Arisha Smith is business leaders and innovator. The Dallas resident is the principal and founder of Idyllic Interactive, an agency set to assist you in growing your business and make smarter data driven decisions. Read to learn how she got to the top. What inspires you to show at work everyday? Since I own my own business, it's the opportunity to create a legacy for my children.
In the past 20 years, we've seen a number of industries transformed by artificial intelligence, ranging from fast food to enterprise revenue management. My company uses AI to provide vehicle maintenance, so I've seen the various applications of the technology firsthand. I've also seen that, despite the hype, there is still a great deal of resistance to AI in the mainstream. At its core, AI is just the science of making things smart. Machine learning is an aspect of AI that uses large amounts of data to learn how to perform a specific task.
Artificial intelligence is more than just hype now, the technology has already found its way into multiple areas of our lives. It's in the digital ads we view online every day. The technology is also increasingly powering our businesses. AI's ability to enable new ways of competing – including transforming business models, together with understanding possible risks and ethical issues – mean that embracing this new technology should be part of every future-facing CEO's job spec. Unfortunately, this isn't always the case, with many leaders still seeing it as something to be delegated to CTOs.
Unless model optimize directly the business KPI, model offline performance gain wouldn't always translate to business value gain. For example, the hotel recommendation model optimizing towards click through rate would recommend similar hotels as to user previously clicked ones, which encouraged users to click and compare more hotel listings. However this may drawn the user into the paradox of choice and hurt actually hurt booking rate. There's no obvious solution to solve this issue, but the paper suggested to use offline metric only as a health check, and evaluate models base more on online testing of business metric. One example given in the paper is when diversity was hypothesized to be important for a recommender system and a new model with lower RMSE but more diversity was developed, it should be tested online against current model regardless of its lower RMSE, to see if it provides better business value gain.
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