AI Content Detector Market size is growing at a moderate pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period i.e. 2024 to 2031.

Global AI Content Detector Market Drivers

Rising Concerns Over Misinformation: The proliferation of fake news, misinformation, and inappropriate content on digital platforms has led to increased demand for AI content detectors. These systems can identify and flag misleading or harmful content, helping to combat the spread of misinformation online.

Regulatory Compliance Requirements: Stringent regulations and legal obligations regarding content moderation, data privacy, and online safety drive the adoption of AI content detectors. Organizations need to comply with regulations such as the General Data Protection Regulation (GDPR) and the Digital Millennium Copyright Act (DMCA), spurring investment in AI-powered content moderation solutions.

Growing Volume of User-Generated Content: The exponential growth of user-generated content on social media platforms, forums, and websites has overwhelmed traditional moderation methods. AI content detectors offer scalable and efficient solutions for analyzing vast amounts of content in real-time, enabling platforms to maintain a safe and healthy online environment for users.

Advancements in AI and Machine Learning Technologies: Continuous advancements in artificial intelligence and machine learning algorithms have enhanced the capabilities of content detection systems. AI models trained on large datasets can accurately identify various types of content, including text, images, videos, and audio, with high precision and speed.

Brand Protection and Reputation Management: Businesses prioritize brand protection and reputation management in the digital age, as negative content or misinformation can severely impact brand image and consumer trust. AI content detectors help organizations identify and address potentially damaging content proactively, safeguarding their reputation and brand integrity.

Demand for Personalized User Experiences: Consumers increasingly expect personalized online experiences tailored to their preferences and interests. AI content detectors analyze user behavior and content interactions to deliver relevant and engaging content, driving user engagement and satisfaction.

Adoption of AI-Powered Moderation Tools by Social Media Platforms: Major social media platforms and online communities are investing in AI-powered moderation tools to enforce community guidelines, prevent abuse and harassment, and maintain a positive user experience. The need to address content moderation challenges at scale drives the adoption of AI content detectors.

Mitigation of Online Risks and Threats: Online platforms face various risks and threats, including cyberbullying, hate speech, terrorist propaganda, and child exploitation content. AI content detectors help mitigate these risks by identifying and removing harmful content, thereby creating a safer online environment for users.

Cost and Resource Efficiency: Traditional content moderation methods, such as manual review by human moderators, are time-consuming, labor-intensive, and costly. AI content detectors automate the moderation process, reducing the need for human intervention and minimizing operational expenses for organizations.

Analysis of ‘Share of Americans Not Having Sex’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/share-of-americans-not-having-sex on 14 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

• The dataset contains aggregated survey results asking about sex frequency of Americans over the years. This dataset was created by Makeover Monday and contains data from year 1989 to 2000.

How to use this dataset

• Analyze the change in sex frequency over the years
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Makeover Monday urf

--- Original source retains full ownership of the source dataset ---

According to Cognitive Market Research, the global AI Training Dataset Market size will be USD 2962.4 million in 2025. It will expand at a compound annual growth rate (CAGR) of 28.60% from 2025 to 2033.

North America held the major market share for more than 37% of the global revenue with a market size of USD 1096.09 million in 2025 and will grow at a compound annual growth rate (CAGR) of 26.4% from 2025 to 2033.
Europe accounted for a market share of over 29% of the global revenue, with a market size of USD 859.10 million.
APAC held a market share of around 24% of the global revenue with a market size of USD 710.98 million in 2025 and will grow at a compound annual growth rate (CAGR) of 30.6% from 2025 to 2033.
South America has a market share of more than 3.8% of the global revenue, with a market size of USD 112.57 million in 2025 and will grow at a compound annual growth rate (CAGR) of 27.6% from 2025 to 2033.
Middle East had a market share of around 4% of the global revenue and was estimated at a market size of USD 118.50 million in 2025 and will grow at a compound annual growth rate (CAGR) of 27.9% from 2025 to 2033.
Africa had a market share of around 2.20% of the global revenue and was estimated at a market size of USD 65.17 million in 2025 and will grow at a compound annual growth rate (CAGR) of 28.3% from 2025 to 2033.
Data Annotation category is the fastest growing segment of the AI Training Dataset Market

