The edge is not a new concept, but it’s about to take off in a major way.
According to Gartner, by 2022, over 50% of enterprise-generated data will be created and processed outside the data center and cloud.
To keep up with the proliferation of new devices and applications that require real-time decisions, organizations will need a new strategy. That’s where edge computing comes into play.
Edge computing involves moving processing power closer to the source of the data to reduce network congestion and latency, and extract maximum value from your data. By taking this approach, companies can use this information to further enhance business outcomes.
However, there is a caveat. You must account for data growth. The new applications at the edge are continuously producing massive amounts of data, and often, organizations require real-time responses based on that data.
One way to do so is with artificial intelligence (AI) and machine learning (ML). AI and ML enable companies to parse the data and maximize the value of their assets, while accelerating the push to the edge.
The Role Of AI And ML At The Edge
AI and ML are transforming how we leverage application and instrumentation data. Real-time analytics are now possible.
In two years, we will see a 10% improvement in asset utilization based solely on a 50% increase in new industrial assets having some form of AI deployed on edge devices, per an IDC FutureScape report.
Take wind turbine farms, for example. Wind turbines are typically in remote settings and widely distributed. This makes computing extremely difficult. It’s inefficient to try to stream all the data back to a centralized data center for processing.
Edge computing removes these physical limitations. The data is collected from sensors on the turbines and processed closer to the source at the edge, reducing latency.
The addition of ML and AI at the edge then enables business intelligence and data warehousing. You can spot historical trends, optimize inventory, identify anomalies, and even prevent future issues, resulting in less downtime and higher profitability.
Everyday Use Cases For AI And ML In Edge Computing
The use of AI and ML in edge computing usually falls within two emerging technologies: natural language processing and convolutional neuro networking.
Natural language processing involves parsing human speech and human handwriting. It also incorporates text classification. Some common use cases include:
- Smart retail: AI analyzes customer service conversations and recognizes historically successful interactions
- Call centers: AI analyzes calls and creates metadata that offers predictions and suggestions for automated customer responses
- Smart security: For consumers, smart devices listen for noises that sound like broken glass; for public safety, AI detects gunshots
- Legal assistants: AI assistants review legal documents and make suggestions for language clarity and strength
Convolutional neuro networks focus on visualization algorithms. These can identify faces, people, street signs, and other forms of visual data. Some common use cases with this technology include:
- Quality control: Inspect for defects in factories and other facilities
- Facial recognition: Find people at risk in a crowd; control access to a facility or workplace
- Smart retail: Look at personal attributes of shoppers to make product suggestions that elevate the customer experience, and can be used to recommend additional items
- Health care: Assist doctors by analyzing an image to check for things like tumors
- Industrial: Safety in a factory setting — look for the locations of workers if someone gets injured; identify dangerous machinery and shut it down if there’s a malfunction
There are countless applications for ML and AI in edge computing. Whether you’re analyzing video or audio data, these technologies can enhance safety and security, improve interactions with customers, and achieve more efficient business outcomes. By processing, interpreting, and acting on the data at the edge using AI and ML, the results arrive with the speed required for safety, sales, and manufacturing situations.