What Can We Learn from Machines?

Machine learning software starts like an infant: exploring its systems and surrounding, and learning pathways and protocols. Then it “grows up” and learns ever-more efficient ways to achieve tasks and solve complex problems. It never stops learning.

AI is just another kind of learning machine, created within set parameters for a purpose we define. It could be an e-commerce platform that learns what a customer likes (because you liked X, we thought you might like Y), or an industrial maintenance routine that keeps machines moving.

Machine learning is built on, or from, big data. The sensors at the core of Industry 4.0 collect huge, comprehensive data sets, which then must go through a three-step process to make them useful:

1. Visualise – put data in a form where we can see and work with it
2. Evaluate – evaluate its patterns and purpose
3. Act – to change systems in line with findings

And then Repeat.

This is made possible by the other main components of machine learning: algorithms and mathematical models. From these bases, combing huge computing power with precision training, software learns to use the data for itself. Seeing and connecting patterns, pathways and predictions, it takes over the processes of visualising, evaluating, acting, improving and learning. It is becoming AI.

The possibilities for machine learning in industry are limitless. But the most important factor is finding the right partner to work with.

So while machine learning is built on data, AI starts with HI: Human Intelligence. The value of machine learning in our connected world is that the smarter our software gets, the smarter we can work. And the smarter we can work, the greater benefits for our industries, our lives and our world.