How to turn AI failure into AI success
The enterprise is rushing headfirst into AI-driven analytics and processes. However, based on the success rate so far, it appears there will be a steep learning curve before it starts to make noticeable contributions to most data operations.
While positive stories are starting to emerge, the fact remains that most AI projects fail. The reasons vary, but in the end, it comes down to a lack of experience with the technology, which will most certainly improve over time. In the meantime, it might help to examine some of the pain points that lead to AI failure to hopefully flatten out the learning curve and shorten its duration.
AI’s hidden functions
On a fundamental level, says researcher Dan Hendrycks of UC Berkeley, a key problem is that data scientists still lack a clear understanding of how AI works. Speaking to IEEE Spectrum, he notes that much of the decision-making process is still a mystery, so when things don’t work out, it’s difficult to ascertain what went wrong. In general, however, he and other experts note that only a handful of AI limitations are driving many failures.
