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.

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.

Comparison of Image Input Price: USD per 1k images at 1MP (1024x1024) by Model

Comprehensive comparison of Output Speed (Output Tokens per Second) vs. Price (USD per M Tokens) by Model

Artificial Intelligence in Space Exploration Market Outlook

The artificial intelligence in space exploration market is projected to witness significant growth, with a market size valued at approximately USD 2.5 billion in 2023 and expected to grow to USD 6.8 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 11.3% during the forecast period. This growth can be attributed to the increasing demand for advanced technologies to enhance the efficiency and effectiveness of space missions. As space exploration becomes more complex, AI technologies are poised to revolutionize how we explore and understand the universe, providing unprecedented capabilities in terms of automation, data processing, and mission planning.

One of the key growth factors driving the market is the increasing volume of data generated from space missions, which requires sophisticated AI systems for efficient analysis and interpretation. As the number of satellites in orbit grows and space missions become more frequent, the amount of data collected is unparalleled. AI technologies, particularly machine learning and data analytics, are critical in processing this data to derive meaningful insights, optimizing operational efficiency, and improving decision-making processes. Additionally, AI's ability to enhance predictive maintenance of spacecraft systems significantly reduces operational costs and extends the lifespan of these expensive assets.

Another growth factor is the rising interest and investment from commercial space companies. These enterprises are leveraging AI to gain a competitive edge in satellite operations and spacecraft navigation. By employing AI-driven technologies, companies can automate routine operations, reduce human error, and enhance the overall reliability of their missions. Furthermore, AI assists in mission planning and execution, which are crucial for the success of commercial space endeavors. With the continuous support from private investments and the increasing involvement of startups in the space sector, AI in space exploration is set to expand its market influence significantly.

Additionally, government agencies and research institutions are investing heavily in AI to further their space exploration goals. By integrating AI technologies into their operations, they aim to improve mission outcomes, enhance safety, and reduce costs. These institutions are also collaborating internationally to develop AI applications for space exploration, fostering innovation and sharing of critical technological advancements. Such collaborations are expected to boost the adoption of AI in space exploration, promoting the development of new applications and technologies that can address emerging challenges and opportunities in space missions.

Space Mining is emerging as a pivotal aspect of the future of space exploration, offering the potential to unlock vast resources beyond Earth. As the demand for rare minerals and metals increases, space mining presents a promising solution to resource scarcity on our planet. The development of AI technologies is crucial in this domain, enabling the automation of mining operations on asteroids and other celestial bodies. AI-driven systems can efficiently analyze geological data to identify resource-rich areas, optimize extraction processes, and ensure the safety and sustainability of space mining activities. This advancement not only supports the economic viability of space missions but also paves the way for new industries and opportunities in the space sector.

Regionally, North America dominates the market, driven by the strong presence of key players and significant investments in AI and space exploration technologies. The region's well-established infrastructure and government support through organizations like NASA play a crucial role in market growth. Meanwhile, Asia Pacific is expected to witness the highest growth rate, with countries like China and India increasing their focus on space technologies and AI integration. These countries are investing in developing their space capabilities and have ambitious plans for future space missions, creating substantial opportunities for AI technology vendors.

Technology Analysis

Machine learning stands at the forefront of AI technologies utilized in space exploration, offering powerful capabilities for data processing, anomaly detection, and predictive analytics. It plays a crucial role in automating spacecraft operations, enabling real-time decision-making, an

Dataset Card for atitaarora/state-of-ai-2024

  Dataset Description

This dataset contains images converted from PDFs using the PDFs to Page Images Converter Space.

Number of images: 212 Number of PDFs processed: 1 Sample size per PDF: 100 Created on: 2024-10-11 15:05:25

  Dataset Creation





  Source Data

The images in this dataset were generated from user-uploaded PDF files.

  Processing Steps

PDF files were uploaded to the PDFs to Page Images… See the full description on the dataset page: https://huggingface.co/datasets/atitaarora/state-of-ai-2024.

