What is Edge AI?
Edge AI is a new kind of artificial intelligence that uses machine learning to process data at the edge of the network. It is being touted as a game-changer in many industries and is growing rapidly. The Global Edge AI market was valued at $3.5 billion in 2019 but could reach $155.9 billion by 2030.
A survey by IBM revealed that leading organizations expect an average ROI of 24% over three years from Edge computing investments. Some of the top industries projecting increases in operational responsiveness include: Automotive, Telecommunications, Retail, Energy & Utilities, Media & Entertainment.
How does Edge AI work?
Edge AI is a combination of Artificial Intelligence and Edge Computing. Edge AI computing is when devices collect data and process it, either locally or on the closest server. Machine Learning algorithms then analyze and share this stream of data.
It is essentially a small army of devices collecting and analyzing data in real-time. It’s not a stretch to say that Edge AI may represent the best opportunity for IoT (Internet of Things) to reach it’s full potential.
Edge AI: Why Now?
The edge is the place to be.
The edge is where information is gathered, processed, and acted upon. It’s a place where smart devices, sensors, and other edge technologies all converge to process data in real-time. The edge has become the center of attention for many businesses because it can help them make more informed decisions, reduce costs, and even increase reliability.
Traditional cloud computing models don’t always meet these needs because they often require too much bandwidth and processing power. This means that many businesses have not been able to keep up with this data explosion. To keep pace with the increasing demand many corporations will have to make investments in IOT to fully leverage the power of cloud computing and machine learning at scale.
Edge AI features are designed specifically to enhance a business’s ability to capitalize the power of “The Edge”. Here are some of the most important benefits of edge computing:
Real-Time Data Processing
Edge AI makes it possible to process data in real-time without having to send it back to the cloud or data center first. This means information can be analyzed on the spot – no matter how far away from the data center that spot may be!
Reduction In Internet Bandwidth And Cloud Costs
Sending data back and forth between the cloud and the edge over an internet connection can be expensive – especially for businesses sending large amounts of data repeatedly. With Edge AI technology integrated into your system, however, it’s possible to reduce these costs significantly by keeping all of this activity within an owned network instead of relying heavily on internet connections outside of it.
Edge AI improves decision-making by increasing speed, accuracy, and reliability. The ability to make effective decisions is a key component of business success, so the faster and more accurate your decisions are, the better. With Edge AI, you can get real-time insights that help you make decisions faster and more accurately than ever before.
Edge AI helps improve the reliability of your data by removing any human error or bias from the equation. This means fewer mistakes are made in your reports and analysis, which leads to increased confidence among company leaders and stakeholders.
Edge AI trends and the future
The market for Edge AI is expected to grow rapidly over the next several years due to increased demand from companies who want better insights into their customers’ behavior in order to improve their products or services. The most impacted industries are manufacturing, transportation and traffic, energy consumption (including smart homes), retail sales, banking, and financial services (including healthcare).
Edge AI use cases by Industry
Edge AI is going to be a nearly ubiquitous technology in the future. We walk through some of the industries that are most likely to be early adopters of Edge AI and maximize potential ROI.
Edge AI Use Cases in Manufacturing
An increasing number of factories are adopting AI-enabled solutions. The next era of manufacturing could have an impact not seen since the industrial revolution. Companies will potentially have a nearly unlimited constant supply of data that they can use to increase efficiency for robot workers. This new phase of manufacturing will streamline operations and maximize efficiency.
Here are some examples of how artificial intelligence can be used by manufacturing companies to improve productivity:
- Machine learning tools that can detect and predict failures in real-time, reducing downtime and increasing productivity.
- AI-powered data analysis to assist in the design of products and production process optimization.
- Manufacturing is one of the most data-intensive industries. With manufacturing plants generating millions of data points every day, AI can reduce costs and improve efficiency.
- Data collection and analysis for quality assurance: AI can be used to scan a product’s surface for defects and even identify them before they are visible to the human eye. This allows manufacturers to reduce waste and improve their products’ quality.
- Predictive maintenance: AI can analyze historical data on machinery to predict when it will likely break down so that repairs can be scheduled accordingly.
- Manufacturing is one of the most important industries in the world, and it’s also one of the most complex. Manufacturing companies have to coordinate with many different parties and often face a myriad of challenges that vary widely from facility to facility. Predictive maintenance will allow companies to unlock productivity gains at scale.
Edge AI Use Cases in Transportation and Traffic
Artificial intelligence (AI) is poised to transform the transportation industry. Self-driving cars are one of the most visible manifestations of this new technology, but there are many other applications as well.
- Edge AI can be used to optimize traffic in cities by analyzing historical data and predicting when a traffic jam is likely to occur.
- Edge AI can be used to predict the probability of an accident, which can help authorities take corrective measures before it happens.
Edge AI Use Cases in Energy
There are many different ways that AI Edge Computing is already impacting the Energy Industry. Arguably the most important of which is increasing reliability by using edge hardware in concert with machine learning to respond to power outages in real-time. That’s only the beginning though.
Here are 3 other use cases for Edge AI Computing in the Energy Sector:
- Improving Cyber Security: As the world becomes more and more dependent on data and technology, cyber security is becoming increasingly important.
