As of 2024, customer data was the leading source of information used to train artificial intelligence (AI) models in South Korea, with nearly ** percent of surveyed companies answering that way. About ** percent responded to use public sector support initiatives.

Abstract:

In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems.
The major contributions have been materialized in the form of novel algorithms.
Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms.
Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance.
Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research.
Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation.

General Information:

This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset.

You may find details of this dataset from the original paper:

Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics".

If you use the data, implementation, or any details of the paper, please cite!

BIBTEX:

_

@inproceedings{nedelkoski2020multi,
 title={Multi-source Distributed System Data for AI-Powered Analytics},
 author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Mandapati, Ajay Kumar and Becker, Soeren and Cardoso, Jorge and Kao, Odej},
 booktitle={European Conference on Service-Oriented and Cloud Computing},
 pages={161--176},
 year={2020},
 organization={Springer}
}

_

The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth. We provide two datasets, which differ on how the workload is executed. The sequential_data is generated via executing workload of sequential user requests. The concurrent_data is generated via executing workload of concurrent user requests.

The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format.

Important: The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods. Please read the IMPORTANT_experiment_start_end.txt file before working with the data.

Our GitHub repository with the code for the workloads and scripts for basic analysis can be found at: https://github.com/SashoNedelkoski/multi-source-observability-dataset/

According to a survey conducted in 2022 in the public sector in South Korea, more than ** percent answered to use non-customer in-house data for training artificial intelligence (AI) models. More than a ***** of the surveyed public organizations were using public data.

Brand performance data collected from AI search platforms for the query "social determinants of health data sources".

AI Data Resource Service Market Outlook

The global AI Data Resource Service market size was valued at approximately $5.2 billion in 2023 and is projected to reach around $21.8 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 17.1% during the forecast period. This significant growth can be attributed to various factors including the exponential increase in data generation, advancements in artificial intelligence technologies, and the rising need for efficient data management solutions across different sectors.

One of the primary growth factors for the AI Data Resource Service market is the rapid expansion of data generation from various sources such as Internet of Things (IoT) devices, social media, and enterprise data systems. Organizations are increasingly seeking advanced solutions to manage, analyze, and extract valuable insights from this vast amount of data. AI data resource services offer enhanced capabilities to handle and process data efficiently, thereby driving their adoption across different industries.

Another important factor contributing to the market's growth is the continuous advancements in AI technology. Progressive developments in machine learning algorithms, natural language processing, and predictive analytics are enhancing the capabilities of AI data resource services. These advancements enable organizations to gain deeper insights, automate complex processes, and improve decision-making, thereby adding significant value to their operations and propelling market growth.

The demand for AI data resource services is further fueled by the increasing need for real-time data analytics and the growing emphasis on data-driven decision-making. In today’s competitive business environment, organizations are striving to leverage data analytics to gain a competitive edge. AI data resource services provide the necessary tools and frameworks to process data in real-time, enabling faster and more accurate business insights. This trend is particularly prevalent in sectors such as finance, healthcare, and retail, where timely and precise data analysis is critical.

From a regional perspective, North America currently holds the largest market share in the AI data resource service market. The region's dominance can be attributed to the presence of major technology companies, a robust IT infrastructure, and significant investments in AI research and development. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period. The rapid digitization of economies, increasing adoption of AI technologies, and supportive government initiatives in countries like China and India are driving the market expansion in this region.

Component Analysis

The AI Data Resource Service market can be segmented by component into software, hardware, and services. Each of these components plays a critical role in the overall functionality and effectiveness of AI data resource solutions, and their demand varies across different industries and applications.

In the software segment, the market is driven by the increasing adoption of AI-driven analytics solutions and data management platforms. These solutions enable organizations to efficiently process and analyze large volumes of data, derive actionable insights, and enhance their decision-making processes. The continuous advancements in AI algorithms and the development of new software tools are further propelling the growth of this segment.

The hardware segment is also witnessing significant growth due to the rising demand for high-performance computing systems, storage solutions, and data centers. These hardware components are essential for supporting the extensive computational requirements of AI data processing tasks. With the proliferation of big data and the increasing complexity of AI models, the need for advanced hardware infrastructure is becoming more critical, driving the growth of this segment.

The services segment encompasses various professional and managed services that assist organizations in implementing, maintaining, and optimizing their AI data resource solutions. This includes consulting services, system integration, training, and support services. The growing complexity of AI technologies and the need for specialized expertise are driving the demand for these services. Organizations are increasingly relying on external service providers to ensure the successful deployment and operation of their AI data resources.

