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5-Question Chatbot Session with Don Gooding

Why did you want to get involved with Introspective Systems?
I’ve known Kay and Caryl since they were in the Top Gun accelerator program in 2012 during my time running MCED. Caryl is off the charts brilliant and Kay is a take-no-prisoners entrepreneur, which makes for a fierce founding duo. I invested a bit of money in back in 2013, and like some of the fine Bordeaux in my wine cellar, they took a little while to mature. But 2017 has been a year of tremendous progress. So like all smart investors who look for the Big Mo – momentum that is – I jumped in with both feet in December.

How do you see your role as Board Chair evolving at the company?
I’ve evolved from a small investor and occasional advisor, to lead investor and constant advisor, and now to Chair. The short-term objective is to help the company get the funding needed to paddle onto the simultaneous waves of Edge Computing, AI Everywhere, and Complex Computing. After the board adds investors and industry experts, I’ll no doubt be pivoting to whatever is needed next to make the company successful.

What do you think the IoT and AI landscape will look like in the next 24 months?
They’ll be merging a lot, for one. Edge computing for IoT will go from pilots to full deployment, and AI at the Edge for IoT makes a huge amount of sense. Filtering data is a great AI use case: come up with the actionable information locally via AI, and don’t flood the cloud with petabytes. Special-purpose Machine Learning systems will start to be integrated into coherent enterprise-wide systems. And the limitations of Machine Learning will stimulate integration of a broader suite of AI tools that rightfully includes the full data analytics toolbox.

Can you tell us about Four Colors of Money for Entrepreneurs?
Sure! It’s a blog, video channel and soon a podcast to provide entrepreneurs a 360-degree view of their funding options. Many successful entrepreneurs end up using all four colors of money to launch and grow – bootstrapping, grants, debt, and equity. But since most information sources have their biases – like “VC is the only way to go,” or “use online debt since banks will turn you down” – I’m trying hard to level the playing field.

Who do you think I’d have a better blind date with, Siri or Alexa?
I’ve got an occasional relationship with Siri, but Apple isn’t known for playing well with others. I’m currently blind to Alexa so I’d say go for it! The Amazon ecosystem sounds like a perfect match for xGraph. The one in Brazil, that is.

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Introspective Systems’ CEO Kay Aikin joins the Accel VT cohort in its first sprint in December 2018.

Positioning xGraph in the New Energy Economy – Introspective Systems at Accel VT

By Kay Aikin, CEO

I’m a few weeks home from attending the first sprint at Accel VT, and have had some time to think about what it will mean for Introspective Systems to participate in this program. Designed to support entrepreneurs in the energy marketplace, Accel VT leads a cohort through a series of exercises designed to test assumptions, expose business vulnerabilities, and prepare climate economy companies for investment. There are three sprints in all, and I am excited for this opportunity for a number of reasons.

Introspective Systems isn’t a pure “energy” company – we develop a platform for building applications that manage complex environments. Our xGraph platform helps developers solve problems that use large volumes of streaming data from a variety of sources and in a variety of formats, require rapid and often autonomous decision making, and that need to “learn” over time. Energy is one of our target markets – our platform is particularly suited to tackle problems such as modernizing our ancient energy grid.

Among the first lessons for me in our first sprint is understanding how our technology can fit with others being designed to modernize energy systems. The best description for xGraph within the energy market is as a control technology that helps utilities and consumers better manage electricity flow. Our cohort includes four hardware companies that are distributed energy producers, two control technologies including Introspective, and two energy financial services firms. Each has a role to play in this complex environment, and I’m learning from my peers what challenges they face, what they need to realize goals, and where my technology can and will fit.

One problem that utilities would like to fix is fluctuating energy use. Smooth, steady, predictable power consumption is what they ideally need to provide enough energy at a reasonable price. But electricity users usually don’t cooperate. They use a bunch of energy for 10 minutes, then next to nothing for an hour, then a lot for two minutes … Generating and pricing that kind of energy on demand is a big, big problem for utilities. Electricity might be there when the user flicks the switch, but utilities need to buy power weeks and even months in advance to make it happen.