- In the Energy Sector, this means protecting sensitive information such as power grid control systems, which are often connected to the internet. AI can be used to detect when attacks take place by monitoring network activity for suspicious patterns or anomalies.
- Reducing Electricity Demand: The most obvious use case for Edge AI is to reduce the total amount of electricity used by customers.
- We’ve already seen proof that this is possible, with microgrids in Puerto Rico using machine learning algorithms to help homes and businesses cut their energy usage during peak hours. This can also be done using other edge hardware like solar panels and batteries.
- Reduce Energy Losses in Power Distribution: Improve Grid Reliability Through Self-Healing Systems that can Detect and Respond to Disturbances.
- This reduces cost and complexity of Energy Infrastructure.
Edge AI Use Cases in Retail
The retail industry has always been an early adopter of technology and is a great example of how AI will visibly impact the everyday experience for consumers. AI can be used to improve customer experience, increase sales and reduce costs.
Here are three ways Edge AI will impact Retail:
- Personalized Recommendations: Make Better Recommendations based on Customers’ Preferences.
This reduces cost and complexity of Retailers’ Infrastructure.
- Improved Marketing: Use AI to Personalize Customer Experiences Based on Their Data.
Amazon is probably the best-known example, but retailers like Walmart and Target have also gotten into this space as well. The goal here is to provide customers with a more tailored shopping experience that better fits their needs and interests than a generic recommendation algorithm would be able to.
- Improved Product Discovery: Use AI to Better Target Customers’ Needs and Interests
Amazon is a great example here as well; it uses data about customers’ shopping habits (both online and offline), social media accounts, etc. to provide them with recommendations based on what it knows they already like.
Edge AI Use Cases in Smart Homes
The adoption of devices like Amazon’s Alexa is simply the first wave of Edge AI to enter the home. Real estate property owners and developers are increasingly building “smart” features into their units. Some of the most interesting use cases for Edge AI in smart homes are:
- Smart homes can anticipate your needs based on past behavior, which is a robust use case for Edge AI.
- For example, if you leave for work every morning at 7 am and take the same route, then Alexa could automatically start playing your usual morning playlist when it hears you pull up in your driveway.
- Edge AI can increase security for homes.
- A grid of Edge AI networks can improve security for homeowners. Intelligent Video or VCA (Video Content Analysis) can be used in virtual tripwire scenarios to improve perimeter detection.
Edge AI Use Cases in Banking and Financial Services
The world of finance has quickly turned into the world of Fintech. Edge AI is going to be one of the most important tools to power the future of commerce in the next few decades. Here are 3 applications of Edge AI computing in Banking, Finance and Insurance (BFSI) industries:
- Fraud Detection and Prevention: Edge AI can help banks to prevent fraud and money laundering through advanced analytics.
It will also help in real-time monitoring of transactions, preventing fraudsters from taking advantage of gaps in the system.
- Data Mining and Analysis: Edge AI is a perfect fit for data mining and analysis, which are the most important functions of BFSI industries.
- Edge AI can help companies better understand their customers, analyze their behavior patterns and predict future trends. It will also make it easier for them to customize their offerings based on individual needs.
Next-generation credit scoring and risk models.
- Automated fraud detection
- Smart portfolio management.
Edge AI Use Cases in Healthcare
The future of healthcare is a connected one. Edge AI will be a valuable tool for large organizations looking for hybrid computing options that are resilient and secure.
Here are 2 biggest applications for Edge AI in Healthcare:
- Patient Telemonitoring: Edge AI can be used to monitor patients remotely, reducing risks and costs.
- This is especially important for patients with chronic diseases that require frequent monitoring, but who may not have easy access to specialists in remote areas. By analyzing data from multiple sensors, AI algorithms can detect changes in a patient’s condition more quickly and accurately than humans alone.
- Smart Devices: Edge AI can be used to create smart devices that can diagnose patients and administer medication.
- This could include providing real-time video streams of medical procedures, monitoring vital signs from the patient’s perspective, and alerting doctors when a patient is struggling with an adverse reaction to treatment.
Edge AI Use Cases in the Retail Industry
The retail industry has proven to be willing to experiment with the latest and greatest technology. The hybrid online and in-store shopping experience that many buyers prefer will necessitate that retailers adopt Edge AI.
Here are the 2 biggest applications of Edge AI Computing in the Retail Industry:
- Retailers can use Edge AI to improve their customer experience: By using AI to analyze data and make predictions about a customer’s needs, retailers will be able to provide a more personalized experience.
- For example, an online retailer could recommend books based on past purchases or suggest products that would interest you based on your search history.
- Retailers can use Edge AI to improve their supply chain: AI can be used to optimize inventory levels, predict demand, and route products to specific stores or locations.
- Edge AI is particularly helpful for retailers who have multiple locations and large numbers of SKUs.
Even though we have listed many of the ways that Edge AI is being used across a variety of industries, it’s really only scratching the surface. Very soon, we will see more and more uses for Edge AI emerge. The network of smart connected Edge AI computing devices using ML/AI to improve in real-time will increase efficiency and reliability for mission-critical applications across a variety of industries.
The only question is:
Who will leverage the Edge best?