100+ datasets found
  1. Public opinions and social trends, Great Britain: artificial intelligence...

    • ons.gov.uk
    xlsx
    Updated Sep 19, 2025
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    Office for National Statistics (2025). Public opinions and social trends, Great Britain: artificial intelligence (AI) [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/wellbeing/datasets/publicopinionsandsocialtrendsgreatbritainartificialintelligenceai
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    xlsxAvailable download formats
    Dataset updated
    Sep 19, 2025
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    United Kingdom
    Description

    Understanding of and attitudes towards the use of artificial intelligence (AI); indicators from the Opinions and Lifestyle Survey (OPN).

  2. Public opinions and social trends, Great Britain: artificial intelligence...

    • ons.gov.uk
    xlsx
    Updated Sep 19, 2025
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    Office for National Statistics (2025). Public opinions and social trends, Great Britain: artificial intelligence (AI) by personal characteristics [Dataset]. https://www.ons.gov.uk/businessindustryandtrade/itandinternetindustry/datasets/publicawarenessopinionsandexpectationsaboutartificialintelligence
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    xlsxAvailable download formats
    Dataset updated
    Sep 19, 2025
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    United Kingdom
    Description

    Indicators from the Opinions and Lifestyle Survey (OPN) on the public's awareness, opinions and expectations about artificial intelligence (AI) Uses longer data collection periods to allow estimates from various personal characteristics.

  3. Effect of AI and other trends on professions in the next five years...

    • statista.com
    Updated Aug 18, 2025
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    Bergur Thormundsson (2025). Effect of AI and other trends on professions in the next five years worldwide 2024 [Dataset]. https://www.statista.com/topics/3104/artificial-intelligence-ai-worldwide/
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    Dataset updated
    Aug 18, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Bergur Thormundsson
    Description

    AI has become a necessary tool used by many businesses for increased efficiency and reducing human error. In a 2024 survey, 42 percent of respondents from different professions stated that in the next five years AI and GenAI will have transformational impact, while 36 percent indicated high impact.

  4. Influence of AI on student ideas in the UK 2024

    • statista.com
    Updated Jun 24, 2025
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    Statista (2025). Influence of AI on student ideas in the UK 2024 [Dataset]. https://www.statista.com/statistics/1616470/ai-idea-diversity-students/
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    Dataset updated
    Jun 24, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Nov 12, 2024 - Nov 25, 2024
    Area covered
    Worldwide
    Description

    In 2025, ** percent of teachers who do not use AI tools, agree that their students' submitted assignments are less varied, compared to ** percent of teachers who use AI tools agreeing to the same statement.

  5. Impact of Artificial Intelligence on Education

    • kaggle.com
    Updated Jun 9, 2025
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    INK (2025). Impact of Artificial Intelligence on Education [Dataset]. https://www.kaggle.com/datasets/irakozekelly/impact-of-artificial-intelligence-on-education/discussion
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 9, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    INK
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    This dataset supports a study examining how students perceive the usefulness of artificial intelligence (AI) in educational settings. The project involved analyzing an open-access survey dataset that captures a wide range of student responses on AI tools in learning.

    The data underwent cleaning and preprocessing, followed by an exploratory data analysis (EDA) to identify key trends and insights. Visualizations were created to support interpretation, and the results were summarized in a digital poster format to communicate findings effectively.

    This resource may be useful for researchers, educators, and technologists interested in the evolving role of AI in education.

    Keywords: Artificial Intelligence, Education, Student Perception, Survey, Data Analysis, EDA
    
    Subject: Computer and Information Science
    
    License: CC0 1.0 Universal Public Domain Dedication
    
    DOI: https://doi.org/10.18738/T8/RXUCHK
    
  6. Opinion of U.S. parents and youth on AI impact on school learning, 2025

    • statista.com
    Updated Jul 4, 2025
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    Statista (2025). Opinion of U.S. parents and youth on AI impact on school learning, 2025 [Dataset]. https://www.statista.com/statistics/1613455/parents-and-youth-on-ai-impact-on-school-learning/
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    Dataset updated
    Jul 4, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 8, 2025 - Jan 20, 2025
    Area covered
    United States
    Description

    According to a January 2025 survey, 43 percent of parents in the United States felt that artificial intelligence (AI) had a negative impact on their children learning at school. Among U.S. kids and teens, 48 percent of respondents reported a positive impact of AI on their school education. As of April 2024, 63 percent of U.S. teens used AI chatbots for school assignments.

