AI has already changed and will continue to change the way that we live. These are the latest Artificial Intelligence statistics you need to know.

Most people expect things to take less time with AI in the next *** 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.

Global AI usage will skyrocket over the next few years, reaching a potential market value of $190.61 billion by 2025.

The adoption rate of artificial intelligence (AI) is expected to rapidly grow in the information technology sector (IT). In 2022, nearly ** percent of IT executives expected their companies to have widescale adoption in AI in their respective companies.

The largest impact that AI will make is on the current workforce. AI will automate tasks and even entire jobs that humans have previously done.

Artificial Intelligence will make a big difference in the future. But how is it used right now?

During a global survey of students conducted in mid-2024, it was found that a whopping ** percent said they were using artificial intelligence tools in their schoolwork. Almost a ****** of them used it on a daily basis.

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.

This index compiles empirical data on AI and big data surveillance use for 179 countries around the world between 2012 and 2022— although the bulk of the sources stem from between 2017 and 2022. The index does not distinguish between legitimate and illegitimate uses of AI and big data surveillance. Rather, the purpose of the research is to show how new surveillance capabilities are transforming governments’ ability to monitor and track individuals or groups. Last updated February 2022.

This index addresses three primary questions: Which countries have documented AI and big data public surveillance capabilities? What types of AI and big data public surveillance technologies are governments deploying? And which companies are involved in supplying this technology?

The index measures AI and big data public surveillance systems deployed by state authorities, such as safe cities, social media monitoring, or facial recognition cameras. It does not assess the use of surveillance in private spaces (such as privately-owned businesses in malls or hospitals), nor does it evaluate private uses of this technology (e.g., facial recognition integrated in personal devices). It also does not include AI and big data surveillance used in Automated Border Control systems that are commonly found in airport entry/exit terminals. Finally, the index includes a list of frequently mentioned companies – by country – which source material indicates provide AI and big data surveillance tools and services.

All reference source material used to build the index has been compiled into an open Zotero library, available at https://www.zotero.org/groups/2347403/global_ai_surveillance/items. The index includes detailed information for seventy-seven countries where open source analysis indicates that governments have acquired AI and big data public surveillance capabilities. The index breaks down AI and big data public surveillance tools into the following categories: smart city/safe city, public facial recognition systems, smart policing, and social media surveillance.

The findings indicate that at least seventy-seven out of 179 countries are actively using AI and big data technology for public surveillance purposes:

• Smart city/safe city platforms: fifty-five countries • Public facial recognition systems: sixty-eight countries • Smart policing: sixty-one countries • Social media surveillance: thirty-six countries

Artificial Intelligence in Big Data Analysis Market Outlook

The global market size for artificial intelligence in big data analysis was valued at approximately $45 billion in 2023 and is projected to reach around $210 billion by 2032, growing at a remarkable CAGR of 18.7% during the forecast period. This phenomenal growth is driven by the increasing adoption of AI technologies across various sectors to analyze vast datasets, derive actionable insights, and make data-driven decisions.

The first significant growth factor for this market is the exponential increase in data generation from various sources such as social media, IoT devices, and business transactions. Organizations are increasingly leveraging AI technologies to sift through these massive datasets, identify patterns, and make informed decisions. The integration of AI with big data analytics provides enhanced predictive capabilities, enabling businesses to foresee market trends and consumer behaviors, thereby gaining a competitive edge.

Another critical factor contributing to the growth of AI in the big data analysis market is the rising demand for personalized customer experiences. Companies, especially in the retail and e-commerce sectors, are utilizing AI algorithms to analyze consumer data and deliver personalized recommendations, targeted advertising, and improved customer service. This not only enhances customer satisfaction but also boosts sales and customer retention rates.

Additionally, advancements in AI technologies, such as machine learning, natural language processing, and computer vision, are further propelling market growth. These technologies enable more sophisticated data analysis, allowing organizations to automate complex processes, improve operational efficiency, and reduce costs. The combination of AI and big data analytics is proving to be a powerful tool for gaining deeper insights and driving innovation across various industries.

From a regional perspective, North America holds a significant share of the AI in big data analysis market, owing to the presence of major technology companies and high adoption rates of advanced technologies. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, driven by rapid digital transformation, increasing investments in AI and big data technologies, and the growing need for data-driven decision-making processes.