Snapshot img

Artificial Intelligence (AI) in the Telecommunication Market Size 2024-2028

The artificial intelligence (ai) in the telecommunication industry market size is forecast to increase by USD 38.05 billion at a CAGR of 66.2% between 2023 and 2028.

in the telecommunications industry, the adoption of artificial intelligence (AI) is becoming increasingly prevalent due to several key factors. The surging demand for autonomous-driven network solutions is a major growth driver, as ai enables networks to self-heal and optimize performance in real-time. Furthermore, substantial investments in 5G network infrastructure are fueling the integration of ai to enhance network efficiency and capacity. However, challenges persist, including the high cost of implementing ai and the poor availability of skilled workforce to manage and maintain these advanced systems. Despite these hurdles, the potential benefits of ai in telecommunications, such as improved customer experience and network reliability, make it a worthwhile investment for industry players.

What will be the Size of the AI in the Telecommunication Industry Market During the Forecast Period?

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The artificial intelligence (AI) market in the telecommunications industry is experiencing significant growth, driven by the increasing adoption of ai technologies to enhance network efficiency, optimize operations, and improve customer experience. Big data analytics, robotics, and generative ai tools are key areas of focus, with ai algorithms and machine learning techniques, such as natural language processing and computer vision, playing a crucial role. The availability and quality of digital data are essential for the effective implementation of ai solutions. Edge ai and ai as a Service (SaaS) products are gaining popularity, with ai platform companies offering licensing options for customized solutions.
Autonomous ai and advanced algorithms, including deep learning techniques, are pushing the boundaries of what is possible in telecommunications. The integration of ai with quantum computers and supercomputers is expected to further accelerate innovation in this space. Overall, the ai market in telecommunications is a dynamic and innovative sector, poised for continued growth and transformation.

How is this AI in the Telecommunication Industry segmented and which is the largest segment?

The AI in the telecommunication 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.

Component

  Solutions
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  South America



  Middle East and Africa

By Component Insights

The solutions segment is estimated to witness significant growth during the forecast period. Artificial Intelligence (ai) is revolutionizing the telecommunication industry by automating manual processes and delivering superior results. ai platforms, installed on enterprise premises, offer cognitive functions such as learning, reasoning, problem-solving, social intelligence, and general intelligence. These platforms enable telecom companies to automate tasks using machine vision and speech recognition applications. ai solutions include software tools and platforms, with ai platforms being the architecture that powers cognitive functions. The primary benefit of ai in telecommunications is resource and time savings, as it automates processes and delivers better results than manual methods. Big data analytics, robotics, generative ai tools, ai art generators, and various ai techniques such as natural language processing, computer vision, and machine learning are integral to ai platforms.

Ethical and regulatory concerns surrounding ai are being addressed by technological innovators, ensuring transparency and privacy. ai is transforming industries like healthcare, food and beverages, finance, and more, with tech giants leading the digitalization charge. ai applications are also being used in customer service, sales and marketing, supply chain management, human resource management, finance and accounting, cybersecurity, and legal and compliance. Edge ai and ai as a Service (SaaS) products are gaining popularity, along with customizable ai and ai marketplaces. ai is driving automation in call centers, chatbots, and virtual assistants, making customer service more efficient and effective.

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The Solutions segment was valued at USD 420.10 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 39% to the growth of the global

Developers using the DOL-wide API have access to a variety of queries providing usage metrics for their app's key.

Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices and sensors enabled many context-aware applications to recognize physical activities. This study presents XAI-HAR, a novel XAI-empowered human activity recognition (HAR) approach based on key features identified from the data collected from sensors located at different places in a smart home. XAI-HAR identifies a set of new features (i.e., the total number of sensors used in a specific activity), as physical key features selection (PKFS) based on weighting criteria. Next, it presents statistical key features selection (SKFS) (i.e., mean, standard deviation) to handle the outliers and higher class variance. The proposed XAI-HAR is evaluated using machine learning models, namely, random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB) and deep learning models such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM). Experiments demonstrate the superior performance of XAI-HAR using RF classifier over all other machine learning and deep learning models. For explainability, XAI-HAR uses Local Interpretable Model Agnostic (LIME) with an RF classifier. XAI-HAR achieves 0.96% of F-score for health and dementia classification and 0.95 and 0.97% for activity recognition of dementia and healthy individuals, respectively.

Success.ai provides a robust, enterprise-grade solution with access to over 150 million verified employee profiles, encompassing comprehensive B2B and B2C contact data. This extensive database is crafted to assist organizations in targeting key decision-makers, enhancing recruitment processes, and powering dynamic B2B marketing initiatives. Our offerings are designed to meet diverse industry needs, from small businesses to large enterprises, ensuring global coverage and up-to-date information.

