With headlines emerging about artificial intelligence (AI) reaching “sentience,” it’s clear that the power of AI remains both revered and feared. For any AI offering to reach its full potential, though, its executive sponsors must first be certain that the AI is a solution to a real business problem.
And as more enterprises and startups alike develop their AI capabilities, we’re seeing a common roadblock emerge — known as AI’s “last mile” problem. Generally, when machine learning engineers and data scientists refer to the “last mile,” they’re referencing the steps required to take an AI solution and make it available for generalized, widespread use.
What Is the ‘Last Mile Problem’?
The last mile describes the short geographical segment of delivery of communication and media services or the delivery of products to customers located in dense areas. Last mile logistics tend to be complex and costly to providers of goods and services who deliver to these areas.
(Source: Investopedia).
Democratizing AI involves both the logistics of deploying the code or model as well as using the appropriate approach to track the model’s performance. The latter becomes especially challenging, however, since many models function as black boxes in terms of the answers that they provide. Therefore, determining how to track a model’s performance is a critical part of surmounting the last-mile hurdle. With less than half of AI projects ever reaching a production win, it’s evident that optimizing the processes that comprise the last mile will unlock significant innovation.
The biggest difficulty developers face comes after they build an AI solution. Tracking its performance can be incredibly challenging as it’s both context-dependent and varies based on the type of AI model. For instance, while we must compare the results of predictive models to a benchmark, we can examine outputs from less deterministic models — such as personalization models — with respect to their statistical characteristics. This also requires a deep understanding of what a “good result” actually entails. For example, during my time working on Google News, we created a rigorous process to evaluate AI algorithms. This involved running experiments in production and determining how to …….
Source: https://builtin.com/artificial-intelligence/ai-last-mile-problem