Jack White and Struggles

I watched an interesting music documentary a few years ago, called It Might Get Loud. The documentary features three great guitar players including Jack White. It was a very insightful look into the struggle around the creative process from some very original talents.

Jack White says in the documentary, “If you don’t have a struggle inside or around you, you have to make one up.” He even mentions it in the trailer, check it out.

He was talking about the creative process and doing great work. What I realized was that the struggle is a necessary part to real creativity that counts. White describes having lots of obstacles on stage when he plays and how it’s awkward to step around and over things. It pushes him to be more creative and he feels like his performance is better.

The friction in your environment is a critical component of your creativity.

Jack White: “If this guitar had only 3 strings, what could I do with that?”.

It’s critical that you approach your work with anticipation of the ‘struggle’. Struggle everyday, as much as you can, to not make assumptions. Focus on the actual problem, don’t just come to the table with a solution and then try to wedge it in.

What can you do to build a better functioning system?

Can you eliminate something and still get the same functional result (careful here)?

There is a lot we can all learn from original thinkers who produce original work. It’s not easy, but that’s all right. The struggle helps produce an original, that makes an impact.

More on Lillian Gilbreth

Lillian Gilbreth is one of the most prolific women engineers in history. We have profiled Ms Gilbreth earlier on the Skellig blog.

Some of her notable achievements are: 

  • first female professor in the engineering school at Purdue University
  • first woman elected to the National Academy of Engineering
  • second woman to join the American Society of Mechanical Engineers
  • until 2005, the only woman awarded the prestigious Hoover Award, jointly bestowed by five leading engineering organizations recognizing “great, unselfish, non-technical services by engineers to humanity” 
  • dubbed “the mother of modern management” 
  • In the 1940’s, was called “a genius in the art of living”  
  • two of her most well-known inventions are shelves inside refrigerator doors, including the egg keeper and butter tray, and the foot-pedal trash can
  • she filed patents on an improved electric can opener and the wastewater hose for clothes washers
  • as an industrial engineer working at General Electric, she interviewed over 4,000 women to design the proper height for stoves, sinks and other kitchen fixtures as she worked on improving kitchen designs
  • taught college and university courses at Bryn Mawr, Newark College of Engineering, Rutgers University, and the University of Wisconsin
  • resident lecturer at MIT in 1964
  • served as an advisor to at least five US presidents on civil defense and women’s issues
  • received more than 20 honorary degrees and several prestigious awards and was included in American Men of Science, Who’s Who of American Women, and Notable American Women: The Modern Period


Preparing for AI and Machine Learning

As Henry Ford said, “If I’d asked customers what they wanted they would have said a faster horse.” Something similar is brewing in terms of machine learning in our industry.

Machine learning and AI could be great for pharma and biotech but our technology choices over the past 30 years are a bit of a problem. In industrial pharma automation, there was no apple computer to make some tough decisions on behalf of the customers. Instead, we have all sorts of half-decent “I want!” technology choices.

Back to Monopolies?

The big suppliers have taken the “give them what they ask for” approach too far. Clients are presented with a plethora of similar spec options that still need to be custom integrated. We start projects in a way too open-ended manner.

The issue here is the platforms have failed this industry in making hard calls. Nobody wants a faster horse.

Instead of focusing on being flexible enough to support every field bus ever invented, we should pick a bus technology and focus on making it really great.

It’s 2018 and we don’t have plug-and-play figured out. We can’t unplug a piece of equipment easily and plug it back in without a custom integrated, “kinda works” solution. Plug-and-play was figured out by the PC industry in the mid ’90s.

Our industry is led by big suppliers with a disease. This is the disease of give the customer more choice. It’s not working. We need focus. We need better choices, not more choices.

There are rumors the big guys are working on plug-and-play. But it goes to show how far behind we really are. Are we going to see an ad from one of the big guys in 2019 announcing a plug-and-play solution and have any reaction other than, “This should have been delivered 20 years ago”?

Moving Ahead with Machine Learning

We have widely adopted virtualization, so there is hope for us yet.

Now we see that the next big thing is machine learning and AI. Its promise is huge. However, we can’t take advantage of this until we decide on a few things. We don’t want to start integration projects with a blank slate. Instead of class-based control modules for devices, we need class-based equipment like bioreactors. We need these standard designs so that machine learning and AI can look at a wider dataset and learn. It can’t be applied in a useful way when everything is designed so custom and complex.

The big platform companies should have simplified their offerings before now. They haven’t. Now the ultimate power is with the customer. When they say “Enough is enough, we don’t want your platform only, instead we want your solution”.

The solution is more standard, less custom. Then machine learning and AI can compare bioreactor to bioreactor and we can see useful patterns. Hopefully we won’t have to wait till 2048 to see this happen.