Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data.Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature representation.Results: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data samples.Conclusion: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.

This document reports current trends and examples of Federal R&D investments, program information, and activities in artificial intelligence that directly address the R&D challenges and opportunities noted in The National Artificial Intelligence Research and Development Strategic Plan: 2019 Update, available at https://www.nitrd.gov/pubs/National-AI-RD-Strategy-2019.pdf

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Artificial Intelligence Platforms Market Size 2024-2028

The artificial intelligence platforms market size is forecast to increase by USD 64.9 billion at a CAGR of 45.1% between 2023 and 2028. The market is experiencing significant growth due to the rising demand for AI-based solutions in various industries. Businesses are increasingly adopting AI technologies to automate processes, enhance productivity, and improve customer experiences. Another trend driving AI platforms market growth is the increasing interoperability among neural networks, enabling seamless data exchange and collaboration between different AI systems. However, the market also faces challenges such as the rise in data privacy issues and ethical concerns related to AI usage. As data becomes a valuable asset, ensuring its security and privacy is paramount for businesses implementing AI solutions. This dynamic market landscape underscores the critical role of artificial intelligence platforms in driving innovation and efficiency across various sectors such as education and telecommunications. Additionally, there is a need for clear regulations and guidelines to address ethical concerns and ensure transparency in AI decision-making. Overall, the market for artificial intelligence platforms is expected to continue its growth trajectory, driven by these trends and challenges.

What will be the Size of the Artificial Intelligence Platforms Market During the Forecast Period?

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Artificial Intelligence Platforms Market Segmentation

The AI platforms market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.

Deployment Outlook 

  On-premise
  Cloud-based


Application Outlook

  Retail
  Banking
  Manufacturing
  Healthcare
  Others


Region Outlook

  North America

    U.S.
    Canada





  Europe

    U.K.
    Germany
    France
    Rest of Europe





  APAC

    China
    India





  Middle East & Africa

    Saudi Arabia
    South Africa
    Rest of the Middle East & Africa


  South America

    Chile
    Brazil
    Argentina

By Application Insights

The retail segment is estimated to witness significant growth during the forecast period. Artificial intelligence (AI) is revolutionizing various industries by enabling advanced data processing, pattern identification, and decision-making capabilities. In healthcare, AI is used for medical imaging analysis, drug discovery, and patient care. In the food and beverages sector, AI is employed for supply chain optimization and product innovation. Digital technologies, including AI software, are transforming banking by facilitating algorithmic trading, fraud detection, and credit risk assessment.

Industry adoption of AI is also prominent in business intelligence, customer experience, and operational efficiency. The emergence of technologies such as big data, IoT, customer relationship management (CRM), and workflow automation are accelerating technological transformations in the sector. AI is used to provide personalized recommendations, automate processes, and optimize workflows. Intelligent virtual assistants, chatbots, natural language processing, speech recognition, and conversational AI interactions are increasingly being used to enhance customer experience.

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The retail segment accounted for USD 662.60 million in 2018. Industry-specific AI Solutions are being developed for finance, where they are used for regulatory support, ethical considerations, data privacy, and security concerns. AI as a service (AIaaS) and cloud computing platforms are enabling businesses to leverage AI capabilities without having to build and maintain their own infrastructure.

Autonomous systems are being adopted for process optimization in manufacturing and logistics. In conclusion, AI is transforming industries by enabling advanced data processing, pattern identification, and decision-making capabilities. Its applications include healthcare, food and beverages, banking, business intelligence, customer experience, and operational efficiency. AI is also being used to develop industry-specific solutions for finance, and to enable autonomous systems for process optimization. Despite the numerous benefits, ethical considerations, data privacy, and security concerns remain key challenges.

Regional Analysis

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North America is estimated to contribute 66% to the growth of the global artificial intelligence platforms market during the market forecast period. Technavio's analysts have elaborately explained the regional trends an

This dataset is an inventory of the uses of artificial intelligence (AI) at USDA. The inventory was developed and published as required by OMB M-24-10, "Advancing Governance, Innovation, and Risk Management for Agency Use of Artificial Intelligence". The inventory attributes were collected in accordance with a data standard established by OMB.