Overall,

AI training draws heavily from the whole web, the largest data source with trillions of tokens, followed by sources like the indexed web and common crawl. This represents the estimated finality of tokens available in 2025, leading to a potential blockage for any AI models training on them.

Brand performance data collected from AI search platforms for the query "how to unify marketing data sources".

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.

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

According to Cognitive Market Research, the global Ai Training Data market size is USD 1865.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.50% from 2023 to 2030.

The demand for Ai Training Data is rising due to the rising demand for labelled data and diversification of AI applications.
Demand for Image/Video remains higher in the Ai Training Data market.
The Healthcare category held the highest Ai Training Data market revenue share in 2023.
North American Ai Training Data will continue to lead, whereas the Asia-Pacific Ai Training Data market will experience the most substantial growth until 2030.

Market Dynamics of AI Training Data Market

Key Drivers of AI Training Data Market

Rising Demand for Industry-Specific Datasets to Provide Viable Market Output

A key driver in the AI Training Data market is the escalating demand for industry-specific datasets. As businesses across sectors increasingly adopt AI applications, the need for highly specialized and domain-specific training data becomes critical. Industries such as healthcare, finance, and automotive require datasets that reflect the nuances and complexities unique to their domains. This demand fuels the growth of providers offering curated datasets tailored to specific industries, ensuring that AI models are trained with relevant and representative data, leading to enhanced performance and accuracy in diverse applications.

In July 2021, Amazon and Hugging Face, a provider of open-source natural language processing (NLP) technologies, have collaborated. The objective of this partnership was to accelerate the deployment of sophisticated NLP capabilities while making it easier for businesses to use cutting-edge machine-learning models. Following this partnership, Hugging Face will suggest Amazon Web Services as a cloud service provider for its clients.

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Advancements in Data Labelling Technologies to Propel Market Growth

The continuous advancements in data labelling technologies serve as another significant driver for the AI Training Data market. Efficient and accurate labelling is essential for training robust AI models. Innovations in automated and semi-automated labelling tools, leveraging techniques like computer vision and natural language processing, streamline the data annotation process. These technologies not only improve the speed and scalability of dataset preparation but also contribute to the overall quality and consistency of labelled data. The adoption of advanced labelling solutions addresses industry challenges related to data annotation, driving the market forward amidst the increasing demand for high-quality training data.

In June 2021, Scale AI and MIT Media Lab, a Massachusetts Institute of Technology research centre, began working together. To help doctors treat patients more effectively, this cooperation attempted to utilize ML in healthcare.

www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Restraint Factors Of AI Training Data Market

Data Privacy and Security Concerns to Restrict Market Growth

A significant restraint in the AI Training Data market is the growing concern over data privacy and security. As the demand for diverse and expansive datasets rises, so does the need for sensitive information. However, the collection and utilization of personal or proprietary data raise ethical and privacy issues. Companies and data providers face challenges in ensuring compliance with regulations and safeguarding against unauthorized access or misuse of sensitive information. Addressing these concerns becomes imperative to gain user trust and navigate the evolving landscape of data protection laws, which, in turn, poses a restraint on the smooth progression of the AI Training Data market.

How did COVID–19 impact the Ai Training Data market?

The COVID-19 pandemic has had a multifaceted impact on the AI Training Data market. While the demand for AI solutions has accelerated across industries, the availability and collection of training data faced challenges. The pandemic disrupted traditional data collection methods, leading to a slowdown in the generation of labeled datasets due to restrictions on physical operations. Simultaneously, the surge in remote work and the increased reliance on AI-driven technologies for various applications fueled the need for diverse and relevant training data. This duali...

