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.

AI & Machine Learning Research Papers Dataset

This dataset contains a curated collection of 1296 research papers focused on advancements in Artificial Intelligence and Machine Learning. It is intended as a resource for researchers, educators, and developers to explore and analyze diverse topics within AI and ML.

  Dataset Summary

Total Papers: 1296 Domains Covered: Artificial Intelligence (AI) Machine Learning (ML) Deep Learning Natural Language Processing (NLP) Computer… See the full description on the dataset page: https://huggingface.co/datasets/khushwant04/Research-Papers.

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.

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.

SALT: Sales Autocompletion Linked Business Tables Dataset

Dataset for our paper SALT: Sales Autocompletion Linked Business Tables Dataset presented at NeurIPS'24 Table Representation Workshop.

  News

07/10/2025: 🎉🎉🎉 Dataset is now integrated into RelBench 🎉🎉🎉 01/11/2025: Updated paper (some results changed due to minor dataset changes, screenshots added to appendix) 12/19/2024: Train/test splits released 12/15/2024: Preliminatry dataset now also available on… See the full description on the dataset page: https://huggingface.co/datasets/sap-ai-research/SALT.

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.

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

One third of the top-tier artificial intelligence (AI) researchers currently working for institutions in the United States received their undergraduate degree in the United States. Ranked second is China which is the home country of ** percent of the leading U.S. AI researchers. India followed with ** percent.

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.

Machine learning is an artificial intelligence (AI) technology which allows machines to learn by using algorithms to interpret data from connected ‘things’ to predict outcomes and learn from successes and failures. Read More

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.

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...

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.

Global artificial intelligence (AI) market worth at USD 219.25 Billion in 2024, is expected to surpass USD 3983.94 Billion by 2034, with a CAGR of 33.64% from 2025 to 2034.

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.

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.

BCC Research Market Report says global AI consulting services market The market is projected to grow from $11.4 billion in 2022 to $64.3 billion in 2028, at a CAGR of 34.2%.

Successful outline proposals to the artificial intelligence (AI) for science hubs funding opportunity.

Global Artificial Intelligence Data Center Market Report is Segmented by Data Center Type (CSP Data Centers, Colocation Data Centers, Others (Enterprise and Edge)), by Component (Hardware, Software Technology, Services - (Managed Services, Professional Services, Etc. )). ). The Report Offers the Market Size and Forecasts for all the Above Segments in Terms of Value (USD).

In 2023, around ***** 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.