  7. Z

    MIT focus group data on AI and deepfake technology

    • data.niaid.nih.gov
    • zenodo.org
    Updated Dec 10, 2024
    + more versions
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    Elena Denia (2024). MIT focus group data on AI and deepfake technology [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_11061770
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    Dataset updated
    Dec 10, 2024
    Dataset provided by
    John Durant
    Elena Denia
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Interview protocols, recordings, and transcripts of three focus groups to investigate the social perception of AI and deepfake technology at the Massachusetts Institute of Technology. The focus groups are described below:

    Focus Group #1 (engaged public): 12 participants in a 3-session Make A Fake class; the students were offered a full course refund in return for their participation in the study, which took place immediately following the final session of the class on Monday 27 February, 2023.

    Focus Group #2 (attentive public) 14 visitors to the MIT Museum who volunteered to participate in the discussion after being recruited in the museum itself. The activity was scheduled for the week following recruitment, Monday 24 April, 2023, and as compensation for their involvement participants were offered a refund of their museum admission fee, and two more tickets for another day.

    Focus Group #3 (nonattentive public): 13 pedestrians who were recruited with the help of 4 MIT volunteers working in the immediate environs of the Boston Public Library and the adjacent Prudential Center Shopping Mall. Participants were offered a $70 Amazon Gift Card in consideration for one hour of conversation on the same day of their recruitment, Saturday 27 May, 2023.

    NOTE: Recordings from different devices are attached to better capture the voices of each conversation (devices: MacBook Air and iPad Pro).

  8. o

    The Effect of AI Labeling on Perceptions of Images

    • osf.io
    url
    Updated Sep 20, 2024
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    Wei Zhong; Zeve Sanderson; Joshua Tucker (2024). The Effect of AI Labeling on Perceptions of Images [Dataset]. http://doi.org/10.17605/OSF.IO/ZJHD4
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    urlAvailable download formats
    Dataset updated
    Sep 20, 2024
    Dataset provided by
    Center For Open Science
    Authors
    Wei Zhong; Zeve Sanderson; Joshua Tucker
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Recent advancements in the sophistication and availability of generative artificial intelligence (AI) technologies have sparked widespread concerns about the public’s ability to navigate the digital information environment (Helmus, 2022; Zhou, Zhang, Luo, Parker, & De Choudhury, 2023), especially during elections (Cybersecurity & Infrastructure Security Agency, 2024). A key concern is whether online users can identify synthetic images, text, audio, and multimedia content. This has led to significant focus from scholars, policymakers, and technologists on developing methods for identifying synthetic content at scale across the creation and distribution pipeline.

    Technical work has concentrated on data provenance tracking, including digital watermarks and provenance data that embed information about where, when, how, and by whom content was created and processed. While such methods are necessary for identifying synthetic media at scale, a growing consensus has emerged around the need for labeling synthetic media to inform users that content was created or altered by generative AI, especially given the public’s limited ability to identify synthetic media (Farid, 2022; Mai, Bray, Davies, & Griffin, 2023). To this end, the application of visible content labels for synthetic media—similar to those used for labeling fact-checked information on social media (Wittenberg & Berinsky, 2020)—has emerged as a key digital media literacy strategy, garnering interest from a range of stakeholders. For example, the use of content labels was included in the Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence published in October 2023 (House, 2023), the AI Labeling Act introduced in October 2023, and the Protecting Consumers from Deceptive AI Act introduced in March 2024. In addition, large technology companies, including Meta, Google, and TikTok, have announced AI content labeling policies for their products and services, while many others have agreed to principles that include content labeling.

