5G and AI convergence

The Promise of AI and 5G Convergence in the Internet of Context

Written by Dr. Kirk Borne

In the world of digital transformation, there is no lack of “hot topics” to discuss. Emerging technologies are truly emerging everywhere. What is most exciting — and what demonstrates their greatest promise — is that these new technologies are converging. Among the hottest technologies that have been (or soon will be) converging are Big Data, Machine Learning (ML), Artificial Intelligence (AI), Internet of Things (IoT), drones, Augmented Reality (AR), Virtual Reality (VR), immersive mixed reality environments, digital twins, Blockchain, and (soon) 5G (fifth-generation cellular wireless technology). We will focus here on only a few of those.

Data comes from sensors, measuring and monitoring the states and behaviors of people, products, and processes. There is a massive amount of data streaming from these sources. We don’t need to explain Big Data to this audience. Data-collecting sensors are ubiquitous, both large and small, both external and embedded, both personal (wearables) and industrial. Sensors in the IoT and in its industrial counterpart IIoT deliver digital data in abundance.

ML algorithms discover and learn patterns in streaming data. Applying these algorithms to processes, actions, and machines leads us to implementations of AI, including robotics and autonomous vehicles. We can help secure and/or encrypt the data flows from sensors to AI operations via Blockchain technology. When those AI operations become time-critical, we can greatly speed up the essential data rates (bandwidth) via 5G networks. Ultra-Reliable Low-Latency Communication (URLLC) will move from an operational requirement in autonomous systems to an operations reality with 5G.

mobility applications for travelers

Almost Like Magic

One such implementation of these converging technologies is in the platooning of autonomous vehicles, which drive in tight formation, synchronously, at high speed. When people see test runs of platooning on the road, they might be startled by the precision and tight coordination of the driving – it is almost like magic.

It has been written (by Sir Arthur C. Clarke) that any sufficiently advanced technology is indistinguishable from magic.1 So, the great magic that is now being revealed to us comes through the convergence of the current flood of emerging technologies. Before we get on with that, I do want to mention that although Arthur C. Clarke is most famous for his science fiction writings (one of which became an awe-inspiring blockbuster motion picture), I was actually most deeply moved and inspired in my youth by his nonfiction masterpiece “The Promise of Space”.

Using Machine Learning to Turn Data into Insights

Measurements of things are collected as raw data by IoT sensors. That data then flows from the sensors (often through “space” via 5G wireless networks) as “sensory” inputs to things (devices, machines, computer logic) and to people (individuals and enterprises). Data-fueled ML algorithms then provide the power to drive amazing AI applications. This perhaps represents a revitalized 21st century version of “the promise of space”.

How does ML do this? ML represents a class of mathematical algorithms that learn from experience. This means that the algorithms learn the patterns, trends, segments, behaviors, what’s normal, and what’s abnormal in a system via the data emanating from that system’s sensors. The exploratory, investigative, and hypothesis-driven discovery of new insights from data using ML algorithms is called data science. The products of these activities are the analytics, which bring our data to life and put our discoveries into action for value creation.

Enterprise AI isn’t What You Think

AI is the application of ML in cyber-physical systems to create action, to make decisions, and to steer resources in response to signals from sensors. Experts now agree that enterprise AI is best understood not as “Artificial” intelligence, but as something much more practical and real: Assisted, Augmented, or Actionable intelligence. That includes both human-assisted machine-based actions and machine-assisted human actions. That’s the real power of AI.

In many application areas where we are using sensor (device) data and ML algorithms, it is possible for the AI to emulate the human cognitive abilities of foresight and optimization. Specifically, ML enables predictive modeling (forecasting) and prescriptive modeling (optimization) of systems’ behaviors and outcomes for optimal operating decisions and actions. In the wireless industry, examples of this include forecasting (predicting) loads on networks and optimized (prescriptive) load balancing.

The exciting role that IoT and IIoT big data play in these use cases and applications is to allow the insertion of multiple, diverse, and even “third party” data sources into the data-fueled and ML-powered decision processes. Those additional “external” data sources provide rich context to the signals coming from the primary systems. Since we anticipate that much of those contextual data will come from ubiquitous devices and sensors, the IoT might be re-labeled as the “Internet of Context” for AI applications.

Given the ability of rich contextual data to deliver multi-perspective insights, even an “ability to see around corners”, I have sometimes envisioned an API-accessible platform for IoT data coupled with ML predictive algorithms that can deliver Forecasting-as-a-Service to other enterprise processes, systems, and applications.

The Ultimate Data Outcomes

Consequently, predictive and prescriptive AI is driven by three categories of data: (1) historical data, (2) current streaming data; and (3) independent co-occurring contextual data. We thereby are able to build better models based on such enriched data sources. For example, we will not simply base a forecast on what is the past history of network load as a function of time and the network’s current state, but we will also seek out and discover contextual events that were occurring at the specific times of historical network peak loads or previous anomalous network behaviors, such as social events, economic activities, sporting events, weather patterns, or any other patterns in streaming data from relevant sensors.

Applications of ML and AI in the context of streaming data (across 5G networks) are revealing a new promise of emerging technologies: the promise of convergence. Data makes that possible. Data is the ultimate fuel for discovery, innovation, and value creation. Those outcomes are being powerfully realized through assisted, augmented, and actionable intelligence, which is informed through IoT and other sensors, creating an Internet of Context for smart AI across the whole enterprise.

Now, take the conversation to Twitter! Agree or disagree with this perspective on 5G and the Internet of Context? Want to ask Kirk a question? Tweet @KirkDBorne using the hashtag #datamakespossible right now!

[1] “Clarke’s Third Law” from Profiles of the Future (revised edition, 1973)

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