If you attended CES in Las Vegas, you’re probably finally starting to emerge from your technology-overload coma and trying to make sense of it all. We’ve got you covered!

If you haven’t read it yet, check out my recap from the annual trade show: “Clarke’s CES 2024 Report: Are You Smarter Than Your Technology?” I hope it will get you thinking about some of the great technologies that were on display and even entertain you a bit.

In the event, however, that you’re prioritizing your real job over reading my (admittedly lengthy) report, we’ve still got you covered! CableLabs is sponsoring a webinar in which I’ll discuss the technologies and solutions I saw and give my analysis on how they’re relevant (or not) to the broadband industry and to consumers in general. If you’re a CableLabs member, register now for the webinar on March 6.

In the meantime, here’s a brief summary to whet your appetite.

AI Spreading Outward

The primary theme of the show was that intelligence is becoming artificial. You might believe you already know all about artificial intelligence (AI), but now the technology is becoming more pervasive, spreading to common electronics.

Consider, for example, the Flappie cat door, whose motion sensor and night-vision camera will keep an eye on your cat and prevent him from coming inside the house until he drops the mouse he’s captured. Other AI-powered tools enable you to also measure your vitals just by looking in a mirror or communicate with people in your own voice when you can’t speak like you used to.

Because products like these rely on strong network connectivity, broadband operators will be an innate part of their success with customers. The industry opportunity doesn’t end there. Somebody must install, provision and maintain these products. Operators have the technical staff, vehicle fleets and monthly billing relationship with customers that give us a distinct advantage in the pursuit of new business opportunities beyond simple connectivity.

AI also presents operators with the prospect of new, futuristic market opportunities. AI-assisted cameras in cities can identify vehicles, animals and people. They can spot vehicle congestion and reroute traffic, and even call police, ambulances or firefighters.

AI Focused Inward

Cable companies can also use AI to anticipate, repair and even proactively prevent problems in their own network infrastructure. The benefits include a more reliable network at a reduced cost, fascinating new revenue opportunities and an improved ability to meet high-demand needs and provide appropriate service levels. Our combination of proven networking technologies and private networks can be leveraged to provide continuous connectivity to support new business models or simply make existing businesses more efficient.

A sometimes-overlooked opportunity in our industry is to increase our customer base by engineering our solutions toward new kinds of customers. For example, AI-powered “glasses” for the blind can replicate some of the benefits of a guide dog (sadly, not love). Not only can they guide a person around obstacles, but they can also integrate GPS for navigation and even help identify items on a grocery shelf. Let’s see the dog do that!

Many companies today are focused on conducting their business with an eye toward sustainability. Electric vehicles of every kind are replacing fossil-fueled alternatives. Fancy “leather” goods are being manufactured from pineapple waste. Bioengineered house plants are being bred to work as efficient (and decorative) air filters.

And, as always, great people are a natural advantage for the cable industry. AI and the other emerging technologies featured at CES are allowing those people to be more efficient, productive and happy.

A Limitless Future

New technology is improving your health, your daily life and your bottom line. You owe it to yourself to learn more about the emerging AI technologies that will make a difference in your near future.

In the webinar on March 6, I’ll cover some of the crazy ideas that blossom on the way to innovation. This is the kind of conversation that will improve your water cooler game as you stand around your Nube “no-plumbing” water generator that condenses and purifies moisture from thin air!

The heterogenous nature of future wireless networks comprising of multiple access networks, frequency bands and cells - all with overlapping coverage areas - presents wireless operators with network planning and deployment challenges. Machine Learning (ML) and Artificial Intelligence (AI) can assist wireless operators to overcome these challenges by analyzing the geographic information, engineering parameters and historic data to:

• Forecast the peak traffic, resource utilization and application types
• Optimize and fine tune network parameters for capacity expansion
• Eliminate coverage holes by measuring the interference and using the inter-site distance information