Component Analysis

The AI in big data analysis market is segmented by components into software, hardware, and services. The software segment encompasses AI platforms and analytics tools that facilitate data analysis and decision-making. The hardware segment includes the computational infrastructure required to process large volumes of data, such as servers, GPUs, and storage devices. The services segment involves consulting, integration, and support services that assist organizations in implementing and optimizing AI and big data solutions.

The software segment is anticipated to hold the largest share of the market, driven by the continuous development of advanced AI algorithms and analytics tools. These solutions enable organizations to process and analyze large datasets efficiently, providing valuable insights that drive strategic decisions. The demand for AI-powered analytics software is particularly high in sectors such as finance, healthcare, and retail, where data plays a critical role in operations.

On the hardware front, the increasing need for high-performance computing to handle complex data analysis tasks is boosting the demand for powerful servers and GPUs. Companies are investing in robust hardware infrastructure to support AI and big data applications, ensuring seamless data processing and analysis. The rise of edge computing is also contributing to the growth of the hardware segment, as organizations seek to process data closer to the source.

The services segment is expected to grow at a significant rate, driven by the need for expertise in implementing and managing AI and big data solutions. Consulting services help organizations develop effective strategies for leveraging AI and big data, while integration services ensure seamless deployment of these technologies. Support services provide ongoing maintenance and optimization, ensuring that AI and big data solutions deliver maximum value.

Overall, the combination of software, hardware, and services forms a comprehensive ecosystem that supports the deployment and utilization of AI in big data analys

AI in Healthcare Statistics: AI in healthcare has been a hot topic for the past few years, and the report says that the industry is expected to reach $187.95 billion by the end of 2030. The fact of this platform in 2023 suggests a huge boom in the market size worldwide, with a compound annual increase rate (CAGR) of 40.1% from 2023 to 2030. The worldwide Artificial intelligence in the healthcare marketplace length changed into worth $20.65 billion in 2023 which has increased from last year. These AI in Healthcare Statistics include insights from various aspects and sources that will provide effective light on the importance of AI in the healthcare industry around the world in recent times. In 2023, the Market share records the gradual adoption of AI which is advancing the sector, and has been observed that 85% of organizations have already implemented AI. Additionally, 1/2 of the executives claimed that AI is indicating a tremendous shift inside and outside the industry. Aid of AI-based healthcare companies used solutions like telemedicine and remote tools and sensors backed by means of large information that can reduce healthcare charges improve access, and promote better outcomes, and performance. Key Takeaways According to AI in Healthcare Statistics, the platform when implemented Artificial Intelligence has experienced a huge increase, with a CAGR of 40.1% from 2023 to 2030 and a global market size expected to attain $187.95 billion by 2030. Around the world, approximately 40% of healthcare industries are regularly using AI and Machine Language in the sector. In 2023, Healthcare executives are increasingly adopting AI in their techniques, and nearly 1/2 of the executives surveyed are already using it. This is being adopted globally, with answers like telemedicine and faraway tools and sensors backed through huge information that could lessen healthcare charges and equitably improve admission to, results, and performance.

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

Here are the top 3 most significant challenges that companies worry about:.

Artificial Intelligence In Education Market size was valued at USD 3.2 Billion in 2023 and is projected to reach USD 42 Billion by 2031, growing at a CAGR of 44.30% during the forecast period 2024-2031.

Global Artificial Intelligence In Education Market Drivers

The market drivers for the Artificial Intelligence In Education Market can be influenced by various factors. These may include:

Personalized Learning: AI makes it possible to design learning routes that are specifically catered to the strengths, weaknesses, and learning style of each student, increasing engagement and yielding better results.

Adaptive Learning Platforms: AI-driven adaptive learning platforms leverage data analytics to continuously evaluate student performance and modify the pace and content to help students grasp the material.

Efficiency and Automation: AI frees up instructors' time to concentrate on teaching and mentoring by automating administrative activities like scheduling, grading, and course preparation.

Improved Content Creation: AI tools can produce interactive tutorials, games, and simulations at scale, which makes it easier to create a variety of interesting and captivating learning resources.

