Tag: Humans-in-the-loop

  • VentureNet: Reducing barriers to innovation and business development through secure data exchange

    VentureNet: Reducing barriers to innovation and business development through secure data exchange

    September 1, 2022 @ 8:00 am – September 30, 2023 @ 8:00 am CEST

    (PROJECT)

    The VentureNet data exchange project was a research project completed in 2023 in collaboration with SINTEF and the Norwegian Centre for Research and Innovation in Cybersecurity in Critical Sectors (SFI NORCICS) based at the Norwegian University of Science and Technology (NTNU). The project received co-financing from the Research Council of Norway and Innlandet County through the FORREGION program.

    Background

    A major EU industrial research project on data harmonization, euBusinessGraph, identified major challenges with access to business data, citing that “it is extremely expensive, time consuming and error prone to find, interpret and reconcile”, especially across industries, geographic regions and languages.

    Machine learning (ML) plays an important role in automating data processing tasks such as natural language processing (NLP), but human input is needed to train ML algorithms and thereby improve the accuracy of ML models. One of the challenges identified by data scientists is a lack of machine learning models with humans-in-the-loop (HITL). 80% of the cost of Big Data and Artificial Intelligence (AI) projects is time spent by data scientists finding, interpreting and harmonizing data. A lack of HITL limits the effectiveness of ML models and their potential to reduce cost in Big Data and AI projects.

    VentureNet aims to create a virtual business accelerator hub powered by its proprietary Information Management System (IMS), which centralizes access to multiple digital ecosystems, facilitating data exchange and reducing manual data entry. Developed in collaboration with SINTEF and NTNU, the project focuses on creating a secure data exchange framework with Human-in-the-Loop (HITL) capabilities that can be scaled up in European data markets.

    Project Results

    The project delivered a report introducing a novel framework and new technical solution for data exchange that will facilitate dynamic harmonization and enrichment of business data with HITL ML models. The envisioned solution is a digital B2B workspace and marketplace that will automate data management tasks and processes and facilitate involvement of non-technical data providers (small businesses) in data enrichment with HITL models.

    SINTEF compared VentureNet’s IMS to leading low-code platforms and AI solutions, such as Oracle APEX, AWS Amplify, Open AI GPT, Sheet GPT, and Google Natural Language API and concluded the following:

    We compared VentureNet against a few low-code/no-code platforms. Low-code/no-code platforms significantly accelerate software development by simplifying and automating code generation, making it possible for non-developers to create applications, thereby democratizing development and reducing the technical skill barrier. However, these platforms can limit customization capabilities, restrict advanced functionality, and lead to vendor lock-in situations, potentially impeding scalability, and long-term growth for more complex or unique business requirements.

    Potential for higher accuracy and filtering of hallucinations, using HITL, especially in sensitive domains, such as healthcare.

    The principal advantage of the VentureNet platform lies in its comprehensive integration capabilities. It can fulfill all project requirements, including form processing, API management, low-code graphical user interface elements, and data enrichment. Unlike other generic platforms, VentureNet enables the development of highly customized solutions tailored to specific project needs.

    Although it is possible to assemble similar platforms using excellent off-the-shelf tools, coordinating, and integrating these disparate components requires significant time and effort. Additionally, the complexities associated with billing processes can often pose substantial challenges. VentureNet solves these concerns by offering a unique solution that simplifies project management and reduces operational overhead. This approach ensures a streamlined workflow and efficient project execution.

    Contact VentureNet to learn more about the project results and collaboration opportunities.

    VentureNet

  • Digitalization, innovation and network effects in rural and mountainous regions: a study of the status quo and opportunities for the IMS

    Digitalization, innovation and network effects in rural and mountainous regions: a study of the status quo and opportunities for the IMS

    June 1, 2021 @ 8:00 am – May 31, 2022 @ 5:00 pm CEST

    (PROJECT)

    The IMS project was completed in 2022 with support from the Eastern Norway Research Institute (a division of the University of Inland Norway) and SINTEF, one of Europe largest independent research institutions. The project was managed by an affiliate of VentureNet, which VentureNet acquired in 2023, and the project received co-financing from the Research Council of Norway and Innlandet County through the FORREGION program.