One way to even out usage and improve forecasting is by changing the way energy is priced – the holy grail is real-time pricing, and until now, no company has been able to do that. It’s exactly the type of complex problem that xGraph was designed to manage, so participating in Accel VT means we can work with other companies who are developing different pieces of the puzzle to understand how to make our technology work for them.

The end game for us is to enable a new, real-time pricing network. Utilities will be able to charge hyper-localized rates for selling energy at the exact point and time of use in the grid. Consumers with fluctuating power use or other expensive behaviors can be rewarded with lower rates when they change their consumption patterns for the better. When we deliver real-time pricing, everyone in the system will win. It will balance the grid, make it easier to control, enable major generators to generate steady and predictable power and allow increased distributed power production like solar.. In my time at Accel VT, I want to learn how Introspective can help our peers create better market share for their technology and products.

I’m looking forward to our next sprint in mid-January, and will report on new lessons learned. We’re all competing for two $25,000 prizes and I hope to report back in February that Introspective Systems has one of the winning checks.

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From Deep Blue to xGraph: Gaming Out AI Challenges

posterIt’s been 20 years since Deep Blue beat Garry Kasparov in chess. As impressive a win as that was for artificial intelligence and computing, the game has changed. Chess players have about five million options during the course of an average game; StarCraft has roughly 10300 choices per move. Our full stack programmer, Amanda Castonguay, is using our xGraph platform solution to find a way to beat StarCraft.

This is more than a game – it’s a representation of how computing problems have become exponentially more complicated over time. They have massive decision spaces with an ever-growing number of “moves” or options. Any one of those choices can kick off a chain of unintended consequences, meaning complex problems can and will morph and grow even more complex. They have a high volume of streaming data – more than two zettabytes per year are flooding data centers at super high velocity.

Compounding this is the need for speed: self-driving cars can’t wait for data to go from every vehicle on the street to the cloud and back to find out whether or not they’re too close to the car (or kid) in front of them. Latency is one of the issues we’re trying to solve with XGraph, and Amanda’s work is part of this.
xGraph is our new technology platform. It’s an executable graph framework for intelligent and collaborative edge computing that solves big problems: those that have massive decision spaces, tons of data, are highly distributed, dynamically reconfigure, and need instantaneous decision-making.

Amanda recently presented her StarCraft research at the 2017 IEEE MIT Undergraduate Research Technology Conference (URTC). This official IEEE conference brings together undergraduates around the world to present, discuss, and develop solutions to advance technology for humanity. We’re excited that she had the opportunity to share her findings. If you’d like to follow along, sign up for our newsletter to learn more about what we’re doing to beat StarCraft, manage drone swarms, fix our energy grid, discover earthquakes, and more.

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Transactive Energy for Microgrids

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Our CEO Kay Aikin wrote a guest blog for Microgrid Knowledge the premier industry newsletter focused on Microgrid technologies and policy.

MRRA Microgrid

MRRA Microgrid

Toward Market-based Microgrid Control Systems

The largest machine on earth is often said to be our electrical grid. By the end of 2016 there were some 7600 power plants greater than 1 MW with many times more, smaller resources and an even larger number of control systems. The grid is truly a complex machine that is made up of systems collected into ever larger systems.  In the controls world this is called a systems of systems. This is the ultimate in complexity and is true on all large systems like the electric grid and ecological systems.

The advantage of microgrids is they help us tame the complexity the grid by limiting the number of possible interactions within the grid. Smaller more predictable systems are less prone to unintended consequences as can happen in the electrical grid like the Northeast blackout of 2003. However, even with microgrids and the emergence of energy management systems and smart devices the control networks of a microgrid will become even more complex. But this complexity can be used to build better systems as Ecologist Eric Berlow says the more you “embrace complexity the better chance you have finding simple answers”. A great Ted talk illustrating this concept can be seen at:

It has been shown that market-based systems can be amazingly stable in complex environments because of the many naturally balancing feedback loops within the system. Using this complexity, to lead to simplicity. In the electrical engineering field, the GridWise Alliance has called this idea of market-based system “Transactive Energy”. Much of the research in this area has been done by the Pacific Northwest National Laboratory and one of the successful trials was the Olympia Peninsula Project (OPP) consisting of a field demonstration of price signal-based control of distributed energy resources. The demonstration showed that market-based control was able to manage distribution constraints and reduce peak loads. This was followed with the Pacific Northwest Demonstration Project (PNDP) ending in 2015.

