When you think of 3M you immediately think of Post-It Notes or Scotch tape. If you're old school or local, maybe you know that 3M was founded as Minnesota Mining and Manufacturing Company. But have you ever thought about this company, which has $30 billion in annual sales, employs 88,000 people worldwide and produces more than 55,000 products, as an IoT company? All that material science must have an opportunity in IoT.
For that we turned to Dr. Jennifer F. Schumacher, the technical supervisor and co-founder of the Computational Intelligence group in the Corporate Research Laboratory at 3M Company. She manages a team and portfolio of 35 new technology Introduction programs which are mechanizing, electrifying, and digitizing 3M materials. Her current initiative is to drive technology platform development in computer vision, machine learning, and deep learning
When people talk about 3M, the first thing that usually comes to mind is Post-it® Notes, and they might not think about 3M at the ready for future advances. How is a materials science company playing in the IoT space?
They say you’re never more than ten feet from a 3M product – that is a lot of potential “things” we could integrate into the IoT space. In fact, we have already digitized the simple Post-it® Notes through the Post-it® Plus App, it integrates physical and digital notes and lets you connect with others to share, for example, outputs from brainstorming sessions.
What have been some of the roadblocks you and your team have faced in convincing people that a materials science company is also a tech and data science company? How are you working to overcome this?
The data science/machine learning group at 3M is relatively new, and as such, many of the technologies we are developing are not ready for public disclosure yet. Therefore, it is difficult to communicate externally that 3M is actually working on these things, and difficult to recruit talent in this high-demand skillset space. We are addressing this by attending key conferences and interacting more with universities, for example we are sponsoring a seminar series at the University of Wisconsin – Madison.
You have a PhD in neuroscience and an expertise in human vision. How does this apply to your work at 3M when it comes to data science and the IoT?
I initially leveraged my expertise in human vision to develop the 3M™ Display Quality Score – a metric that predicts how well a human will prefer a digital display based on its resolution, contrast, color saturation, etc. I then translated this skillset from understanding how people see, to teaching computers how to see, or ‘computer vision’. The opportunity to learn new things and adapt skillsets makes the job fun.
I believe that in a world full of data, it will be the ones that ask the right questions that have the advantage. Formal training in science has helped me hone my skills in asking the right questions so the most efficient and effective experiments can be carried out first. Much of my formal training has been multi-disciplinary, and I think this breadth of knowledge and cross pollination of ideas and concepts is the key to innovation. 3M’s approach to science is aligned to this approach of cross pollinating ideas and heavy collaboration.
Explain how machine learning can be applied to 3M products?
Machine learning thrives on data. 3M products are, or could be, producing data. We can then leverage the insights from the algorithms to enhance the product itself (for example, the Victory Series™ buccal tubes, which were optimized for fit) or to create an entirely new solution (we have several in the pipeline, so stay tuned!)
What can 3M do to adapt to the current digital economy and help your customers adapt?
There is a global trend of greater economic opportunity in service-based business models rather than product-based. I think 3M will need to start adapting some of these service-based models to adapt to the current digital economy, and we can do so by providing complete solutions (products + services) to our customers.
What do you think the most pressing challenges are when it comes to IoT? How is 3M working to solve these?
The most pressing challenge I see is finding the most impactful applications – there are plenty of ‘cool’ factor solutions or products, but what are the sustainable solutions, the ones that significantly improve the quality of life or enable new capabilities? 3M’s vision statement concludes with ‘3M Innovation Improving Every Life’, so I think we align our research goals with significant global technology trends and sustainability issues that would have this broad impact.
What excites you most about the future of IoT?
The more trivial decisions that a smart system can take care of, the more time I can spend dreaming and implementing what the next technology to improve lives will be.