Source: Forbes
rtificial intelligence (AI), which includes machine learning, deep learning, reinforcement and analytics algorithms, is a powerful tool, but it can’t just be installed and expected to immediately start delivering value. The algorithms must be trained on curated data sets before they can be applied to actual production data to deliver the value that businesses seek.
Examples of AI training requirements include labeling photos to enable AI systems to identify people or objects, adding context to text passages for text recognition, and mapping variations in voice recordings for speech recognition systems. While the tools and platforms are evolving and making such training more accessible, data is the other critical side of the equation.
Training algorithms involves creating a model that employs labeled or contextual examples of available or historical data from which machines can learn. Although humans learn through intuition and experience, machines can learn only through data patterns. This training process needs to occur both when an algorithm is first tested and deployed, as well as throughout its lifetime. Training is delivered not only from incoming data, but also through humans specifying the meaning behind labels or categories that go into the model.
“Training an algorithm, put simply, is the process of taking data collected and using it to generate an estimate or expected result,” said Jennifer Shin, founder of 8 Path Solutions and data science lecturer at New York University’s Stern School of Business.
“The training set can be thought of as the information we have available at the time we make a decision. For instance, let’s say you are interested in buying a new pair of shoes and you have decided that your selection will be based on the last three purchases. In this case, your past purchases would be your training set.”The accuracy of algorithms depends on whether or not training sets are complete, Shin cautions. “In reality, there is no way to know for sure whether your data set has all the factors that influenced your past purchases, such as price and maintenance,” she said. “And if these variables are not included in your training set, this will result in a less accurate algorithm.”
Here are some guidelines to establish a robust algorithm training process:
1. The more good data, the better
Not only must data sets be as comprehensive as possible, but the data also needs to be clean and useful. “Models built on a few thousand rows are generally not robust enough to be successful for large-scale business practices,” wrote Robert Munro and Qazaleh Mirsharif in “The Essential Guide to Training Data.” Tellingly, Sam Ransbotham and MIT-BCG researchers found that one of the most common misconceptions about AI is that “sophisticated AI algorithms alone can provide valuable business solutions without sufficient…
The post Keep Your AI Algorithms Accurate And Adaptable appeared first on FeedBox.