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If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
For instance, specific data that a neural network might not be able to process, such as the reasoning behind the results of an insurance claim -- might not have a straightforward representation in machine learning because of possible interpretations. This issue of overfitting is a typical problem of AI, and a variety of use cases, and data might bring up additional challenges that the human brain can handle and adapt to more easily and creatively. For example, if there are exceptions to the rules in issues of fraud detection in the financial industry, both experts and customers alike would want to know all of the elements that led to the AI's decision and require some transparency regarding the outcome. Few things are more frustrating for business owners than a missed target or a misplaced investment, but cognitive biases can hinder intelligent decisions and cost every year. But if your business faces a sudden uncertainty, a proclivity for deep thinking, over-analyzing, and compensating for lower performance through shortcuts doesn't help.
With billions of dollars at stake, decision-makers need to set boundaries and parameters for AI to avoid any downsides to technology usage. It is critical to know how to avoid common mistakes with neural networks to feel confident about your solution stack. AI processes information differently, and it's essential to understand how each works before applying it in business. For instance, specific data that a neural network might not be able to process, such as the reasoning behind the results of an insurance claim -- might not have a straightforward representation in machine learning because of possible interpretations. In this situation, the output of a neural network might not have quality results.