Artificial Intelligence in Construction Market size was valued at USD 892.66 Million in 2023 and is poised to grow from USD 1197.06 Million in 2024 to USD 12518.34 Million by 2032, growing at a CAGR of 34.1% during the forecast period (2025-2032).

BiorXiv Pdf

BiorXiv PDF dataset is a collection of PDF documents gathered from the BiorXiv website. This initiative aims to democratize artificial intelligence research by providing researchers with access to readily available training datasets. It is part of our broader effort to publish open access research papers as collective datasets. BiorXiv is a renowned preprint publication in the field of biology and related disciplines. It is operated by Cold Spring Harbor Laboratory (CSHL)… See the full description on the dataset page: https://huggingface.co/datasets/laion/biorXiv-pdf.

The full text of this article can be freely accessed on the publisher's website.

XAI-FUNGI: Dataset from the user study on comprehensibility of XAI algorithms

We present the dataset which was created during a user study on evaluation of explainability of artificial intelligence (AI) at the Jagielloninan University as a collaborative work of computer science (GEIST team) and information sciences research groups. The main goal of the research was to explore effective explanations of AI model patterns to diverse audiences.

The dataset contains material collected from 39 participants during the interviews conducted by the Information Sciences research group. The participants were recruited from 149 candidates to form three groups that represented domain experts in the field of mycology (DE), students with data science and visualization background (IT) and students from social sciences and humanities (SSH). Each group was given an explanation of a machine learning model trained to predict edible and non-edible mushrooms and asked to interpret the explanations and answer various questions during the interview. The machine learning model and explanations for its decision were prepared by the computer science research team.

The resulting dataset was constructed from the surveys obtained from the candidates, anonymized transcripts of the interviews, the results from thematic analysis, and original explanations with modifications suggested by the participants. The dataset is complemented with the source code allowing one to reproduce the initial machine leaning model and explanations.

The general structure of the dataset is described in the following table. The files that contain in their names [RR]_[SS]_[NN] contain the individual results obtained from particular participant. The meaning of the prefix is as follows:

• RR - initials of the researcher conducting the interview,
• SS - type of the participant (DE for domain expert, SSH for social sciences and humanities students, or IT for computer science students),
• NN - number of the participant

The detailed structure of the files presented in the previous Table is given in the Technical info section.

The source code used to train ML model and to generate explanations is available on Gitlab

Artificial Intelligence of Things (AIoT) Market size was valued at USD 65.50 billion in 2022 and is poised to grow from USD 82.01 billion in 2023 to USD 495.09 billion by 2031, growing at a CAGR of 25.2% during the forecast period (2025-2032).

Round 12 Train Dataset This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of pdf malware classification AIs trained Contaigio dataset feature vectors. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 120 AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

MedrXiv Pdf

Introducing MedrXiv Pdf, a dataset that offers access to all PDFs published until September 15, 2024. This resource aims to facilitate artificial intelligence research and the training of domain-specific scientific models. As part of our efforts to democratise knowledge in the scientific domain, we have compiled this dataset. While most papers included have non-restrictive and open access licences, certain PDFs may have additional restrictions. Researchers are encouraged to… See the full description on the dataset page: https://huggingface.co/datasets/laion/medrXiv-pdf.

Original data sources (Raw and processed ) from the pre-test/post-test questionaries of the article How do Machines learn. xlsx and pdf files are provided. Links for supplementary materials are also avaiable here.

Artificial Intelligence (AI) Market size was valued at USD 285.9 Billion in 2023 and is poised to grow from USD 331.42 Billion in 2024 to USD 1213.68 Billion by 2032, growing at a CAGR of 15.92% during the forecast period (2025-2032).

Global Artificial Intelligence (AI) in Computer Vision Market size was valued at USD 20.7 billion in 2022 and is poised to grow from USD 25.8 billion in 2023 to USD 148.8 billion by 2031, growing at a CAGR of 24.5% during the forecast period (2024-2031).

