We'd like to be able to basically pet that puppy, right? But that's not the kind of accuracy we want when we're trying to run our operations. That's really helpful for keeping you alive, because you're going to avoid dogs, and the downside perhaps to avoiding dogs is, you don't get to pet the cute puppy, but you don't get bitten either. If you've been bitten by a dog, your brain is gonna assume that all dogs are out to get you. Our brains also over-generalize from bad events.
So, we need to kind of get our brains in the mode of thinking about what probabilities really are.
That's certainly not all the time, but that's really not a rare event.
But a 15% chance means that on average, one time out of seven, it will happen. If I see there's a 15% chance of something happening, our brains conclude, it's just not gonna happen. Humans also tend to simultaneously under and overestimate the chances of something happening. Even a few sensors generating only one data point per second can quickly overwhelm just about anyone, let alone trying to keep up with this 24/7, 365. If humans are pretty good at making predictions, then why are we even bothering with machine learning? First, humans just cannot keep up with the flood of data that we're generating today. So, you have to pick your tasks for machine learning with that in mind. That's about where we are with machine learning right now, the smart toddler stage. I was really excited about this improvement and I was trying to describe to someone how cool this was, and I realized I was trying to get them excited about my car basically being a toddler. You know, my car shows a visualization of what's around it and recently, that visualization was updated to include things like traffic cones and bicycles. For example, recognize a stop sign or a truck, understand a sentence, or pick out all of the pictures with dogs in them. In fact, some of the hottest research topics of machine learning today are things that a five-year-old can do easily. Our brains have an amazing capacity to recognize patterns and find trends. But if machine learning isn't rooted in data, then it's just machine wild guessing. I'll talk a bit later about what to do when we don't have as much data as we'd like. I also like that we have the words past data in this definition, because machine learning should be rooted, as much as possible, in real data. Otherwise, it's just compiling data to let an expert human look at later, which can be useful at times, but it's not what we're after here. Our machine learning should let us make some prediction to be useful and there should be some automation to that prediction. There's a wide overlap between data analytics, machine learning and artificial intelligence and experts disagree on the exact dividing lines between them, but I like this definition a lot. The first step is to figure out what machine learning even is. In this session, we'll see some ways to combine Ignition and the power of machine learning to find the solutions that are hiding in your data. You're probably using Ignition to store and visualize that data, but can you use Ignition to make predictions that will make your operation smoother, make operators happier and increase the bottom line? So, IIoT can give us a huge stream of data. This is definitely a chat today, so I'll be taking your questions both during and at the end of today's webinar.
I'm one of the senior software engineers here at Inductive Automation and I have a master's degree in Computer Science, where I worked on machine learning, data mining, and data warehousing.
This is our weekly series of live chats, and today's topic is Machine Learning and Ignition. Thank you for showing up for Ignition Community Live today. You are probably using Ignition to store and visualize that data, but can you use Ignition to make predictions that will make your operation smoother, make operators happier, and increase the bottom line?In this session we’ll see some ways to combine Ignition and the power of machine learning to find the solutions hiding in your data.