Machine Learning and Six Sigma: Insights and Improvements
The human brain is a powerful thing. Often likened to a computer, it processes 400 billion bits of information per second and is always learning. The human capacity for learning is unlike any other species, but it is not a characteristic applicable only to people. Science fiction stories have explored the concept of artificial intelligence for decades, with more advances made each day, especially in an area called Machine Learning.
Technology is advancing exponentially and, in the next few years, we expect to see tremendous leaps in the realm of possibility. The further computer technology advances, the more likely real artificial intelligence will start to appear. Six Sigma also has a role to play here, but to understand the relationship between machine learning and Six Sigma, we first have to understand the former.
What Is Machine Learning?
Machine learning is the name for a kind of artificial intelligence, used in computers that enable them to learn. Ever since the invention of the modern computer, and well before that, humans have been forced to manually input data. Programming computers in this way are not only time-consuming and tedious but also inefficient.
A perfect artificial intelligence will mirror the way the human brain works in every way, sharing its capacity for learning. Machine learning aims to develop new computer programs to achieve true artificial intelligence. The problem is getting computers to learn and grow, something human beings can do without much help, whereas computers are more reliant on their masters.
Computational statistics and mathematical optimization also factor into the difficulty of creating true AI, as prediction making is an essential aspect of computer learning. AIs currently exist in very primitive forms, such as when you play chess on your computer. Unless you’re a master, there’s little chance you’re going to beat it. This is because the AI will predict every possible decision you can make before you do.
There’s a difference, however, between chess-playing computers and those that manage entire production lines and factories. As such, to progress, an AI needs to be able to reach beyond its reliance on human input, and learn for itself. Data mining, whose focus is on analyzing exploratory data, is an important practice for unsupervised learning.
How Can Six Sigma Aid Machine Learning?
Lean Six Sigma, in particular, aims to improve process performance in computing by reducing variance. Reducing variation can be defined as the squared differences between LSS actual and prediction measures. Using the following model: X –> Process, Y = f(x) –> Y to identify associations and patterns between the input and output variables. This enables us to regulate how our input and output variables function, so we can drive them towards desired values. Reducing variance will help manage your process targets, and ensure you reach the goals you wish to.
Lean Six Sigma can contribute to improving computer processes as it strives to deliver hard, accurate data on which to base decisions. By using variance models to make the most of your forecasts, you can achieve significant process improvement for machine learning. The ideal machine should be an efficient one, after all, and Six Sigma methodology can help drive improvements in the AI field. Remember, accurate, reliable, valid data is the best kind. It can help make your machine learning more effective and act as a platform for unsupervised learning.
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