AI algorithms, or step-by-step operations to be performed by a computer, are an important part of the AI workflow. A more important aspect when performing AI algorithms is obtaining quality training data. When comparing more training data versus better AI algorithms, more training data always wins in terms of long-term benefits.
When you try out new features and algorithms, things can move backwards. That’s rare in training, where you almost always get improvements and the improvements themselves are usually bigger. Obviously, exploring features and algorithms helps get a handle on the data and that can pay dividends beyond accuracy metrics. But in terms of benefits, more data beats better algorithms.
To test this theory out on your own set of data, try CrowdFlower AI. CrowdFlower AI combines the powerful human-in-the-loop SaaS platform with machine learning (powered by Microsoft ML) to create an effective AI engine for text classification cases.