Depriving buyers of the same critical

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If your AI system is being sold as AI simply because it uses ML, stop misrepresenting it by calling it an AI system and call it an ML system instead. Another way to look at this is to imagine you sell squares, but instead of telling people they are squares, you describe them as quadrilaterals. That is technically accurate, but with a quadrilateral, all the buyer knows is that they are getting something with four sides. If you told them it was a square, they would know that it has four sides, all four sides are the same length, and the angles between them are 90 degrees.

You’re context when you mix AI and ML when dominican republic mobile database your product. There are specific questions to ask about ML systems. How is data populated, labeled (if at all), and updated? What types of models are used and how are they trained? What results do they produce, and how can they be tailored to specific performance goals and risk tolerances? But without knowing that they’re evaluating an ML system, buyers may ask general questions about why a system qualifies as AI instead of understanding how it functions, which can prevent them from fully understanding the product and ultimately lead to a missed opportunity.

Using the right language is a critical step forward in navigating the hype around AI and ML. If AI is the right term to describe a product, then use it, but be prepared to justify why it’s appropriate and accurate. If ML is a better term to describe a product, then leave out AI and be precise. Product descriptions should guide buyers on what they need to ask to understand whether a purchase is right for them, and they should make it easier for sellers to communicate the product’s strengths and uses of the technology. We will all begin to effectively cut through the hype when we use AI and ML with precision and accuracy.