Data scarcity presents a significant obstacle in the field of biomedicine, where acquiring diverse and sufficient datasets can be costly and challenging. Synthetic data generation offers a potential solution to this problem by expanding dataset sizes, thereby enabling the training of more robust and generalizable machine learning models. Although previous studies have explored synthetic data generation for cancer diagnosis, they have predominantly focused on single-modality settings, such as whole-slide image tiles or RNA-Seq data. To bridge this gap, we propose a novel approach, RNA-Cascaded-Diffusion-Model or RNA-CDM, for performing RNA-to-image synthesis in a multi-cancer context, drawing inspiration from successful text-to-image synthesis models used in natural images. In our approach, we employ a variational auto-encoder to reduce the dimensionality of a patient’s gene expression profile, effectively distinguishing between different types of cancer. Subsequently, we employ a cascad..., , , # RNA-CDM Generated One Million Synthetic Images

https://doi.org/10.5061/dryad.6djh9w174

One million synthetic digital pathology images were generated using the RNA-CDM model presented in the paper "RNA-to-image multi-cancer synthesis using cascaded diffusion models".

Description of the data and file structure

There are ten different h5 files per cancer type (TCGA-CESC, TCGA-COAD, TCGA-KIRP, TCGA-GBM, TCGA-LUAD). Each h5 file contains 20.000 images. The key is the tile number, ranging from 0-20,000 in the first file, and from 180,000-200,000 in the last file. The tiles are saved as numpy arrays.

Code/Software

The code used to generate this data is available under academic license in https://rna-cdm.stanford.edu .

Manuscript citation

Carrillo-Perez, F., Pizurica, M., Zheng, Y. et al. Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models...

The data presented in this data project were collected in the context of the research project “AI TRACE - Synaesthetic Engagement of Artificial Intelligence with Digital Arts and its Audience”. The research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.)under the “2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (Project Number: 782). AI TRACE aimed at developing an ethically compliant behavioural analysis and visualization tool in the form of a metalanguage that can be used in the museum sector to track, analyse and present data collected from exhibition visitors in the form of a personalized 3D digital object. AI TRACE showcases Artificial Intelligence subsystems. The data presented in this data project were collected during the Preparatory Activity event that took place in October 2021 during the 17th edition of the Athens Digital Arts Festival (ADAF). The research activity was hosted at the new premises of the Museum of Modern Greek Culture, at a specially designed exhibition space. The purpose of this activity was to collect data for methodological testing and for feeding the AI subsystem. The data files derived from the research activities and provided here are:

1. Data that was gathered from a pre-visit questionnaire which was completed by 231 survey participants. The questionnaire explores visitors' motivations for visiting and preferences of a digital arts exhibition, their relationship with digital arts, their mood and attitude before the visit, and certain demographic characteristics. For the purpose of the research, visitors of the Athens Digital Arts Festival who voluntarily participated in the survey were approached. The dataset can be used to analyse various characteristics of museum visitors, such as their motivation to visit, their interests and their relationship with art, in relation to their personal characteristics (gender, age, status).
2. Timing-and-tracking (T&T) data -more specifically time spent in each exhibit and trajectory- which were extracted from video recordings in the exhibition space. Regular festival visitors were recruited at the entrance of the exhibition area by the research assistants. Participants (N=273) were asked to sign an informed consent form before filling the online anonymous questionnaire and entering the exhibition space. A custom-made software was used to blur their faces during video recording for data anonymization purposes. The tracking data were extracted via BORIS (Behavioral Observation Research Interactive Software), an open-source event logging software for video/audio coding and live observations that is developed by the University of Torino. The datasets can be used to analyse visitors’ behavior in relation to demographic characteristics, their motivations, interests and relation to arts, as well as their pre-visit mood and attitudes.
3. The data file containing both questionnaire data and timing-and-tracking (T&T) data for a subset of the sample (N=223). This data can be used in combination to analyse the behaviour of museum visitors in relation to their demographic characteristics, motivations for visiting, their preferences and their relationship with digital arts, as well as in relation to their mood and attitude before the visit.

This is a dataset obtained from an online survey conducted in August 2020.