    While interest in visible content labeling has grown, a key challenge remains in the difficulty of identifying synthetic content, particularly given the scale of social media platforms and the increasing sophistication of open foundation models (Kapoor et al., 2024). For instance, according to Meta’s synthetic content policy, labeling occurs only if synthetic content meets specific criteria: either industry-standard AI indicators are detected, or users disclose the synthetic nature of the content. Limitations in accurately determining content provenance, which are present for every social media platform aiming to label synthetic content, can result in many synthetic images remaining unlabeled and some authentic images mistakenly being labeled as synthetic. This variability in labeling accuracy—resulting in both false positives (labeling authentic content as synthetic) and false negatives (failing to label synthetic content)—should inform the empirical study of the efficacy of content labeling for synthetic media.

    The logic of content labeling for synthetic media is straightforward: given the public’s challenges in identifying synthetic media (Farid, 2022; Mai et al., 2023), applying a visible label is expected to improve the public’s ability to discern whether AI was used in content creation. This approach aligns with previous studies on the effects of applying fact-check labels to false or misleading information. Several studies, primarily focused on text-based content, have found that fact-checking labels can reduce the likelihood of believing misinformation (Clayton et al., 2019; Martel & Rand, 2023; Pennycook, Bear, Collins, & Rand, 2020) and reported willingness to share it (Martel & Rand, 2023; Mena, 2020; Wittenberg & Berinsky, 2020). However, previous research has also shown that fact-checking labels can have unintended consequences. Specifically—and relevant to synthetic content labels—correctly applying fact-checking labels to some content can increase the perceived truthfulness and willingness to share other false headlines that remain unlabeled (Pennycook et al., 2020). While this existing work on fact-checking labels informs the study of synthetic content labels, additional research is necessary to understand the effects of content labels in the context of synthetic images.

    In a recent study, labels identifying news headlines as AI-generated decreased their perceived accuracy and users’ willingness to share them (Altay & Gilardi, 2023). This effect occurred regardless of whether the headlines were true or false, and whether they were created by humans or AI (Altay & Gilardi, 2023). Given the difficulty of identifying synthetic text, key stakeholders have shifted their focus to identifying and labeling AI-generated audio, images, videos, and multimedia content. For example, Meta announced that content labels would be applied to synthetic media across Facebook and Instagram, but not to text content. Visual content tends to be more believable and memorable than text or speech because it can convey nuanced messages that are difficult to communicate verbally (Grabe & Bucy, 2009). Therefore, understanding the effects of AI labels on visual media is particularly important.

    In a similar study, Wittenberg, Epstein, Peloquin-Skulski, Berinsky, and Rand (2024) showed social media posts with images—each of which were both synthetic and false—to respondents and varied whether a label was applied and, if so, the content of the label. They found that all labels decreased belief in the false claims presented in the images, but there were heterogeneous effects on the willingness to share based on which label was used. A focus on misinformation has been central in other studies on AI-generated content (Goldstein et al., 2023; Zhou et al., 2023). However, labeling exists in a digital information environment with diverse information types—authentic and synthetic, true and false. Previous research has found that true information dwarfs misinformation for most online users (Allen, Howland, Mobius, Rothschild, & Watts, 2020; Guess, Aslett, Tucker, Bonneau, & Nagler, 2021), and recent reporting has suggested that AI-powered misinformation has not, as of yet, emerged as a significant factor in political communications (Frenkel, 2024). As a result, measuring the efficacy of labeling must account for a broad range of content that users are likely to encounter "in the wild."

    Our study differs from Wittenberg et al. (2024) in five primary ways. First, we include both authentic and synthetic images. Second, we include images depicting both true and fabricated events. Third, we have a process for pairing images to attempt, as much as possible, to minimize differences between images and thus isolate the effect of the label. Fourth, we use the synthetic content labeling schema first introduced by Meta in April 2024, rather than varying the label to include information about the veracity of the content. Finally, in our second study, we randomly assign exposure to the Meta synthetic content policy. In this way, we view these studies as complementary: while Wittenberg et al. (2024) focus on the effects of labels on synthetic misinformation images and evaluate the relative efficacy of labeling language, our study evaluates the effects of content labels on a range of image types (synthetic/authentic, false/true) and examines the impact of exposure to a platform content policy.