The AI Data Resource Service market is experiencing robust growth, projected to reach $703 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 15.3% from 2025 to 2033. This expansion is driven by the increasing demand for high-quality data to train and improve the performance of artificial intelligence models across various sectors. The proliferation of AI applications in healthcare, finance, autonomous vehicles, and customer service fuels this demand. Key trends include the rising adoption of synthetic data generation techniques to address data scarcity and privacy concerns, alongside an increasing focus on data annotation and labeling services catering to the diverse needs of AI model development. While challenges exist, such as ensuring data quality, managing data security and compliance, and the need for skilled professionals, the overall market outlook remains extremely positive. The competitive landscape is characterized by a mix of established players like Amazon, Google, and Appen, and smaller, specialized firms focusing on niche areas. The market's rapid expansion presents significant opportunities for companies capable of providing high-quality, reliable, and ethically sourced data resources, and continued innovation in data augmentation and annotation techniques. The substantial growth anticipated through 2033 suggests a considerable expansion in market value beyond the 2025 figure. Assuming a consistent CAGR of 15.3%, a substantial increase in market value is projected. Major players are investing heavily in Research and Development to improve data acquisition, processing, and annotation capabilities, further accelerating market growth. Moreover, the increasing integration of AI into various industries ensures the continued reliance on high-quality data resources, thereby solidifying the long-term outlook for sustained expansion of the AI Data Resource Service market. Geographical expansion into emerging markets also presents a significant opportunity for growth, as businesses in these regions increasingly adopt AI solutions. Strategic partnerships and mergers and acquisitions among existing players are likely to further shape the competitive landscape and drive innovation in this dynamic market.

AI in Drug Discovery Market Outlook

The global AI in drug discovery market size is projected to reach a valuation of USD 5.1 billion by 2032, driven by advancements in machine learning algorithms and the increasing need for faster and cost-effective drug development processes. The market is expected to exhibit a compound annual growth rate (CAGR) of 40% from 2024 to 2032.

One of the primary growth factors of the AI in drug discovery market is the escalating demand for personalized medicine. Personalized medicine tailors treatment to the individual characteristics of each patient, which requires a deep understanding of genetic and molecular profiles. AI-driven technologies have the capacity to analyze vast amounts of data from various sources, such as genomics and proteomics, far more efficiently than traditional methods. This capability accelerates the identification of potential drug candidates that can be tailored to specific patient profiles, thus driving the market growth.

Another significant growth factor is the cost and time savings associated with AI technologies in drug discovery. Traditional drug discovery processes are often time-consuming and expensive, requiring extensive labor and resources. AI can streamline various stages of drug development, including target identification, drug screening, and clinical trials, thus reducing the time to market for new drugs. For instance, AI algorithms can predict the outcomes of clinical trials, identify the most promising drug candidates, and even optimize chemical structures, significantly cutting down both time and costs involved in the drug development process.

Moreover, the increasing collaboration between pharmaceutical companies and AI technology providers is bolstering market growth. Pharmaceutical giants are investing heavily in AI to enhance their drug discovery pipelines. These collaborations often result in the development of sophisticated AI platforms designed to address specific challenges in drug discovery. Additionally, the growing number of start-ups specializing in AI-driven drug discovery solutions is contributing to the market's dynamism. These start-ups often bring innovative technologies and new approaches that challenge traditional methodologies, further pushing the envelope of what is possible in drug discovery.

Regionally, North America holds the largest share of the AI in drug discovery market, driven by advanced healthcare infrastructure, significant investments in R&D, and the presence of major pharmaceutical companies. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, attributed to increasing healthcare expenditure, growing awareness of AI capabilities, and supportive government initiatives. The region's burgeoning biotechnology sector also plays a crucial role in the adoption of AI technologies for drug discovery.

Component Analysis

The AI in drug discovery market can be segmented by components into software, hardware, and services. The software segment is expected to dominate the market, given the critical role that advanced algorithms and machine learning models play in analyzing complex biological data. AI software solutions are indispensable for tasks such as molecular imaging, data mining, and predictive analytics, which are fundamental steps in the drug discovery process. These tools help in identifying patterns and correlations that would be almost impossible to detect using traditional methods.

In addition to software, hardware also forms an essential component of the AI in drug discovery market. High-performance computing systems and specialized hardware such as GPUs (Graphics Processing Units) are crucial for running complex AI algorithms, especially for tasks that require significant computational power such as molecular simulations. The advancements in hardware technologies are pushing the boundaries of what is achievable in drug discovery, allowing for more complex and accurate models to be developed and tested.

The services segment, which includes consulting, implementation, and maintenance services, also plays a vital role in the AI drug discovery ecosystem. Many pharmaceutical and biotechnology companies rely on specialized service providers to implement and manage their AI-driven drug discovery platforms. These services ensure that AI solutions are effectively integrated into existing workflows and that they are maintained to operate at optimal performance levels. The increasing reliance on these serv

Open Source Data Labelling Tool Market Outlook

The global market size for Open Source Data Labelling Tools was valued at USD 1.5 billion in 2023 and is projected to reach USD 4.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.2% during the forecast period. This significant growth can be attributed to the increasing adoption of artificial intelligence (AI) and machine learning (ML) across various industries, which drives the need for accurately labelled data to train these technologies effectively.

