How to make your AI Algorithms smarter?

You know that data is really important for building AI systems. For example, gathering only one variable about revolutions per minute of your machine is not going to be enough to tell you why a failure happened. However, if you add vibration, temperatures, and data about many conditions that contribute to machine failure, you can begin to build models and algorithms to predict failure. Also, as more data is collected, you can create accuracy requirements, such as This algorithm will be able to predict this failure within one day, with 90% accuracy. How do you acquire data? Well, one way to get data is manual labeling. For example, you might collect a set of pictures like these over here, and then you might either yourself or have someone else go through these pictures and label each of them. So, the first one is a cat, the second one is not a cat, the third one is a cat, the fourth one is not a cat. By manually labeling each of these images, you now have a dataset for building a cat detector. To do that, you need more than four pictures. You might need hundreds of thousands of pictures but manual labeling is a tried and true way of getting a dataset where you have both A and B.

Adopting AI and ML is a journey, not a silver bullet that will solve problems in an instant. It begins with gathering data into simple visualizations and statistical processes that allow you to better understand your data and get your processes under control. From there, you’ll progress through increasingly advanced analytical capabilities, until you achieve that utopian goal of perfect production, where you have AI helping you make products as efficiently and safely as possible.

Explore how TagX can help you make your AI smarter.