You can consider there to be three main methods for implementing Transactive Energy control systems applicable to microgrids including:

  • Centralized (top down)
  • Centralized (auction-based)
  • Distributed (edge-based)

The difference between the Olympia Peninsula and Pacific Northwest Project was that OPP was a double auction very similar to the current ISO systems and PNDP was a top down model where demand response assets and distributed energy resources were optimally dispatched by individual “transactive” nodes using two-way communications. Both both were generally more centralized paradigms.

The third transactive approach is a fully distributed edge control method that relies on pricing signals reflecting the prediction of future conditions creating a different price at many different scales.  Lower-level devices (or entire systems) respond to those pricing signals from higher levels. This method is currently being researched at Maine’s Brunswick Landing Microgrid Project (add link) for the Department of Energy.

While the first two transactive energy approaches have shown promise in that they have been able to balance energy demand, lowering peak demand and managing grid congestion they rely on large two-way communication networks that are particularly vulnerable to cyber assaults.

This cyber vulnerability should be a concern for the microgrid community because for a wide spread deployment of a system of microgrids this communication vulnerability is of particular concern to today’s infrastructure experts.

The edge-based system being researched at Brunswick Landing has pricing signals (potential of 10 or more different grid scales) are continuously re-calculated, only travel in a downward direction and are acted upon only by edge devices have promise, using the “power of complexity to lead to simplicity” Since the scope of influence of a single node is typically only one or two degrees of separation as described by Eric Berlow, this limits the computing power required to calculate the system state and provides for enhanced capabilities using advanced artificial intelligence techniques and limits security risks with limited communications routes. An effective transactive edge-based energy system can provide increased resilience, versatility, reliability and flexibility when used not only in microgrids but the greater electrical grid.

Kay Aikin is CEO of Introspective Systems, a complex systems architecture and engineering company in Portland Maine. Introspective Systems is the project lead at the Brunswick Landing Microgrid Project researching edge-based transactive energy networks.

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DOE Grid Architecture project

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New grid architecture promotes renewable energy

New grid architecture promotes renewable energy

Introspective Systems LLC, was awarded a $146,683 U.S. Department of Energy contract. The project running through March 13, 2017 will develop a new “future” grid architecture that puts intelligence on the edge of the grid.  The project is part of the DOE’s Grid Modernization Initiative.

Introspective Systems’ plan is based upon a fractal computer architecture xGraph developed by Introspective Systems.  xGraph distributes intelligence (analytics processing) to the edge of the computer network. It uses advanced theories in Complex Adaptive Systems (CAS) and Complexity theory. It also uses a unique concept utilizing a fractal structure.  A fractal is a natural phenomenon or a mathematical set that exhibits a repeating pattern that displays at every scale. Introspective Systems is using this concept and applying it to a computer architecture.

In the case of the electrical grid a single house would have a miniature electrical grid that would operate to save the homeowner electricity. Neighbors could then connect together to share infrastructure (solar panels, batteries etc.) within another small scale grid. Then neighborhoods group together pooling resources in the same way. This pattern would duplicate itself all the way up to the the entire nation. The algorithms running in each level of the electrical grid would be similar.

This would solve many problems in the current grid potentially making it more renewable, efficient, reliable and manageable while giving consumers choice. Most importantly the current grid is very vulnerable to terrorist cyber attack. This problem is being worked on by the Department of Energy, Department of Homeland Security as well as the Department of Defense. This fractal grid architecture approach could potentially solve these problems inherently. The structure of the grid would consist of fewer connections between layers. These fewer connections would make the electrical grid less vulnerable to being cyber-hacked.  If there was an attack fewer of these millions of smaller grids would be effected resulting in less damage.