The rapid advancement and integration of AI and ML in numerous sectors serve as a primary growth factor for the Open Source Data Labelling Tool market. With the proliferation of big data, organizations are increasingly recognizing the importance of high-quality, annotated data sets to enhance the accuracy and efficiency of their AI models. The open-source nature of these tools offers flexibility and cost-effectiveness, making them an attractive choice for businesses of all sizes, especially startups and SMEs, which further fuels market growth.

Another key driver is the rising demand for automated data labelling solutions. Manual data labelling is a time-consuming and error-prone task, leading many organizations to seek automated tools that can swiftly and accurately label large datasets. Open source data labelling tools, often augmented with advanced features like natural language processing (NLP) and computer vision, provide a scalable solution to this challenge. This trend is particularly pronounced in data-intensive industries such as healthcare, automotive, and finance, where the precision of data labelling can significantly impact operational outcomes.

Additionally, the collaborative nature of open-source communities contributes to the market's growth. Continuous improvements and updates are driven by a global community of developers and researchers, ensuring that these tools remain at the cutting edge of technology. This ongoing innovation not only boosts the functionality and reliability of open-source data labelling tools but also fosters a sense of community and shared knowledge, encouraging more organizations to adopt these solutions.

In the realm of data labelling, Premium Annotation Tools have emerged as a significant player, offering advanced features that cater to the needs of enterprises seeking high-quality data annotation. These tools often come equipped with enhanced functionalities such as collaborative interfaces, real-time updates, and integration capabilities with existing AI systems. The premium nature of these tools ensures that they are designed to handle complex datasets with precision, thereby reducing the margin of error in data labelling processes. As businesses increasingly prioritize accuracy and efficiency, the demand for premium solutions is on the rise, providing a competitive edge in sectors where data quality is paramount.

From a regional perspective, North America holds a significant share of the market due to the robust presence of tech giants and a well-established IT infrastructure. The region's strong focus on AI research and development, coupled with substantial investments in technology, drives the demand for data labelling tools. Meanwhile, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, attributed to the rapid digital transformation and increasing AI adoption across countries like China, India, and Japan.

Component Analysis

When dissecting the Open Source Data Labelling Tool market by component, it is evident that the segment is bifurcated into software and services. The software segment dominates the market, primarily due to the extensive range of features and functionalities that open-source data labelling software offers. These tools are customizable and can be tailored to meet specific needs, making them highly versatile and efficient. The software segment is expected to continue its dominance as more organizations seek comprehensive solutions that integrate seamlessly with their existing systems.

The services segment, while smaller in comparison, plays a crucial role in the overall market landscape. Services include support, training, and consulting, which are vital for organizations to effectively implement and utilize open-source data labelling tools. As the adoption of these tools grows, so does the demand for professional services that can aid in deployment, customization

IntroductionEurope works to improve cancer management through the use of artificialintelligence (AI), and there is a need to accelerate the development of AI applications for childhood cancer. However, the current strategies used for algorithm development in childhood cancer may have bias and limited generalizability. This study reviewed existing publications on AI tools for pediatric brain tumors, Europe's most common type of childhood solid tumor, to examine the data sources for developing AI tools.MethodsWe performed a bibliometric analysis of the publications on AI tools for pediatric brain tumors, and we examined the type of data used, data sources, and geographic location of cohorts to evaluate the generalizability of the algorithms.ResultsWe screened 10503 publications, and we selected 45. A total of 34/45 publications developing AI tools focused on glial tumors, while 35/45 used MRI as a source of information to predict the classification and prognosis. The median number of patients for algorithm development was 89 for single-center studies and 120 for multicenter studies. A total of 17/45 publications used pediatric datasets from the UK.DiscussionSince the development of AI tools for pediatric brain tumors is still in its infancy, there is a need to support data exchange and collaboration between centers to increase the number of patients used for algorithm training and improve their generalizability. To this end, there is a need for increased data exchange and collaboration between centers and to explore the applicability of decentralized privacy-preserving technologies consistent with the General Data Protection Regulation (GDPR). This is particularly important in light of using the European Health Data Space and international collaborations.