    Background: small-and-medium-enterprises (SMEs) need help with data management.

    Problem: Limited availability and access to private sector (business) data.

    • High cost of generating business data and analysis for startups and SMEs.
    • High cost of harmonizing business data using Big Data and artificial intelligence (AI).

    Opportunities: make business analysis more cost-effective for SMEs using Big Data.

    • “Core” business data points are reused in sales, fundraising and other processes.
    • Gather existing “core” business data using Big Data to save time and cost for SMEs and advisors.
    • Exchange “core” business data across digital platforms to save cost and generate revenue for SMEs, advisors, customers, investors, making SME data more valuable and cost-effective to analyze.

    Challenges required advanced knowledge of regional dynamics and data science.

    • Lack of digital platforms designed to make use of business data exchange and virtual networking.
    • How to build a network of businesses and advisors that benefit from enriching and exchanging data.
    • Technical bottlenecks in Big Data related to data harmonization, enrichment and exchange.

    Project objective

    Inform design of a main research project to develop new machine-learning and data exchange models for Big Data and digital B2B platforms that accelerate digitalization, innovation and network effects for SMEs in rural areas.

    Hypotheses for lower growth rates in rural and mountainous regions:

    • The Lemon Problem: lower trust in SMEs due to a lack of verified data (“Information Asymmetry).
    • Lower access to capital and specialist expertise in non-metropolitan areas (“Agglomeration Effects”).
    • Lower growth ambition? The Inland region of Norway has a high proportion of micro-enterprises.
    • Is this due to a perception of lower access to private capital?
    • Possible lower access to specialist/M&A expertise?
    • Other factors, such as cultural factors, systemic inequalities or biases, or lower competitiveness?

    Project Results

    Key results from the University of Inland Norway

    • Statistical analysis of ICT companies in the Inland region using publicly available data from Proff.
    • Use of Centralization Index as a basis for future ethnogeography studies and statistical analysis.
    • Mapping of advisory services and financial services available for companies in Innlandet.
    • Questionnaire templates to evaluate knowledge and utilization of digital tools and advisory services.
    • Other relevant suggestions from HINN colleagues for use in future research projects:
      • Social network analysis and gender study methods to analyze cultural and systemic factors linked to growth.
      • Analysis of industry clusters and network effects to identify best practice and factors linked to value creation.

    Key results from SINTEF

    • Clarified that the IMS has an innovative approach to data enrichment, which can be used to develop advanced machine-learning models with Humans-In-The-Loop, a current challenge for data scientists.
    • Identified opportunities to reduce time and cost of data entry by using machine-learning techniques for data harmonization and enrichment.
    • Identified areas for further development: Data Fusion, Dynamic Data Enrichment, Data Exchange.
    • Delivered a toolbox and recommendations for potential suppliers and partners to enhance the IMS:
      • Several leading data sets providing broad access to available business data in Norway and Europe.
      • Leading ontologies and vocabularies for harmonizing and enriching data with machine-learning.
      • Advanced methods and software for data mining, harmonization, and enrichment.

    Project Outcomes

    Clear path for development of advanced Big Data solutions (software, methods and data).

    • Data Harmonization/Fusion: integrate multiple data sources to dynamically produce structured data.
    • Data Enrichment: develop HITL machine-learning models to dynamically enrich missing/incomplete data.
    • Data Exchange: re-use “core” business data securely and cost-effectively across platforms and users.

    Scalable frameworks and templates for further research and studies into regional dynamics.

    Plan and partnerships to build a user ecosystem and test platform for data exchange.

    Clear project opportunities and expressions of interest from leading research institutions.

    • SINTEF invitation to join the EU Horizon “EnrichMyData” industrial research project with OECD.
    • Dialog with SINTEF and NTNU-NorCIS to establish NRC Collaboration Project in cybersecurity.
    • Private sector consortium of digital platforms in NRC Innovation Project (IPN) for data exchange.
    • Assisting INN to establish “Inn-the-Loop” NRC Social Impact Project (KSP) in artificial intelligence.
    • Dialog with INN and IESE Business School to apply for EU Horizon Expanding Investments Ecosystems.

    Want to learn more?

    Contact VentureNet for more information about the project results and collaboration opportunities.

    VentureNet

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