In the survey, participants were introduced to the concept of a smartphone-based shopping assistant application with the help of pictures and videos when shopping with and without the application. Participants were presented with three different shopping scenarios. In each scenario, we showed products on a shelf (groceries, luxury chocolate, shoes, books). The first shopping scenario was a regular shopping scenario (RSS), the second was an augmented reality shopping scenario (ARSS), and the third was an augmented reality shopping scenario with explainable AI features (XARSS). For each scenario participants had to answer questions about how they perceived the scenario and how it influenced their overall purchase intention.

The present work was conducted within the Innovative Training Network project PERFORM funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 765395. The EU Research Executive Agency is not responsible for any use that may be made of the information it contains.

The global digital twin market size reached USD 23.4 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 219.6 Billion by 2033, exhibiting a growth rate (CAGR) of 25.08% during 2025-2033. The market is rapidly expanding, driven by recent advancements in the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) technologies, increasing focus on sustainable development, rising urbanization and industrialization, burgeoning complexity of industrial processes, heightened focus on predictive maintenance, and the integration of digital twins with augmented reality (AR) and virtual reality (VR) technologies.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the global, regional, and country levels for 2025-2033. Our report has categorized the market based on type, technology, and end use.

IMPORTANT: This deposit contains a range of supplementary material related to the deposit of the SIPHER Synthetic Population for Individuals, 2019-2021 (https://doi.org/10.5255/UKDA-SN-9277-1). See the shared readme file for a detailed description describing this deposit. Please note that this deposit does not contain the SIPHER Synthetic Population dataset, or any other Understanding Society survey datasets.

The lack of a centralised and comprehensive register-based system in Great Britain limits opportunities for studying the interaction of aspects such as health, employment, benefit payments, or housing quality at the level of individuals and households. At the same time, the data that exist, is typically strictly controlled and only available in safe haven environments under a “create-and-destroy” model. In particular when testing policy options via simulation models where results are required swiftly, these limitations can present major hurdles to coproduction and collaborative work connecting researchers, policymakers, and key stakeholders. In some cases, survey data can provide a suitable alternative to the lack of readily available administrative data. However, survey data does typically not allow for a small-area perspective. Although special license area-level linkages of survey data can offer more detailed spatial information, the data’s coverage and statistical power might be too low for meaningful analysis.

Through a linkage with the UK Household Longitudinal Study (Understanding Society, SN 6614, wave k), the SIPHER Synthetic Population allows for the creation of a survey-based full-scale synthetic population for all of Great Britain. By drawing on data reflecting “real” survey respondents, the dataset represents over 50 million synthetic (i.e. “not real”) individuals. As a digital twin of the adult population in Great Britain, the SIPHER Synthetic population provides a novel source of microdata for understanding “status quo” and modelling “what if” scenarios (e.g., via static/dynamic microsimulation model), as well as other exploratory analyses where a granular geographical resolution is required

As the SIPHER Synthetic Population is the outcome of a statistical creation process, all results obtained from this dataset should always be treated as “model output” - including basic descriptive statistics. Here, the SIPHER Synthetic Population should not replace the underlying Understanding Society survey data for standard statistical analyses (e.g., standard regression analysis, longitudinal multi-wave analysis). Please see the respective User Guide provided for this dataset for further information on creation and validation.

This research was conducted as part of the Systems Science in Public Health and Health Economics Research - SIPHER Consortium and we thank the whole team for valuable input and discussions that have informed this work.

