Think about a time when you hiked to a secluded area to enjoy the wilderness. What happens to your electronic devices? Most likely, your phone loses its connectivity. At that point, there’s only so much you can do with your smartphone. You can still use the calculator, the camera, the music, and perhaps more. But you cannot access Google Drive, Microsoft Word, and other apps that rely on connectivity. Edge AI is algorithms for Artificial Intelligence that are processed locally. Take Google Home and Alexa, for instance. They have local algorithms that recognize voice commands. Once activated, the Voice Assistant connects to the internet and takes data from there (playing music, checking the weather, etc.)
We see how the iPhone uses motion sensors to count steps in healthcare. With Edge AI, we can optimize the healthcare apps to give notifications to users: “you need X amount to complete your daily 10,000 steps.” Manufacturers can install data about calorie intake to fulfill a specific exercise before launching. So, if you tell your smartphone you will climb a mountain for some time, the app will remind you when to drink, rest, and eat snacks.
Meanwhile, we see Tesla cars with 360 cameras in the automotive industry. The vehicle can see two cars ahead to predict when it needs to hit the break. This algorithm is locally applied, or rather, it is an Edge AI application. Hence, Edge AI can be beneficial for making a split-second decision that involves safety.
The energy industry also gets benefits from Edge AI applications. Solar power in a remote area might have limited connectivity to the internet. Thus, an Edge AI app would help the operations. The AI algorithms would be applied locally to support solar panels to catch sunlight by tilting and rotating the panels.
It is often the case that airplanes need maintenance with huge space. A locally managed Edge AI application would help the maintenance process, given that there could be low connectivity in such a vast area.
According to Toward Data Science, there are four reasons for Edge AI:
An example of a customer-facing app would be an Edge AI app installed in cooking appliances. With machine learning, the appliance can tell its users some updates, such as ‘yesterday you cooked this type of meal at 200 Fahrenheit, but the result was not good. Would you like to increase the temperature?’ The manufacturer can install an Edge AI app that records data and gives basic instructions for several menus.
Meanwhile, an example of an employee-facing app would be Edge AI apps on surgical instruments. In the hospital, doctors can use smart instruments to perform operations. For example, they can inform the limits of how far to cut.
When applying Edge AI, we can start small by implementing Edge AI in one critical process. We have to understand how the business operations work and decide where to optimize them with local algorithms.
As your business grows to the Mature stage, you want to improve your products and services. One way to enhance your apps is to connect it to the internet. With machine learning, Edge AI can improve its algorithms, and with more data from the internet, the Edge AI app would improve.
Finally, in the Growth stage, you would want to explore new fields for your business. For example, you know that your next growing customer segment is Gen Z. At this point, you can implement Edge AI apps to select local areas. In one way, you’re doing A/B testing.
The main goal of Edge AI is to make AI work possible offline. A good app should seamlessly synchronize data once a device is connected online. For example, an airplane maintenance app would manage data locally, and once the aircraft reaches a connected WiFi, it updates the maintenance data to the cloud. Furthermore, the AI algorithm would enable the app to do more. AI could help users identify issues and offer solutions to fix them specific to maintenance instead of searching around Help Guides for troubleshooting.
McKinsey mentioned how industries could have real-time data because of Edge. This local real-time data makes it possible to monitor activities such as equipment maintenance, health, safety, and labor in local Edge AI apps.
A good UX design for Edge AI is responsive local apps that ensure privacy and security. Hackers use online connections to target computer users. But local apps are not connected to the internet; hence it is more secure. Moreover, Edge AI apps should take user experience to the next level with its locality. For example, a FitBit watch can track sleep, calories burnt, and heartbeat locally.
Technology is always evolving, and investing in it will be necessary for a company’s future. Keeping in mind that the price of the technology will be more affordable as the years pass, you can consider scaling up infrastructure and programs.
McKinsey interviewed Microsoft’s Kevin Scott, who explained how AI technology helped the pandemic. The AI function simulates molecular models for compound discovery. This phenomenon illustrates how technology is a worthwhile investment.
Edge AI is a locally focused app to enable Artificial Intelligence devices. The goal is to make a seamless app that functions offline and updates to the cloud when connected to the internet.
At Designial, we can help you start mobile app development, perform digital transformation, and more. Contact us for further information.
Light mode is good improves focus by 20%. Google offers apps in both modes. Find out which one is right for your app. Talk to designial.
Dark mode saves battery life, makes product accessible and improves customer experience. Apple, Google apps support dark mode. Do your apps?
If your customers complain about app being too confusing, learn how to declutter and improve the user experience. Contact designial.