5G can be a key enabler to drive the ML and AI integration into the network edge. The figure below shows how 5G enables simultaneous connections to multiple IoT devices generating massive amounts of data. The integration of ML and AI with 5G multi-access edge computing (MEC) enables wireless operators to offer:

• High level of automation from the distributed ML and AI architecture at the network edge
• Application-based traffic steering and aggregation across heterogeneous access networks
• Dynamic network slicing to address varied use cases with different QoS requirements
• ML/AI-as-a-service offering for end users

ML and AI for Beamforming

5G, deployed using mm-wave, has beam-based cell coverage unlike 4G which has sector-based coverage. A machine learned algorithm can assist the 5G cell site to compute a set of candidate beams, originating either from the serving or its neighboring cell site. An ideal set is the set that contains fewer beams and has a high probability of containing the best beam. The best beam is the beam with highest signal strength a.k.a. RSRP. The more activated beams present, the higher the probability of finding the best beam; although the higher number of activated beams increases the system resource consumption.

The user equipment (UE) measures and reports all the candidate beams to the serving cell site, which will then decide if the UE needs to be handed over to a neighboring cell site and to which candidate beam. The UE reports the Beam State Information (BSI) based on measurements of Beam Reference Signal (BRS) comprising of parameters such as Beam Index (BI) and Beam Reference Signal Received Power (BRSRP). Finding the best beam by using BRSRP can lead to multi-target regression (MRT) problem while finding the best beam by using BI can lead to multi-class classification (MCC) problem.

ML and AI can assist in finding the best beam by considering the instantaneous values updated at each UE measurement of the parameters mentioned below:

• Beam Index (BI)
• Beam Reference Signal Received Power (BRSRP)
• Distance (of UE to serving cell site),
• Position (GPS location of UE)
• Speed (UE mobility)
• Channel quality indicator (CQI)
• Historic values based on past events and measurements including previous serving beam information, time spent on each serving beam, and distance trends

Once the UE identifies the best beam, it can start the random-access procedure to connect to the beam using timing and angular information. After the UE connects to the beam, data session begins on the UE-specific (dedicated) beam.

ML and AI for Massive MIMO

Massive MIMO is a key 5G technology. Massive simply refers to the large number of antennas (32 or more logical antenna ports) in the base station antenna array. Massive MIMO enhances user experience by significantly increasing throughput, network capacity and coverage while reducing interference by:

• Serving multiple spatially separated users with an antenna array in the same time and frequency resource
• Serving specific users with beam forming steering a narrow beam with high gain to send the radio signals and information directly to the device instead of broadcasting across the entire cell, reducing radio interference across the cell.

The weights for antenna elements for a massive MIMO 5G cell site are critical for maximizing the beamforming effect. ML and AI can be used to:

• Identify dynamic change and forecast the user distribution by analyzing historical data
• Dynamically optimize the weights of antenna elements using the historical data
• Perform adaptive optimization of weights for specific use cases with unique user-distribution
• Improve the coverage in a multi-cell scenario considering the inter-site interference between multiple 5G massive MIMO cell sites

ML and AI for Network Slicing

In the current one-size-fits-all approach implementation for wireless networks, most resources are underutilized and not optimized for high-bandwidth and low-latency scenarios. Fixed resource assignment for diverse applications with differential requirements may not be an efficient approach for using available network resources. Network slicing creates multiple dedicated virtual networks using a common physical infrastructure, where each network slice can be independently managed and orchestrated.

Embedding ML algorithms and AI into 5G networks can enhance automation and adaptability, enabling efficient orchestration and dynamic provisioning of the network slice. ML and AI can collect real time information for multidimensional analysis and construct a panoramic data map of each network slice based on:

• User subscription,
• Quality of service (QoS),
• Network performance,
• Events and logs

Different aspects where ML and AI can be leveraged include:

• Predicting and forecasting the network resources can enable wireless operators to anticipate network outages, equipment failures and performance degradation
• Cognitive scaling to assist wireless operators to dynamically modify network resources for capacity requirements based on the predictive analysis and forecasted results
• Predicting UE mobility in 5G networks allowing Access and Mobility Management Function (AMF) to update mobility patterns based on user subscription, historical statistics and instantaneous radio conditions for optimization and seamless transition to ensure better quality of service.
• Enhancing the security in 5G networks preventing attacks and frauds by recognizing user patterns and tagging certain events to prevent similar attacks in future.