The global PDF solutions market is experiencing robust growth, driven by the increasing reliance on digital documents across various sectors. The market, estimated at $5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $9 billion by 2033. This growth is fueled by several key factors. The rising adoption of cloud-based solutions offers enhanced collaboration and accessibility, contributing significantly to market expansion. Furthermore, the increasing demand for secure and efficient document management systems across government, education, and enterprise sectors fuels the need for advanced PDF editing and reader tools. The integration of artificial intelligence (AI) and machine learning (ML) into PDF solutions is enhancing features like automated document processing and intelligent search capabilities, further driving market growth. Specific application segments, such as government and enterprise, exhibit high growth potential due to the substantial volume of documents they handle daily, requiring sophisticated PDF management solutions. The increasing preference for user-friendly interfaces and cross-platform compatibility is another important trend. While potential restraints such as the availability of free or open-source alternatives exist, the overall market outlook remains positive, driven by the consistent need for efficient, secure, and feature-rich PDF solutions. The market segmentation reveals a diverse landscape. The PDF editor segment currently holds a larger market share compared to the PDF reader segment, reflecting the increasing demand for advanced functionalities beyond simple document viewing. Geographically, North America and Europe dominate the market, driven by high technological adoption and established digital infrastructure. However, the Asia-Pacific region is expected to show significant growth in the coming years, fueled by rapid economic development and increasing digitalization across various sectors in countries like China and India. Key players like Adobe, Nitro, and Foxit are consolidating their market position through innovation and strategic partnerships, while smaller companies are focusing on niche applications and specialized functionalities to gain market share. The competitive landscape remains dynamic, with ongoing innovations and mergers and acquisitions expected to shape the market further in the coming years.

Global Artificial Intelligence (AI) Robots Market size was valued at USD 12 billion in 2022 and is poised to grow from USD 14.58 billion in 2023 to USD 69.24 billion by 2031, growing at a CAGR of 21.5% in the forecast period (2024-2031).

The full text of this article can be freely accessed on the publisher's website.

The global Enterprise PDF Document Solutions market is projected to reach USD 14.24 billion by 2030, exhibiting a CAGR of 8.5% during the forecast period. The expanding need for efficient document management solutions and the increasing adoption of cloud-based enterprise services are driving the market growth. PDF document solutions offer enhanced security features, improved collaboration capabilities, and real-time editing functionalities, making them a preferred choice for organizations seeking to streamline their document workflow. Key market trends include the rising adoption of mobile PDF solutions for remote work and the growing demand for automated PDF processing. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) capabilities within PDF document solutions is expected to further enhance their efficiency and functionality. The market is characterized by the presence of established players such as Adobe and Nitro, as well as emerging vendors offering innovative solutions. The competitive landscape is expected to remain dynamic as companies invest in research and development to stay ahead of the curve. Regional growth drivers, key market segments, and company profiles are provided in the market research report.

The PDF to AI Converter market is experiencing robust growth, driven by the increasing need for automated document processing and the rising adoption of artificial intelligence across various industries. The market's expansion is fueled by several key factors: the surge in digital document creation, the demand for efficient data extraction from PDFs, the need for improved accessibility for individuals with disabilities, and the increasing reliance on automation to streamline workflows. While precise market sizing data is unavailable, a reasonable estimation, considering the growth in related AI and document processing markets, would place the 2025 market value at approximately $250 million. A Compound Annual Growth Rate (CAGR) of 20% seems plausible, suggesting significant market expansion through 2033. Key segments likely include converters focusing on specific PDF functionalities (e.g., image extraction, data table conversion, OCR) and those catering to diverse industry verticals (e.g., legal, healthcare, finance). Market restraints could include concerns surrounding data security and privacy, the complexity of AI integration into existing workflows, and the need for user-friendly interfaces to encourage widespread adoption. The competitive landscape is dynamic, with established players like Adobe and emerging companies offering specialized solutions. The forecast period (2025-2033) presents substantial opportunities for innovation and market penetration. We anticipate continued growth driven by advancements in AI algorithms leading to more accurate and efficient conversions. The development of user-friendly interfaces will be crucial for widespread adoption across different user skill levels. Furthermore, strategic partnerships between AI solution providers and document management systems (DMS) vendors will fuel market expansion. The focus should be on enhancing the accuracy and speed of conversions while addressing data security and privacy concerns effectively. Meeting these challenges will be pivotal in shaping the future trajectory of the PDF to AI converter market.

Global Artificial Intelligence (AI) in Retail Market is valued at USD 5.59 Billion in 2022 and is expected to grow from USD 7.42 Billion in 2023 to reach USD 71.23 Billion by 2031, at a CAGR of 32.68% during the forecast period (2024-2031).