
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
Machine learning is powering most of the recent advancements in artificial intelligence including autonomous systems, computer vision, natural language processing, predictive analytics, and a wide range of applications among the seven patterns of AI. However, in order for these systems to be able to create accurate generalizations, these machine learning systems must be trained on data. The more advanced forms of machine learning, especially deep learning neural networks, require significant amounts of data to be able to create models with acceptable levels of accuracy. If machine learning systems are going to learn from this data, then this data needs to be clean, accurate, complete, and well-labeled so the resulting machine learning models are accurate. Whereas it has always been the case that garbage in is garbage out in computing, it is especially the case with regards to machine learning data. For companies looking to get started on their AI Data Engineering Lifecycle, we have put together this checklist to help.
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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
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
The topic of ethics and responsibility comes up frequently in the context of Artificial Intelligence, and for good reason. The concept of intelligent machines is
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
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
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
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