The global digital twin market is expected to expand rapidly, reaching a value of USD 8.33 billion by 2033, growing at a CAGR of 33.30% during the forecast period (2025-2033). The increasing adoption of digital twin technology in various industries, such as manufacturing, healthcare, and retail, is a key driving factor for the market growth. Additionally, the growing availability of high-speed internet connectivity, such as 5G, and the increasing penetration of IoT and IIoT devices are contributing to the market expansion. Key market segments include type (parts twin, product twin, process twin, and system twin), technology (IoT and IIoT, 5G, big data analytics, blockchain, and artificial intelligence), and end-user (aerospace & defense, automotive & transportation, manufacturing, healthcare, retail, energy & utilities, home & commercial, IT and telecom, and others). Geographically, North America is expected to hold the largest market share, followed by Europe and Asia Pacific. Key market players include General Electric (US), AI (US), IBM (US), Siemens AG (Germany), PTC (US), Microsoft Corporation (US), ANSYS (US), Oracle (US), SAP (Germany), and Robert Bosch (Germany). Recent developments include: June 2021: FARO Technologies Inc. announced the acquisition of HoloBuilder. HoloBuilder’s SaaS platform will add fast and easy reality-capture photo documentation and remote access capability to FARO’s highly accurate 3D point cloud-based laser scanning to create the industry’s first end-to-end Digital Twin solution., March 2021: The Lamina Tower, an ultra-luxury residential condominium tower in the Middle East, partnered with Cityzenith LLC to create a multi-purpose digital twin using the SmartWorldOS Digital Twin desktop application to create a 3D view of the property and surroundings, along with other requirements.. Key drivers for this market are: Increasing demand for real-time data visibility and predictive maintenance Growing adoption of IoT and advanced analytics technologies Government initiatives and regulatory compliance Focus on sustainability and energy efficiency Advancements in artificial intelligence and machine learning. Potential restraints include: Data privacy and security concerns Interoperability and standardization issues Lack of skilled professionals in digital twin implementation Data management and storage challenges High upfront costs of implementation. Notable trends are: Integration with augmented reality (AR) and virtual reality (VR) Use of generative AI to create synthetic data for training digital twins Edge computing for decentralized data processing Adoption of artificial twin intelligence (ATI) for autonomous decision-making Application in new industries, such as healthcare and retail.

Accurate and robust 6DOF (Six Degrees of Freedom) pose estimation is a critical task in various fields, including computer vision, robotics, and augmented reality. This research paper presents a novel approach to enhance the accuracy and reliability of 6DOF pose estimation by introducing a robust method for generating synthetic data and leveraging the ease of multi-class training using the generated dataset. The proposed method tackles the challenge of insufficient real-world annotated data by creating a large and diverse synthetic dataset that accurately mimics real-world scenarios. The proposed method only requires a CAD model of the object and there is no limit to the number of unique data that can be generated. Furthermore, a multi-class training strategy that harnesses the synthetic dataset's diversity is proposed and presented. This approach mitigates class imbalance issues and significantly boosts accuracy across varied object classes and poses. Experimental results underscore the method's effectiveness in challenging conditions, highlighting its potential for advancing 6DOF pose estimation across diverse applications. Our approach only uses a single RGB frame and is real-time. Methods This dataset has been synthetically generated using 3D software like Blender and APIs like Blendeproc.

Artificial intelligence to help enhance payments was significantly more an option for younger respondents than it was for their older counterparts in 2024. This is according to a survey held in 14 different countries across North America, Europe, and Latin America. The source observed in 2023 already that most respondents - regardless of age - were not yet comfortable with the idea of AI in digital payments. This revealed itself, especially, in the reply from 10 percent of the respondents that they would perhaps use artificial intelligence in two years' time when it had become more established. In 2024, the source did not ask how many people actively used AI during their payments journey. Examples of AI in day-to-day digital payments for consumers The source lists three specific use cases of artificial intelligence in consumer-driven payments: Smart wallets, AI-powered checkouts, and chatbots. One example includes Amazon's Just Walk Out (JWO) in its Amazon Go shops in the United States. The technology uses machine learning to identify what customers picked off the shelves and then bill them automatically. This solution aims at the innovation consumers hope to see most in shopping, especially online: A seamless payments experience. Payment providers had a similar impression, in that they observed a demand among their clients for real-time payments. More so than for lower payment processing costs or cross-border payment solutions. The source adds certain payment solutions might already be using AI in the background, but that consumers are simply not aware of them. AI pros and cons for financial services The finance industry is expected to make heavy use of artificial intelligence's capabilities for years to come. AI's ability to monitor trends and improve data analytics, especially, is popular among financial service providers. Another popular use is that AI can help process large quantities of data. This is especially useful for larger investment-style banks. There are concerns, though. Data issues and growing concerns about keeping talent on board to help out with issues or data sciences ranked as the top AI concerns in 2024.

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Synthetic Monitoring Market Size 2024-2028

The synthetic monitoring market size is forecast to increase by USD 343.7 mn at a CAGR of 6.1% between 2023 and 2028.

