We live in a day and age where we rely on our Smartphones for a lot of the things we do all of the time. Most advanced smartphones today have artificial intelligence or AI. Manufacturers have found ways for their Smartphone chips and processing units to develop and grow in AI (Artificial Intelligence). You may have heard of AI Deep Learning but not know what it is. Smartphones use what we call Mobile Deep Learning. So what is Mobile Deep Learning, and how is it used in smartphones?
Our Smartphone mobile devices use Deep Learning by using neural networks to perform better in certain aspects of our phone’s functions. Functions are numerous and include understanding and interpreting scenes or images when using the Smartphone’s camera, facial recognition, augmented reality, and any other process requiring automation.
Because our smartphones grow more intelligent every year as their chips are improving and as the neural network we have via the internet are increasing larger every day. That’s why Mobile Deep Learning has wide use in today’s society. We live in a generation where we rely more on how our smartphones can learn about our environment and how we use them daily.
What is Deep Learning?
When it comes to developing advanced machines and computers that are smart enough to handle the demands of today’s technologically advancing digital world, the best companies, engineers, and experts are now developing mobile devices that are advanced enough to control whatever the ordinary person is demanding on a regular basis.
Believe it or not, we are now developing artificial intelligence that can, in a sense, learn thanks to the large amounts of data available. Many AI Algorithms are designed and specified for data collection for AI processing in such industries as self-driving cars and mobile devices. As the AI chips and processors become more powerful and energy-efficient, this facilitates the AI’s growth potential. This subset of artificial intelligence is called Machine Learning.
Machine learning is the process of how machines and devices can retain information over time using a complex set of algorithms that allow them to remember through experience. So, machine learning happens because specific devices are programmed with algorithms that use training data. This retention enables them to make predictions independently without using a program to tell them what to do. In short, the algorithms perpetuate the next steps in a program and allow the machines to “think” to a certain degree and make decisions that are not based on a fixed set of options given to them by a program.
What is Mobile Deep Learning?
What is Mobile Deep Learning? It is AI Deep Learning specific to Mobile Devices such as Smartphones, Tablets, Autonomous Driving Vehicles, all the way to Robots. Anything related that is Mobile by design and definition and not sitting on a desk or in a room stationery like a desktop computer. Frequently, in this case, the mobile device serves as a sensor and or user interface.
Mobile Deep Learning’s benefit is if all of the calculations necessary for the AI’s action can be performed locally on a smartphone or a robot, for instance. Then there is no latency in communication time or any concerns for cloud or server reliability.
Therefore, Deep Learning is at the forefront of many advances in artificial intelligence applications. AI technologies are at the center of future mobile applications. This progress can only be achieved through and combined with the advances in the hardware side of Mobile Devices. SoC, CPUs, Mobile GPUs, ASIC, and FPGAs.
We discuss AI Tech in our article “Smartphone AI: Can Your Mobile Phone Really Become Self-Aware?”
Deep Learning Is A Subset Of Machine Learning
Deep learning is a subset of machine learning. Deep Learning is the evolution of Machine Learning and is much more concerned with algorithms developed to be quite similar to a real human brain’s complex neural network. In that sense, the algorithms are quite parallel to how the human brain thinks and learns.
The great thing about deep learning is that it can access and use large artificial neural networks because of how vast our data network has become thanks to the internet. It is also thanks mainly to the computers and device’s speed and power that we have been using as described above.
Jeff Dean, a senior employee with Google’s Systems and Infrastructure Group and Google AI Lead, gave a talk and discussed Deep Learning. He said in the talk that deep learning involves an extensive and Deep Neural Network that refers to a large network. This large network is where the “deep” term is connected to how the AI needs to penetrate and go through specific different layers while learning complicated processes.
This explanation is the hierarchical aspect of deep learning, where simple and easy processes branch out into complicated procedures, which also branch out into much more complicated functions. This structural definition describes why there are so many “deep layers” involved in deep learning. The machine’s AI learns as it goes through different layers of processes that will only get more complicated the deeper it goes.
Are Smartphones Making Use Of Deep Learning?
It isn’t a secret that our smartphones are equipped with chips and processors that are now fast and powerful enough to crunch large quantities of data to allow them to perform and function at their best.
So, when we look at and use our smartphones, we probably notice how certain apps can identify and predict specific actions you would take. For example, our Spotify app can suggest new songs depending on the type of music we usually listen to. Meanwhile, you might have also noticed how your Gmail or Outlook can filter out spam emails without you having to label them as spam.
The simple reason is that our different devices, as well as the network that large companies such as Spotify and Google make use of, are using deep learning or deep neural networks that will allow them to recognize and classify a certain type of data available to them without the need for humans to intervene and tell them which data is supposed to be classified to where.