The Big Data Technology Market size was valued at USD 349.40 USD Billion in 2023 and is projected to reach USD 918.16 USD Billion by 2032, exhibiting a CAGR of 14.8 % during the forecast period. Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems that wouldn’t have been able to tackle before. Big data technology is defined as software-utility. This technology is primarily designed to analyze, process and extract information from a large data set and a huge set of extremely complex structures. This is very difficult for traditional data processing software to deal with. Among the larger concepts of rage in technology, big data technologies are widely associated with many other technologies such as deep learning, machine learning, artificial intelligence (AI), and Internet of Things (IoT) that are massively augmented. In combination with these technologies, big data technologies are focused on analyzing and handling large amounts of real-time data and batch-related data. Recent developments include: February 2024: - SQream, a GPU data analytics platform, partnered with Dataiku, an AI and machine learning platform, to deliver a comprehensive solution for efficiently generating big data analytics and business insights by handling complex data., October 2023: - MultiversX (ELGD), a blockchain infrastructure firm, formed a partnership with Google Cloud to enhance Web3’s presence by integrating big data analytics and artificial intelligence tools. The collaboration aims to offer new possibilities for developers and startups., May 2023: - Vpon Big Data Group partnered with VIOOH, a digital out-of-home advertising (DOOH) supply-side platform, to display the unique advertising content generated by Vpon’s AI visual content generator "InVnity" with VIOOH's digital outdoor advertising inventories. This partnership pioneers the future of outdoor advertising by using AI and big data solutions., May 2023: - Salesforce launched the next generation of Tableau for users to automate data analysis and generate actionable insights., March 2023: - SAP SE, a German multinational software company, entered a partnership with AI companies, including Databricks, Collibra NV, and DataRobot, Inc., to introduce the next generation of data management portfolio., November 2022: - Thai Oil and Retail Corporation PTT Oil and Retail Business Public Company implemented the Cloudera Data Platform to deliver insights and enhance customer engagement. The implementation offered a unified and personalized experience across 1,900 gas stations and 3,000 retail branches., November 2022: - IBM launched new software for enterprises to break down data and analytics silos that helped users make data-driven decisions. The software helps to streamline how users access and discover analytics and planning tools from multiple vendors in a single dashboard view., September 2022: - ActionIQ, a global leader in CX solutions, and Teradata, a leading software company, entered a strategic partnership and integrated AIQ’s new HybridCompute Technology with Teradata VantageCloud analytics and data platform.. Key drivers for this market are: Increasing Adoption of AI, ML, and Data Analytics to Boost Market Growth . Potential restraints include: Rising Concerns on Information Security and Privacy to Hinder Market Growth. Notable trends are: Rising Adoption of Big Data and Business Analytics among End-use Industries.

The open-source data labeling tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in various AI applications. The market's expansion is fueled by several key factors: the rising adoption of machine learning and deep learning algorithms across industries, the need for efficient and cost-effective data annotation solutions, and a growing preference for customizable and flexible tools that can adapt to diverse data types and project requirements. While proprietary solutions exist, the open-source ecosystem offers advantages including community support, transparency, cost-effectiveness, and the ability to tailor tools to specific needs, fostering innovation and accessibility. The market is segmented by tool type (image, text, video, audio), deployment model (cloud, on-premise), and industry (automotive, healthcare, finance). We project a market size of approximately $500 million in 2025, with a compound annual growth rate (CAGR) of 25% from 2025 to 2033, reaching approximately $2.7 billion by 2033. This growth is tempered by challenges such as the complexities associated with data security, the need for skilled personnel to manage and use these tools effectively, and the inherent limitations of certain open-source solutions compared to their commercial counterparts. Despite these restraints, the open-source model's inherent flexibility and cost advantages will continue to attract a significant user base. The market's competitive landscape includes established players like Alecion and Appen, alongside numerous smaller companies and open-source communities actively contributing to the development and improvement of these tools. Geographical expansion is expected across North America, Europe, and Asia-Pacific, with the latter projected to witness significant growth due to the increasing adoption of AI and machine learning in developing economies. Future market trends point towards increased integration of automated labeling techniques within open-source tools, enhanced collaborative features to improve efficiency, and further specialization to cater to specific data types and industry-specific requirements. Continuous innovation and community contributions will remain crucial drivers of growth in this dynamic market segment.

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.