Market Dynamics of AI Training Dataset Market

Key Drivers for AI Training Dataset Market

Government-Led Open Data Initiatives Fueling AI Training Dataset Market Growth

In recent years, Government-initiated open data efforts have strongly driven the development of the AI Training Dataset Market through offering affordable, high-quality datasets that are vital in training sound AI models. For instance, the U.S. government's drive for openness and innovation can be seen through portals such as Data.gov, which provides an enormous collection of datasets from many industries, ranging from healthcare, finance, and transportation. Such datasets are basic building blocks in constructing AI applications and training models using real-world data. In the same way, the platform data.gov.uk, run by the U.K. government, offers ample datasets to aid AI research and development, creating an environment that is supportive of technological growth. By releasing such information into the public domain, governments not only enhance transparency but also encourage innovation in the AI industry, resulting in greater demand for training datasets and helping to drive the market's growth.

India's IndiaAI Datasets Platform Accelerates AI Training Dataset Market Growth

India's upcoming launch of the IndiaAI Datasets Platform in January 2025 is likely to greatly increase the AI Training Dataset Market. The project, which is part of the government's ?10,000 crore IndiaAI Mission, will establish an open-source repository similar to platforms such as HuggingFace to enable developers to create, train, and deploy AI models. The platform will collect datasets from central and state governments and private sector organizations to provide a wide and rich data pool. Through improved access to high-quality, non-personal data, the platform is filling an important requirement for high-quality datasets for training AI models, thus driving innovation and development in the AI industry. This public initiative reflects India's determination to become a global AI hub, offering the infrastructure required to facilitate startups, researchers, and businesses in creating cutting-edge AI solutions. The initiative not only simplifies data access but also creates a model for public-private partnerships in AI development.

Restraint Factor for the AI Training Dataset Market

Data Privacy Regulations Impeding AI Training Dataset Market Growth

Strict data privacy laws are coming up as a major constraint in the AI Training Dataset Market since governments across the globe are establishing legislation to safeguard personal data. In the European Union, explicit consent for using personal data is required under the General Data Protection Regulation (GDPR), reducing the availability of datasets for training AI. Likewise, the data protection regulator in Brazil ordered Meta and others to stop the use of Brazilian personal data in training AI models due to dangers to individuals' funda...

Success.ai’s Company Data Solutions provide businesses with powerful, enterprise-ready B2B company datasets, enabling you to unlock insights on over 28 million verified company profiles. Our solution is ideal for organizations seeking accurate and detailed B2B contact data, whether you’re targeting large enterprises, mid-sized businesses, or small business contact data.

Success.ai offers B2B marketing data across industries and geographies, tailored to fit your specific business needs. With our white-glove service, you’ll receive curated, ready-to-use company datasets without the hassle of managing data platforms yourself. Whether you’re looking for UK B2B data or global datasets, Success.ai ensures a seamless experience with the most accurate and up-to-date information in the market.

Why Choose Success.ai’s Company Data Solution? At Success.ai, we prioritize quality and relevancy. Every company profile is AI-validated for a 99% accuracy rate and manually reviewed to ensure you're accessing actionable and GDPR-compliant data. Our price match guarantee ensures you receive the best deal on the market, while our white-glove service provides personalized assistance in sourcing and delivering the data you need.

Why Choose Success.ai?

• Best Price Guarantee: We offer industry-leading pricing and beat any competitor.
• Global Reach: Access over 28 million verified company profiles across 195 countries.
• Comprehensive Data: Over 15 data points, including company size, industry, funding, and technologies used.
• Accurate & Verified: AI-validated with a 99% accuracy rate, ensuring high-quality data.
• Real-Time Updates: Stay ahead with continuously updated company information.
• Ethically Sourced Data: Our B2B data is compliant with global privacy laws, ensuring responsible use.
• Dedicated Service: Receive personalized, curated data without the hassle of managing platforms.
• Tailored Solutions: Custom datasets are built to fit your unique business needs and industries.