Mobile Deep Learning’s Need For Power
A problem with Deep Learning is that it tends to be quite taxing, especially when it comes to the processing power and memory that Mobile Devices need to use for this kind of process. A large part of the design and engineering of Mobile Applications revolves around using less energy and resources.
That’s why our Mobile Devices have needed to be connected to the internet to use deep learning. Whatever kind of processing is required for Deep Learning is done remotely by servers. These servers are powerful enough in terms of their processing power and memory capacity.
So, this is why you need to be connected to the internet to make use of Siri. At this point, whatever processing that Siri needs to do has to be done remotely on a different server instead of using your smartphone’s processor and memory.
Autonomous Deep Learning on Mobile Devices
So, the short part of the story is that smartphones technically are not capable of deep learning on their own because how most smartphones today are not yet powerful enough to perform the complex processes needed in deep learning.
However, smartphones act like terminals that will use the internet so that deep learning can be done remotely by a more powerful server. Whatever needs to be computed will be sent over to servers through the internet and sent back to your smartphone. This “Magic Act” is how smartphones are making use of deep learning.
Although AI will probably always access some form of the Internet because of the vastness of the information, the growth and potential for Autonomous Deep Learning on Mobile Devices at some levels are surely coming.
Mobile Deep Learning Devices Hardware Architectures
The basis for improvement and growth of Mobile Device capabilities is based on improved Mobile Device Architecture and its continued improvements. Below are some of the names of the components.
- System On A Chip (SoC)
- Mobile Graphic Processing Units (GPUs)
- Central Processing Unit (CPU)
- Tensor Processing Units (TPUs)
- Field Programmable Gate Arrays (FPGA)
- Application Specific Integrated Circuit (ASIC)
Please see some of our other articles on Smartphone AI, such as “Smartphone AI: Helpful Artificial Intelligence For The Beginner” or “How Will The Internet of Things Affect You? IoT.”
How Is Deep Learning Used In Smartphones?
While deep learning is yet to be done by most smartphones (except by the ones that are smart and powerful enough like Apple’s newest iPhone models) on their own due to how inadequate they are in terms of their processing capabilities and memory capacities, deep learning is still used by smartphones in a lot of different ways. These are some of them:
One of the more popular ways that deep learning is being used in smartphones with the best and highest processing powers is through its camera. Taking high-quality photos has always been one of the most important factors consumers want in a great smartphone. Some of the best smartphones, such as the iPhone 11 and above, can use deep learning to take the best photos imaginable.
Such phones already come with certain features such as Image Signal Processors, Deep Learning Image Algorithms, and Neural Processing Units that allow them to shoot high-quality photos without human’s need to intervene and make adjustments themselves.
In the case of the iPhone and the Pixel, they use multiple cameras and deep learning to make complex decisions such as recognizing people and objects while also calculating color balance on their own so that you would always end up with photos of the highest quality possible.
Privacy and Security
In the realm of privacy and security, you could also trust your smartphone to use deep learning to improve the way you approach your privacy and security. While smartphones that aren’t powerful enough would usually have to send over your biometrics and individual data to a server, which will then make use of deep learning, the newer phones of today are powerful enough to process specific data on their own so that you no longer need your phone to send over such data to a remote server.
And on the entertainment side of things, Augmented Reality can improve thanks to Mobile Deep Learning in advanced Smartphones. There are now plenty of different companies that use augmented reality apps so that you will be able to try on products without having to go to the store. This innovation is a great boon for online shopping, which usually lacks actual physical shopping due to how consumers cannot try the products themselves.
So, technically, deep learning has found a niche in smartphones today, thanks to how powerful some newer devices have become. This transformation is merely a growing niche as Mobile Deep Learning is still relatively new and unexplored. The more powerful our smartphone’s AI capacities become, the more efficient and widely-used deep learning will become as well.
Mobile Deep Learning for Beginners
Artificial intelligence in smartphones is now growing as our handheld devices are becoming smarter and smarter thanks to the advanced tech incorporated into them and the growing global network available.
We are always learning and want to pass on whatever we can. We provide a list of links and some publicly available codebases to use. Tools and Links for mobile deep learning applications
- TensorFlow: https://www.TensorFlow.org/
- Caffe2: https://caffe2.ai/
- Apple Machine Learning: https://developer.apple.com/machine-learning/
- Core ML for IOS: https://developer.apple.com/documentation/coreml
- Deep Learning Kit for Apple Devices: https://github.com/DeepLearningKit/DeepLearningKit
- Snapdragon neural processing SDK from Qualcomm: https://developer.qualcomm.com/software/qualcomm-neural-processing-sdk
- Deep Learning for Java: https://deeplearning4j.konduit.ai/
- Apache MXNet: https://mxnet.apache.org/versions/1.7.0/