The market is experiencing significant growth due to various factors. One key trend is the increasing adoption of SaaS solutions, which offer cost-effective and flexible monitoring options for businesses. Additionally, the integration of machine learning (ML) and big data analytics in synthetic monitoring solutions is enabling predictive analysis and automation, enhancing the overall value proposition. Cybersecurity concerns are also driving the market, as synthetic monitoring provides an effective means of detecting and mitigating threats in real-time. Mobile apps, telecommunication, automotive, and IT services are major industries adopting synthetic monitoring for digital transformation and ensuring optimal application performance. Cloud computing is another major growth factor, as businesses increasingly move their operations to the cloud and require robust monitoring solutions to ensure availability and reliability.
Data security is a critical concern, and synthetic monitoring offers advanced data visualization tools and API management capabilities to help mitigate risks. Moreover, the availability of open-source software and the growing popularity of social networking platforms are also contributing to the market growth. Predictive analytics and automation are becoming essential features of synthetic monitoring solutions, enabling businesses to proactively address potential issues and optimize their digital operations. Overall, the market is poised for significant growth, driven by these key trends and factors.

What will be the Size of the Synthetic Monitoring Market During the Forecast Period?

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The market is experiencing significant growth as businesses increasingly prioritize IT infrastructure reliability and customer experience. This market encompasses solutions that use big data and advanced technologies such as machine learning (ML) and artificial intelligence (AI) for fault detection and predictive analytics. The adoption of cloud-based infrastructure, DevOps practices, and digital strategy have driven the demand for real-time issue resolution and IT infrastructure optimization. Cybersecurity remains a critical concern, with synthetic monitoring playing a crucial role in detecting and mitigating threats. User experience monitoring, ITIL, and IT service management are key areas of focus, as businesses aim to enhance customer satisfaction and improve operational effectiveness.
Data sharing, data analysis, and data visualization are also essential components of synthetic monitoring, enabling IT governance, business continuity, and data security. The market caters to various industries, including financial institutions, mobile app usage, and website updates. Mobile device outages, social media marketing, and cloud migration are other areas where synthetic monitoring solutions are increasingly being employed to ensure IT infrastructure management and digital transformation. Technology adoption continues to shape the market, with IT strategy, IT infrastructure optimization, and business intelligence driving the need for more sophisticated monitoring solutions.

How is this Synthetic Monitoring Industry segmented and which is the largest segment?

The synthetic monitoring 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.

Type

  API monitoring
  Website monitoring
  Mobile applications monitoring


Geography

  APAC

    China
    India


  North America

    Canada
    US


  Europe

    Germany


  South America



  Middle East and Africa

By Type Insights

The api monitoring segment is estimated to witness significant growth during the forecast period. API monitoring involves assessing the performance, availability, and functional correctness of APIs, primarily in production, to ensure application success. Synthetic monitoring is a preferred method for API monitoring, enabling the detection of API outages and subpar calls that can lead to application failure. This practice is essential when utilizing third-party components, such as payment services and CRM systems like Salesforce. The increasing reliance on third-party services is anticipated to boost the demand for API monitoring solutions, contributing to the expansion of the market during the forecast period. API monitoring plays a pivotal role in industries like healthcare, retail, automotive, and telecom, where IT systems, customer behavior, and mobile application demand necessitate optimal infrastructure performance and user experience.

Infrastructure, mobile devices, and web deployment a

Digital persona enrollment data.

As of 2023, about 30 percent of surveyed employees from companies in the United States of America and United Kingdom claim to use artificial intelligence (AI) in the logic-based task of data analysis. Approximately 25 percent claim to use it for routine administrative tasks. These numbers are forecasted to grow, as the share of employees that wish to use the technology for both tasks is much higher, lying around 60 percent.

Links

• isSupplementTo publication-article https://doi.org/10.46298/jtcam.12590
• isNewVersionOf dataset https://zenodo.org/records/10090469

Authors

• Leygue, Adrien, Ecole Centrale de Nantes, ORCID: 0000-0003-0714-822X

Language

• English

License

• Creative Commons Attribution 4.0

Funding sources

• This work was performed by using HPC resources of Centrale Nantes Supercomputing Center on the cluster Liger, granted and identified D1705030 by the High Performance Computing Institute(ICI).