Our database spans 195 countries and covers 28 million public and private company profiles, with detailed insights into each company’s structure, size, funding history, and key technologies. We provide B2B company data for businesses of all sizes, from small business contact data to large corporations, with extensive coverage in regions such as North America, Europe, Asia-Pacific, and Latin America.

Comprehensive Data Points: Success.ai delivers in-depth information on each company, with over 15 data points, including:

Company Name: Get the full legal name of the company. LinkedIn URL: Direct link to the company's LinkedIn profile. Company Domain: Website URL for more detailed research. Company Description: Overview of the company’s services and products. Company Location: Geographic location down to the city, state, and country. Company Industry: The sector or industry the company operates in. Employee Count: Number of employees to help identify company size. Technologies Used: Insights into key technologies employed by the company, valuable for tech-based outreach. Funding Information: Track total funding and the most recent funding dates for investment opportunities. Maximize Your Sales Potential: With Success.ai’s B2B contact data and company datasets, sales teams can build tailored lists of target accounts, identify decision-makers, and access real-time company intelligence. Our curated datasets ensure you’re always focused on high-value leads—those who are most likely to convert into clients. Whether you’re conducting account-based marketing (ABM), expanding your sales pipeline, or looking to improve your lead generation strategies, Success.ai offers the resources you need to scale your business efficiently.

Tailored for Your Industry: Success.ai serves multiple industries, including technology, healthcare, finance, manufacturing, and more. Our B2B marketing data solutions are particularly valuable for businesses looking to reach professionals in key sectors. You’ll also have access to small business contact data, perfect for reaching new markets or uncovering high-growth startups.

From UK B2B data to contacts across Europe and Asia, our datasets provide global coverage to expand your business reach and identify new markets. With continuous data updates, Success.ai ensures you’re always working with the freshest information.

Key Use Cases:

• Targeted Lead Generation: Build accurate lead lists by filtering data by company size, industry, or location. Target decision-makers in key industries to streamline your B2B sales outreach.
• Account-Based Marketing (ABM): Use B2B company data to personalize marketing campaigns, focusing on high-value accounts and improving conversion rates.
• Investment Research: Track company growth, funding rounds, and employee trends to identify investment opportunities or potential M&A targets.
• Market Research: Enrich your market intelligence initiatives by gain...

Singapore was the nation with the highest combined value where enterprises were exploring or had actively deployed AI within their business in 2023. China, India, and the UAE were all close behind, with over ** percent of respondents claiming exploration or deployment of AI. Western countries, in particular European mainland nations such as France, Germany, and Italy, had the highest rate of non-usage or no exploration of AI, though even the U.S. had a similar share of enterprises not engaged with AI. This may reflect the specialized industries that thrive in those countries, needing individualized human skills to operate.

These files represent the data and accompanying documents of an independent research study by a student researcher examining the searchability and usability of machine learning dataset metadata.

The purpose of this exploratory study was to understand how machine learning (ML) practitioners are searching for and evaluating datasets for use in their work. This research will help inform development of the ML dataset metadata standard Croissant, which is actively being developed by the Croissant MLCommons working group, so it can aid ML practitioners' workflows and promote best practices like Responsible Artificial Intelligence (RAI).

The study consisted of a pre-interview Qualtrics survey ("Survey_questions_pre_interview.pdf") that focused on ranking various metadata elements on a Likert importance scale.

The interview consisted of open questions ("Interview_script_and_questions.pdf") on a range of topics from search of datasets to interoperability to AI used in dataset search. Additionally, participants were asked to share their screen at one point and recall a recent dataset search they had performed.

The resulting survey dataset ("Survey_p1.csv") and interview ("Interview_p1.txt") of participants are presented in open standard formats for accessibility. Identifying data has been removed from the files so there will be missing columns and rows potentially referenced in the files.

Analysis of ‘Which Social Media Millennials Care About Most?’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/which-social-media-millennials-care-about-moste on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This data was collected by Whatsgoodly, a millennial social polling company.

