Data sets for machine learning solve big data problems, from data collection and storage through analysis and the training of machine learning models to the deployment of real-time prediction endpoints. VentureBeat brought this interesting information to our attention in their article, “Database technology evolves to combine machine learning and data storage.”
This new approach offers not just the potential for tapping the power of artificial intelligence (AI) algorithms, but also a more flexible search engine that isn’t locked into searching for exact matches.
Most organizations have little visibility and knowledge of how AI systems make the decisions they do and how the results are being applied in the various fields. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. “Explainable AI” is used to describe an AI model, its expected impact and potential biases. Explainability becomes critical when the results can have an impact on data security or safety.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Access Innovations, changing search to found.
