Machine Learning in Mobile Development: Perspectives and Decentralization
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Mobile development specialists will benefit from the revolutionary changes that machine learning can offer on devices today. The point is to what extent this technology enhances any mobile applications, namely, it provides a new level of convenience for users and allows you to actively use powerful features, for example, provide the most accurate recommendations based on geolocation, or instantly detect diseases in plants.
Such a rapid development of mobile machine learning is the answer to a number of common problems with which we managed to torment ourselves in classical machine learning. In fact, everything is obvious. In the future, mobile applications will require faster data processing and further reduction of delays.
You may have already wondered why mobile applications based on AI cannot simply run logical inference in the cloud. First, cloud technologies depend on central nodes (imagine a huge data center, where both extensive data storage and large computing power are concentrated). With such a centralized approach, it is impossible to cope with processing speeds sufficient to create smooth mobile interactions based on machine learning. Data must be processed centrally, and then sent back to the device. This approach takes time, money and does not guarantee the privacy of the data itself.
So, outlining these basic advantages of mobile machine learning, let's explore in more detail why the machine learning revolution unfolding before our eyes should be of interest to you personally as a mobile developer.
Mobile application developers know that increased latency can be a black mark for a program, regardless of how good its capabilities are, or how respectable the brand is. Earlier, Android devices had serious delays in many video applications, which is why video and audio viewing was often out of sync. Similarly, a high-latency social network client can turn communication into real torture for the user.
The implementation of machine learning on the device is becoming more important because of such problems with delays. Imagine how image filters for social networks work, or recommendations of restaurants linked to geolocation. In such applications, the delay should be minimal, only in this case, it can work at the highest level.
As mentioned above, cloud processing is sometimes slow, and the developer is required to delay to zero – only in this case, the possibility of machine learning in a mobile application will work as it should. Machine learning on devices opens up such data processing capabilities, which really makes it possible to reduce the delay to almost zero.
Smartphone manufacturers and the giants of the technical market are gradually becoming aware of this. Apple has been the flagship in this industry for a long time, developing ever more sophisticated chips for smartphones using its Bionic system, in which the neural engine Neural Engine was implemented, helping to drive neural networks right on the device while achieving incredible speeds.
Apple also continues, step by step, to develop Core ML, its machine learning platform for mobile applications; TensorFlow Lite library adds support for GPUs; Google continues to add pre-loaded features to its ML Kit machine learning platform. With the help of these technologies, you can develop applications that enable lightning-fast data processing, eliminate any delays and reduce the number of errors.
Such a combination of accuracy and seamless user interactions is the main indicator that mobile application developers must take into account when introducing machine learning capabilities. And to guarantee such functionality, it is required to adopt machine learning on devices.
Improved security and privacy
Another huge benefit of edge computing, which cannot be overemphasized, is how much it improves the security and privacy of users. Ensuring the safety and privacy of data in an application is an integral part of the developer’s tasks, especially given the need to comply with the GDPR (General Data Protection Regulations), new European laws, which will undoubtedly affect mobile development practice.
Since data is not required to be sent for processing to the north or to the cloud, cybercriminals have fewer opportunities to exploit any vulnerabilities that arose during the transfer phase; therefore, data integrity is maintained. So it becomes easier for mobile application developers to comply with the GDPR data security regulations.
Machine learning on devices also provides decentralization, in much the same way as blockchains. In other words, it is more difficult for hackers to set up a connected network of hidden devices with a DDoS attack than to conduct a similar attack on a central server. This technology can also be useful when working with drones and to monitor compliance with the law.
Apple’s above-mentioned smartphone chips also contribute to the user's safety and privacy – so they can serve as the basis for Face ID. This feature of the iPhone works on the basis of a neural network deployed on devices and collecting data on all the various views of the user face. Thus, the technology serves as an exceptionally accurate and reliable method of identification.
Such and newer equipment with AI support will pave the way for more secure user interactions with the smartphone. In fact, developers get an extra level of encryption to protect user data.
No internet connection required
Apart from problems with latency, sending data to the cloud to process and extract the findings requires a good Internet connection. Often, especially in developed countries, there is no reason to complain about the Internet. But what to do in areas where communication is worse? When machine learning is implemented on devices, neural networks live on the phones themselves. Thus, a developer can deploy technology on any device and anywhere, regardless of the quality of the connection. Plus, this approach leads to the democratization of ML opportunities.
Health care is one of the industries that can particularly benefit from machine learning on devices, because developers can create tools that check vital signs, or even provide robotic surgery without any kind of Internet connection. This technology is also useful for students who wish to turn to lecture materials without having an Internet connection – for example, being in a transport tunnel.
Ultimately, machine learning on devices will provide developers with tools to create tools that will be useful to users from all over the world, regardless of the situation with the Internet connection. Given that the power of new smartphones will be at least not lower than that of the current ones, users will forget about the problems with delays when they work with the application offline.
Reducing costs for your business
Machine learning on devices is also designed to save you a fortune – because with it you will not have to pay external contractors who would implement and support many solutions. As mentioned above, in many cases you can do without the cloud, and without the Internet.
GPU and AI-specific cloud services are the most expensive solutions you can buy. When you run models on a device, you don’t have to pay for all these clusters, due to the fact that today more and more sophisticated smartphones equipped with neuromorphic processors (NPU) appear.
Avoiding the nightmarish ponderous data processing that takes place between the device and the cloud, you save tremendously; therefore, it is very profitable to implement machine learning solutions on devices. In addition, you save because your application bandwidth requirements are significantly reduced.
The engineers themselves also greatly save on the development process, since they do not have to assemble and maintain additional cloud infrastructure. On the contrary, it is possible to achieve a greater power of the smaller team. Thus, human resource planning in development teams is much more efficient.
Undoubtedly, in the 2010s, the clouds became a real boon, simplifying data processing. But high technologies are developing exponentially, and machine learning on devices may soon become the de facto standard not only in the field of mobile development, but also in the field of the Internet of Things.
Thanks to reduced latency, improved security, offline capabilities and cheaper prices in general, it is not surprising that major mobile developers make high stakes on this technology. Mobile application developers also need to look at it in order to keep up with the times.