• Global Coverage: With data spanning 195 countries, Success.ai delivers profiles that include crucial contact details like email addresses, phone numbers, and physical addresses.
• Tailored Data Solutions: Adapted to your specific business needs, our data sets include B2B contact data, phone number data, email address data, address data, and small business contact data.
• Real-Time Accuracy: Continuously updated to maintain the utmost accuracy and relevance, helping you make informed decisions swiftly.
• Compliance and Ethics: Our data collection and processing are fully compliant with global standards, ensuring ethical usage across all business practices.
• Strategic Use Cases: Ideal for targeted lead generation, personalized marketing campaigns, strategic sales outreach, and comprehensive market research.

Why Choose Success.ai?

• Best Price Guarantee: We offer competitive pricing, ensuring you get the best value for comprehensive contact data.
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• Comprehensive Reach: From local businesses to global enterprises, access detailed contact data for over 150 million profiles.
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Key Use Cases:

• B2B Marketing: Leverage accurate email and phone data to execute precise marketing campaigns.
• Sales Enhancement: Utilize verified contact details to reach decision-makers and close deals more effectively.
• Recruitment Efficiency: Access up-to-date contact information to source and recruit top talent globally.
• Customer Insights: Enhance your understanding of customer bases with detailed address and demographic data.
• Network Expansion: Utilize comprehensive B2C contact data to broaden your consumer outreach and engagement.

Success.ai stands as your premier partner in harnessing the power of detailed contact data to drive business growth and operational efficiency. Our commitment to delivering tailored, accurate, and ethically sourced data ensures that you can engage with your target audience effectively and responsibly.

Get started with Success.ai today and experience how our B2B and B2C contact data solutions can transform your business strategies and lead you to achieve measurable success.

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Analysis of ‘Number of pupils in community schools with elementary school’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/08afa9c4-7aa7-47ce-a05c-56d37c198505 on 18 January 2022.

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

Number of pupils in community schools with elementary school in the years: 2013/14, 2014/15, 2015/16, 2016/17, 2017/18, 2018/19, 2019/20, 2020/21.

Source: https://transparenz.schleswig-holstein.de/dataset/minderheitenbericht-2021

The 2021 minority report contains a variety of tables that you can find listed here.

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

Success.ai offers a comprehensive, enterprise-ready B2B leads data solution, ideal for businesses seeking access to over 150 million verified employee profiles and 170 million work emails. Our data empowers organizations across industries to target key decision-makers, optimize recruitment, and fuel B2B marketing efforts. Whether you're looking for UK B2B data, B2B marketing data, or global B2B contact data, Success.ai provides the insights you need with pinpoint accuracy.

Tailored for B2B Sales, Marketing, Recruitment and more: Our B2B contact data and B2B email data solutions are designed to enhance your lead generation, sales, and recruitment efforts. Build hyper-targeted lists based on job title, industry, seniority, and geographic location. Whether you’re reaching mid-level professionals or C-suite executives, Success.ai delivers the data you need to connect with the right people.

Key Categories Served: B2B sales leads – Identify decision-makers in key industries, B2B marketing data – Target professionals for your marketing campaigns, Recruitment data – Source top talent efficiently and reduce hiring times, CRM enrichment – Update and enhance your CRM with verified, updated data, Global reach – Coverage across 195 countries, including the United States, United Kingdom, Germany, India, Singapore, and more.

Global Coverage with Real-Time Accuracy: Success.ai’s dataset spans a wide range of industries such as technology, finance, healthcare, and manufacturing. With continuous real-time updates, your team can rely on the most accurate data available: 150M+ Employee Profiles: Access professional profiles worldwide with insights including full name, job title, seniority, and industry. 170M Verified Work Emails: Reach decision-makers directly with verified work emails, available across industries and geographies, including Singapore and UK B2B data. GDPR-Compliant: Our data is fully compliant with GDPR and other global privacy regulations, ensuring safe and legal use of B2B marketing data.

Key Data Points for Every Employee Profile: Every profile in Success.ai’s database includes over 20 critical data points, providing the information needed to power B2B sales and marketing campaigns: Full Name, Job Title, Company, Work Email, Location, Phone Number, LinkedIn Profile, Experience, Education, Technographic Data, Languages, Certifications, Industry, Publications & Awards.