    What impact does synthetic content labeling have on perceptions of images? In this study, we propose an online experiment to assess the impact of AI labels on perceptions of image veracity, provenance, and sharing intentions. We develop a method for pairing similar synthetic and authentic images, attempting to isolate the impact of the label rather than the effects of differences across the images. Previous work on fact-checking labels found that biases in the samples of stimuli selected by researchers decreased the external validity of the results (Clemm von Hohenberg, 2020); our method is transparent and replicable, aiming to overcome this limitation. Additionally, we utilize the “Made with AI” label introduced by Meta in April 2024. Previous research on AI labeling, including studies by Altay and Gilardi (2023) and Epstein, Arechar, and Rand (2023), has primarily involved experimental labeling schemes developed by the researchers themselves. Such approaches may compromise the external and ecological validity of the findings. Epstein et al. (2023) also suggest that the specific language used in AI labels can influence user interpretation, raising concerns about the generalizability of these results to other labeling strategies or those implemented by companies in real-world settings. By adopting Meta’s initial labeling schema—both its language and design—we sidestep the need to develop our own label.

    Our research is centered around two primary questions. First, we aim to evaluate how labels such as “Made with AI” influence perceptions of an image’s provenance, veracity, and self-reported likelihood of being shared. Implementing these labels presents significant challenges given the vast volume of content on social media platforms, the realistic qualities of images generated by advanced AI, and the variety of AI services (especially those from open-source models that may not follow standard watermarking practices). Measuring the effects of synthetic content labels across false and true positives and negatives is a critical aspect of our experimental design. Second, we seek to understand the impact of exposing participants to Meta’s AI content policy on the effectiveness of labeling. Previous research has shown that labeling false news as false can decrease belief in labeled headlines but can also increase the perceived veracity and willingness to share false headlines that remain unlabeled (Pennycook et al.,

  9. G

    On-Device AI Market Research Report 2033

    • growthmarketreports.com
    csv, pdf, pptx
    Updated Aug 22, 2025
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    Growth Market Reports (2025). On-Device AI Market Research Report 2033 [Dataset]. https://growthmarketreports.com/report/on-device-ai-market
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    pptx, csv, pdfAvailable download formats
    Dataset updated
    Aug 22, 2025
    Dataset authored and provided by
    Growth Market Reports
    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    On-Device AI Market Outlook




    According to our latest research, the global On-Device AI market size stood at USD 9.7 billion in 2024, with a robust compound annual growth rate (CAGR) of 22.8% projected from 2025 to 2033. By the end of 2033, the market is forecasted to reach an impressive USD 74.3 billion. This remarkable expansion is primarily driven by the surging demand for real-time data processing, enhanced user privacy, and lower latency across a myriad of consumer and industrial applications. As per the latest research, the proliferation of smart devices and advancements in edge computing technologies are accelerating the adoption of On-Device AI solutions globally.




    The growth trajectory of the On-Device AI market is significantly influenced by the increasing integration of artificial intelligence capabilities into everyday consumer electronics. The widespread adoption of smartphones, wearables, and smart home devices, all equipped with AI-powered features, is fueling market expansion. These devices leverage on-device AI to deliver personalized experiences, such as voice assistants, image recognition, and predictive analytics, without the need to transfer data to centralized cloud servers. This not only enhances performance and responsiveness but also addresses growing concerns regarding data privacy and security, making On-Device AI a preferred solution across various sectors.




    Another crucial growth factor is the evolution of hardware and software ecosystems that support AI processing at the edge. Semiconductor manufacturers are introducing specialized AI chips and neural processing units (NPUs) that enable efficient, low-power AI computations directly on devices. Simultaneously, advancements in AI algorithms and frameworks are optimizing model sizes and inference speeds, making it feasible to deploy sophisticated AI functionalities on resource-constrained devices. The convergence of these technological developments is opening new avenues for innovation in applications ranging from autonomous vehicles and industrial automation to healthcare diagnostics and retail analytics.




    Furthermore, the growing emphasis on data sovereignty and regulatory compliance is propelling organizations to adopt On-Device AI solutions. Governments and regulatory bodies across regions are enacting stringent data protection laws, compelling enterprises to process sensitive information locally rather than relying on cloud-based infrastructures. This shift is particularly evident in sectors such as healthcare, automotive, and finance, where real-time decision-making and data confidentiality are paramount. The ability of On-Device AI to deliver instant insights while maintaining compliance with data privacy regulations is a key differentiator driving its adoption across diverse end-user segments.