Data-driven Insights: AI analytics give teachers useful information on learning preferences, trends in student performance, and areas for development. This information helps them make data-driven decisions and implement interventions.

Accessibility and Inclusion: AI technologies can provide students with individualized help who face linguistic challenges or disabilities by accommodating a variety of learning methods and needs.

Global Demand for Education Technology: The use of artificial intelligence (AI) in education is being fueled by the growing demand for education technology solutions worldwide, which is being driven by factors including the expanding penetration of the internet, the digitization of classrooms, and the growing significance of lifelong learning.

Government Initiatives and Corporate Investments: Government initiatives supporting digital literacy and STEM education as well as corporate investments in AI firms specializing in education technology drive market expansion.

Acceleration caused by the Pandemic: The COVID-19 pandemic has prompted the demand for AI-powered solutions that can improve the delivery of remote education and assist distant learning, hence accelerating the adoption of online and blended learning models.

Institutions aiming to stand out from the competition and draw in students are spending more in AI-powered learning technology as a means of providing cutting-edge instruction and maintaining an advantage over rivals in the market.

The global number of AI tools users in the 'AI Tool Users' segment of the artificial intelligence market was forecast to continuously increase between 2025 and 2031 by in total ***** million (+****** percent). After the tenth consecutive increasing year, the number of AI tools users is estimated to reach *** billion and therefore a new peak in 2031. Notably, the number of AI tools users of the 'AI Tool Users' segment of the artificial intelligence market was continuously increasing over the past years.Find more key insights for the number of AI tools users in countries and regions like the market size in the 'Generative AI' segment of the artificial intelligence market in Australia and the market size change in the 'Generative AI' segment of the artificial intelligence market in Europe.The Statista Market Insights cover a broad range of additional markets.

Artificial Intelligence (AI) Training Dataset Market Outlook

According to our latest research, the global Artificial Intelligence (AI) Training Dataset market size reached USD 3.15 billion in 2024, reflecting robust industry momentum. The market is expanding at a notable CAGR of 20.8% and is forecasted to attain USD 20.92 billion by 2033. This impressive growth is primarily attributed to the surging demand for high-quality, annotated datasets to fuel machine learning and deep learning models across diverse industry verticals. The proliferation of AI-driven applications, coupled with rapid advancements in data labeling technologies, is further accelerating the adoption and expansion of the AI training dataset market globally.

One of the most significant growth factors propelling the AI training dataset market is the exponential rise in data-driven AI applications across industries such as healthcare, automotive, retail, and finance. As organizations increasingly rely on AI-powered solutions for automation, predictive analytics, and personalized customer experiences, the need for large, diverse, and accurately labeled datasets has become critical. Enhanced data annotation techniques, including manual, semi-automated, and fully automated methods, are enabling organizations to generate high-quality datasets at scale, which is essential for training sophisticated AI models. The integration of AI in edge devices, smart sensors, and IoT platforms is further amplifying the demand for specialized datasets tailored for unique use cases, thereby fueling market growth.

Another key driver is the ongoing innovation in machine learning and deep learning algorithms, which require vast and varied training data to achieve optimal performance. The increasing complexity of AI models, especially in areas such as computer vision, natural language processing, and autonomous systems, necessitates the availability of comprehensive datasets that accurately represent real-world scenarios. Companies are investing heavily in data collection, annotation, and curation services to ensure their AI solutions can generalize effectively and deliver reliable outcomes. Additionally, the rise of synthetic data generation and data augmentation techniques is helping address challenges related to data scarcity, privacy, and bias, further supporting the expansion of the AI training dataset market.

The market is also benefiting from the growing emphasis on ethical AI and regulatory compliance, particularly in data-sensitive sectors like healthcare, finance, and government. Organizations are prioritizing the use of high-quality, unbiased, and diverse datasets to mitigate algorithmic bias and ensure transparency in AI decision-making processes. This focus on responsible AI development is driving demand for curated datasets that adhere to strict quality and privacy standards. Moreover, the emergence of data marketplaces and collaborative data-sharing initiatives is making it easier for organizations to access and exchange valuable training data, fostering innovation and accelerating AI adoption across multiple domains.