Use Cases Across Industries: Success.ai’s B2B data solution is incredibly versatile and can support various enterprise use cases, including: B2B Marketing Campaigns: Reach high-value professionals in industries such as technology, finance, and healthcare. Enterprise Sales Outreach: Build targeted B2B contact lists to improve sales efforts and increase conversions. Talent Acquisition: Accelerate hiring by sourcing top talent with accurate and updated employee data, filtered by job title, industry, and location. Market Research: Gain insights into employment trends and company profiles to enrich market research. CRM Data Enrichment: Ensure your CRM stays accurate by integrating updated B2B contact data. Event Targeting: Create lists for webinars, conferences, and product launches by targeting professionals in key industries.

Use Cases for Success.ai's Contact Data - Targeted B2B Marketing: Create precise campaigns by targeting key professionals in industries like tech and finance. - Sales Outreach: Build focused sales lists of decision-makers and C-suite executives for faster deal cycles. - Recruiting Top Talent: Easily find and hire qualified professionals with updated employee profiles. - CRM Enrichment: Keep your CRM current with verified, accurate employee data. - Event Targeting: Create attendee lists for events by targeting relevant professionals in key sectors. - Market Research: Gain insights into employment trends and company profiles for better business decisions. - Executive Search: Source senior executives and leaders for headhunting and recruitment. - Partnership Building: Find the right companies and key people to develop strategic partnerships.

Why Choose Success.ai’s Employee Data? Success.ai is the top choice for enterprises looking for comprehensive and affordable B2B data solutions. Here’s why: Unmatched Accuracy: Our AI-powered validation process ensures 99% accuracy across all data points, resulting in higher engagement and fewer bounces. Global Scale: With 150M+ employee profiles and 170M verified work emails, Success.ai provides extensive coverage for UK B2B data, B2B marketing data, and global contacts. Competitive Pricing: We offer the most competitive rates on the market, undercutting major competitors like Lusha, Cognism, and ZoomInfo. Tailored Solutions: Our white-glove service ensures we deliver exactly what you need, in the format that suits your workflow (CSV, Excel, etc.). Real-Time Updates: Our data is continuously updated, so you always have the latest information, unlike static da...

Analysis of ‘AAA07 - Number of Livestock in June’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/616d2ab8-d4e7-4d8b-aa95-780f8410a374 on 12 January 2022.

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

Number of Livestock in June

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

This data represents the number of individuals registered in the Department of Consumer Protection's Medical Marijuana Program by registration type on the last day of the month.

This story provides information on individuals who exit homelessness to permanent housing destinations and then return to homelessness within 2 years from their exit in the Austin/Travis County Continuum of Care (CoC) in a given fiscal year.

Over the last years, studies using artificial intelligence (AI) for the detection and prediction of diseases have increased and also concentrated more and more on vulnerable groups of individuals, such as infants. The release of ChatGPT demonstrated the potential of large language models (LLMs) and heralded a new era of AI with manifold application possibilities. However, the impact of this new technology on medical research cannot be fully estimated yet. In this work, we therefore aimed to summarise the most recent pre-ChatGPT developments in the field of automated detection and prediction of diseases and disease status in infants, i.e., within the first 12 months of life. For this, we systematically searched the scientific databases PubMed and IEEE Xplore for original articles published within the last five years preceding the release of ChatGPT (2018–2022). The search revealed 927 articles; a final number of 154 articles was included for review. First of all, we examined research activity over time. Then, we analysed the articles from 2022 for medical conditions, data types, tasks, AI approaches, and reported model performance. A clear trend of increasing research activity over time could be observed. The most recently published articles focused on medical conditions of twelve different ICD-11 categories; “certain conditions originating in the perinatal period” was the most frequently addressed disease category. AI models were trained with a variety of data types, among which clinical and demographic information and laboratory data were most frequently exploited. The most frequently performed tasks aimed to detect present diseases, followed by the prediction of diseases and disease status at a later point in development. Deep neural networks turned out as the most popular AI approach, even though traditional methods, such as random forests and support vector machines, still play a role—presumably due to their explainability or better suitability when the amount of data is limited. Finally, the reported performances in many of the reviewed articles suggest that AI has the potential to assist in diagnostic procedures for infants in the near future. LLMs will boost developments in this field in the upcoming years.

A curated database of legal cases where generative AI produced hallucinated citations submitted in court filings.