    Regionally, the Asia Pacific market is emerging as a powerhouse in the On-Device AI landscape, fueled by rapid digitization, a burgeoning consumer electronics industry, and significant investments in AI research and development. North America continues to lead in terms of innovation and early adoption, while Europe is witnessing steady growth backed by supportive regulatory frameworks and a strong industrial base. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, driven by increasing smartphone penetration and the expansion of smart infrastructure. This diverse regional momentum underscores the global relevance and transformative potential of On-Device AI technologies.





    Component Analysis




    The On-Device AI market is segmented by component into hardware, software, and services, each playing a pivotal role in the overall ecosystem. The hardware segment, comprising AI-optimized processors, sensors, and accelerators, forms the foundation for enabling efficient on-device computations. Leading semiconductor vendors are investing heavily in developing next-generation AI chips that deliver high performance while minimizing power consumption. The

  10. O

    On-premises Conversational AI Platforms Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Feb 15, 2025
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    Archive Market Research (2025). On-premises Conversational AI Platforms Report [Dataset]. https://www.archivemarketresearch.com/reports/on-premises-conversational-ai-platforms-29225
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    doc, pdf, pptAvailable download formats
    Dataset updated
    Feb 15, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global on-premises conversational AI platforms market is projected to reach $X million by 2033, growing at a CAGR of XX% from 2025 to 2033. The growing adoption of artificial intelligence (AI) and machine learning (ML) technologies, increasing demand for customer engagement and satisfaction, and rising need for cost-effective and scalable solutions are driving market growth. Furthermore, advancements in natural language processing (NLP) and deep learning are enabling conversational AI platforms to understand and respond to customer inquiries more effectively, further fueling market expansion. Key trends shaping the market include the integration of conversational AI platforms with other enterprise software applications, such as customer relationship management (CRM) and enterprise resource planning (ERP), and the increasing adoption of cloud-based deployment models. Additionally, the growing number of mergers and acquisitions among vendors is expected to consolidate the market and strengthen the competitive landscape. Major players in the on-premises conversational AI platforms market include SAP, IBM, Microsoft, Kore.ai, and Ada, among others. These companies are focusing on developing innovative features and expanding their geographical reach to gain a competitive edge. This report provides an in-depth analysis of the on-premises conversational AI platforms market. It covers the current market landscape, key trends, growth drivers, challenges, and opportunities. The report also provides company profiles of the leading players in the market.

  11. e

    Artificial Intelligence Activity in UK Businesses, 2021 - Dataset - B2FIND

    • b2find.eudat.eu
    Updated Apr 15, 2023
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    (2023). Artificial Intelligence Activity in UK Businesses, 2021 - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/ac6afe0c-de86-50ae-9b9c-4c4311ee1795
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    Dataset updated
    Apr 15, 2023
    Area covered
    United Kingdom
    Description

    Abstract copyright UK Data Service and data collection copyright owner. The Department of Digital, Culture, Media and Sport's (DCMS) Office for Artificial Intelligence commissioned research to model current and future estimates of (i) the adoption of Artificial Intelligence (AI) technologies in the UK, and (ii) the expenditure on AI technologies and AI-related labour in the UK.The associated report provides an assessment of the scale of AI activity in UK businesses and scenarios for growth over the next twenty yearsTo gather data on AI adoption and spending to inform the modelling in this study, a survey of private businesses was conducted in conjunction with YouGov.The total survey sample was 2,019 private businesses, including 1,127 small businesses (55.8%), 291 medium businesses (14.4%) and 601 large businesses (29.8%). Respondents spanned all regions of Great Britain and all private sectors. Businesses in Northern Ireland have thus not been surveyed. After removing spurious responses, the identification process of which included an analysis of expenditure responses relative to firm size (in terms of turnover), the sample reduced to 2,009 quality responses. Main Topics: The survey asks respondents whether they have adopted AI technologies (including the following six technologies: machine learning, natural language processing and generation, computer vision/image processing and generation, data management and analysis, hardware, and robotic process automation), how did they source these technologies, and their expenditure on these technologies and the associated labour expenditure.The survey asked how businesses expected their expenditure on AI and AI-related labour to increase in the next year and next 5 years to support modelling the trajectory of AI expenditure in the UK.The study data also includes variables related to the size of the business, the business sector, turnover, and main industry worked in.