Classification of Mars Terrain Using Multiple Data Sources Alan Kraut1, David Wettergreen1 ABSTRACT. Images of Mars are being collected faster than they can be analyzed by planetary scientists. Automatic analysis of images would enable more rapid and more consistent image interpretation and could draft geologic maps where none yet exist. In this work we develop a method for incorporating images from multiple instruments to classify Martian terrain into multiple types. Each image is segmented into contiguous groups of similar pixels, called superpixels, with an associated vector of discriminative features. We have developed and tested several classification algorithms to associate a best class to each superpixel. These classifiers are trained using three different manual classifications with between 2 and 6 classes. Automatic classification accuracies of 50 to 80% are achieved in leave-one-out cross-validation across 20 scenes using a multi-class boosting classifier.

The dataset comprises responses to 116 questions, with contributions from both human and AI sources. The data is organized into a single folder called "AI classifier dataset," containing 100 Excel files and one JSON list file named "dataset.jsonl." Each Excel file contains three attributes: "Question", "Human", and "AI" except one file, 457c895.xlsx, which has columns "Question", "Answer," and "AI or Human."The JSON file includes four attributes for each entry: an ID, the original question, the answer, and Is_it_AI. In total, the JSON list file contains 4,231 rows of data. The source code folder contains the website design code for the question distribution and data collection website.

AI-Powered Cognitive Search Market Outlook

The global AI-powered cognitive search market size is projected to grow from $2.35 billion in 2023 to $10.45 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 18.1% over the forecast period. This impressive growth is driven by the increasing demand for advanced data analytics tools and the need for enhanced customer experience across various industries. The integration of artificial intelligence (AI) technology in search algorithms has significantly improved the ability to retrieve relevant information, thus fueling the market expansion.

One of the primary growth factors for the AI-powered cognitive search market is the exponential increase in data generation across industries. With the advent of big data, organizations are accumulating vast amounts of unstructured data, which traditional search methods struggle to manage effectively. AI-powered cognitive search leverages machine learning, natural language processing (NLP), and other AI technologies to analyze and index this data, allowing organizations to derive actionable insights and make data-driven decisions. This capability is particularly valuable in sectors such as healthcare, BFSI, and IT, where the rapid retrieval of relevant information can significantly impact operational efficiency and customer satisfaction.

Furthermore, the growing emphasis on personalized customer experiences is propelling the adoption of AI-powered cognitive search solutions. Modern consumers expect quick and accurate responses to their queries, and businesses are increasingly recognizing the need to enhance their search functionalities to meet these expectations. By implementing AI-powered cognitive search, companies can provide more relevant search results and recommendations, thereby improving customer engagement and loyalty. This trend is especially prominent in the retail and e-commerce sectors, where personalized interactions can drive higher conversion rates and revenues.

Additionally, advancements in AI technologies, such as deep learning and NLP, are continuously enhancing the capabilities of cognitive search solutions. These technologies enable search systems to understand the context and intent behind user queries, leading to more accurate and relevant search results. As a result, organizations are investing heavily in AI research and development to stay competitive in the market. The ongoing innovation in AI-powered cognitive search tools is expected to create new growth opportunities and drive market expansion over the forecast period.

The regional outlook for the AI-powered cognitive search market indicates significant growth across various geographies. North America currently holds the largest market share, primarily due to the presence of leading technology companies and high adoption rates of AI technologies. However, the Asia Pacific region is expected to witness the highest CAGR during the forecast period, driven by the increasing digital transformation initiatives and the rising demand for advanced analytics solutions in countries like China and India. Europe and Latin America are also anticipated to experience substantial growth, supported by the growing awareness of AI benefits and the increasing investments in AI infrastructure.

Component Analysis

The AI-powered cognitive search market can be segmented by components into software and services. The software segment is expected to hold the largest market share, driven by the increasing adoption of AI-based search solutions across various industries. These software solutions incorporate advanced algorithms and AI technologies such as machine learning and NLP to enhance search accuracy and relevance. The continuous advancements in AI technologies are further boosting the capabilities of cognitive search software, enabling them to provide more sophisticated and intuitive search experiences.

Within the software segment, several sub-segments can be identified, including enterprise search software, cognitive search platforms, and industry-specific search solutions. Enterprise search software is designed to cater to the needs of large organizations, providing comprehensive search capabilities across diverse data sources. Cognitive search platforms, on the other hand, offer more specialized functionalities, often tailored to specific use cases or industries. Industry-specific search solutions are customized to address the unique requirements of sectors such as healthcare, retail, and BFSI, enhancing their ability to retrieve relevant information quickly and accurately.