4 Ways Machine Learning is Revolutionizing Mobile App
4 Ways Machine Learning is Revolutionizing Mobile Apps
Machine Learning is quickly becoming one of the top choices of mobile app developers. Building machine learning apps is really looking like the way for the future. Also, it is a leading category among funded startups, securing higher than $2 billion in funding. Machine Learning is revolutionary for mobile apps primarily because it allows more user experiences.
Moreover, it is able to take advantage of most of the functionalities of mobile phones such as accelerometer, location and so on. Machine Learning app examples include snapchat filters, oval money and carat app. It is important for executives to understand how Machine Learning will impact their business. Let’s explore further that why as an executive, you want to watch out for Machine Learning revolution happening in mobile apps.
1. Machine Learning causes Low Latency
One of the ways Machine Learning is transforming mobile apps is it causes low latency. Regardless of how good the features are or how good the brand is, mobile app developers know that no user wants to be at an app with high latency.
For example, a frustrating user experience is produced on a social media app if the latency is high. With its acute data processing capabilities, Machine Learning gives way for near-zero latency on the device. Apple is developing advanced chips with Bionic system with an integrated neural motor. This allows neural networks to operate directly on the iPhone device, producing amazingly fast speeds.
2. Machine Learning enhances Security and Privacy
Moreover, executives also need to keep an eye on Machine Learning in mobile apps because it provides greater security and privacy. Machine Learning makes sending the data to cloud or server unnecessary. This makes it harder for cybercriminals to attack any vulnerability during data transfer which in turn make compliance with GDPR regulations on data security easy for mobile app developers.
Moreover, just like Blockchain, decentralization is provided by Machine Learning on mobile apps. It is more difficult to attack a connected network of hidden devices through DDoS attack in contrast with attack on a centralized server.
Furthermore, the privacy and security in Apple phones is vastly improved by the introduction of new chips mentioned above. Face ID on iPhone is powered by these chips. To serve as a more secure and accurate identification method, Face ID is based on a neural network in the mobile phone device that collects data in different ways in which you can see the face of the user.
This paves the way for safer smartphone experience for users which helps business leaders offer greater security and privacy to the data of their customers.
3. Non-requirement of Internet Connection
Normally, data is sent to cloud for processing. This requires an active internet connection which is often not possible especially in developing countries. Neural networks live on the smartphones themselves with machine learning on mobile phone devices. Therefore, regardless of internet connectivity, mobile app developers are able to implement this technology on any device at any time.
One of the industries that can benefit from Machine Learning on mobile devices is Healthcare. This is because, without internet connection, ML permits the verification of crucial signs or even those that help remote surgery with robots, for mobile app developers. Furthermore, students in a place with no internet connection can also benefit from this technology.
Therefore, regardless of their internet connectivity situation, Machine Learning allows mobile app developers to produce mobile applications which can prove handy anywhere across the globe. Consequently, Executives can significantly boost their revenues by incorporating Machine Learning in their mobile app development.
4. Decrease in costs for executives
Because machine learning decreases the dependency on internet connection, executives will save money by not having to pay these external providers to maintain these solutions. Moreover, smartphones have sophisticated NPU (Neural Processing Units) these days. This means that executives do not have to pay for the expensive cloud maintenance services like AI-specific chips and GPUs anymore.
Also, bandwidth demands are also reduced by having this inference on the device, which in turn massively saves costs. Even further, executives do not have to worry about the maintenance of additional cloud infrastructure which is costly. Rather, they would need small engineering teams and could scale these teams more efficiently.
Summing it all up, executives can significantly reduce the costs of their business by using Machine Learning in mobile app development because it massively saves costs through it lowering dependency on cloud services and offering low latency. Moreover, executives can ensure greater security of customer data as well through machine learning. Therefore, it is a no-brainer to incorporate in your mobile app development.