The Industrial Internet is the future- and it’s being built now.

IoT and Analytics are transforming industry, and who know industry like the upper Midwest?

Add to the decades of institutional experience a community of educated tech adopters, then just add water (liquid capital) and stir. BAM!
Forget Silicon Valley, this is Silicon Lakes.

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New Investment

TeamGenius is player evaluation software for managing tryouts, coach evaluations, camps, and more. Team Genius is focused on building stronger young adults and communities through their powerful, simple software tool. Streamline scoring with the mobile application, add transparency to the evaluation process, and ditch the paper evaluation forms, clipboards, and spreadsheets!

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Minnebar 13

With no formal workshops, “BarCamps” are user-generated and participant-led by tech and business community leaders. Over 100 sessions were held this year at Minnebar13. Participants, speakers, and staff braved the ridiculous Minnesota blizzard to hunker down at Best Buy HQ for Minnestar’s premier tech conference.

MinneStar is currently running a 100 Day Challenge where the Board of Directors is matching donations by new community members. Join Rob and Ryan Weber and contribute to this important part of the Twin Cities tech community!

Great North Labs at Minnebar

Ryan Weber presented How Running Lean Can Help You Raise Capital, about how the stages of funding correlate to the phases of customer development. His Exponential Technology and Leadership talk delved into disruptive technology and innovation.

Rob Weber focused on How Entrepreneurs are Impacting Cities. Participants learned core concepts on entrepreneurial thinking and leveraging local industry expertise to create the next big thing.

Upcoming Events

EntreFEST May 17-18, Cedar Rapids, IA

State of Innovation: Ag-Tech May 22nd, Minneapolis, MN

Drone Focus Conference 2018 May 30, Fargo, ND

New Venture Challenge– May 30, Chicago, IL

 

Welcome New Advisors, to the Great North Labs Team!

Brad Lehrman – Attorney, Soffer Law Group, PLLC
Jeffrey Robbins – Attorney for Entrepreneurs and Angel and Venture Investors, Messerli & Kramer
Mitch Coopet – Co-founder of Aftercode
Paul Borochin – Assistant Professor of Finance at UConn School of Business
Art Rosenberg – President and Owner, Capital Commercial Realty Group, LLC
Shawn Teal – President, Forest Hill Capital

See our Team

IoT and Analytics – Organizing the Industrial Internet

 

Figure 1: The third revolution: IoT and Analytics.  [Image credit: General Electric]                               
 
The Evolution of IoT – Where we Came From
The first generation of IoT systems (IoT 1.0) was built mostly with data collected from IP-based sensors by monitoring applications. Whether standalone or embedded in phones, low-cost sensors, compact packaging and distributed power enabled new endpoints and systems. These monitoring applications served needs such as asset tracking, fitness monitoring, mood lighting, physical security, and others.
The second generation (IoT 2.0) leveraged the capabilities of infrastructure tools such as edge gateways, publish-subscribe buses, data warehouses, and API-based integration. The edge gateways allowed IP network segments to connect to sensor bus segments using a diverse set of protocols (e.g., RS-422, RS-485, BACnet, CAN, Fieldbus, Hart, LonWorks, Profibus, Seriplex, Zigbee, Z-wave, and others). The gateways extended the reach of these IoT systems across the many incumbent protocols and enabled the integration of the IP segments with legacy systems. The publish-subscribe buses made data-driven software architectures easier to implement and scale. The data warehouses enabled the integration of structured, semi-structured and unstructured data. The integration APIs enabled ingestion of data at scale. Together, these new building blocks enabled larger-scale IoT applications such as home monitoring, smart metering, power grid management, parking systems, next-generation environmental controls in buildings, windmill farms, warehouse management, etc., with varying degrees of commercial success based on the benefit provided vs. the insertion economics of each use case.
 