It was published by Brietbart on 3/17/17.

Link to article here: http://www.breitbart.com/tech/2017/03/17/report-snapchat-is-most-important-social-network-among-millennials/

This dataset was created by Adam Halper and contains around 500 samples along with Segment Type, Count, technical information and other features such as: - Segment Description - Answer - and more.

How to use this dataset

• Analyze Percentage in relation to Question
• Study the influence of Segment Type on Count
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Adam Halper

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Analysis of ‘🍒 Social Influence on Shopping’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/social-influence-on-shoppinge on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This data was collected on our social survey mobile platform Whatsgoodly. We have 300,000 millennial and Gen Z members, and have collected 150,000,000 survey responses from this demographic to date.

This dataset was created by Adam Halper and contains around 1000 samples along with Count, Segment Type, technical information and other features such as: - Segment Description - Percentage - and more.

How to use this dataset

• Analyze Answer in relation to Question
• Study the influence of Count on Segment Type
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Adam Halper

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Analysis of ‘International Educational Attainment by Year & Age’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/international-comp-attainmente on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

The National Center for Education Statistics (NCES) is the primary federal entity for collecting and analyzing data related to education in the U.S. and other nations. NCES is located within the U.S. Department of Education and the Institute of Education Sciences. NCES fulfills a Congressional mandate to collect, collate, analyze, and report complete statistics on the condition of American education; conduct and publish reports; and review and report on education activities internationally.

• Table 603.10. Percentage of the population 25 to 64 years old who completed high school, by age group and country: Selected years, 2001 through 2012
• Table 603.20. Percentage of the population 25 to 64 years old who attained selected levels of postsecondary education, by age group and country: 2001 and 2012
• Table 603.30. Percentage of the population 25 to 64 years old who attained a bachelor's or higher degree, by age group and country: Selected years, 1999 through 2012
• Table 603.40 Percentage of the population 25 to 64 years old who attained a postsecondary vocational degree, by age group and country: Selected years, 1999 through 2012
• Table 603.50 Number of bachelor's degree recipients per 100 persons at the typical minimum age of graduation, by sex and country: Selected years, 2005 through 2012
• Table 603.60. Percentage of postsecondary degrees awarded to women, by field of study and country: 2013
• Table 603.70. Percentage of bachelor's or equivalent degrees awarded in mathematics, science, and engineering, by field of study and country: 2013
• Table 603.80. Percentage of master's or equivalent degrees and of doctoral or equivalent degrees awarded in mathematics, science, and engineering, by field of study and country: 2013
• Table 603.90. Employment to population ratios of -25 to 64-year-olds, by sex, highest level of educational attainment, and country: 2014

Source: https://nces.ed.gov/programs/digest/current_tables.asp

This dataset was created by National Center for Education Statistics and contains around 100 samples along with Unnamed: 20, Unnamed: 24, technical information and other features such as: - Unnamed: 11 - Unnamed: 16 - and more.

How to use this dataset

• Analyze Unnamed: 15 in relation to Unnamed: 6
• Study the influence of Unnamed: 1 on Unnamed: 10
• More datasets

Acknowledgements

If you use this dataset in your research, please credit National Center for Education Statistics

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

The AI Training Dataset in Healthcare market is rapidly evolving, driven by the increasing need for advanced data analytics and machine learning applications in the medical field. This market encompasses various structured and unstructured datasets used to train artificial intelligence algorithms for tasks such as i

Between 2023 and 2027, the majority of companies surveyed worldwide expect big data to have a more positive than negative impact on the global job market and employment, with ** percent of the companies reporting the technology will create jobs and * percent expecting the technology to displace jobs. Meanwhile, artificial intelligence (AI) is expected to result in more significant labor market disruptions, with ** percent of organizations expecting the technology to displace jobs and ** percent expecting AI to create jobs.