  12. w

    Global On-Device AI Market Research Report: By Application (Smartphones,...

    • wiseguyreports.com
    Updated Aug 4, 2025
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    (2025). Global On-Device AI Market Research Report: By Application (Smartphones, Smart Home Devices, Wearable Devices, Industrial Automation, Automotive), By Technology (Machine Learning, Natural Language Processing, Computer Vision, Speech Recognition), By End Use (Consumer Electronics, Healthcare, Manufacturing, Transportation), By Deployment (Cloud-Based, On-Premises) and By Regional (North America, Europe, South America, Asia Pacific, Middle East and Africa) - Forecast to 2035 [Dataset]. https://www.wiseguyreports.com/reports/on-device-ai-market
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    Dataset updated
    Aug 4, 2025
    License

    https://www.wiseguyreports.com/pages/privacy-policyhttps://www.wiseguyreports.com/pages/privacy-policy

    Time period covered
    Aug 25, 2025
    Area covered
    Global
    Description
    BASE YEAR2024
    HISTORICAL DATA2019 - 2023
    REGIONS COVEREDNorth America, Europe, APAC, South America, MEA
    REPORT COVERAGERevenue Forecast, Competitive Landscape, Growth Factors, and Trends
    MARKET SIZE 20247.4(USD Billion)
    MARKET SIZE 20258.8(USD Billion)
    MARKET SIZE 203550.0(USD Billion)
    SEGMENTS COVEREDApplication, Technology, End Use, Deployment, Regional
    COUNTRIES COVEREDUS, Canada, Germany, UK, France, Russia, Italy, Spain, Rest of Europe, China, India, Japan, South Korea, Malaysia, Thailand, Indonesia, Rest of APAC, Brazil, Mexico, Argentina, Rest of South America, GCC, South Africa, Rest of MEA
    KEY MARKET DYNAMICSEdge computing advancements, Increasing data privacy concerns, Rising demand for real-time processing, Growth of IoT devices, Enhanced machine learning algorithms
    MARKET FORECAST UNITSUSD Billion
    KEY COMPANIES PROFILEDNVIDIA, Cerebras Systems, Baidu, Microsoft, Sony, Google, Qualcomm, Apple, ARM, Amazon, Samsung, HUAWEI, Intel, IBM
    MARKET FORECAST PERIOD2025 - 2035
    KEY MARKET OPPORTUNITIESIncreased edge computing adoption, Demand for privacy-focused solutions, Growth in IoT devices, Advancements in NLP technology, Rising interest in real-time analytics
    COMPOUND ANNUAL GROWTH RATE (CAGR) 19.0% (2025 - 2035)
  13. Opinion on AI changing current occupations globally 2023-2024, by...

    • tokrwards.com
    • statista.com
    Updated Jun 26, 2025
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    Bergur Thormundsson (2025). Opinion on AI changing current occupations globally 2023-2024, by demographic groups [Dataset]. https://tokrwards.com/?_=%2Ftopics%2F13638%2Fethical-artificial-intelligence-ai%2F%23D%2FIbH0Phabzc8oKQxRXLgxTyDkFTtCs%3D
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    Dataset updated
    Jun 26, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Bergur Thormundsson
    Description

    In 2023 and 2024 different generational groups were asked their point of view regarding AI's impact on the performance of their current jobs and how this will affect how jobs are done. Although they all show a trend upwards confirming the statement, the highest percentage increase is to be seen in milennials and baby boomers with a 3 percent each. Only 49 percent of baby boomers think their jobs will be affected, in contrast to 67 percent of gen z who agreed to the statement.

