Machine vision has been rapidly finding its place in the world. Oranges are seen and plucked from trees. Gaze detection targets the dangerously unaware driver. Industrial robots moving around a factory floor rely on it for safe obstacle detection. IoT endpoints lie at the frontier of embedded vision.

As 5G improves the way we live and work, opportunity for communications increases on a global level. The storytelling that PR and marketing professionals will be tasked with will help educate the masses about the impact of 5G technology across industries. It's a big job, but someone has to do it! As communicators, we are responsible for highlighting how increased data, low latency and faster edge computing is going to make a tangible difference. Merritt Group put out a recent infographic detailing this topic and I'm here to dive deeper today.

The breadth of IoT's distribution over the coming years will frustrate efforts to harness it as a unified resource. IoT application developers are embedding algorithmic capabilities in resource-constrained endpoints such as mobile phones, business machines, and consumer products of every type. More IoT edges are acquiring the ability to make decisions and take actions autonomously. More IoT devices are embedding machine learning and other sophisticated analytic algorithms. Though they're growing more powerful, IoT endpoints will continue to rely on resources located elsewhere in the sprawl, such as on adjacent devices, nearby gateways, and always-on public clouds.

Arm has unveiled two new IP cores designed to power machine learning in endpoint devices, IoT devices and other low-power, cost-sensitive applications. The Cortex-M55 microcontroller core is the first to use Arm's Helium vector processing technology, while the Ethos-U55 machine learning accelerator is a micro-version of the company's existing Ethos NPU (neural processing unit) family. The two cores are designed to be used together, though they can also be used separately. Enabling AI and machine learning applications on microcontrollers and other cost-sensitive, low-power resource-constrained devices is known as the tinyML sector. With the rise of 5G initiating a trend for more intelligence in endpoint devices, tinyML is expected to grow exponentially into a market that encompasses billions of consumer and industrial systems.

And that's largely correct, in many cases, but it's increasingly not the whole story – IoT endpoints are getting closer and closer to the ability to do their own analysis, leading to simpler architectures and more responsive systems. It's not the right fit for every use case, but there are types of IoT implementation that are already putting the responsibility for the customising their own metrics on the devices themselves, and more that could be a fit for such an architecture. There are three main areas where letting the endpoint do its own data analysis – in whole or in part – is becoming increasingly common – smart cities, industrial settings and transportation. In smart cities smart cameras can do certain kinds of analysis right there on the device, helping planners understand pedestrian and motorised traffic patterns. The difference between doing analytics completely on an endpoint device or partially on a device is an important one, according to Gartner research vice president Mark Hung.

Autonomous agents are a huge trend in consumer, business, industry, and other domains. They're popping up in everything from physical devices -- such as Internet of things (IoT) endpoints and mobile handsets -- to cloud services such as virtual personal assistants and smart advisers. Autonomous IoT devices will allow us to multitask like never before. As we incorporate more of them into our lives, we can offload much of the drudgery we once needed to handle manually. We will let self-driving cars manage our commute, offload the more strenuous yardwork to our robotic household assistants, and depend on personal drones to keep an eye on the neighborhood.