Analysis of ‘🐕 Cat VS Dog popularity per state’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/cat-vs-dog-popularity-in-u-se on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

http://i.imgur.com/LGI7wTt.png" alt="Imgur" style="">

This dataset was created by Andrew Duff and contains around 0 samples along with Percentage Of Cat Owners, Mean Number Of Dogs Per Household, technical information and other features such as: - Percentage Of Households With Pets - Mean Number Of Cats - and more.

How to use this dataset

• Analyze Percentage Of Dog Owners in relation to Number Of Pet Households (in 1000)
• Study the influence of Percentage Of Cat Owners on Mean Number Of Dogs Per Household
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Andrew Duff

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Analysis of ‘😷 NYC Leading Causes of Death’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/nyc-leading-causes-of-deathe on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

NYC Leading Causes of Death Data

Rows: 3840; Columns: 6

The data includes items, such as:

• Year
• Ethnicity
• Sex
• Cause of Death
• Count
• Percent

Source: NYC Open Data

https://data.cityofnewyork.us/Health/New-York-City-Leading-Causes-of-Death/jb7j-dtam

This dataset was created by Data Society and contains around 4000 samples along with Ethnicity, Sex, technical information and other features such as: - Percent - Count - and more.

How to use this dataset

• Analyze Cause Of Death in relation to Year
• Study the influence of Ethnicity on Sex
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Data Society

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

According to Cognitive Market Research, the global Artificial Intelligence AI in Insurance market size is USD 4681.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 33.60% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 1872.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 31.8% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 1404.36 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 1076.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 35.6% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 234.06 million in 2024 and will grow at a compound annual growth rate (CAGR) of 33.0% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 93.62 million in 2024 and will grow at a compound annual growth rate (CAGR) of 33.3% from 2024 to 2031.
The Hardware held the highest Artificial Intelligence AI in Insurance market revenue share in 2024.

Market Dynamics of Artificial Intelligence AI in Insurance Market

Key Drivers of Artificial Intelligence AI in Insurance Market

Data Explosion and Processing Power to Increase the Demand Globally

The proliferation of data and advances in processing capacity are causing a revolution in the insurance sector. Insurance companies must overcome the difficulty of efficiently evaluating and utilizing the massive volumes of data that are being collected, which range from driving patterns to client demographics. The ability of artificial intelligence (AI), which can analyze data more accurately and quickly than humans, makes it an important answer. Insurance companies may make better judgments about risk assessment, pricing, and personalized offerings by using AI algorithms to extract insightful information from large, complicated datasets. This improves operational effectiveness and consumer happiness.

Improved Risk Assessment and Underwriting to Propel Market Growth

The insurance business collects data, including a wide range of information, including driving habits and client demographics. By dramatically improving data processing capabilities, artificial intelligence (AI) offers a disruptive possibility. Insurers can quickly and accurately extract useful insights from complicated datasets with unprecedented speed and precision using AI analysis. Thanks to this increased efficiency, Insurance companies can make faster, more informed decisions—from risk assessment to customized policy offerings. Insurance companies can improve operational efficiency, effectively manage risks, and ultimately offer more individualized services to their clients by utilizing AI's capacity to navigate the data explosion. This will help the industry become more adaptable and resilient to changing market conditions.

Restraint Factors Of Artificial Intelligence AI in Insurance Market

Rising Risk Assessment to Limit the Sales

Using sophisticated data analytics, AI algorithms are transforming risk assessment and underwriting in the insurance sector. These algorithms are highly skilled at analyzing complex datasets to identify trends and predict dangers with previously unheard-of accuracy. Insurers can increase customer satisfaction and loyalty by providing low-risk customers with more competitive rates when they are reliably identified as such. Furthermore, insurers can quickly and efficiently identify possible fraudulent activity due to AI's skill in detecting anomalies. Insurance companies benefit from streamlined underwriting procedures, reduced losses, and increased profitability due to improved risk assessment and fraud detection. AI technologies improve the insurance sector's capacity to customize policies, reduce risks, and stop fraudulent activity, creating a more robust and customer-focused market.

Impact of COVID-19 on the Artificial Intelligence AI in the Insurance Market

Artificial Intelligence (AI) in the insurance industry has been greatly impacted by the COVID-19 epidemic, creating both potential and challenges. The crisis highlighted the significance of artificial intelligence (AI) in insurance, even as it slowed down conventional...