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Systems in IoT World

Systems of Systems focused IoT Design

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As Internet of Things applications become more widespread software complexity with the system will grow exponentially. The IoT is not a single monolithic system but a system of systems. To cope, we must be able to create better architectures to manage these complex systems of systems.

Hardware has progressed tremendously in the last 40 years by encapsulation and Miniaturization. Numerous technologies have been encapsulated into single chips and miniaturized. This has sped time to market, processing speed and a host of benefits that have allowed Moore’s Law to be proven true. Software however has not scaled this way relying on monolithic architectures built around centralized databases that define how the pieces talk and work together. As systems architects we are designing IoT systems in much the same way as we design our applications. Some central brain with memory receives signals, makes decisions and sends signals to actuators for implementation.

The Internet scaled because each node (browser) provided processing and could be completely separated. A node operates independently and is separated from all other nodes. This is separation of concerns ideal in most good software design. This has been very successful for the Internet but it appears we are forgetting this lesson with Azure IoT and Samsung’s new Artik among others. When we design our Internet of things architecture we need realize the same separation of concerns but on a larger scale by decoupling our processes so everyone can be independent of each other. Centralized (ie: cloud data analytics) databases and architectures are a high friction architecture that only makes the solutions more complex. Security, communications and scaling being the biggest challenges for this centralized approach.

Nature doesn’t have this centralized “brain”. The swamp does not tell a cattail how to be a cattail, there is no “ecosystem” brain telling the cattail and swamp to communicate between themselves (in this case nutrients, water etc.) Each “thing” in the swamp is its own agent. But we are fundamentally designing the Internet of Things in an old paradigm where there is some central controller orchestrating the dance of the things.

We have to get to a systems thinking view of entire technical ecosystem. There are global goals but most of the thinking is done locally. Today in the environmental movement we hear the slogan “Think Globally act Locally” but this doesn’t say everything. If we literally applied this to the complex technological system world, we would build software architecture’s that have some kind of cloud controller. But what the environmental movement is really saying is to get back to natures’ principals. There is a global imperative, which, considers the entire architecture in its design and goals but the intelligence and actions are collaborative at the lowest level.

That doesn’t say that we only have local control and action. There are layers of control/action. So a group of cattails share a resource and create equilibrium between them. The frog uses the cattails roots as a place to hide its eggs and provides fertilizer to the cattails roots in exchange. And many groups of things (Cattails, frogs, fish etc) form the basis for the swamp and the swamp is one control/action network in a greater ecosystem of the watershed. This brings us to the concept of fractal layers. With fractal layers of control we can break larger systems up into systems of systems (SoS) and those systems into smaller systems. With those new SoS’s designs come possibilities for unique interactions and the optimal control of systems. One of the core principals in Systems of Systems science is how emergent behavior develops out of these interactions. This being a core benefit of AI systems.

On the 29th of April, Harvard Business Review posted an article “The Internet of Things needs Design, not just Technology” where they argue for the need of better product design in the Internet of Things. Producing value for the customer rather than just being a technology looking for a customer. Creating business value in data is driving most of the current solutions in the IoT space. What data can I, as a business, get from having an IoT device in a home? Not what value can I give to the consumer?

I believe the problem is even more fundamental than this business issue, in that we need to start with a fundamental design question. What architecture best serves the interests of the consumer? One that sends all information to the cloud or one that uses systems thinking to develop a more nature focused Systems of Systems that all levels of the ecosystem can benefit from.