  14. Shoppers feelings on AI usage for shopping experiences worldwide 2025

    • tokrwards.com
    • statista.com
    Updated Jun 20, 2025
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    Statista Research Department (2025). Shoppers feelings on AI usage for shopping experiences worldwide 2025 [Dataset]. https://tokrwards.com/?_=%2Ftopics%2F11640%2Fartificial-intelligence-and-extended-reality-in-e-commerce%2F%23D%2FIbH0PhabzN99vNwgDeng71Gw4euCn%2B
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    Dataset updated
    Jun 20, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Description

    A survey conducted in 2025 showed that shoppers have mixed feelings about AI when shopping online. Although over 60 percent seemed to understand that this technology is helpful when recommending products, around 45 percent do not like to have any interactions with AI during their shopping experience. One in four shoppers signaled being worried with frauds and scams if using this technology.

  15. Consumer sentiment on AI personalization in the UK 2024, by generation

    • tokrwards.com
    • statista.com
    Updated Jul 9, 2025
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    Statista (2025). Consumer sentiment on AI personalization in the UK 2024, by generation [Dataset]. https://tokrwards.com/?_=%2Fstatistics%2F1609123%2Fconsumer-sentiment-on-ai-personalization-in-the-uk%2F%23D%2FIbH0PhabzN99vNwgDeng71Gw4euCn%2B
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    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Dec 2024
    Area covered
    United Kingdom
    Description

    A 2024 survey carried out in the United Kingdom showed that Gen Z (** percent) and Millennial (** percent) shoppers were the most enthusiastic toward AI personalization when shopping online. In turn, Boomers were more skeptical, with ** percent of them appreciating product and service recommendations implemented with AI-powered technologies.

  16. D

    Notable AI Models

    • epoch.ai
    csv
    Updated Jul 24, 2025
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    Epoch AI (2025). Notable AI Models [Dataset]. https://epoch.ai/data/ai-models
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    csvAvailable download formats
    Dataset updated
    Jul 24, 2025
    Dataset authored and provided by
    Epoch AI
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Global
    Variables measured
    https://epoch.ai/data/ai-models-documentation#records
    Measurement technique
    https://epoch.ai/data/ai-models-documentation#records
    Description

    Our most comprehensive database of AI models, containing over 800 models that are state of the art, highly cited, or otherwise historically notable. It tracks key factors driving machine learning progress and includes over 300 training compute estimates.

  17. Concerns regarding AI's negative effects on everyday life in the U.S. 2021

    • tokrwards.com
    Updated Jul 1, 2025
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    Statista (2025). Concerns regarding AI's negative effects on everyday life in the U.S. 2021 [Dataset]. https://tokrwards.com/?_=%2Fstatistics%2F1302334%2Fnegative-effects-of-ai-us%2F%23D%2FIbH0PhabzN99vNwgDeng71Gw4euCn%2B
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    Dataset updated
    Jul 1, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Sep 8, 2021 - Sep 10, 2021
    Area covered
    United States
    Description

    Most respondents believe artificial intelligence (AI) will have considerable negative effects on everyday life in the United States. Concerns regarding business, privacy, and employment opportunity ranked the highest among respondents. Concerns regarding racial and gender bias was considerably lower, where most in fact had positive views on increased AI adoption. Loss of personal privacy had the largest share at ** percent.

  18. Impact of AI on world aspects from 2025-2028

    • epiphanyinfotech.com
    Updated Feb 16, 2024
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    Bergur Thormundsson (2024). Impact of AI on world aspects from 2025-2028 [Dataset]. https://www.epiphanyinfotech.com/wp-content/uploads/2023/03/artificial-intelligence-ai-worldwide
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    Dataset updated
    Feb 16, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Bergur Thormundsson
    Description

    Most people expect things to take less time with AI in the next 3-5 years, that is to say, improve the efficiency of time usage. However, most did not share this feeling regarding the job market, which was expected to be worse with the usage of AI in that field.

  19. Clinicians' who believed clinical decisions will be based on AI by 2031, by...

    • statista.com
    Updated Jun 25, 2025
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    Statista (2025). Clinicians' who believed clinical decisions will be based on AI by 2031, by region [Dataset]. https://www.statista.com/statistics/1298955/clinicians-views-on-ai-use-to-make-decisions-by-2031-by-region/
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    Dataset updated
    Jun 25, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Oct 2021 - Dec 2021
    Area covered
    Worldwide
    Description

    According to a survey conducted in December 2021, ** percent of clinicians in the Asia Pacific and South America regions believed that in future they will make the majority of their decisions using clinical decision support tools that use artificial intelligence (AI). On the other hand, fewer than ** percent of clinicians surveyed in Europe and North America agreed that the majority of clinical decisions in ten years' time will be based on AI.