With future heterogenous wireless networks implemented with varied technologies addressing different use cases providing connectivity to millions of users simultaneously requiring customization per slice and per service, involving large amounts of KPIs to maintain, ML and AI will be an essential and required methodology to be adopted by wireless operators in near future.

Deploying ML and AI into Wireless Networks

Wireless operators can deploy AI in three ways:

• Embedding ML and AI algorithms within individual edge devices for to low computational capability and quick decision-making
• Lightweight ML and AI engines at the network edge to perform multi-access edge computing (MEC) for real-time computation and dynamic decision making suitable for low-latency IoT services addressing varied use case scenarios
• ML and AI platform built within the system orchestrator for centralized deployment to perform heavy computation and storage for historical analysis and projections

Benefits of Leveraging ML and AI in 5G

The application of ML and AI in wireless is still at its infancy and will gradually mature in the coming years for creating smarter wireless networks. The network topology, design and propagation models along with user’s mobility and usage patterns in 5G will be complex. ML and AI can will play a key role in assisting wireless operators to deploy, operate and manage the 5G networks with proliferation of IoT devices. ML and AI will build more intelligence in 5G systems and allow for a shift from managing networks to managing services. ML and AI can be used to address several use cases to help wireless operators transition from a human management model to self-driven automatic management transforming the network operations and maintenance processes.

There are high synergies between ML, AI and 5G. All of them address low latency use cases where the sensing and processing of data is time sensitive. These use cases include self-driving autonomous vehicles, time-critical industry automation and remote healthcare. 5G offers ultra-reliable low latency which is 10 times faster than 4G. However, to achieve even lower latencies, to enable event-driven analysis, real-time processing and decision making, there is a need for a paradigm shift from the current centralized and virtualized cloud-based AI towards a distributed AI architecture where the decision-making intelligence is closer to the edge of 5G networks.

The Role of CableLabs

The cable network carries a significant share of wireless data today and is well positioned to lay an ideal foundation to enable 5G with continued advancement of broadband technology. Next-generation wireless networks will utilize higher frequency spectrum bands that potentially offer greater bandwidth and improved network capacity, however, face challenges with reduced propagation range. The 5G mm-wave small cells require deep dense fiber networks and the cable industry is ideally placed to backhaul these small cells because of its already laid out fiber infrastructure which penetrates deep into the access network close to the end-user premises. The short-range and high-capacity physical properties of 5G have high synergies with fixed wireless networks.

A multi-faceted CableLabs team is addressing the key technologies for 5G deployments that can benefit the cable industry. We are a leading contributor to European Telecommunication Standards Institute NFV Industry Specification Group (ETSI NFV ISG). Our SNAPS™ program is part of Open Platform for NFV (OPNFV). We are working to optimize Wi-Fi technologies and networks in collaboration with our members and the broader ecosystem. We are driving enhancements and are standardizing features across the industry that will make the Wi-Fi experience seamless and consistent. We are driving active contributions to 3GPP Release 16 work items for member use cases and requirements.

Our 10G platform complements 5G and is also a key enabler to provide the supporting infrastructure for 5G to achieve its full potential. CableLabs is leading the efforts for spectrum sharing to enable coexistence between Wi-Fi and cellular technologies, that will enable multi-access sharing with 3.5 GHz to make the 5G vision a reality.

A version of this blog post was published on February 21, 2019, on the S&P Global Market Intelligence site. 

We are surrounded by embedded sensors and devices with more processing power than many of the computers standing on our desks. Machine learning modules inside phones, home control systems, thermostats, and the ubiquitous voice operated gadgets, constitute a whole technological species that now coexist with us through the same Internet environment we populate with our own communication devices. These are the simple components of what it is commonly called the “Internet of Things” (IoT).