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The whole city in your hands concept

10 Challenges: IoT Electrical Grid- Part 1

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In January, the Department of Energy released 220 million dollars for 80 grid modernization projects.  The aim is to increase the resiliency, reliability and security of the nations electrical grid. Scanning the list of projects few projects strike at the real electrical grid challenge. The projects like “DER siting and Optimization Tool for California” are focused on very narrow concerns. As a nation we need to confront a larger problem. What structure will a new grid architecture take? A new

grid architecture must be one that adapts and evolves as new technologies appear. It must also provide for the following core principles:

  • Backward and forward compatible
  • Open-Sourced
  • Secure
  • Resilient, reliable and robust

My thoughts are that a new Internet of Things paradigm designed from first principles can accomplish these requirements. However, there are challenges. Technical challenges are the first that DOE addressing. My opinion is the most important challenge is social. To start off, I believe that the technical challenges are solvable. They are basically engineering problems in security, communication and algorithm development. The social challenges are more profound as they revolve around entrenched business interests and government regulation standing in the way. At Introspective Systems we believe that a proper IoT based architecture we can solve both of these problems.

Ten challenges for implementation of the Internet of Things Electrical grid

Technical challenges

  1. Scalability
  2. Resource constrained devices
  3. Physical and Cyber security
  4. Adaption
  5. Openness

Social challenges

  1. View that IoT devices are a source of consumer data driving business value
  2. Electrical grid manager’s centralized control view
  3. Technological risk adverse grid managers
  4. Complex and fragmented electrical grid standards
  5. Slow to innovate Regulation to support new business innovation

Please leave a comment and tell me your views

Part 2: Our next blog post in the series will discuss these challenges in detail

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Ride the Third Wave with Partnerships -Steve Case

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About 6 months ago I had the privilege of presenting at the Portland Maine stop of the Rise of the Rest Tour. While I did not win a very deserving local entrepreneur team, Rapport LLC did and received a $100,000 personal investment by the founder of America Online Steve Case. A picture of Steve Case (and my legs) was published by Getty Images detailing the pitch completion and now is on my office wall.

Three weeks ago Steve’s first book “The Third Wave” was published and I received a copy. The book details Steve’s vision of the future third wave of Internet technology. AOL was a part of the first wave, which was hardware centric, and Google, Facebook and Twitter were the second wave being much more about social connections.

Today we are at the earliest hills of a huge mountain range that will be the third wave. This is a melding of hardware and the connections. In this case the connections will be both connections and insights between people but also machines. He did a great job in explaining his views of how the third wave is much more like the first rather than second. He had a particular view that business in the third wave will have greater barriers to success and to succeed they will need to develop partnerships to a much greater extent than in the past. No longer will you be able to build a web app and find it going viral. Successful entrepreneurs will see partnerships everywhere with competitors, complementary industries and most importantly government both as a regulator and a customer.

So his marching orders for us entrepreneurs was to look to partnerships as the way to break through the barrier. I strongly recommend that every entrepreneur producing a new third wave technology either IoT or data analytics pick up a book.

PORTLAND, ME - OCTOBER 2: Steve Case, founder of AOL, at far right, listens as Kay Aikin, CEO of Introspective Systems, a Portland-based software company specializing in big data challenges, makes the company's pitch during Case's Rise of the Rest tour at Port City Music Hall Friday, October 2, 2015. Case runs the tour - which has visited 14 cities throughout the country - and is aimed at finding small local businesses to invest in. The winner earns a $100,000 investment from Case. (Photo by Gabe Souza/Staff Photographer)

PORTLAND, ME – OCTOBER 2: Steve Case, founder of AOL, at far right, listens as Kay Aikin, CEO of Introspective Systems, a Portland-based software company specializing in big data challenges, makes the company’s pitch during Case’s Rise of the Rest tour at Port City Music Hall Friday, October 2, 2015. Case runs the tour – which has visited 14 cities throughout the country – and is aimed at finding small local businesses to invest in. The winner earns a $100,000 investment from Case. (Photo by Gabe Souza/Staff Photographer)

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Applied Science: Bridging Science and Engineering

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Who makes the great innovations of the world? Are they scientists or engineers? Introspective Systems purpose is to solve wicked problems through innovation. To push the boundaries of what is possible in science and engineering. We like to say that we use applied science as the bridge between science and engineering. At Introspective Systems our lead founder is titled Chief Innovation Officer. Ours is Dr. Caryl Johnson who came from both the engineering world and the science world with degrees arguably from the preeminent engineering university in the world MIT and the preeminent science university Caltech. We purposely created this organizational position to separate the technical engineering focused functions from the innovation. It should be pointed out that we also don’t have a Chief Scientist for a reason as well. We use basic scientific knowledge in many fields combining them to solve problems. We often explain it this way.

