By Pradip Madan, Ryan Weber, and Rob Weber

 

 

In the US, several tech ecosystems have become centers of tech innovation in addition to the much-vaunted Silicon Valley. Silicon Alley is in NYC; Austin is known as Silicon Hills; Silicon Mountain includes Boulder, Colorado Springs, Denver and Fort Collins; Silicon Forest is in the Greater Portland region; and Silicon Prairie covers Omaha, Des Moines, and Kansas City (depending on who you ask). But what about the upper Midwest? Can it rightfully be called “Silicon Lakes”?  

The ‘Silicon’ brand has not only spread through the US, but has also found limited purchase overseas, as in Silicon Wadi in Israel. More importantly, the essence of Silicon Valley has become rooted in various international regions, including the thriving tech ecosystems of Hong Kong, Shenzhen, Beijing, Cheng-du, and Dalian in China; Bangalore, Pune, and Hyderabad in India; Haifa, Israel; Tsukuba, Japan; Suwon, Korea; and Hsinchu, Taiwan.

On a smaller scale, innovation hubs are also springing up from Barcelona to Buenos Aires and Paris to Johannesburg, and can be found near universities and in buildings repurposed as co-location centers for innovative tech companies.

 

 

The “Silicon” Recipe

So, what is the essence of Silicon Valley? How do you define the nature of innovative regions and hubs, beyond the “Silicon” brand? By identifying the nature of particular attributes across these hubs including culture, talent, capital, and collaboration, we can begin to see common characteristics, and what it takes to form a successful innovation hub.   

   1. Culture

First, you need the right culture. This attribute is best characterized by the culture Intel established in the late 1970s and early 1980s. Key traits include openness, transparency, and optimism combined with discipline, inclusion, talent and meritocracy; clear shared goals with an emphasis on value creation and collaboration; a platform to succeed with a permission to fail, and the resilience and acceptance to repeat despite failure.

At Intel, there are open cubicles for Intel’s rank and file alike, including the CEO. Measurable individual key objectives are shared with colleagues, as is encouragement to push the edge. The employees are a cultural cornucopia, including the best new college graduates (NCGs) hired from the best schools. These employees manifested the culture, and while Intel was not unique in creating it, its powerful brand did a lot to popularize it.

Today, this culture is multiplied across the many unicorns and the thousands of successful tech companies, from startups to mid-sized enterprises, that make up Silicon Valley. 

   2. Talent and Capital

On a physical level, the proximity of academic centers such as Stanford University and UC-Berkeley provide a fountainhead of talent and ideas in Silicon Valley. Stanford’s contribution to the development of Silicon Valley is particularly well-known and can be characterized as a successful pairing of advanced engineering and commercialization.

Commercialized advancements begat wealth, and now Silicon Valley is decades into significant wealth creation. This wealth and entrepreneurial thinking has led to a ready flow of risk capital, the availability of which is another key attribute of innovation hubs.

   3. Collaboration

Complex problems benefit collaborative thinking, which benefits from diverse experiences. For many years, the complex problems of the human body have required in vitro testing of single molecular pathways for several months, testing in mouse models for 1+ years, and in clinical trials for more than 1-2 years. Realizing the need to accelerate this process by examining multiple variables at the same time, and that computational methods ranging from vertical search to structural biology could accelerate insights, Stanford University established Bio-X in 2003 as a center for interdisciplinary collaboration between the computational and life sciences.

 

 

The Innovation Ecosystem as a Rainforest

In his book “The Rainforest: The Secret to Building the Next Silicon Valley”, author Victor Hwang delves into the ingredients for a healthy “innovation ecosystem”. He uses the rainforest as a metaphor to identify the success factors for tech entrepreneurship: a natural eco-system in which abundant species thrive based on autonomy, symbiosis, and survival of the fittest as core principles.

“However, the key to the mystery of Silicon Valley is the software.  And that software works like a ‘rainforest’—an ecosystem that thrives because its many elements combine to create new and unexpected flora and fauna. Those elements thrive through rapid mixing, just as they do in a natural biological system.” – Victor Hwang, in Forbes 

The Rainforest Thrives in the Valley

The companies in Silicon Valley largely embody the above practices. A hundred miles away in any direction, in Central Valley, in Wine Country, or in Pebble Beach/Monterey, the atmosphere changes tangibly, and the regions are untouched by tech concepts and unicorns. It is not that the communities are deliberately or inalterably different, it’s simply that the awareness of these cultural attributes has not been as powerful or pervasive, or to put it colloquially, it’s not ‘in the air and water’. The same transitions exist around most other “Silicon” ecosystems or innovation hubs.

… But Takes Root in any “Silicon” Soil

What made an impact at Intel was the investments its legendary CEO, Andy Grove, personally made in training, writing books, giving lectures, taking the time to teach new college grad employees, and promoting concepts such as measurable goals, transparency, and constructive confrontation. Similar cultural emphases at companies like Google, Facebook, and Apple help drive innovation today, while the many opportunities to mingle at conferences, meetups and BarCamps, (where like-minded engineers share ideas and solve problems), and the proliferation of open courseware, enable innovation to thrive on a much larger scale.

We instilled these principles in the open workspaces in our own past company, located in three places: St. Cloud, Minneapolis (in the repurposed Grain Exchange), and in San Francisco. Commingling the Midwestern values of the founders with the lessons of Silicon Valley’s success experienced by our Board members, we created a culture of entrepreneurial success.  

The open work environment of the co-working space Fueled Collective, in the historic Grain Exchange building, downtown Minneapolis. Where grain was once traded, ideas are currency.

Conversely, is there evidence that policy-based institutions do not have impact? Look around Silicon Valley for unicorns traceable to policy-based cause-and-effect. The city governments of Santa Clara (Intel headquarters), Cupertino (Apple Headquarters), Palo Alto (HP and Tesla headquarters), Mountain View (Google headquarters), or Menlo Park (Facebook headquarters) have not been the agents of change.

So, does that mean policy-driven innovation hubs do not succeed? We believe that policy-based initiatives (ultra-high-speed broadband, net neutrality, etc.) are important enablers, but without entrepreneurial zeal, they are never enough, and ultimately wither. Relatively speaking, regions such as China have benefited from the visible hand of policy initiatives, but in the end, even there, the invisible hand of entrepreneurship has been the necessary ingredient.

 

Silicon Lakes

The upper Midwest has the culture, the talent, and the capital to be an innovation hub. It has interdisciplinary collaboration – but it can use more. At Great North Labs we are working closely with St. Cloud State University and other local organizations to foster a similar ecosystem of interdisciplinary collaboration. We work with private and corporate LPs to seed startups in the upper Midwest, using the shared knowledge of our advisors and contacts to facilitate their success. We also educate, participate in events, and promote connections in the local tech communities. Our aim is to create a powerful innovation ecosystem in this region and connect it to the larger community of “Silicon” geography around the world. Welcome to Silicon Lakes!

 

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.

              

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