Machine Learning and Artificial Intelligence have been promoted as sure-fire solutions, yet mis-application has led to hilarious and terrifying errors. Algorithms touted as new over decades of academic work have recently been revealled as performing worse than the originals. What should you consider when deciding if your use case can benefit? How to avoid Garbage In Garbage Out. We look beneath the magical thinking surface to uncover why AI/ML work better than humans in the right applications, and may never surpass humans in others.A
Artificial Intel: Flaws, Faux Pas and Successes