Power Of Knowledge Line Style Illustration

A basic scientist is typically focused on a defined domain and explores the basic functioning of nature and why things happen. They may have a problem in the back of their mind but generally don’t try to solve the problem but to understand the system. So they start at a starting point (A) and announce success when they gain some understanding of the system. An engineer is given a problem to solve with clearly defined ending point (B) and starting points (A). They are provided a tool set to solve the problem and their job is to find the most efficient method to get there. An applied scientist is given a hard problem to solve but isn’t given a pre-defined approach. They are given a (B) but can start almost anywhere and combine multiple scientific domains.

As applied scientists we solve problems not by incremental engineering improvements but by asking basic fundamental questions. By using science and engineering we find new and better ways to solve tough problems. Our last successful project for the United States Geological Survey was finding an entirely new way to locate earthquakes using basic physical laws, probability science and distributed computational analytics. The result was a 6-fold increase in earthquake location performance. This applied science work not only gave earth scientists new ways to understand how earthquakes are propagated but gave us a new computational platform to solve other complex problems.

We think the best innovation is an applied science; using broad based scientific knowledge to solve unsolvable problems and being the bridge between the basic sciences and engineering.

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Female and a Scientist: The problems in securing investment

Female ScientistThis has been a blog post in the making for about 4 years. My partner and I who is an accomplished scientist, started our company Introspective Systems in earnest back in 2012. At the time I was trying to get into a local accelerator in our state and was interviewed by a three entrepreneurs /investors. One turned out to be my lead investor a year later but something happened that reminded me of the struggle again. One of the entrepreneurs on the interview board was celebrated as one of the success stories of our small state and his comments about me boiled down to: She is a women in the tech/computer industry, doesn’t know the market, won’t succeed and we shouldn’t spend any time on her.

Now it is over 4 years later and his business has just closed after spending millions in government grants, investor money and government commercialization loans. We are still here and growing. I have to admit it was a tough road and last year we went into the valley of death and are now working our way out of it with a new focus, new product (actually where we first started) and contracts that are paying the bills while we prepare to open source our platform. But we are still alive, kicking.

This came into focus when talking to one of the companies that I mentor who has a women CEO in an all female company. (Full disclosure I have mentored 3 companies in the last two years and 2 of them have female CEO’s- Sorry I am biased as well) She is in the oil industry bringing high-level data science to a very old school and male dominated industry. We were talking and telling me about the problems in getting investment. Another of her female friends is in the Fintech industry and she has tried to sell her idea to the biggest investment firms in Silicon Valley with no luck. She is now crowd-funding her crowd-financing business. Ironic that someone in the crowd-financing invoices business has to crowd finance her own business.

Why is it so hard for high technology focused, women founded, women run companies to get investment money? According to a Dow Jones study there is a direct correlation between the number of women leaders in a company and its success. Successful companies have twice the number of women executives. There are many other studies that back up these findings.

So women face a definite bias in the tech industry even though they perform better. Men generally make the investment decisions so they “go with what they know” which is other men. But I even get it from other women. I went to a women investor super-angel group 18 months ago that most will have heard of and did what I thought was a good pitch. Twenty minutes later, my sponsor (who is a technology focused women) told me they were not going to invest because they didn’t know how they could help. Thinking back I realized that the women in the room, who were all successful, were public relations, media, advertising or financial executives and didn’t know anything about computer software or artificial intelligence. So where do you turn?

I have to ask how do women break this self-fulfilling prophesy?

Kay Aikin


First posted March 29, 2016 on Linkedin