Data structure and information

Synthetic data used in the case study (section 3) of the paper.

The data in XDMF (Milou.xdmf ) + hdf5 (Milou.hdf5) format comprises:

1. The 2D computational mesh with triangular linear elements
2. The nodal Forces for all loading steps (nodal quantity)
3. The displacement for all loading steps (nodal quantity)
4. Cauchy stress fields for all loading steps (cell quantity)

Explore a detailed Digital Wallet Transactions Dataset featuring 5,000 synthetic records. Ideal for analyzing payment behaviors, spending patterns, and developing AI models.

Examples of synthetic health datasets and their characteristics.

During the September 2023 survey, 70 percent of responding brand and agency marketers from the United Kingdom stated that data privacy and compliance was a challenge of implementing artificial intelligence (AI) within digital advertising. The was the most frequently named issue. Technical complexity of artificial intelligence (AI) integration ranked second, mentioned by 63 percent of respondents.

The DNA digital data storage market is experiencing robust growth, driven by the increasing demand for long-term, secure, and high-density data storage solutions. The market's capacity to store vast amounts of information within a compact space, coupled with its inherent stability and longevity, addresses the limitations of traditional storage methods. This burgeoning sector is projected to reach a market size of $1.5 billion by 2025, demonstrating a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033. This significant growth trajectory is fueled by several key factors, including the escalating volume of digital data generated globally, concerns over data security and longevity, and advancements in DNA sequencing and synthesis technologies that are continuously driving down costs and increasing efficiency. The cloud-based segment is expected to lead the market due to its scalability and accessibility, while the archival application segment will maintain a substantial share, driven by the need for long-term data preservation in various sectors such as government, healthcare, and media. Major players like Twist Bioscience, Illumina, and Western Digital are actively shaping the market landscape through strategic partnerships, research and development initiatives, and the introduction of innovative products and services. Geographical expansion is also a prominent trend, with North America and Europe currently dominating the market. However, regions like Asia Pacific are expected to witness significant growth over the forecast period, fueled by increasing digitalization and investment in research and development within the life sciences and technology sectors. While the high initial cost of DNA synthesis and the need for specialized infrastructure represent challenges to widespread adoption, ongoing technological advancements and economies of scale are expected to progressively mitigate these hurdles, ultimately accelerating market expansion.

The democratization of AI offers new opportunities for small businesses in digital marketing. This study presents a practical AI toolkit with ten key applications to enhance efficiency, creativity, and decision-making. It bridges AI advancements with real-world use, optimizing marketing and operations.

In 2021, the market for artificial intelligence (AI) in marketing was estimated at 15.84 billion U.S. dollars. The source projected that the value would increase to more than 107.5 billion by 2028.

What is AI and who uses it?

Artificial intelligence (AI) has become one of the most impactful digital innovations of the past few decades. The term refers to the ability of a computer or machine to mimic the competencies of the human mind, with the current ecosystem consisting of machine learning, robotics, artificial neural networks, and natural language processing. All of these features and algorithms are highly versatile and adaptable to the specific requirements of the user, explaining why they have become embedded into many different industries, ranging from telecommunications and financial services to healthcare and pharma. Overall, the global artificial intelligence market was valued at around 327 billion U.S. dollars in 2021.

AI at the marketing wheel

AI is deeply embedded into the digital marketing landscape, and based on the latest reports, more than 80 percent of industry experts integrate some form of AI technology into their online marketing activities. This vast adaptation of artificial intelligence for marketing purposes is no surprise considering that its benefits include task automation, campaign personalization, and data analysis, to name but a few. When asked about marketers' main application areas of AI in a recent survey, roughly 50 percent of respondents from the U.S., Canada, the UK, and India mentioned ad targeting. Other popular activities they trusted AI with included personalizing content, optimizing e-mail send times, and calculating conversion probability.