The real revolution is taking place in a different, less visible setting, an industrial one, that ranges from manufacturing and refineries to health care. Within these very large systems and organizations, myriads of embedded smart sensors are connected through shared API’s, leading to a new form of networked computing power that will likely dwarf what we conceive of as the present-day Internet.

The Challenges of Embedded Systems

This vast industrial array of connected sensors has many characteristics that make it different from the consumer smart devices with which most people are familiar. First, the pervasiveness and interconnectivity of these sensors, coupled with the unpredictability of their inputs, make their response times autonomous from human intervention. For instance, a fitness tracker running out of power does not necessitate an urgent response. In comparison, the failure or delayed emergency signal from a smart sensor controlling several refinery valves can trigger an undesirable chain reaction from other sensors and actuators leading to consequential systemic failures.

Second, these smart sensors constitute an open and asynchronous distributed system which cannot predict the behavior of the environment in which it is embedded. This system is also decentralized since it would be hard for a central unit to receive and transmit up-to-date information on the state of the whole system.

Third, the distributed nature of this industrial internet of things makes it open to a host of security threats, since a single break into a component of the distributed fabric can compromise the entire system.

While it is easy to create machine learning algorithms that report on the behavior of parts of the system, it is hard for these programs to be able to reason and act swiftly in response to inputs and malfunctions of a large, interconnected embedded system. A notable improvement can be achieved by using Edge Computing, which entails sensing and processing the information from embedded systems in close spatial proximity to them.

Far more complicated is the aggregation of such local information at a coarser level, so that one can obtain global and timely information and take corrective action when needed. The reason why this is difficult is that sensors differ in the precision and sophistication with which they sense and process data, while at times reporting faulty readings.

Consensual AI Solutions

One way to remedy these problems is to design distributed algorithms that can cooperate in the overall task of diagnosing and acting on systems of embedded sensors. Examples include the sensing of local anomalies that are aggregated intelligently in order to decide on a given action, the collective detection of malware in parts of the network, and effective responses to predetermined traffic and content patterns, to name a few.

We know that such distributed systems are extremely effective at solving the global problems posed by interconnected local units because this is the manner in which humans successfully manage large distributed tasks. Organizations are created at a dizzying speed to deal with problems of control and distribution in a number of industries and services, and they function by interweaving local expertise that is able to detect and solve problems that require timely solutions, from network malfunctions to supply chain interruptions.

This distributed form of artificial intelligence is not an illusory goal. A few such systems have already been designed and tested and have shown dramatic improvements in the times needed to solve hard problems such as optimization and graph coloring. These problems are characterized by the fact that as their size increases linearly, the time to finding a solution rises exponentially. A common example is the traveling salesman problem, which can be seen as a metaphor for the laying of networks in a manner that minimizes the number of traversals needed to cover multiple cities and users. As more nodes are added to such a network, the number of possible solutions increases exponentially, leading to the impossibility of finding the node that minimizes the connections in finite times.

The beauty of cooperative systems is that once deployed, they can exhibit combinatorial implosions. These implosions are characterized by a sudden collapse in the number of possible avenues to a solution due to the effectiveness of cooperation. As a result, problems that took an enormous amount of time to solve are now rendered in linear or polynomial time. These implosions occur when both the quality and the number of messages exchanged by these agents or programs increases while working on the solution of complex problems.

In the context of these networked embedded sensors, consensual systems have the ability to aggregate disparate, and at times, incorrect information to quickly and reasonably diagnose problems. These types of cooperative systems will allow for the control of these large embedded systems without having to resort to an exhaustive analysis of all the data that floods the network. Even better, one hopes that as algorithmic forms of common-sense reasoning (e.g. haven’t I seen this problem before?) are developed in the future we will then reach the dream of fully embedded sensors being able to control their systems in a form that one would call intelligent.