From a regional perspective, North America currently dominates the AI training dataset market, accounting for the largest revenue share in 2024, driven by significant investments in AI research, a mature technology ecosystem, and the presence of leading AI companies and data annotation service providers. Europe and Asia Pacific are also witnessing rapid growth, with increasing government support for AI initiatives, expanding digital infrastructure, and a rising number of AI startups. While North America sets the pace in terms of technological innovation, Asia Pacific is expected to exhibit the highest CAGR during the forecast period, fueled by the digital transformation of emerging economies and the proliferation of AI applications across various industry sectors.

Data Type Analysis

The AI training dataset market is segmented by data type into Text, Image/Video, Audio, and Others, each playing a crucial role in powering different AI applications. Text da

Global Big Data And Artificial Intelligence market size is expected to reach $8.52 Billion by 2029 at 7%, increase in data volume driving the growth of the market due to rising iot adoption and digital transactions

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.

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.

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The artificial intelligence in energy market share is expected to increase by USD 6.78 billion from 2020 to 2025, and the market’s growth momentum will decelerate at a CAGR of 34.19%.

This artificial intelligence in energy market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers artificial intelligence in energy market segmentations by solution (software, hardware, and services) and geography (North America, Europe, APAC, MEA, and South America). The artificial intelligence in energy market report also offers information on several market vendors, including ABB Ltd., Alphabet Inc., Flex Ltd., General Electric Co., Intel Corp., International Business Machines Corp., Microsoft Corp., Origami Energy Ltd., Siemens AG, and Verdigris Technologies Inc. among others.

What will the Artificial Intelligence In Energy Market Size be During the Forecast Period?

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Artificial Intelligence In Energy Market: Key Drivers, Trends, and Challenges

Based on our research output, there has been a positive impact on the market growth during and post COVID-19 era. The growing demand for data integration and visual analytics is notably driving the artificial intelligence in energy market growth, although factors such as existing issues of ai may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the artificial intelligence in energy market industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Artificial Intelligence In Energy Market Driver

One of the key factors driving the global AI market is the growing demand for data integration and visual analytics. Rising proliferation and complexity have made the process of deploying and maintaining reliable data interfaces difficult. Enterprises around the world are, therefore, adopting data integration solutions. AI allows real-time synthesizing of data to facilitate real-time analysis for effective decision-making, thus enabling enterprises to monitor, transform, and deliver data; understand business processes; and bridge the gap between businesses and IT. Similarly, AI helps energy companies to integrate technical and business process data from different sources and convert them into meaningful business insights. With the exponential increase in data volume, the need for analyzing, transforming, monitoring, and interpreting data has become a priority for business operations. With globalization, customers, suppliers, and companies are scattered across the world and require real-time information exchange. To accomplish this, energy companies require AI platforms to link multiple enterprise systems with the web and cloud-based applications. Additionally, energy companies are integrating data with AI-powered video analytics systems to explore and analyze various types of data, such as sales data, for informed decision-making. Enterprises are also integrating business analytics software with their businesses for the dynamic representation of data. Hence, the demand for AI in the energy sector is likely to increase significantly during the forecast period.

Key Artificial Intelligence In Energy Market Trend

Increasing adoption of cloud-based solutions is another factor supporting the global AI market growth in the forecast period. With the increasing applications of robotics in repetitive and risky tasks, end-users are increasingly seeking avenues to ensure the elimination of limitations of industrial automation and robotics technologies. These limitations arise due to factors such as the cost, computational capacity, storage, size, power supply, motion mode, and working environment. Thus, the adoption of cloud-based AI solutions is increasing in the energy sector to enhance the capabilities of existing systems. Furthermore, the emergence of AI-as-a-service (AIaaS) is trending among various industrial users of AI, as it allows individuals and companies to access AI for various applications without large initial investment and with a lower risk of failure. AIaaS can allow energy companies to experiment on samples of multiple public cloud platforms to test various machine learning algorithms. AIaaS helps vendors in the market to increase their awareness about AI and its benefits, such as efficiency and maintenance of a company’s grid system and asset management of solar farms and gas plants. Companies like Alphabet, IBM, and GENERAL ELECTRIC are investing heavily in the development of prediction and maintenance systems for the energy industry and are planning