Think ultra-smart robots and device-free communication belongs to the realm of science fiction? Think again. The next generations of wireless connectivity – both 5G and 6G – stand to unlock entirely new levels of smart technology thanks to the advances they will bring to artificial intelligence and machine learning. Both technologies already have numerous uses in the world today, but machine learning especially harbors the potential to radically remake our world.

Machine learning has been around since the late 1950s, and it’s expected to play a central role in 6G in the future. Read on to learn what machine learning is, and how it will be applied to 6G networks to make them nimble, practical, and optimized.

Machine Learning Explained

Machine learning is a subset of artificial intelligence that uses algorithms, patterns identification, and inference to learn implicitly. It contrasts with other types of programming where all of the knowledge that a software or program has is explicitly entered by a user.

Many uses for machine learning already exist, and we use it more frequently than people expect. For example, virtual assistants like Siri or Alexa use machine learning to understand concepts or language from the context of the conversation. If you pay attention, you’ll notice that you never have to correct Alexa on the same mistake twice. 

Machine learning also plays a role in things like Facebook ads (deciding what to show you), malware protection (identifying new potential threats), and online customer support (chatbots). If you’re inputting information about your preferences and there’s clearly no human on the other end of the line, chances are an algorithm is carefully sorting that data and determining what content you’ll find enjoyable or helpful.

Machine learning is fundamental to artificial intelligence because the dynamic contexts of daily existence are too complicated to code from the ground up. With machine learning, an AI can learn to spot clues and meanings in much the same way that humans do – with far less effort on the developer’s part.

Uses for Machine Learning with 6G

It’s impossible to consider the future of 6G without also considering the role machine learning plays in it. 6G will enhance the capabilities of machine learning substantially, but that’s because it will need machine learning to function.

As we’ve noted elsewhere, one of the biggest challenges with 6G is that the technology to realize it simply doesn’t exist. 5G enhances performance, bringing faster speeds, lower latency, and more data transfer capacities. We’re accomplishing this using mostly what we already have. (That’s also why we’re facing the infrastructure challenges with 5G.)

To get to 6G, we need to develop the technology that will solve these challenges. Currently, most envisioned applications for machine learning with 6G lie with the potential advantages it will deliver to infrastructure – advantages that will look more like necessities. Here’s a closer look at three ways that will occur.

1. Communication-Efficient Distributed Training and Inference

Artificial intelligence, like the human brain, needs a lot of energy. In wireless connectivity lingo, it needs significant data transfer capacity. However, even with terabyte-level frequencies, the extensive use of AI will tax the entire system and prove impractical.

To get around this resource problem, some researchers are suggesting “distributed training and inference” structures. In other words, resources to perform “smart” functions will be more dispersed across clouds, networks, and devices. The paper from the Hong Kong University of Science and Technology notes it’s not a perfect solution, but a step towards what’s needed.

2. Smart Network Reconfigurability

By creating a dispersed resource environment, researchers hope to significantly reduce the chances of bottlenecks from occurring in bandwidth. However, machine learning will be needed to further address the reallocation and reconfiguration of resources efficiently, in response to changing network conditions. A paper from IEEE refers to the ability to do this as quantum machine learning.

3. Big Data Analytics

5G will bring about the mainstreaming of big data due to its potential for making the Internet of Things practical. That trend will continue with 6G, and we’ll be able to use data analytics in unprecedented ways. Currently, expected uses include applications in healthcare, the military, and certain commercial uses where self-sustaining and proactive machine learning delivers better insights about individuals, situations, or markets.

Learn Everything You Need to Know on 6G Research and Development

Machine learning has many uses. Its list of applications will only grow as 5G matures and 6G enters development. By leveraging the connectivity and data transfer capacities of the next generations of wireless connectivity, machine learning will transform networks into quick, capable, lightweight grids that can anticipate and respond to needs flawlessly.

Of course, we’ve got a long way to go to get there. Current research, while hopeful, remains fully cognizant of the infrastructural and technological challenges facing machine learning for 6G. Still, just as 5G will make the Internet of Things a practical reality in this decade, 6G will be the age of machine learning, and our world will get not just more connected, but even smarter.

What is 6G? That’s the question everybody’s asking. Here’s everything you need to know.