From the course: How to Measure Anything in AI: Quantitative Techniques for Decision-Making
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Measuring the current state to measure AI performance
From the course: How to Measure Anything in AI: Quantitative Techniques for Decision-Making
Measuring the current state to measure AI performance
- One thing I've noticed about people trying to measure AI is that they often don't have the current performance understood well enough to benchmark against any potential improvements from utilizing AI. In order to measure the change in the speeds of things, the quality of output, the cost of output, et cetera, you need to know your current performance. In other words, how fast is a specific process now? What is the current level of quality? And what is the current cost and benefit of some process? Do they know what the value of saving time is? Do they know the cost of correcting errors on a specific task? Do they know the value of getting to the market faster? Do they know the value of higher-quality analysis for decision making? Do they know the cost of losing or gaining a customer? If they don't know these before testing AI, they won't know the improvement or the value of the improvement from AI. If you list the processes where you might use AI, you can start to fill in the blanks…
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