Synthetic Data Generation Market: Research Snapshot Feb. 2022

Machine learning training data is not always readily available. In many cases, “ground truth” data is unavailable, can be difficult to collect, or is considered private, making its use difficult. Synthetic data is often used in scenarios where there isn’t sufficient training data available to be used in machine learning algorithms, especially in supervised machine …

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Data Labeling Market: Research Snapshot Dec. 2021

In order for machine learning systems to be able to create accurate generalizations, they must be trained on data. Advanced forms of machine learning, especially deep learning neural networks, require significant volumes of data to be able to create models with desired levels of accuracy. Customers often don’t have the resources to label large data …

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Cognilytica’s Comprehensive Ethical AI Framework

Many of the existing ethical AI frameworks currently released by government agencies, corporations, multi-stakeholder organizations and non-profit groups are lacking in different aspects of their ethical AI frameworks. As such, Cognilytica is releasing for general usage a template for ethical AI frameworks that can serve as a basis for individual organization AI implementations or as …

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Worldwide AI Country Strategies and Competitiveness 2021

The race for competitive advantage in artificial intelligence (AI) is not just the domain of companies and organizations. Countries vie with each other for dominance in the space of AI, seeking competitive advantages for their industrial ecosystem, military and defense, academic institutions, and areas of private and public sector enterprise. Countries that can take advantage …

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Machine Learning Platforms 2020

Document ID: CGR-MLP20 | Last Updated: Dec. 8, 2020 Machine learning systems are core to enabling each of the seven patterns of AI. Machine learning platforms facilitate and accelerate the development of machine learning models by providing functionality that combines many necessary activities for model development and deployment. In this report, Cognilytica evaluates five major categories …

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Digitization and Digitalization Report (June 2020)

Document ID: CGR-DIG20 | Last Updated: Jun. 23, 2020 Digitization focuses on the capturing and extraction of value from non-digital data by converting it to a usable digital state. Digitalization expands upon the idea of digitization by addressing human-bound processes and processes that have previously been dependent on non-digital information. Digitization and Digitalization are seen …

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ML Model Management and Operations 2020 (“MLOps”)

Document ID: CGR-MOM20 | Last Updated: Mar. 11, 2020 As the markets for AI shift from those organizations that have the technical expertise required to build models from scratch to those enterprises and organizations looking to consume models built by others, the focus shifts from tooling and platforms focused solely on model development to tools …

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Worldwide AI Laws and Regulations 2020

Document ID: CGR-REG20 | Last Updated: Feb. 20, 2020 The pace of adoption for artificial intelligence (AI) and cognitive technologies continues unabated with widespread, worldwide, rapid adoption of AI and its various patterns. As a result, governments around the world are moving quickly to ensure that existing laws, regulations, and legal constructs remain relevant in …

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