Autonomous technology: Thinking beyond automobiles
Over the past few years, advancements in autonomous vehicles have captured the attention of people who have long envisioned a world where machines operate without constant human involvement. While cars are a popular use case, they are far from the only machines working autonomously in 2022.
Today, autonomous technology is thriving across a diverse set of industries well beyond automotive, including manufacturing, agriculture, infrastructure and even elder care. Autonomous technology includes both hardware — like robots and Internet of Things (IoT) devices — and autonomous systems, such as computer-assisted banking, which does the kind of number crunching that would take humans hours and even days to do.
These technologies are developing quickly thanks to continuous advancements in the realms of data analysis and engineering. Here's a look at how companies are harnessing the opportunities afforded by autonomous innovations.
What is autonomous technology?
Autonomous technology is a stream of innovation in which sensors, artificial intelligence (AI), augmented reality and analytical capabilities work together without human intervention to perform tasks and respond to changes in a device or system's environment. The ability to adapt can range from stopping motion when the device senses something blocking its way; to identifying and removing objects from a production line that do not meet quality standards; to adjusting the level of water, nutrients and light in a greenhouse.
Autonomous technologies are built on large volumes of data that must first be collected, annotated and validated before a device or system can be trained to perform optimally.
The market for autonomous technologies is growing fast. In 2020, there were 3 million multipurpose industrial robots — a threefold growth since 2010. A 2021 survey on robotics adoption conducted by Automation World magazine said 44.9% of respondents used robots in their assembly and manufacturing facilities.
Of those, 65% used industrial robots, while 35% used collaborative robots, also known as "cobots." An emerging technology, cobots are designed to share the same working environment as humans, bringing automation to production processes previously completed exclusively by humans.
Applications of autonomous technologies
Autonomous technologies are not limited to factory floors or automobiles.
In agriculture, for example, autonomous technologies can be used to optimally irrigate crops and enable the successful planting of high-density, efficient vertical gardens in areas with limited agricultural land. Meanwhile, cities can use machine learning to make traffic signaling intuitive, in an effort to improve traffic flow, reduce congestion and improve safety for cyclists and pedestrians.
Autonomous technologies are also used in infrastructure maintenance and are especially helpful for repairing hard-to-reach elements, such as an underwater pipelines or the underside of bridges.
Some are even speculating that autonomous technologies will help solve for the supply and demand issue currently experienced by the elder care industry. In the near future we may see "social robots" help lessen the burden of staffing shortages. These robots would be responsible for lifting patients, checking vitals and fetching items for people with limited mobility.
The use cases for autonomous devices and systems are expanding as the technologies become more sophisticated and finely tuned.
The essential guide to AI training data
Discover best practices for the sourcing, labeling and analyzing of training data from TELUS International, a leading provider of AI data solutions.
Autonomous technology: advantages and roadblocks
One of the most salient advantages of autonomous technology is efficiency.
With autonomous technology, there is a tight coupling of sensor data, analysis and response, which is well-demonstrated by computer vision technologies. For example, machine vision programs can take an image as input, identify basic shapes and perform semantic segmentation to identify entities within that image. This level of analysis gives the robot the ability to make decisions and complete actions based on what it sees — like removing a malformed product from an assembly line or even identifying tumors on MRIs.
Scalability is another advantage of autonomous technologies. There are initial investments for data ingestion, analysis and model building, but once that work is done it can be applied to a large number of devices and applications.
Another, sometimes overlooked, strength of autonomous technology is the consistency of operations. This can lead to a reduction in mistakes.
While these advantages make a compelling case for the adoption of autonomous technologies, there are challenges to consider as well.
When it comes to cobots, employees may be apprehensive about welcoming emerging technologies into their workplaces for fear of job loss. It can be difficult for organizations to navigate the sensitivities related to who does what in human-robot interactions.
And then there's the data component. Machine learning often demands huge amounts of training data. An industrial robot working on an assembly line might be relatively easy to train to perform a set of very specific functions compared to the autonomous technology that's required to navigate complex environments such as city streets. The collection, annotation and validation of data is resource-intensive and time-consuming,
In a similar vein, specialized industries or processes require unique training data. Generating this data requires deep domain expertise in order to create datasets that are comprehensive enough to capture the full range of entities and properties the AI will need for its reasoning. Not every organization has that kind of knowledge or the required tools readily available. Getting external help from an industry partner can bridge this gap and move projects along faster.
Use a trusted partner
Autonomous technologies are finding their way into a broad range of applications across industries. While the potential benefits of autonomous technologies are clear, there are also challenges to implementing the technology — especially when trying an internal, DIY approach.
Reach out to learn how our team of experts at TELUS International can help you adopt effective AI strategies and strong data infrastructure for the implementation of autonomous technology at your company.