The measurement and diagnosis of the severity of failures in rotating machines allow the execution of predictive maintenance actions on equipment. These actions make it possible to monitor the operating parameters of the machine and to perform the prediction of failures, thus avoiding production losses, severe damage to the equipment, and safeguarding the integrity of the equipment operators. This paper describes the construction of a dataset composed of vibration signals of a rotating machine. The acquisition has taken into consideration seven distinct operating scenarios, with different speed values. Unlike the few datasets that currently exist, the resulting dataset contains simple and combined faults with several severity levels. The considered operating setups are normal condition, unbalance, horizontal misalignment, vertical misalignment, unbalance combined with horizontal misalignment, unbalance combined with vertical misalignment, and vertical misalignment combined with horizontal misalignment. The dataset described in this paper can be utilized by machine learning researchers that intend to detect faults in rotating machines in an automatic manner. In this context, several related topics might be investigated, such as feature extraction and/or selection, reduction of feature space, data augmentation methods, and prognosis of rotating machines through the analysis of failure severity parameters.

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Cloud Artificial Intelligence (AI) Market Size 2024-2028

The cloud artificial intelligence (ai) market size is forecast to increase by USD 12.61 billion at a CAGR of 24.1% between 2023 and 2028.

The market is experiencing significant growth, driven by the emergence of technologically advanced devices and the increasing adoption of 5G and mobile penetration. These factors enable the integration of AI technologies into various applications, leading to improved efficiency and productivity. However, the market also faces challenges from open-source platforms, which offer free AI solutions, making it difficult for market players to compete on price. Despite this, the market is expected to continue its growth trajectory, driven by the increasing demand for AI solutions in various industries, including healthcare, finance, and retail. Organizations are leveraging cloud-based AI solutions to gain insights from their data, automate processes, and enhance customer experiences.The market analysis report provides a comprehensive overview of these trends and challenges, offering valuable insights for stakeholders looking to capitalize on the growth opportunities In the cloud AI market.

What will be the Size of the Cloud Artificial Intelligence (AI) Market During the Forecast Period?

Request Free SampleThe market is experiencing robust growth, driven by the increasing adoption of machine learning (ML), deep learning, neural networks, and generative AI technologies. These advanced algorithms are revolutionizing various industries by emulating human intelligence in speech recognition, digital media, diagnostics, cybersecurity, and business decision-making. Hyperscale cloud platforms are becoming the preferred infrastructure for AI applications due to their ability to handle massive data processing requirements. Cloud AI solutions are transforming IT services by automating routine tasks, enhancing data analytics, and improving human capital management. They offer significant cost savings by eliminating the need for expensive hardware and maintenance. Moreover, AI-driven cloud management and data management solutions enable predictive analytics, personalization, productivity, and security enhancements.In addition, AI is playing a pivotal role in threat detection and cybersecurity, ensuring business continuity and data protection. Overall, the cloud AI market is poised for exponential growth, as organizations continue to leverage AI to gain a competitive edge In their respective industries.

How is this Cloud Artificial Intelligence (AI) Industry segmented and which is the largest segment?

The cloud artificial intelligence (ai) 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. ComponentSoftwareServicesGeographyNorth AmericaUSEuropeGermanyUKAPACChinaJapanSouth AmericaMiddle East and Africa

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

Artificial Intelligence (AI) software replicates human learning and behavior, revolutionizing various business sectors. AI development involves creating new software or enhancing existing solutions to deliver analytics results and trigger actions based on them. Applications of AI include automating business processes, personalizing services, and generating industry-specific insights. The digitization trend has driven industrial transformations, with healthcare being a prime example. According to BDO's Healthcare Digital Transformation Survey, 93% of US healthcare organizations adopted digital transformation strategies in 2021, integrating AI, computing, and enterprise resource planning software. AI functionality encompasses speech recognition, machine learning (ML), deep learning, neural networks, generative AI, automation, decision-making, and more.Hyperscale cloud platforms, IT services, infrastructure, data analytics, human capital management, cost savings, cloud management, data management, predictive analytics, personalization, productivity, security, threat detection, integration, talent gap, and chatbots are significant AI applications. AI tools process data, power business intelligence, and enable lower costs through ML-based models and GPUs. Enterprise datacenters, virtualization, public clouds, private clouds, and hybrid cloud solutions leverage AI for non-repetitive tasks. AI streamlines workloads, automates repetitive tasks, monitors and manages IT infrastructure, and offers dynamic cloud services. AI is transforming industries, from retail inventory management to financial organizations, providing competitive advantages through cost savings and improved decision-making capabilities.

Get a glance at the Cloud Artificial Intelligence (AI) Industry repo