Four studies were conducted in this PhD project. In the first two studies, a novel AI algorithm with 3D-DCGAN for designing dental crowns was proposed and evaluated. Dental crowns designed by AI algorithm on pre-molars and molars were compared respectively to natural teeth, CEREC biogeneric design and technician design with the parameters of cusp angle, 3D volume discrepancy, occlusal contact point number and area, and in silico fatigue load. The results revealed that the AI algorithm can design a dental crown mimicking natural tooth morphology, such that the performance of load outweigh than other designs. The latter two studies examined the application of AI image segmentation. In the third study, the AI tool was used to quantitatively measure the initial bacterial adhesion on scanning electron microscope images. To evaluate the efficiency of different dental suction systems in the COVID-19 pandemic, in the fourth study this AI tool was used to measure the number and area of aerosols/droplets produced by a high-speed dental handpiece powered by an electrical surgical motor. The AI tool was shown to be accurate and efficient in measuring and detecting for these purposes, able to find a new relationship, and can be an alternative method in evaluation of initial bacterial adhesion and dental aerosol/droplet measurement.

AI Facial Skin Analyzer Market Outlook

In 2023, the global AI facial skin analyzer market size was valued at approximately USD 920 million, with a projected compound annual growth rate (CAGR) of 18.5% from 2024 to 2032. By 2032, the market is forecasted to reach around USD 3.39 billion. The rapid growth in the market is primarily driven by advancements in artificial intelligence (AI) and machine learning technologies, increasing consumer awareness about skincare, and the rising demand for personalized beauty solutions.

One of the key growth factors propelling the AI facial skin analyzer market is the technological advancements in AI and machine learning. These technologies enable more accurate and comprehensive skin assessments, which can recommend personalized skincare regimens. The integration of deep learning algorithms allows for the analysis of a vast array of skin conditions, including acne, wrinkles, and pigmentation, thereby delivering high-precision results to users. This technological evolution is not only enhancing the performance of skin analyzers but also driving their adoption across various sectors, including healthcare and personal care.

Additionally, the rising consumer awareness and increasing inclination towards maintaining skin health contribute significantly to market growth. With a higher disposable income and growing interest in skincare, consumers are becoming more proactive in seeking advanced solutions for their skin concerns. AI-based skin analyzers offer a convenient and effective means to monitor skin health, track changes over time, and receive tailored advice. This heightened consumer interest and demand for advanced skincare solutions are expected to drive the market forward substantially over the forecast period.

The burgeoning beauty and personal care industry also plays a critical role in the market's expansion. Beauty salons and dermatology clinics are increasingly incorporating AI facial skin analyzers into their services to provide enhanced customer experiences. These devices enable professionals to offer evidence-based skincare recommendations and treatments, thereby improving customer satisfaction and loyalty. Moreover, the trend of home-use skin analyzers is growing, allowing consumers to leverage these advanced technologies in the comfort of their own homes, further fueling market growth.

The Facial Skin Analysis System is an integral component of the AI facial skin analyzer market. These systems combine advanced imaging technologies with AI algorithms to provide detailed insights into skin health. By capturing high-resolution images of the skin, these systems can analyze various parameters such as texture, tone, and hydration levels. The data collected is then processed by sophisticated AI models to identify potential skin issues and recommend personalized skincare solutions. This approach not only enhances the accuracy of skin assessments but also empowers consumers with actionable insights to improve their skincare routines. As the demand for personalized beauty solutions continues to grow, the Facial Skin Analysis System is poised to play a crucial role in meeting consumer expectations and driving market growth.

Regionally, the Asia Pacific region is anticipated to witness significant growth in the AI facial skin analyzer market due to the rising consumer base and increasing awareness about skincare. Countries like China, Japan, and South Korea are at the forefront, with a growing number of beauty and skincare brands integrating AI technologies into their products and services. Additionally, North America and Europe are expected to continue their strong market presence, driven by technological innovations and high consumer spending on advanced skincare solutions.

Product Type Analysis

The AI facial skin analyzer market is segmented by product type into hardware and software. The hardware segment includes the physical devices used to capture and analyze skin images, while the software segment encompasses the applications and platforms that process the data and provide insights.

The hardware segment is expected to hold a significant share of the market due to the increasing adoption of high-tech devices by dermatology clinics and beauty salons. These devices, equipped with advanced imaging technologies and sensors, can provide detailed and accurate analysis of various skin parameters. The durability and reliability of hardware components also

Total number of programs held, at County libraries, which focus on Science, Technology, Engineering and Mathematics (STEM). Updated annually.