Most mobile apps that we know, use, and love currently uses machine learning techniques or neural network forms to personalize user experience. Typical examples of this are Apple Music and Spotify, which are music industry’s prominent leaders. These apps use recommendations that are powered by AI to generate music suggestions. Other than these, the commonest examples of AI used in applications are the common voice assistance tools like Alexa, Google Assistant, and Siri.
When it comes to mobile machine learning, it is quite different from the web-based machine learning and web-based mobile learning.
Mobile Machine Learning
The field of mobile machine learning is one aspect of machine learning that’s growing very fast. It does not involve clustering high-powered GPU machines and data centers. Instead, it will be possible to run machine learning operations on mobile devices while avoiding network bottleneck.
The results generated by the applications mentioned above are from the combination of cloud services and on-device network. However, in the last few years, we’ve seen a significant evolution of the on-device deep learning techniques. They currently cover daily use cases without needing to make network calls. Some of these are speech recognition, gesture recognition, object recognition, image recognition, text classification, and translation.
It might be, however, though, to choose a mobile app deep learning application because of the absence of resources that are relevant for getting started.
This article by an author at essay writing service discusses three of the most demanding challenges to integrate elements of AI into the mobile application and to develop quality AI applications.
Finding Features of Mobile Apps That Work Well For AI
Understandably, there’s a substantial difficulty in introducing AI to your app from the beginning. The first thing that organizations need to look out for, first of all, is the best options that can quickly adapt to a rapidly changing, digital-first market. This would also include deciding on the long term plan for whatever stack that you’re using and determining the possibility of applying your chosen model on the device.
This also means that you’re able to efficiently deal with tactical areas such as integrating the app’s backend to legacy systems already in existence, ensuring there’s access to data, adopting agile methods of development, and implementing architectures based on API.
Once you are done with the planning stage, you will start to see positive results soon enough.
There are some features that modern apps require AI to employ. Some of these are:
- Automated reasoning
This is the science behind computer’s ability to solve problems like puzzles and prove theorems using logical reasoning. This technology is why humans can be defeated when playing games against AI-powered systems and handle industry tasks such as market stock trading better than humans.
Some services, such as Uber, use similar algorithms for calculating numerous data points from their drivers that have taken similar routes at different times. This app uses this information to predict the estimated fare, time to destination, etc.
- Recommendation systems
This is one of the most effective and straightforward uses of AI in mobile applications. It is used for almost all solutions. Many apps don’t launch within a year due to their inability to supply relevant content to the users continuously. So, it feels in continuously engaging the users.
This service provides fresh and up-to-date content regularly. But it will not interest the user and engage them if it is not relevant to them. With on-device AI, it is possible to monitor users’ choices and use the information to continuously keep the algorithm of the in-depth learning updates with fresher data points. This is how they ensure that the apps’ recommendations are relevant to the user or what they love.
Examples of other features that modern apps require AI to employ are:
- Computer vision
- Learning behavioral patterns
Training AI Model on the Mobile Devices
Another major challenge is machine learning’s ability to collate and use the data from the user for training. Using the device to train data is still relatively new, and many times businesses train on the server, then send back the improved model as updates.
Most mobile apps use machine learning for inference, but not much learning actually occurs on the device. But there are some cases in which it’s important for the learning to take place on the device as this will ensure that the training for the model is specific to the individual users. An example is a predictive keyboard. It has a generic model with which it is trained in one language. However, as time goes on, it starts to learn and customize its model based on the user input and can predict what the user is likely to write next.
If you’re building your own AI model, for instance, there are several ways that you can implement it:
- Use public datasets and not learn from the user at all. This model requires offline training and then sends an improved version in the form of updates. It is, however, difficult to achieve something like the predictive keyboard with this technique.
- Use central learning, which stores the user data on servers. If the user data are stored on servers already, this data can be used for training your AI model. Then the personalization will be based on the behavior of the individual users. However, there’s the issue of security, privacy, and scalability using this strategy. If privacy is essential to your business, then this model is wrong for you.
- Distributed learning offers a model that’s pre-trained to the user. It is then fine-tuned by the application depending on user data. The cost of this model training is distributed among all of the users. The drawback to this is that other users can’t use your model or benefit from it.
The App’s Usage of Mobile Resources
Another challenge is the way mobile apps are going to be using mobile resources. When mobile apps are developed, it has to be with the mind of resources utilization. You would do things in the cloud with GPU clusters that are not possible on mobile devices. Therefore, it is worth considering how the model and algorithm used affect mobile resources such as memory and battery power.
Apart from monitoring the resources utilization, it would help if you had a different plan to work for devices that have lower specifications and cannot run tasks that are processor intensive.
While solutions like Apple’s Core Machine Learning and Google’s TensorFlow Lite are there for embedded and mobile devices, the resources that teach their implementation in a production environment are very few.
In the last few years, mobile apps have tried to integrate AI models for better user experience. APIs were the earliest adoptions, but now devices can do more complex tasks such as image processing and text clarification on their own, thanks to powerful processors and modern sensors. But there are still many challenges to developing quality AI apps on mobile devices, and this article covers three of them.
Emma Coffinet is a content creator for websites, blogs, articles, white papers, and social media platforms. She is keen on capturing the attention of a target audience. She keeps herself well-read with the changing trends of the web world. Emma loves to pen down her knowledge in an engaging and simplified way. She also enjoys leading, motivating, and being part of a productive team, equally comfortable working on her own initiative.