During the last couple of years, apps have become increasingly complex and much smarter with a focus on personalization. Spotify is not only a music player, it also generates personal playlists based on what you listen to. Google maps is not just a map but shows you location-based recommendations.
Paired with this evolution, performance expectations are higher and more difficult to manage than ever. Enabled by specialized smartphone chips and machine learning algorithms optimized for mobile devices, the mobile landscape is moving from an occasional AI-enabler towards being the dominant platform for AI.
Increase reliability and speed
Mobile on-device machine learning algorithms are taking over cloud-based processing as the most viable way to perform inference, which means making predictions with your model. Model training is performed in the cloud while predictions are made using the power of your smartphone.
Cloud technologies rely on central nodes, meaning you need a data center with storage space and high computing power. In order for this to work, you need a stable connection between the data center and your mobile device, making it less reliable.
This centralized approach is incapable of handling processing speeds necessary to create the desired smooth mobile experiences users crave. Data must be processed on this centralized data center and then needs to be sent back down to the device, resulting in a delayed execution.
Take Snapchat. Snapchat applies facial filters real-time. If this wouldn't be applied on-device, imagine how bad the experience would be every time you fire up the filter bar. Features like this one greatly rely on speed of execution to even be considered by users.
Secure your data
When your data is kept on your device, protecting and respecting privacy is much easier. Data doesn't need to be sent to a separate server or cloud instance to process it. GDPR regulations can be met more easily and hackers will have a much harder time trying to take down your decentralized network of devices.
Companies are building more specialized chips, like Samsung adding a "Data-security first" chip in their latest Galaxy S20 smartphones to ensure safer data processing.
Imagine a future where surveillance drones with built-in detection mechanisms can operate autonomously even after disconnecting or an attempted cybercrime. The data it uses can stay on the device and is thus safer - unless someone takes the drone down of course.
Look at the costs
AI-related instances are typically the most expensive cloud services on the market. On-device running of models will thus decrease cloud infrastructure cost since predictions can be done locally. This also means that developers can shift their time away from building and maintaining cloud infrastructure and spend it more useful on model performance for example.
Wondering what AI in mobile could mean for your business or idea? Be sure to contact us!
