At Instacart, Hybrid AI Solves The Stubborn AI Reliability Problem
Article excerpt
By Eric Siegel, Contributor. Like many AI systems, Instacart’s product-replacement system needs to gauge its own confidence in order to turn predictions into actions. AI takes on complex tasks that are impossible to solve perfectly. In fact, that may be as good a definition for AI as any, given that the field eludes definitive objective definition. The rise of generative AI in recent years has played no small part in increasing the ambition of AI projects. AI systems are now heralded as potentially assuming the role of customer service agent, analyst, educator or virtual assistant. I'm skeptical that it will soon achieve that degree of full-fledged autonomy, but emerging approaches promise to tame large language models, even if only for somewhat more modest deployment goals. But even predictive AI, which has been around for decades (formerly "predictive analytics"), takes on a task that can only be imperfectly solved: prediction. Analytical methods are advancing astronomically, yet we are not developing a magic crystal ball. We can’t feasibly expect systems that predict with high confidence in general who will click, buy, lie or die. Both genAI and predictive AI systems must clean up after their own imperfections. They can't solve the problem perfectly, so they need a failsafe. Instacart, which lets you order groceries and household goods for home delivery from most any...
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By leveraging AI to predict which replacement item is most likely to satisfy the customer, Instacart can offer the best possible substitute when an item is out of stock.
