Artificial intelligence (AI) and machine learning not only provide new means for business users to gain quick insights, but also the possibility of improving and adding intelligence to their own operations. Venture Beat brought this interesting information to our attention in their article, “Database technology evolves to combine machine learning and data storage.”
Traditional databases companies are strengthening their connections with AI algorithms. Cloud companies are also bundling the option into their data storage products. All want to find a way to support the demands of the computation-heavy AI algorithms as they plow through larger and more complex sets of data.
However, most organizations have little visibility and knowledge on how AI systems make the decisions they do, and as a result, how the results are being applied in the various fields that AI and machine learning are being applied.
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. Why is this important? Because 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, the intelligence and the technology behind world-class explainable AI solutions.
