As of 2023, most surveyed companies in the United States and Europe, or ** percent, claim to be either industry leaders in terms of data, analytics, and artificial intelligence (AI) function advancements or about the same as their industry peers.

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.

As large language models (LLMs) such as GPT have become more accessible, concerns about their potential effects on students’ learning have grown. In data science education, the specter of students’ turning to LLMs raises multiple issues, as writing is a means not just of conveying information but of developing their statistical reasoning. In our study, we engage with questions surrounding LLMs and their pedagogical impact by: (a) quantitatively and qualitatively describing how select LLMs write report introductions and complete data analysis reports; and (b) comparing patterns in texts authored by LLMs to those authored by students and by published researchers. Our results show distinct differences between machine-generated and human-generated writing, as well as between novice and expert writing. Those differences are evident in how writers manage information, modulate confidence, signal importance, and report statistics. The findings can help inform classroom instruction, whether that instruction is aimed at dissuading the use of LLMs or at guiding their use as a productivity tool. It also has implications for students’ development as statistical thinkers and writers. What happens when they offload the work of data science to a model that doesn’t write quite like a data scientist? Supplementary materials for this article are available online.

Generative AI is already employed at Amazon. In 2024, a bit more than one-third of Amazon’s sellers and brands created and optimized product listings with AI tools. Another 14 percent of them shifted from manual to AI-based production of marketing and social media content.

A work-in-progress situation

AI use at Amazon aligns with the general trend observed in the United States. In 2023, only one-third of B2C e-commerce companies fully implemented AI in their operations, while nearly half of them are still in the experimental phase. In comparison, B2B organizations were lagging, as the full implementation rate stood at 25 percent.

What holds companies back?

Implementing AI-based solutions is easier said than done, as companies face numerous challenges ranging from data security to significant business costs – the main factors retail CEOs mentioned in a survey from 2023. In the same survey, over four in ten managers and employees believed AI innovations were hindered by a lack of understanding and expertise in AI use.

The statistic shows the cumulative revenues from the ten leading artificial intelligence (AI) use cases worldwide, between 2016 and 2025. Over the ten years between 2016 and 2025, AI software for vehicular object detection, identification, and avoidance is expected to generate * billion U.S. dollars.

Integrated Systems Health Management includes as key elements fault detection, fault diagnostics, and failure prognostics. Whereas fault detection and diagnostics have been the subject of considerable emphasis in the Artificial Intelligence (AI) community in the past, prognostics has not enjoyed the same attention. The reason for this lack of attention is in part because prognostics as a discipline has only recently been recognized as a game-changing technology that can push the boundary of systems health management. This paper provides a survey of AI techniques applied to prognostics. The paper is an update to our previously published survey of data-driven prognostics.

As of 2023, about half of the surveyed companies claim to take the steps of explaining how the artificial intelligence (AI) works, ensuring a human is involved in the process, and instituting an AI ethics management program to guarantee transparency and data security.

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

We offer comprehensive data collection services that cater to a wide range of industries and applications. Whether you require image, audio, or text data, we have the expertise and resources to collect and deliver high-quality data that meets your specific requirements. Our data collection methods include manual collection, web scraping, and other automated techniques that ensure accuracy and completeness of data.

Our team of experienced data collectors and quality assurance professionals ensure that the data is collected and processed according to the highest standards of quality. We also take great care to ensure that the data we collect is relevant and applicable to your use case. This means that you can rely on us to provide you with clean and useful data that can be used to train machine learning models, improve business processes, or conduct research.

We are committed to delivering data in the format that you require. Whether you need raw data or a processed dataset, we can deliver the data in your preferred format, including CSV, JSON, or XML. We understand that every project is unique, and we work closely with our clients to ensure that we deliver the data that meets their specific needs. So if you need reliable data collection services for your next project, look no further than us.

Introduction

Jasper AI Statistics:Â Jasper AI has emerged as a leading generative AI platform, significantly transforming content creation and marketing workflows. By 2024, the company reported over 100,000 active users and more than 850 enterprise clients. Its revenue reached approximately USD 142.9 million, reflecting substantial growth from previous years.

