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What Problems Should You Solve With AI?

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Talk of artificial intelligence (AI) is inescapable these days. There’s a reason for that: The availability of and access to more computing power and data sets have ushered in breakthroughs of all kinds in everyday life, from cashierless grocery stores to voice-activated devices that respond to our commands from across the room.

The palpable excitement around AI centers on its potential to revolutionize seemingly every facet of every industry. But AI isn’t a cure-all. Just because machines will eventually be able to learn almost anything asked of them doesn’t mean they should. So, what are the specific problems teams working on AI projects should focus on solving? Here are three criteria my team follows.

Use AI to solve problems you can prototype first.

I suggest prototyping every AI application before commercializing it. That will provide an opportunity to test, iterate and fail fast at a low cost and in a safe environment. Without prototyping first, the product has a more limited ability to make a meaningful impact — and can even jeopardize your reputation.

While Waymo’s self-driving technology is now deployed in Chrysler minivans, its original home was in Firefly, a two-seater prototype vehicle that could not exceed 25 mph. Waymo, an Alphabet company, shared its intent for Firefly to be “a platform to experiment and learn, not for mass production.” Prototyping allowed the company to work out various kinks in low-risk settings like freeways before progressing to more complex situations like city streets. Following the same approach in your AI initiatives can ensure your products have an established track record and a sufficient level of polish before they enter prime time.

Use AI to solve problems in applications where you can afford to make mistakes.

In order to continuously improve, we must create AI with a feedback loop that highlights when it makes the wrong decisions. Applying previous knowledge will continue to ensure smarter, more accurate assumptions. This means you should start deploying AI in areas where the cost of making mistakes will not make a significant negative impact on your customer experience or reputation.

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Article Credit: Forbes

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