What you can learn from your smartphone for industrial AI
One night, I was casually scrolling through my smartphone screen. Suddenly, I realized something as simple as it is ingenious: Everything on a smartphone is just one click away. “We need it to be that simple for AI as well,” I thought to myself.
Sounds trivial? It is.
But why am I sharing it? It has not always been like this when it comes to computers.
My early days with computers
My first contact with computers was as a child. I was barely literate when my friend and I tried out the first computers. For games — what else as a kid? We had to use floppy disks and sometimes magnetic tapes to get the games onto the green and orange monochrome screens.
As a teenager, I spent hours and days tweaking our first home computer. Good old MS DOS. I optimized the memory to have more of the coveted 640 kilobytes of RAM available. For Christmas I wished for an external sound card so I wouldn’t have to listen to the annoying beeping of the computer’s speaker.
You may smile. So am I. But it shows me how far we have come in the everyday use of standard desktop computers.
Fast forward. To this day, my team and I spend a lot of time trying to get things to work on computers, locally or in the cloud. Anyone who’s ever written code or solved technical problems with computers knows what I’m talking about.
But that’s presumably not how the vast majority of people use computers. When we have a choice, many of us take the easiest option. At least I do. Gone are the days when I looked up directions on the Internet and printed them out. We have maps at our fingertips on our smartphones in a fraction of a second.
Learn from your smartphone for Industrial AI
I think we are now at a similar threshold in industrial AI. We started writing our own machine learning (ML) algorithms in 2016. We are focused on applying AI to challenging industrial problems. How can we extract more value from industrial data for our customers? That’s what drives us.
In our daily work, we see how difficult it is to bridge the gap between the AI pilot project and the actual use of AI in the production environment. I’m not talking about the difficulty of the content during development of AI models — that’s there. But that’s what specialists like us are for.
The difficulty comes when the AI engineer is no longer sitting next to you. When you are alone and want to evolve the system, for example, because you want to train a new feature of your product into the ML model. Or because something has changed in the existing design of your component that needs to be reflected in the ML algorithm. So you need to retrain your ML model.
It is not surprising, then, that executives look up to these seemingly bottomless pits in industrial AI and fear that costs will continue to rise.
How to take the cost spike out of industrial AI
With this in mind, we have developed a tool that can help: our 1-Click AI Trainer.
The idea is that users no longer need to know in detail how to train an AI. By fully automating the training process and reducing it to a single click, it is now possible for anyone in the industry to train their own AI models. The new model is deployed in an easy-to-use ML ecosystem. In fact, users do not even notice that they are rebuilding part of their AI engine. This makes it possible for non-IT experts to work with AI models in everyday industrial practice.
We believe that we are opening a door to enable the use of AI for company-specific industrial AI problems. With our technology, industrial AI will be as easy to use as a mobile phone. No matter who uses it. AI is then just another tool in the industrial toolbox to answer questions. Simple, and at your fingertip when you need it. Like the smartphone.
Still hesitating? Check out our demo video to see how it works in action.
We hope you enjoyed our series of articles on industrial AI. This is the last of a seven-part series. Thank you for reading.
Further reading
For more articles, click here:
(1) How to bring AI to your manufacturing company
(2) Get machine-readable data for industrial AI
(3) Build sandboxes and let them play
(4) Problems you can solve with ML in your company
(5) Your route to success in industrial AI: Think big, start simple
(6) From pilot to maintainable AI technology stack
(7) What you can learn from your smartphone for industrial AI