To enhance productivity, Jasper AI introduced over 80 AI applications and launched Marketing Workflow Automation tools. With a total funding of USD 131 million and a valuation of USD 1.5 billion as of early 2024, Jasper AI continues to be a pivotal tool for businesses aiming to optimize their content strategies and achieve better marketing outcomes.

On this account, the article looks at some key Jasper AI statistics and trends for 2024, depicting the evolution and influence.

Business's use of Generative AI, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, first quarter of 2024.

One of the core elements of Machine Learning (ML) is statistics and its embedded foundational rules and without its appropriate integration, ML as we know would not exist. Various aspects of ML platforms are based on statistical rules and most notably the end results of the ML model performance cannot be objectively assessed without appropriate statistical measurements. The scope of statistics within the ML realm is rather broad and cannot be adequately covered in a single review article. Therefore, here we will mainly focus on the common statistical concepts that pertain to supervised ML (i.e. classification and regression) along with their interdependencies and certain limitations.

Artificial intelligence (AI) holds tremendous promise to benefit nearly all aspects of society, including the economy, healthcare, security, the law, transportation, even technology itself. On February 11, 2019, the President signed Executive Order 13859, Maintaining American Leadership in Artificial Intelligence. This order launched the American AI Initiative, a concerted effort to promote and protect AI technology and innovation in the United States. The Initiative implements a whole-of-government strategy in collaboration and engagement with the private sector, academia, the public, and like-minded international partners. Among other actions, key directives in the Initiative call for Federal agencies to prioritize AI research and development (R&emp;D) investments, enhance access to high-quality cyberinfrastructure and data, ensure that the Nation leads in the development of technical standards for AI, and provide education and training opportunities to prepare the American workforce for the new era of AI. In support of the American AI Initiative, this National AI R&emp;D Strategic Plan: 2019 Update defines the priority areas for Federal investments in AI R&emp;D. This 2019 update builds upon the first National AI R&emp;D Strategic Plan released in 2016, accounting for new research, technical innovations, and other considerations that have emerged over the past three years. This update has been developed by leading AI researchers and research administrators from across the Federal Government, with input from the broader civil society, including from many of America’s leading academic research institutions, nonprofit organizations, and private sector technology companies. Feedback from these key stakeholders affirmed the continued relevance of each part of the 2016 Strategic Plan while also calling for greater attention to making AI trustworthy, to partnering with the private sector, and other imperatives.

Survey of advanced technology, applications related to artificial intelligence technologies, by North American Industry Classification System (NAICS) and enterprise size for Canada and certain provinces, in 2022.

The global data preparation software market is estimated at USD 579.3 million in 2025 and is expected to witness a compound annual growth rate (CAGR) of 8.1% from 2025 to 2033. Factors such as increasing data volumes, growing demand for data-driven insights, and the adoption of artificial intelligence (AI) and machine learning (ML) technologies are driving the growth of the market. Additionally, the rising need for data privacy and security regulations is also contributing to the demand for data preparation software. The market is segmented by application into large enterprises and SMEs, and by type into cloud-based and web-based. The cloud-based segment is expected to hold the largest market share during the forecast period due to its benefits such as ease of use, scalability, and cost-effectiveness. The market is also segmented by region into North America, South America, Europe, the Middle East and Africa, and Asia Pacific. North America is expected to account for the largest market share, followed by Europe. The Asia Pacific region is expected to witness the fastest growth during the forecast period. Key players in the market include Alteryx, Altair Monarch, Tableau Prep, Datameer, IBM, Oracle, Palantir Foundry, Podium, SAP, Talend, Trifacta, Unifi, and others. Data preparation software tools assist organizations in transforming raw data into a usable format for analysis, reporting, and storage. In 2023, the market size is expected to exceed $10 billion, driven by the growing adoption of AI, cloud computing, and machine learning technologies.

Introduction

E-Learning Statistics: In today’s fast-moving digital world, e-learning has become a key tool for businesses and people who want to keep improving and growing. E-learning is convenient, easy to access, and flexible, making it a game-changer for traditional education. It’s now an essential resource for staying competitive and adaptable in various industries.

Before the global COVID-19 pandemic, online learning was already starting to show up in schools, from elementary through university, as well as in corporate training. Both students and teachers liked the flexibility it offered to everyone taking part in the lessons.