Analysis of ‘Opioid Prescribing Rates - by Geography’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/medicaid-opioid-prescribing-rates-by-geographye on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

The Medicaid Opioid Prescribing Rates by Geography dataset provides information on state comparisons of the number and percentage of Medicaid opioid prescriptions.

Source: https://catalog.data.gov/dataset/medicaid-opioid-prescribing-rates-by-geography
Last updated at https://catalog.data.gov/organization/hhs-gov : 2021-10-01

This dataset was created by US Open Data Portal, data.gov and contains around 1000 samples along with Opioid Prscrbng Rate, La Opioid Prscrbng Rate, technical information and other features such as: - Opioid Prscrbng Rate 1y Chg - Tot Opioid Clms - and more.

How to use this dataset

• Analyze Geo Cd in relation to La Opioid Prscrbng Rate 5y Chg
• Study the influence of La Opioid Prscrbng Rate 1y Chg on Plan Type
• More datasets

Acknowledgements

If you use this dataset in your research, please credit US Open Data Portal, data.gov

--- Original source retains full ownership of the source dataset ---

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

Analysis of ‘🎗️ Cancer Rates by U.S. State’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/cancer-rates-by-u-s-statee on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

In the following maps, the U.S. states are divided into groups based on the rates at which people developed or died from cancer in 2013, the most recent year for which incidence data are available.

The rates are the numbers out of 100,000 people who developed or died from cancer each year.

Incidence Rates by State
The number of people who get cancer is called cancer incidence. In the United States, the rate of getting cancer varies from state to state.

• *Rates are per 100,000 and are age-adjusted to the 2000 U.S. standard population.

• ‡Rates are not shown if the state did not meet USCS publication criteria or if the state did not submit data to CDC.

• †Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. Available at: http://www.cdc.gov/uscs.

Death Rates by State
Rates of dying from cancer also vary from state to state.

• *Rates are per 100,000 and are age-adjusted to the 2000 U.S. standard population.

• †Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. Available at: http://www.cdc.gov/uscs.

Source: https://www.cdc.gov/cancer/dcpc/data/state.htm

This dataset was created by Adam Helsinger and contains around 100 samples along with Range, Rate, technical information and other features such as: - Range - Rate - and more.

How to use this dataset

• Analyze Range in relation to Rate
• Study the influence of Range on Rate
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Adam Helsinger

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

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AI Market In Media And Entertainment Industry Size 2024-2028

The ai market in media and entertainment industry size is forecast to increase by USD 30.73 billion, at a CAGR of 26.4% between 2023 and 2028.

The AI market in the media and entertainment industry is witnessing significant growth, driven by the increasing utilization of multimodal AI to enhance consumer experiences. This technology allows AI systems to process and analyze various forms of data, including text, images, and speech, enabling more personalized and engaging content. Another key trend is the adoption of blockchain technology to securely store and share data for AI model training. This ensures data privacy and security, addressing a major concern for media and entertainment companies.
However, the reliance on external sources of data for training AI models poses a challenge. Ensuring data accuracy, ownership, and ethical usage is crucial to mitigate potential risks and maintain consumer trust. Companies in this industry must navigate these dynamics to effectively capitalize on the opportunities presented by AI and provide innovative, personalized experiences for their audiences.

What will be the Size of the AI Market In Media And Entertainment Industry during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The AI market in media and entertainment continues to evolve, with dynamic applications across various sectors. In game development, AI training datasets enhance player experiences through realistic non-playable characters and intelligent enemy behavior. Recommendation engines personalize content for streaming services, while cybersecurity measures protect against potential threats. AI-powered video editing streamlines production workflows, enabling real-time rendering and automated dubbing. Deep learning algorithms enable sentiment analysis, allowing content distributors to tailor recommendations based on viewer preferences. Machine learning models optimize programmatic advertising, ensuring targeted delivery to specific audiences. Data analytics and licensing agreements facilitate revenue generation in animation studios, while bias detection ensures ethical AI usage.

