![]() Variables that are supposed to be independent are often slightly dependent. Things that are linear are often slightly nonlinear. We don’t have precise equations to model these most basic physical systems, especially once we are looking at five 9s. Measuring things accurately and repeatably is, in general, a mysterious ongoing challenge, as many of our readers know all too well. Friction is a poorly understood phenomenon and is affected by material condition and environmental parameters. Force transducers have very bizarre mathematical behavior. Temperature sensors have non-linearities. Real resistors don’t precisely follow Ohm’s Law. Where the power of this becomes obvious is when we start looking at real-world behaviors of sensors and components. Real Things Aren’t Linear And Inputs Aren’t Independent ![]() This is what is called “learning from data.” Socket and plug displacement found from below using 2D pattern tools Again, these are very easy things to calculate without ML, but the point is to be able to get the right answer and to be able to generalize without ever having to derive or know what the formulas are. We next graduate from simple linear problems to using a neural network to solve the Pythagorean Theorem: that is, if I tell you how long two sides of a right triangle are, can you tell me how long is the hypotenuse? And also tell me the perimeter and area of the triangle. But to see how we can deduce the precise formula without needing to deduce a formula of any kind can give us some insight into the utility of ML. It’s a very simple linear mathematical function that we all typically learn in middle school. Now I know, to be sure, this isn’t an application that requires ML. Yes, there are places for ML in our automation world! When I teach ML for robotics applications at the university level, I start by showing students how to use neural networks in TensorFlow or PyTorch, or even in low-level C-code, for something very simple, like converting back and forth between Celsius and Fahrenheit. Many startups are learning this the hard way. So, there is a basic mismatch between the capabilities and expectations of the AI/ML computer science community and our factory automation and quality needs. But our readers know that QC and QA systems need to be much more reliable than the actual processes they monitor-and our modern processes may be 99.99% or more reliable. Getting to 98% accuracy is almost unheard of.
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