The integration of AI into mobile apps and devices is common today. Google, Amazon, Huawei, Microsoft, and Apple are getting closer to the AI users.
We already have mobile app translators in real time like the Google Translator. There are chips with modules to optimize AI applications such as the Apple A11 Bionic
or the Huawei NPU in its Kirin 970 processor, capable of making the mobile camera "recognize" animals, people, food and other everyday elements thanks to algorithms of artificial intelligence.
In some cases, these intelligent systems are physically located in the data centers of Google, Microsoft or Amazon, but AI technologies are beginning to be seen in other devices that are apparently modest in terms of performance, such as mobile phones.
The AI begins to reach smartphones. It is a trend that has materialized in recent months. Smartphones seem to be moving from being a possibility of becoming reality in view of the developments that Google, Apple or Huawei
are integrating into their most recent apps and devices.
The relationship between AI, Machine Learning and Deep Learning
Before continuing, it may be useful to remember that another of the great "words" of our time, Machine Learning is directly related to AI. Artificial intelligence is a discipline within the field of computer science aimed at creating machines capable of displaying intelligent behavior.
Machine Learning is an application of artificial intelligence to design computers that are capable of functioning without having to program them. That is to say, from a few basic premises, are able to learn without the intervention of a program that governs the behavior of the computer.
Deep Learning, in turn, is one of the possible ways to implement Machine Learning. So, as you see, the three concepts are related, although with a clear hierarchy when thinking about them.
In smartphones, one of the applications of the AI is to adapt the operations of the apps to their owners based on learning algorithms over time. This is one of the possible uses of Artificial Intelligence on mobile devices. There are more applications of AI on mobile apps and devices.
- Voice Processing
One of the applications that benefit from the presence of advanced "intelligent" processing technologies is that of speech recognition and processing. This is the case of Huawei with its neural processing unit (NPU) in the Kirin 970 processor.
Using Machine Learning technologies
, it is possible to process the voice even when speaking with a low volume, thanks to the processor's ability to "learn" to recognize sounds.
- Image and Data Processing
One of the technological novelties of the iPhone 8 and iPhone X is its A11 Bionic processor. The Bionic Neural Engine is designed specifically to process Machine Learning algorithms as those that use features such as Face ID, facial recognition technology.
Another application of image processing is found, again, in Huawei devices with the Kirin 970 processor with its NPU unit. In this case, the camera app is able to recognize if there are cats, dogs, food and other elements, so that it optimizes the shot according to its photographic peculiarities.
- Optimized Simultaneous Translation
Apps such as simultaneous translation also benefit from AI and Machine Learning algorithms. Whether in the pure performance dimension or in the optimization of energy consumption by using specific processing algorithms on a hardware designed "ad hoc" for these tasks, the AI promises to contribute to achieving the ultimate goal of simultaneous translation of conversations in real time.
Huawei incorporates a Microsoft translation app optimized to work on its Kirin 970 processor and its NPU neural processing unit.
Google introduced the Pixel Buds, a wireless headset capable of also functioning as a simultaneous translator, as long as our interlocutor is using the Google translator as well. You should meet with app builders
to integrate these features into the app of your business.
- More Autonomy
An effect of the use of AI technologies in mobile devices and apps is the increase their autonomy. Without going any further, the NPU of Kirin 970 Huawei is 50 times more energy efficient than other processors. It is necessary for the apps to run under the auspices of AI development platforms, such as Google's TensorFlow.
- Smart Camera
In addition to Huawei's Mate 10 camera, which is able to identify the elements in the scene (to some extent), Google introduced its Clips camera which also uses the AI to take pictures automatically if it detects faces, situations or moments that it thinks that are relevant to the user.
This camera recognizes faces and can modify its behavior to take more pictures of those situations where familiar faces appear. It is possible to integrate this feature into your mobile app by meeting with app development companies.
Now, mobile apps can listen to and identify songs. This opens the way to future more intelligent applications in which, for example, apps can "listen" to what we say in a conversation to search for contextual information about it in real time, and offer it in case the user needs it.
- Objects Identification
Another functionality that also has to do with AI is the identification of the places in which we move. Samsung has already presented Bixby Vision months ago, and Google also added Google Lens
that is able to identify many objects in our environment and search over them online.
Until now, the customization of apps was to define the wallpaper, change the text font, the colors of the icons, and so on. Now, the AI allows apps to be personalized in a more "intimate" way.
The AI, through the Machine Learning algorithms, makes it possible to adjust the operating parameters of apps and devices according to the habits of the user.
We are in the dawn of the application of AI in mobile apps and devices and therefore in our daily lives. Once the apps are ready to process Machine Learning or Deep Learning algorithms quickly and efficiently, it is a matter of time that our daily activities are being automated.
The AI, through Machine Learning and Deep Learning
as enablers, points to ways to become the next big thing in technology.