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.

In 2023, AI tools were used daily by IT professionals across various fields. In that year, over ** percent of machine learning engineers globally reported using these tools every day, while data scientists followed closely, with around ** percent stating daily usage. Back-end developers and full-stack developers reported slightly lower usage, with **** percent and **** percent respectively stating that they use AI tools daily.

In a fifth-grade classroom in Phoenix, a student with dyslexia is reading aloud confidently. Her voice carries, not just because of practice, but because an AI tool helped tailor phonics exercises to her unique pace. Across the globe, a college freshman in Seoul aces a calculus test after spending a...

Introduction

AI Industry Statistics: The AI industry has experienced significant growth in recent years, driven by advancements in machine learning, deep learning, and natural language processing. The increasing integration of AI across industries such as healthcare, finance, automotive, and retail is propelling this rapid expansion.

Companies are making substantial investments in AI to improve efficiency, reduce costs, and provide more tailored customer experiences. The potential of AI to transform business operations is vast, ranging from enhancing decision-making with predictive analytics to optimizing supply chains.

In healthcare, AI-driven diagnostics and treatment suggestions are transforming patient care, while the automotive sector is advancing with innovations in autonomous driving. As AI technologies continue to evolve, their influence is expected to grow, reshaping industries and unlocking new avenues for innovation, positioning it as one of the most transformative sectors of the 21st century.

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.

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).

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.

As of 2023, about **** 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.

Imagine walking into a small-town hardware store in 2015. On the shelf sits a modest graphics processing unit, designed mostly for gaming. Fast-forward to today, and that same type of chip, evolved, optimized, and purpose-built, is powering the world’s most advanced AI models, from self-driving cars to generative voice assistants....

During a 2024 global survey among marketing and media leaders, approximately ** percent reported using artificial intelligence (AI) tools a few times per week or daily when writing or generating content. Around ** percent said they used those solutions for social media content generation. According to the same study, Facebook and Instagram were the social media platforms most used by global marketers for organic content and paid ads.

Jasper AI powers thousands of content teams with fast, scalable writing through templates and AI workflows. It fuels marketing and creative workflows, especially for mid-size enterprises and agencies. Today, Jasper is more than a writing tool; it’s an AI copilot. Explore how its performance, reach, and industry role shape content...

The Artificial Intelligence (AI) Synthetic Data Service market is experiencing rapid growth, driven by the increasing need for high-quality data to train and validate AI models, especially in sectors with data scarcity or privacy concerns. The market, estimated at $2 billion in 2025, is projected to expand significantly over the next decade, achieving a Compound Annual Growth Rate (CAGR) of approximately 30% from 2025 to 2033. This robust growth is fueled by several key factors: the escalating adoption of AI across various industries, the rising demand for robust and unbiased AI models, and the growing awareness of data privacy regulations like GDPR, which restrict the use of real-world data. Furthermore, advancements in synthetic data generation techniques, enabling the creation of more realistic and diverse datasets, are accelerating market expansion. Major players like Synthesis, Datagen, Rendered, Parallel Domain, Anyverse, and Cognata are actively shaping the market landscape through innovative solutions and strategic partnerships. The market is segmented by data type (image, text, time-series, etc.), application (autonomous driving, healthcare, finance, etc.), and deployment model (cloud, on-premise). Despite the significant growth potential, certain restraints exist. The high cost of developing and deploying synthetic data generation solutions can be a barrier to entry for smaller companies. Additionally, ensuring the quality and realism of synthetic data remains a crucial challenge, requiring continuous improvement in algorithms and validation techniques. Overcoming these limitations and fostering wider adoption will be key to unlocking the full potential of the AI Synthetic Data Service market. The historical period (2019-2024) likely saw a lower CAGR due to initial market development and technology maturation, before experiencing the accelerated growth projected for the forecast period (2025-2033). Future growth will heavily depend on further technological advancements, decreasing costs, and increasing industry awareness of the benefits of synthetic data.

AI data management market size and share projected to reach USD 95 Billion by 2030, growing at a CAGR of 21% from 2024 to 2030. The swift evolution of artificial intelligence (AI), machine learning (ML), and deep learning technologies serves as a crucial catalyst for the growth of the AI data management market.:

IntroductionDevelopments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand.MethodsThis research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME.ResultsThis research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research.DiscussionsThis research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

FileMarket provides premium Large Language Model (LLM) Data designed to support and enhance a wide range of AI applications. Our globally sourced LLM Data sets are meticulously curated to ensure high quality, diversity, and accuracy, making them ideal for training robust and reliable language models. In addition to LLM Data, we also offer comprehensive datasets across Object Detection Data, Machine Learning (ML) Data, Deep Learning (DL) Data, and Biometric Data. Each dataset is carefully crafted to meet the specific needs of cutting-edge AI and machine learning projects.

Key use cases of our Large Language Model (LLM) Data:

Text generation Chatbots and virtual assistants Machine translation Sentiment analysis Speech recognition Content summarization Why choose FileMarket's data:

Object Detection Data: Essential for training AI in image and video analysis. Machine Learning (ML) Data: Ideal for a broad spectrum of applications, from predictive analysis to NLP. Deep Learning (DL) Data: Designed to support complex neural networks and deep learning models. Biometric Data: Specialized for facial recognition, fingerprint analysis, and other biometric applications. FileMarket's premier sources for top-tier Large Language Model (LLM) Data and other specialized datasets ensure your AI projects drive innovation and achieve success across various applications.

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.

data.ai is ranked #59645 in US with 331.92K Traffic. Categories: . Learn more about website traffic, market share, and more!

Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland roadway incident data without rows where road_tmc and road are inconsistent.

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

AiCoderv2/Ai-data dataset hosted on Hugging Face and contributed by the HF Datasets community