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Not a single day goes by without us hearing about AI. Machine learning, and AI, as these terms are often conflated, have become part of the lexicon in business, technology and finance. Great strides in pattern recognition and the discovery of hidden correlations in vast seas of data are fueling the enthusiasm and hopes of both the technical and business communities.

While this success is worth celebrating, we should not lose track of the fact that there are many other aspects of artificial intelligence beyond machine learning. Common sense reasoning, knowledge representation, inference, to name a few, are not part of the toolbox today but will have to be if we seek forms of machine intelligence that have much in common with our own.

The reason why such forms of machine intelligence are not being used is due to the difficulty that those problems entail. Unlike the recent advances in machine learning, half a century of research in symbolic systems, cognitive psychology and machine reasoning have not produced major breakthroughs.

Intelligent Networks: A New Form of Artificial Intelligence

A more promising future can be expected once we realize that intelligence is not restricted to single brains; it also appears in groups, such as insect colonies, organizations and markets in human societies, to name a few. In all these cases, large numbers of agents capable of performing local tasks that can be conceived as computations, engage in collective behavior which successfully solves a number of problems that transcend the capacity of a single individual to solve. And they often do so without global controls, while exchanging information that is imperfect and at times delayed.

Many of the features underlying distributed intelligence can be found in the computing networks that link our planet. Within these systems processes are created or “born”, migrate across networks and spawn other processes in remote computers. And as they do, they solve complex problems -think of what it takes to render a movie in your screen -while competing for resources such as bandwidth or CPU contested by other processes.

Interestingly, we understand the performance of distributed intelligence, both natural and artificial, much better than the workings of individual minds. This is partly due to the ease with which we can observe and measure the interactions among individuals and programs as they navigate complex informational spaces. Contrast this with the difficulty in learning about detailed cognitive processes within the human brain. And from this large body of knowledge we know that while the overall performance of a distributed system is determined by the capacity of many agents exchanging partial results that are not always optimal, success is determined by those few making the most progress per unit time (think of many agents looking for the proverbial needle in the stack).

Distributed Intelligence: Better than the Best

This suggests a promising path forward for AI; the creation of distributed intelligent programs that can sense, learn, recognize and aggregate information when deployed throughout the network in the service of particular goals. Examples can be the sensing of local anomalies that are aggregated intelligently in order to decide on a given action, the collective detection of malware in parts of the network, sensor fusion, and effective responses to predetermined traffic and content patterns, to name a few.

Distributed AI is not an illusory goal. A few such systems have already been designed and tested and have shown large improvements in the times needed to solve hard computational problems such as cryptarithmetic and graph coloring. These are problems characterized by the fact that as their size increases linearly, the time to finding a solution rises exponentially. A common example is the traveling salesman problem, which can be seen as a metaphor for the laying of networks in such a way that they minimize the number of traversals needed to cover a number of cities and users. While there are a number of powerful heuristics to approach this optimization problem, for large instances one can only hope for solutions that while not optimal, satisfy a certain number of constraints.

The beauty of cooperative systems is that once deployed, they can exhibit combinatorial implosions. These implosions are characterized by a sudden collapse in the number of possible venues to a solution due to the effectiveness of cooperation, and as a result, what took exponential times to solve is now rendered in linear or polynomial time. These implosions appear as both the quality and the number of messages exchanged by AI agents increases while working on the solution of complex problems.

In closing, the emergence of distributed AI will allow for the solution of a number of practically intractable problems, many of them connected with the smooth and safe functioning of our cable networks. Imagine applying different AI solutions to search for security anomalies and combining them in order to identify and act on them. Or monitoring distal parts of the network with different kind of sensors whose outputs are aggregated by intelligent agents. These are just two instances of the myriad problems that could be tackled by a distributed form of artificial intelligence. The more examples we think of and implement, the closer we will get to this vision of a society of intelligent agents who, like the social systems we know, will vastly outperform the single machine learning algorithms we are so familiar with.

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