well as Federally-funded research occurring outside of government, such as in academia. The ultimate goal of this research is to produce new AI knowledge and technologies that provide a range of positive benefits to society, while minimizing the negative impacts. To achieve this goal, this AI R&D Strategic Plan identifies the following priorities for Federally-funded AI research: Strategy 1: Make long-term investments in AI research. Prioritize investments in the next generation of AI that will drive discovery and insight and enable the United States to remain a world leader in AI. Strategy 2: Develop effective methods for human-AI collaboration. Rather than replace humans, most AI systems will collaborate with humans to achieve optimal performance. Research is needed to create effective interactions between humans and AI systems. Strategy 3: Understand and address the ethical, legal, and societal implications of AI. We expect AI technologies to behave according to the formal and informal norms to which we hold our fellow humans. Research is needed to understand the ethical, legal, and social implications of AI, and to develop methods for designing AI systems that align with ethical, legal, and societal goals. Strategy 4: Ensure the safety and security of AI systems. Before AI systems are in widespread use, assurance is needed that the systems will operate safely and securely, in a controlled, well-defined, and well-understood manner. Further progress in research is needed to address this challenge of creating AI systems that are reliable, dependable, and trustworthy. Strategy 5: Develop shared public datasets and environments for AI training and testing. The depth, quality, and accuracy of training datasets and resources significantly affect AI performance. Researchers need to develop high quality datasets and environments and enable responsible access to high-quality datasets as well as to testing and training resources. Strategy 6: Measure and evaluate AI technologies through standards and benchmarks. . Essential to advancements in AI are standards, benchmarks, testbeds, and community engagement that guide and evaluate progress in AI. Additional research is needed to develop a broad spectrum of evaluative techniques. Strategy 7: Better understand the national AI R&D workforce needs. Advances in AI will require a strong community of AI researchers. An improved understanding of current and future R&D workforce demands in AI is needed to help ensure that sufficient AI experts are available to address the strategic R&D areas outlined in this plan. The AI R&D Strategic Plan closes with two recommendations: Recommendation 1: Develop an AI R&D implementation framework to identify S&T opportunities and support effective coordination of AI R&D investments, consistent with Strategies 1-6 of this plan. Recommendation 2: Study the national landscape for creating and sustaining a healthy AI R&D workforce, consistent with Strategy 7 of this plan.

Raw data and questionnaire - Research Transformation: change in the era of AI, open and impactTo explore how the research world is transforming, Digital Science released Research Transformation: change in the era of AI, open and impact to uncover how technology, open access, funding policies and other drivers of change are impacting the academic community. This is anonymised raw data and questionnaire for the research transformation survey developed by Digital Science between 29 May and 12 July 2024. The survey . The survey looked to understand how the research world is transforming, what’s influencing change and how roles are impacted. A total of 380 respondents from 70 countries in Europe, Middle East, and Africa (EMEA), Asia–Pacific (APAC) and the Americas participated in the survey. They held roles within the academic library, research office, faculty and leadership.

Japan Artificial Intelligence Market was valued at USD 7.56 Billion in 2024 and is expected to reach USD 26.80 Billion by 2030 with a CAGR of 23.30% during the forecast period.