Any sufficiently advanced technology is indistinguishable from magic.
The first genre of literature to capture my attention and imagination was science fiction. At an early age, I was introduced to the great writers from the golden age of science fiction: Robert Heinlein, Isaac Asimov, Frederick Pohl, and Philip K. Dick. One of the stars of that august group was Arthur C. Clarke. Clarke was much more than just a science fiction writer, he was an inventor, a futurist, and a philosopher.
I like Clarke’s Third Law because of the truths it contains. We see these truths repeated in the history of humankind. When two cultures with significant technical differences experience an encounter with each other, “magic” is the only logical explanation for the difference by the less developed culture.
“Magic” represents “not sufficiently understood.”
Why, you may be asking, am I schooling you in retro science fiction? In a previous CEO to CEO message, I promised to share some ideas for applying AI to manufacturing problems. If you mention AI in most manufacturing circles, the standard response is … predictive maintenance. It is well understood that one can accurately predict part failures using historical lifespans and forecasted processing demands.
Where is the magic in that?
Last month, I gave an example of AI that was within reach (reducing defects) followed by a second example that was just within reach (optimizing unplanned downtime), further to the right on the “magic” continuum. I would like to do the same this month.
Automated Visual Inspection
Automated visual inspection is not new; it has been around since the 80’s. Many manufacturers have employed it as a nondestructive testing method. But like most things, much has happened in the past 30+ years with the advent of cloud, advanced analytics capabilities, deep learning systems, etc. Unfortunately, manufacturers and industrial vision systems, haven’t been quick to embrace these new capabilities. However, the good news is you don’t necessarily need to rip and replace your machine vision system to take advantage of new technology. I know something about this as we have recently announced our Spyglass Visual Inspection System.
We recognize the dilemma of the capital investment in camera systems, infrastructure and training. But, what if those images, which generally are very good, and the vision system that provides them could be augmented with this new technology? We are currently involved with two customers in the automotive sector doing exactly that: training deep learning / neural net models capable of eliminating false positives and classifying defects. With this additional information, we alert human operators when defects exceed upper or lower control limits providing even more value.
Learn by Observing the Humans
Manufacturers struggle with the ‘graying of the workforce’ problem. As veteran employees with decades of experience retire, all of their years of experience retires with them. Performance and quality suffer as a result. Here comes the magic. What if you took a deep learning model and trained it on a particular production line to optimize on yield? You provide all possible inputs to that model: all the production line telemetry, raw material specifications, ambient temperature, humidity, and operator’s identification. You add to these two additional pieces of information: the quality of the resulting product (from QA lab) and a means of identifying which of the sensors the operators are able to control.
Over time, a deep learning module could monitor all the variables on the production line while in operation.
- It could observe the adjustments the operator makes.
- It will know the yield from existing telemetry.
- It will know the asset utilization.
- It will know the impact to the changes the human makes.
- If it is designed to optimize for yield, it will “learn” which changes result in improvements and discard changes that do not.
Over time, the model could suggest the operator make adjustments to the production line. Once comfortable with the model, it could be connected directly to the production line and operate without the human.
Just like magic.
I will leave you with one more Clarke quote:
When a distinguished but elderly scientist states that something is possible, he is almost certainly right.
When he states that something is impossible, he is very probably wrong.
Want to talk more about the magic of AI?
Connect with me on LinkedIn and let’s talk.