Today’s Frontier
With the larger data sets enabled by frameworks such as Hadoop and big data software such as Pivotal, the third generation of IoT systems (IoT 3.0) is integrating analytics for decision-making. These analytic platforms enable the processing and visualization of the IoT data sets. The large data sets and analytic tools identify aberrations with higher levels of confidence (statistical power) and detect ‘signals’ not seen before, they have lower detection thresholds, greater measurement sensitivity, and higher accuracy.
Applications based on these capabilities range from physical security for homes, buildings, and warehouses; to detection of diseases like lung disease, cancer metastases, or cardiac arrhythmias (see the Mayo Clinic and AliveCor’s recent work); and complex chemical analysis as in rare earth element detection. The availability of computing platforms at the ‘edge’ (e.g., gateways) enables distributed/local analysis.
“The Internet of Things is giving rise to a tsunami of data,” said Great North Labs advisor Ben Edwards (founding team member of home automation pioneer SmartThings). “The billions of residential sensors in people’s homes and the personal sensors on their bodies are sources of data of value to each of us, and depending on what we make available to others, to family members for our safety and well-being, to the retailers we buy from, to the health practitioners who take care of us.”
The proliferation of machine learning algorithms with new programming environments such as Python and dataflow libraries such as TensorFlow has opened up a wide range of new applications. These include anomaly-based security alerts, health and fitness monitoring, genomic analysis and biomarker detection for disease prediction, drones, and self-driving cars.
The addition of machine learning libraries to established platforms such as Matlab, R, SAS, and SPSS, is enabling insertion of machine learning into legacy applications.
The availability of these tools in public and private clouds has made their accessibility and deployment even easier.
Together, with supervised and unsupervised learning, the machine learning software is processing data sets with high data dimensionality, like those from mining, voice processing, drone navigation, and self-driving cars.
The integration platforms and IP-based communication are also enabling the integration of the IoT world with the enterprise world, making applications possible across hybrid computing and control environments such as airports, buildings, cargo ships, factories, hospitals, refineries and oil rigs. While this creates security issues for the enterprise as well as control systems, solutions such as micro-segmentation of hybrid systems are beginning to emerge.
 
Tomorrow – The New Startups
With products from companies such as Nvidia, Intel, Qualcomm, Broadcom, and now Google, real-time computing power is becoming available at the edge. With easier integration and low cost, it is becoming embeddable at sensing endpoints for applications such as drones, self-driving cars and trucks, personal walking/talking robots, personal assistants, point-of-care diagnosis, no-POS retail, smart logistics, and smart city applications from parking lots to secure airports and intelligent highways.
 
Adoption Outlook
Beyond analytics and monitoring, this fourth generation of IoT systems will be able to use analytics and machine learning for controls.
What is the outlook for the adoption of these applications? The answer is: it depends. And it is best found through analogies.
How confident do today’s chess masters or masters of the game of Go today feel betting against the machine? IBM’s Deep Blue computer beat chess champion Garry Kasparov in 1997.  And as Great North Labs advisor Mitch Coopet (CEO of AI-focused Aftercode) points out, “Since 2016, Google’s Alpha Go platform has won against several Go masters using improved deep learning techniques.”
Or, when will the day come when your x-ray machine will have better diagnostic accuracy than your radiologist? Ahem, that day is already here.
Or, when will Alexa be able to detect tonal infection to assess mood? Based on indications from Amazon and makers of social robots and AI assistants, sentiment analysis will progressively improve the way machines will interact with humans.
Or, when will we be comfortable with self-driven cars? Completely autonomous navigation in 5-7 years may be unlikely, but it is equally likely that in 20 years, self-navigation will become a required safety feature for new cars.
Given the range of answers above, it is not a matter of if, but when, that real-time control using machine learning will be common. These systems will be able to handle use cases as diverse as (i) detecting rare earth minerals to help navigate the earthmoving equipment towards richer ore in a mining operation, (ii) making real-time sweeps at airports to pinpoint explosives across large masses of people, luggage, and infrastructure, (iii) ensuring that the robots deployed in automotive assembly stay within the extremely tight tolerances of frame construction, and (iv) predicting the failure of a component in a high value CT scanner or remote ATM to dispatch the skilled repairman in a timely way to avoid downtime (a business that Great North Labs has invested in).
 

The Innovation Ecosystem of the Industrial Internet
“Business Insider projects that there will be 55 billion IoT devices operating in the world by 2025, impacting a broad set of industries including automotive, consumer products, electronics, medical devices, and industrial equipment,” notes Great North Labs advisor Robert Bodor (Vice-President and GM, Americas, at Protolabs).
At Great North Labs, with an ambitious vision, we aim to help build the innovation ecosystem of the Industrial Internet visualized by IoT 3.0. This is because we believe the ingredients to build it are uniquely within reach for us.
The three pillars of any tech-enabled disruption are entrepreneurs/developers, adopters/enablers, and capital.

