Comprehensive dataset about AI overview optimization strategies, machine learning search algorithms, and voice search integration for local businesses.

As of June 2024, global searches for the keyword "generative AI" had experienced an increase in the previous year. The search terms for generative artificial intelligence surged in popularity from mid-February to early March 2024, hitting a score of 100 index points in the week ending March 3. Interest in "generative AI" frequently coincides with searches for ChatGPT, an AI chatbot model developed by the United States-based research company OpenAI.

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.

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.

1. Overview This dataset is a collection of 10,000+ high quality images of supermarket & store display shelves that are ready to use for optimizing the accuracy of computer vision models. All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organisations to enable their creative and machine learning projects.

2. Use case The dataset could be used for various AI & Computer Vision models: Store Management, Stock Monitoring, Customer Experience, Sales Analysis, Cashierless Checkout,... Each data set is supported by both AI and human review process to ensure labelling consistency and accuracy. Contact us for more custom datasets.

3. About PIXTA PIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands. Visit us at https://www.pixta.ai/ or contact via our email admin.bi@pixta.co.jp.

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.

The Artificial Intelligence (AI) in Personalization market has witnessed remarkable growth in recent years, transforming how businesses engage with consumers. As industries increasingly prioritize customer-centric approaches, AI technologies take center stage, enabling companies to analyze vast amounts of data and d

AI Training Data Market size was valued at USD 5,873.75 Million in 2023 and is projected to reach USD 23,873.51 Million by 2031, growing at a CAGR of 22.18% from 2024 to 2031.

Global AI Training Data Market Overview

The rapid adoption of artificial intelligence across industries is a key driver for the global AI training data market. Organizations in sectors such as healthcare, automotive, retail, and finance increasingly rely on AI-powered solutions to improve operational efficiency, enhance customer experiences, and optimize decision-making processes. This widespread adoption creates a growing demand for high-quality, domain-specific training datasets required to build and refine AI models. Additionally, the expansion of AI applications in emerging areas like autonomous vehicles, smart cities, and predictive healthcare further boosts the need for diverse and accurately annotated training data.

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.

Analysis of ‘Statistical Summary Period Attendance Reporting (PAR)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/e5dba22a-4354-44f8-9fe1-5e44de9832b2 on 13 February 2022.

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

Statistical report on attendance by borough, grade. Alternate views of same data by grade level and enrollment (register). All students including YABC, adults, LYFE babies and charters, home instruction, home/hospital, CBO UPK.

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

This research integrates teachers’ AI competence (TAC), students’ learning agility (SLA), and students’ engagement (SE), as factors affecting students’ academic performance (SAP)

The rapid adoption of AI technologies across various industries, including healthcare, finance, and autonomous vehicles, is driving the demand for high-quality training datasets essential for developing accurate AI models. According to the analyst from Verified Market Research, the AI Training Dataset Market surpassed the market size of USD 1555.58 Million valued in 2024 to reach a valuation of USD 7564.52 Million by 2032.

The expanding scope of AI applications beyond traditional sectors is fueling growth in the AI Training Dataset Market. This increased demand for Inventory Tags the market to grow at a CAGR of 21.86% from 2026 to 2032.

AI Training Dataset Market: Definition/ Overview

An AI training dataset is defined as a comprehensive collection of data that has been meticulously curated and annotated to train artificial intelligence algorithms and machine learning models. These datasets are fundamental for AI systems as they enable the recognition of patterns.

Market Overview: The Industrial AI market is witnessing exponential growth, driven by advancements in computing power, data availability, and the proliferation of IoT devices. This market is valued at $X million in 2023 and is projected to reach $X million by 2033, exhibiting a CAGR of X%. The surge in demand for automation, predictive maintenance, and enhanced efficiency fuels this market. Key market players include Intel Corporation, Siemens, IBM, and Microsoft. Industry Dynamics: The key factors driving the growth of the Industrial AI market include the increasing adoption of Industry 4.0 initiatives, the need for improved productivity and quality control, and the growing awareness of the benefits of AI in optimizing industrial processes. Additionally, government initiatives to support digital transformation and the emergence of cloud-based AI solutions are fostering market growth. However, challenges such as data security concerns, the lack of skilled professionals, and the high cost of implementing AI systems can restrain market progress. Nonetheless, the evolving landscape of AI in industries, such as manufacturing, transportation, and healthcare, presents lucrative opportunities for market expansion in the years to come.

On June 4-6, 2019, the NSTC NITRD Program, in collaboration with the NSTC's MLAI Subcommittee, held a workshop to assess the research challenges and opportunities at the intersection of cybersecurity and artificial intelligence. The workshop brought together senior members of the government, academic, and industrial communities to discuss the current state of the art and future research needs, and to identify key research gaps. This report is a summary of those discussions, framed around research questions and possible topics for future research directions. More information is available at https://www.nitrd.gov/nitrdgroups/index.php?title=AI-CYBER-2019.

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.

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.

1. Overview We provide various types of Annotated Imagery Data annotation services, including:
2. Video classification
3. Timestamps
4. Video tracking
5. Video detection ...
6. Our Capacity
7. Platform: Our platform supports human-machine interaction and semi-automatic labeling, increasing labeling efficiency by more than 30% per annotator.It has successfully been applied to nearly 5,000 projects.

• Annotation Tools: Nexdata's platform integrates 30 sets of annotation templates, covering audio, image, video, point cloud and text.

-Secure Implementation: NDA is signed to gurantee secure implementation and Annotated Imagery Data is destroyed upon delivery.

-Quality: Multiple rounds of quality inspections ensures high quality data output, certified with ISO9001

1. About Nexdata Nexdata has global data processing centers and more than 20,000 professional annotators, supporting on-demand data annotation services, such as speech, image, video, point cloud and Natural Language Processing (NLP) Data, etc. Please visit us at https://www.nexdata.ai/datasets/computervision?source=Datarade

Overview of school nutrition program data available on the Texas Open Data Portal from Texas Department of Agriculture

Analysis of ‘Access statistics by moers.de for June 2015 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/ff78d614-47cf-4cfc-868b-7c767fd1f415 on 13 January 2022.

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

The zip file contains the following CSV files:

• Visitors per year_1.csv
• Visitors per month — individual period _1.csv
• Visitors per month 1.csv
• Visitors per Stunde_1.csv
• Visitors per day 1.csv
• Visitors _1.csv
• Main browser versions by operating system_1.csv
• browsers by operating system subversionen_1.csv
• Browser Unterversionen_1.csv
• boarding sides_1.csv
• Origin overview _1.csv
• Use per domain 1.csv
• Page Impressions per Page_1.csv
• Search word statistics Phrases by search engine 1.csv
• Search word statistics Phrasen_1.csv
• Search word statistics Wörter_1.csv

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

Introduction

Zoom vs Google Meet Statistics: In 2024, Zoom and Google Meet remain two of the most widely used video conferencing platforms worldwide. Organisations, educators, and individuals rely upon these tools for meetings, classes, and social interactions.

The article gives an in-depth look at Zoom vs Google Meet statistics that bring to light usage trends, market share, differences in features, and user growth, revenue, and pricing information—all presented in layman's terms.