Don't worry; we've put together a list of important E-Learning Statistics for 2024, bringing together the most useful insights in one handy place.

Data Intelligence Platform Market Outlook

The global data intelligence platform market size was valued at approximately $10 billion in 2023, with an anticipated growth to reach $25.2 billion by 2032, growing at a robust CAGR of 11%. The market's growth is predominantly driven by the increasing demand for data-driven decision-making processes and the need for advanced analytics tools across various industries.

The surge in the adoption of data intelligence platforms is largely influenced by advancements in big data technologies and the growing importance of data governance and security. Organizations across sectors such as BFSI, healthcare, and retail are increasingly leveraging data intelligence solutions to enhance operational efficiency, personalize customer experiences, and drive strategic initiatives. The integration of AI and machine learning with data intelligence platforms has further fueled market growth by providing predictive insights and automation capabilities.

Another significant growth factor is the proliferation of cloud-based solutions, which offer scalability, cost-efficiency, and ease of deployment. Cloud-based data intelligence platforms allow organizations to handle large volumes of data and perform complex analytics without the need for extensive on-premises infrastructure. The shift towards cloud computing is also driven by the growing need for remote working capabilities and digital transformation initiatives, further propelling market expansion.

Moreover, regulatory compliance and the emphasis on data protection laws such as GDPR in Europe and CCPA in the United States have compelled organizations to adopt robust data intelligence solutions. These platforms help ensure that data management practices align with regulatory requirements, thereby mitigating risks and enhancing data security. The rising awareness of the importance of data integrity and privacy is expected to drive the adoption of data intelligence platforms across various sectors.

The emergence of AI-Driven Analytics Platform is revolutionizing the way organizations approach data intelligence. These platforms leverage artificial intelligence to automate complex data processes, providing businesses with real-time insights and predictive analytics. By integrating AI capabilities, companies can enhance their decision-making processes, optimize operations, and gain a competitive edge in the market. The ability to analyze vast amounts of data quickly and accurately allows organizations to identify trends, detect anomalies, and make informed decisions that drive business growth. As AI technology continues to evolve, the potential for AI-Driven Analytics Platforms to transform industries and unlock new opportunities is immense.

Regionally, North America dominates the data intelligence platform market, owing to the presence of leading technology providers and high adoption rates of advanced analytics solutions. The Asia Pacific region is also witnessing significant growth due to the rapid digitalization of enterprises and increased investments in data infrastructure. Europe, on the other hand, is experiencing steady growth driven by stringent data protection regulations and the increasing adoption of cloud-based solutions.

Component Analysis

The data intelligence platform market by component is bifurcated into software and services. The software segment holds a major share in the market, driven by the increased demand for advanced analytics, business intelligence tools, and data management solutions. Software components include various types of analytics platforms, data integration tools, and AI-driven data intelligence solutions. Organizations are investing heavily in these software solutions to gain real-time insights, enhance decision-making processes, and improve overall operational efficiency.

Within the software segment, AI and machine learning-based applications have seen significant traction. These applications enable predictive analytics, automate routine data processing tasks, and provide deeper insights into business trends and customer behaviors. The integration of AI has revolutionized data intelligence platforms by making them more intuitive, efficient, and capable of handling large datasets with ease. This trend is expected to continue, with more companies adopting AI-enabled software solutions to stay competitive.

On the other hand, the services segme

The results of a survey conducted among global students in July 2024 show that helping with resume and cover letter writing is the most common use case for artificial intelligence tools among higher education students. ********** of respondents also said they used AI to assist them in writing, for personalized content recommendations, and research. All in all, ** percent of students worldwide admit to using AI in their schoolwork.

The Artificial Intelligence Non-Player Character (NPC) market is rapidly evolving, driven by advancements in machine learning, natural language processing, and computer graphics. As video games, virtual reality (VR), and simulations become increasingly immersive, AI NPCs are revolutionizing the way players interact

Crash Statistics are summarized crash statistics for large trucks and buses involved in fatal and non-fatal Crashes that occurred in the United States. These statistics are derived from two sources: the Fatality Analysis Reporting System (FARS) and the Motor Carrier Management Information System (MCMIS). Crash Statistics contain information that can be used to identify safety problems in specific geographical areas or to compare state statistics to the national crash figures.