              

Author: Rob Weber
Most colleges and universities are finding it very challenging to cultivate strong startup communities like those found at leading institutions like Stanford and Yale. But if we take a deeper look at these leading institutions, and how others are responding to this challenge, we can build a repeatable model to support the the rise of the rest.
Certainly one component to developing a strong collegiate startup culture is having a strong curriculum, jam packed with not just theory but applied learning activities which enable students to develop skills required for jobs in today’s workforce. A good example of this occurred two years ago  with the creation of a Software Engineering Degree at St. Cloud StateUniversity. Many engineering programs have become dated in our region but SCSU’s Science and Engineering leadership are meeting regularly with industry leaders to identify the practical needs of employers and then developing new degrees in support of satisfying them.
Many companies are looking for strong software engineers. SCSU has long offered an ABET-accredited Computer Science Degree that is strong on fundamentals like Database, Computer Architecture and Operating Systems. Started in 2015, they offer a Software Engineering Degree which adds required courses on the Software Development Process. In addition they are also offering electives in Mobile Development, and Gaming and Visualization (useful for 3D software such as VR/AR programming).
Additionally, four years ago, SCSU opened a brand new 100,000 square foot ISELF facility where students can work with industry leaders on projects utilizing cutting edge technology like VR/AR, Robotics, Nanotech, 3D printers, etc. The vast majority of students today in Computer Science programs would rather be learning coding skills to build useful enterprise or consumer software instead of spending their college years learning how to build infrastructure they are not interested in building.
The ISELF building is not just a place for engineering SCSU students to gather either. The new facility is being utilized by students across a variety of fields from business to liberal arts in support of experiential learning.  Let’s face it, many software engineers don’t make the most aesthetically pleasing software! It may go well beyond SCSU’s campus too. Recently, we held a meeting between SCSU and CSBSJU’s Director for Entrepreneurship, Margrette Newhouse, and both groups of academic leaders expressed an interest in teaming up to get more student-led businesses from CSBSJU to work collaboratively with SCSU’s experiential learning offering.
It has become table stakes for a university to invest in equipping labs with cutting edge, disruptive technology to give students access to equipment that they otherwise won’t have access to. Some of America’s greatest startup stories involved young founders taking full advantage of their school’s resources. Take the story of Google and how their founders waited at loading docks at Stanford for new computers to come so they could increase their network and computing capacity. It isn’t 2000 anymore and students still need access to an even greater number of tools. Ideally, universities should invest in labs that provide access to breakthrough AR/VR technology, robotics, drones, etc.
One often overlooked and easily corrected way to supercharge your university’s startup community is to encourage it to focus its investing activity on regional venture funds that align with the university’s mission, as pointed in Tim Schigel of Refinery Ventures recent post. In Tim’s post, he shares insights as to how universities like Yale are generating outsized returns for their endowment than they would otherwise get in the stock market by investing in venture funds which align with their school’s regional impact mission.
Today, most universities investing in the venture capital asset class send all of their funds outside of their region. This far-away distribution of venture capital creates a vicious cycle where the universities in other regions end up dramatically outperforming them, which causes the original university to be less competitive. If there are no venture funds in your region, universities should consider adopting a policy to take small amounts of their capital and deploy it to first-time fund managers who align with a regional investing strategy.
Startup competitions like the Minnesota Cup organized by the University of Minnesota bring awareness to many startups that otherwise would fly under the radar. Beautiful things happens when you bring awareness to startups in your region. The entrepreneurial community will start to rally behind them, bringing with them valuable business contacts, advice, capital, and more to ensure their success.
And then there is that all so important issue of connecting top employment opportunities to the most talented graduating students. The best startup communities provide organized apprentice programs such as Xtern by TechPoint in Indianapolis. Apprentice programs are critical to the success of new graduates so they can learn applied skills required for these new high demand jobs.
Finally, the university needs to identify regional founders who can lead this charge and support them with a bottom up approach by spreading the word throughout various student groups across different disciplines. Top down approaches don’t work. Entrepreneurs are best led by entrepreneurs as Brad Feld describes in his book Startup Communities.

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