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Machine Learning And AI In Mobile Apps

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AI In Mobile Apps

In Apple/iOsArtificial IntelligenceBlog

You must be familiar with the Will Smith movie, "I Robot". We had seen robots taking over humans in it. And how did they do it? Yes, by Artificial Intelligence (AI). Would it really happen to us? Well, that is a question for which I do not have an answer. AI is now a progressing technology that humans are now trying to develop and incorporate in our mobile phones.

Combining Machine Learning (ML) and AI, our smartphones are going to become one smart device that will know us better than our friends or even us (just joking, but I do see that could happen)!

Well, several of our best minds are currently working on it. Sure, they are facing some challenges. In this write-up, we'll take a look at some of the obstacles that are hindering them from getting this huge innovation into a reality.

  • Challenge 1: To find features on the mobile app that can accommodate AI
    Incorporating AI onto a mobile device may be an idea that can be easily said, but it is not so when it comes to reality. There are some steps while doing this. For example, most organizations will first have to adapt to current market trends. That is not going to be pretty easy since it is changing almost every day. Other crucial areas of concern include securing the mobile's access to data, adapting to agile development techniques, etc. The work is progressing and we could expect some results soon enough.
  • Challenge 2: Getting the device trained according to the user's behavioral inputs
    One of the major attractions of AI is that it would learn to adapt and give suggestions to its users based on learning their behavioural traits while using the device. Now, this is perhaps one of the most sophisticated tasks for the developers since every individual is unique and so would be their behaviours. Today is still one the early days of the process's development, and what businesses are doing now is that they train on a server and then return the model improvements in the form of updates. That is, most of the mobile devices use ML for the interface and very little learning happens on the device.
  • Challenge 3: The APIs alone are not enough
    Today there are Application Programming Interfaces (APIs) that offer AI as a service. But these applications that add some additional capabilities so that your device will look glamorous is perhaps is not the brightest idea. There are several reasons behind it. Developers need to be alert of IP leaks, i.e., each time the API is called, there is a risk of letting the API provider know of their base IP. Also, if the developer is using any of the third party APIs, then they are bound to follow their privacy policies and agreements.
  • Challenge 4: What all are the mobile resources used and how much
    The battery life and memory use being two of the biggest concerns among users, when AI is being incorporated into the device, developers should make a note and monitor on how much of the mobile resources are being used.
  • Challenge 5: The data you have for training purposes are the right one?
    There will never be a shortage of data. That is not what we need right. To train the device, finding the right data is the key.

If you are planning on training an application on the device, then keep these points in your mind.

  1. The larger device models would require more amount of data.
  2. Develop a mechanism for sharing the data between devices, since there is the possibility of the user running the application on multiple devices, such as a tablet, PC or a phone.
  3. Have a clear idea on what you would do when there is an update.

AI will definitely be a breakthrough in the advancements, so far if it can be incorporated in the right way. We have seen a few challenges here. But these challenges are being worked on, and hopefully, we'll see major progress with AI incorporated on mobile devices soon.

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