  20. D

    AI Training Dataset Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
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    Dataintelo (2025). AI Training Dataset Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-ai-training-dataset-market
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    csv, pptx, pdfAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    AI Training Dataset Market Outlook



    The global AI training dataset market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 6.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 20.5% from 2024 to 2032. This substantial growth is driven by the increasing adoption of artificial intelligence across various industries, the necessity for large-scale and high-quality datasets to train AI models, and the ongoing advancements in AI and machine learning technologies.



    One of the primary growth factors in the AI training dataset market is the exponential increase in data generation across multiple sectors. With the proliferation of internet usage, the expansion of IoT devices, and the digitalization of industries, there is an unprecedented volume of data being generated daily. This data is invaluable for training AI models, enabling them to learn and make more accurate predictions and decisions. Moreover, the need for diverse and comprehensive datasets to improve AI accuracy and reliability is further propelling market growth.



    Another significant factor driving the market is the rising investment in AI and machine learning by both public and private sectors. Governments around the world are recognizing the potential of AI to transform economies and improve public services, leading to increased funding for AI research and development. Simultaneously, private enterprises are investing heavily in AI technologies to gain a competitive edge, enhance operational efficiency, and innovate new products and services. These investments necessitate high-quality training datasets, thereby boosting the market.



    The proliferation of AI applications in various industries, such as healthcare, automotive, retail, and finance, is also a major contributor to the growth of the AI training dataset market. In healthcare, AI is being used for predictive analytics, personalized medicine, and diagnostic automation, all of which require extensive datasets for training. The automotive industry leverages AI for autonomous driving and vehicle safety systems, while the retail sector uses AI for personalized shopping experiences and inventory management. In finance, AI assists in fraud detection and risk management. The diverse applications across these sectors underline the critical need for robust AI training datasets.



    As the demand for AI applications continues to grow, the role of Ai Data Resource Service becomes increasingly vital. These services provide the necessary infrastructure and tools to manage, curate, and distribute datasets efficiently. By leveraging Ai Data Resource Service, organizations can ensure that their AI models are trained on high-quality and relevant data, which is crucial for achieving accurate and reliable outcomes. The service acts as a bridge between raw data and AI applications, streamlining the process of data acquisition, annotation, and validation. This not only enhances the performance of AI systems but also accelerates the development cycle, enabling faster deployment of AI-driven solutions across various sectors.



    Regionally, North America currently dominates the AI training dataset market due to the presence of major technology companies and extensive R&D activities in the region. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid technological advancements, increasing investments in AI, and the growing adoption of AI technologies across various industries in countries like China, India, and Japan. Europe and Latin America are also anticipated to experience significant growth, supported by favorable government policies and the increasing use of AI in various sectors.



    Data Type Analysis



    The data type segment of the AI training dataset market encompasses text, image, audio, video, and others. Each data type plays a crucial role in training different types of AI models, and the demand for specific data types varies based on the application. Text data is extensively used in natural language processing (NLP) applications such as chatbots, sentiment analysis, and language translation. As the use of NLP is becoming more widespread, the demand for high-quality text datasets is continually rising. Companies are investing in curated text datasets that encompass diverse languages and dialects to improve the accuracy and efficiency of NLP models.



    Image data is critical for computer vision application

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Office for National Statistics (2025). Public opinions and social trends, Great Britain: artificial intelligence (AI) [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/wellbeing/datasets/publicopinionsandsocialtrendsgreatbritainartificialintelligenceai
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Public opinions and social trends, Great Britain: artificial intelligence (AI)

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2 scholarly articles cite this dataset (View in Google Scholar)
xlsxAvailable download formats
Dataset updated
Sep 19, 2025
Dataset provided by
Office for National Statisticshttp://www.ons.gov.uk/
License

Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically

Area covered
United Kingdom
Description

Understanding of and attitudes towards the use of artificial intelligence (AI); indicators from the Opinions and Lifestyle Survey (OPN).

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