Interactive advertising engages viewers through object detection and metadata tagging, enhancing user experience. Project management software streamlines workflows, from pre-production to post-production. Natural language processing and CGI rendering bring AI-powered content creation tools to life, while cloud rendering and monetization strategies enable scalability and profitability. AI ethics, explainable AI, and facial recognition are crucial considerations in this rapidly evolving landscape. Virtual production and AI-powered post-production workflows revolutionize television production, while social media platforms leverage AI for content moderation and personalized content delivery. Big data processing and model interpretability enable more efficient and effective AI implementation. In the ever-changing media and entertainment industry, AI continues to unfold new patterns and applications, driving innovation and growth.

How is this AI In Media And Entertainment Industry Industry segmented?

The ai in media and entertainment industry industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Technology

  Machine learning
  Computer vision
  Speech recognition


End-user

  Media companies
  Gaming industry
  Advertising agencies
  Film production houses


Offering

  Software
  Services


Application

  Media
  Entertainment


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Technology Insights

The machine learning segment is estimated to witness significant growth during the forecast period.

The media and entertainment industry has been significantly transformed by the integration of artificial intelligence (AI) technologies. Machine learning (ML), in particular, has been instrumental in enhancing video data management and analytics. For instance, Wasabi Technologies' latest object storage solutions employ AI and ML capabilities for automated tagging and metadata indexing of video content. These advancements enable seamless storage of video content in S3-compatible object storage systems, improving content accessibility and searchability. AI is also revolutionizing game development with the use of deep learning algorithms for creating more

Analysis of ‘How Every NFL Team’s Fans Lean Politically?’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/nfl-fandome on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Data behind the story How Every NFL Team’s Fans Lean Politically.

Google Trends Data

Google Trends data was derived from comparing 5-year search traffic for the 7 sports leagues we analyzed:

https://g.co/trends/5P8aa

Results are listed by designated market area (DMA).

The percentages are the approximate percentage of major-sports searches that were conducted for each league.

Trump's percentage is his share of the vote within the DMA in the 2016 presidential election.

SurveyMonkey Data

SurveyMonkey data was derived from a poll of American adults ages 18 and older, conducted between Sept. 1-7, 2017.

Listed numbers are the raw totals for respondents who ranked a given NFL team among their three favorites, and how many identified with a given party (further broken down by race). We also list the percentages of the entire sample that identified with each party, and were of each race.

The data is available under the Creative Commons Attribution 4.0 International License and the code is available under the MIT License. If you do find it useful, please let us know.

Source: https://github.com/fivethirtyeight/data

This dataset was created by FiveThirtyEight and contains around 0 samples along with Unnamed: 10, Unnamed: 4, technical information and other features such as: - Unnamed: 3 - Unnamed: 1 - and more.

How to use this dataset

• Analyze Unnamed: 13 in relation to Unnamed: 21
• Study the influence of Unnamed: 7 on Unnamed: 12
• More datasets

Acknowledgements

If you use this dataset in your research, please credit FiveThirtyEight

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--- Original source retains full ownership of the source dataset ---

1) Data Introduction • The Credit_Card_Transactions Dataset is a representative sample data for building fraud detection models, including anonymized real-world transaction data such as financial transaction type, amount, sender/receiver account balance, and fraud indicators.

2) Data Utilization (1) Credit_Card_Transactions Dataset has characteristics that: • This dataset provides individual transaction records on a row-by-row basis, reflecting the real-world class imbalance problem with the extremely low percentage of fraudulent transactions (isFraud=1). • It is an unprocessed raw data structure that allows you to directly utilize key variables such as transaction time, amount, and account change. (2) Credit_Card_Transactions Dataset can be used to: • Binary classification modeling: Fraud transaction detection models can be developed by applying imbalance processing techniques such as SMOTE and undersampling, and appropriate evaluation indicators such as F1-score and ROC-AUC. • Real-time anomaly detection: It can be used to build a real-time anomaly signal detection system through analysis of transaction patterns (amount, frequency, account change).