The simplest definition of data science is the extraction of actionable insights from raw data. Artificial intelligence (AI) is the intelligence that is possessed by the machines. It is modeled after the natural intelligence that is possessed by animals and humans. AI and data science can be used interchangeably, but there are certain differences between the two fields. This interesting news came to us from Technology Networks in their article, “Why the Future of Analytical Data Must Consider AI Principles.”
As data science initiatives proliferate across institutions, stakeholders utilizing analytical results need to consider their data structures and their ability to extract and transform the exhaustive set of analytical data from relevant instrumentation. It is also important for data management to store resultant datasets in accordance with relevant data integrity principles.
Because of AI, managing and analyzing data depends less on time-consuming manual effort than in the past. People still play a vital role in data management and analytics, but processes that might have taken days or weeks (or longer) are picking up speed thanks to AI.
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
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