HOW WE GOT HERE

Since the beginning of computing there have essentially been three waves of technology innovations. The first wave in the 1960s to the 1980s was driven by the advent of silicon – microprocessors and microcontrollers that enabled calculations and put computational capabilities into business, society and professionals’ hands on a significant scale.  Engineers could design products, businesses could manage orders and inventories and scientists could model diverse realms like human disease, the weather and natural resources.

The second wave in the 1990s and 2000s, brought us interconnectedness or the “connected economy.”  Everything began to be linked and integrated creating a new virtual world where the Internet and the Web, telco networks and satellites enabled shared computing resources interconnecting machines, software, systems, processes and people that executed everything digitally. This shift involved digitally enabling physical actions and execution.

The third wave, which started in the later 2000s, is based on multiple parallel technology developments which began to accelerate and, more importantly, also began to reinforce one another. Cloud computing infrastructure provided unprecedented computing scale. Mobile computing devices extended the reach of computing itself. Machine learning and AI brought intelligence to diverse things, and embedded systems and IoT technology began connecting and integrating a broad array of physical and digital applications.

Each of these technologies is powerful on its own, but catalytic combinations of these capabilities are multiplying their impacts. Human-connected devices and machine-connected IoT devices enable exponentially more data. The cloud then enables us to capture, analyze and model many phenomena through its computational capacity. This in turn sets the stage for AI and machine learning tools to analyze and capture new insights.

THE SHIFT TO COMPLEX ADAPTIVE AND AUTONOMOUS SYSTEMS

The third wave of innovation, which is still evolving, is enabled by cheap pervasive networked sensors and sensor data fusion. This is driving new innovations that are enabling a whole new generation of “awareness” applications. As networks continue to invade the “physical” world, system developers are seeing the new values that come from the growing interactions between sensors, machines, systems and people and the ability to detect patterns from large-scale sensor and machine data.

Applications like computer vision caused the development of systems, based on methods and algorithms that could recognize something and execute an action based on the result. These new capabilities revolve around real-time situational awareness and automated analysis of very large volumes of sensor data. As a result, technology has moved beyond just proposing task solutions — such as executing a work order or a sales order — to sensing what is happening in the world around it, analyzing that new information for patterns, risks and possibilities, presenting alternatives, and automatically taking actions.

These combinatorial innovations are what we call “complex adaptive systems” where multiple technologies converge and reinforce one another.

For example, the development of autonomous vehicles not only depends on advancements in robotics and artificial intelligence to operate vehicles, but also on the maturation of the Internet of Things so an array of sensors can analyze driving conditions and interact with other cars, as well as improvements in lithium and battery technology for cars to be able to efficiently refuel themselves. The interdependence of these technologies has no doubt contributed to their synchronous advancement. For example, many believed a limiting factor in the emergence of driverless cars was the high cost of batteries required to travel long distances. In response, however, battery producers have dramatically increased production to scale down per unit costs.  Over the last ten years, EV battery prices have fallen 90%.

From seemingly disparate innovations powered by self-organizing sensor networks processing voluminous amounts of data, computers are able for the first time to understand and form associations based on statistical methods. Computers can now do what we previously thought only humans could do.

Complex adaptive and autonomous systems have common attributes, including:

• The ability to infer and reason, using substantial amounts of appropriately represented knowledge.
• The ability to learn from their experiences and improve their performance over time.

• The capability of explaining themselves and taking naturally expressed directions.
• The awareness of themselves and ability to reflect on their own behavior and to respond robustly to surprises.

As these systems evolve, we are setting the stage for numerous “invisible” computer-to-computer interactions (a new generation of “machine-to-machine” transactions):

• Autonomous machines making decisions about purchases on our behalf or concluding contracts.
• Multi-agent systems and decentralized autonomous machines enabled to lease themselves out, hire maintenance professionals, and pay for replacement parts.
• Micro-payments between machines, such as a car looking for a specific spare part, or drones and farming systems negotiating directly with each other for services.

Machine data models and analytics allows not only data patterns but a much higher order of intelligence to emerge from large collections of ordinary machine and device data, similar to the neurons of the brain, ants in an anthill, human beings in a society, or information devices connected to each other. The many “nodes” of a network may not be very “smart” in themselves, but if they are networked in a way that allows them to connect effortlessly and interoperate seamlessly, they begin to give rise to complex, system-wide behavior that usually goes by the name “emergence.” That is, an entirely new order of intelligence “emerges” from the system as a whole—an intelligence that could not have been predicted by looking at any of the nodes individually.