Know How Apple Play with Machine Learning To Develop Seamless User Experience?
Apple is the most hyped tech brand in the globe. The user experience is so seamless that users even review their product as if they 'bought an emotion, not a product.' But did you know what tactics Apple uses behind its strategy of creating such market hype?
Their strategy is quite simple but extremely unique. They are utilizing Machine Learning (ML) in the most creative way to achieve the maximum possible advancement in their technical innovation.
To enable intelligent features and innovative app experience, Apple created on-device machine learning capabilities that train, deploy and target trained models in real-time.
It has mastered the technique of how to turn any iOS app into a reliable platform for machine learning by using the Core/Create ML tools and interfaces- Two well-known supervised machine learning components by Apple.
Create ML and Core ML:-
Both are quite relevant and utilize (labeled data sets) supervised learning algorithms and approaches, which makes employing the Apple user interface really convenient. An overview of both of them is provided below.
Machine Learning Algorithms in 'Create ML':-
Create ML uses the infrastructure seen in Apple apps like Photos and Siri. This implies that your image classification and natural language processing models are more compact, quick, and simple to train.
Intelligent Document Solutions offers specialized solutions to assist in ensuring your document is updated and organized. Apple creates ML models to produce content and makes recommendations for improving business procedures.
Machine Learning on 'Core ML':-
Core ML seems to be an exceptional tool. A lot of people's attention has recently been focused on this particular language, drawing a lot of interest from developers worldwide.
Core ML enables developers to concentrate more on building machine learning models while spending less time designing and training the models.
It can be included in their applications without the need to be aware of the system's internal workings. Understanding your data is crucial when creating a machine-learning application.
Apple has given developers a tool to visualize their data related to machine learning models before writing a single line of code.
This effectively makes them aware of how well the model works or functions. Developers can concentrate more on improving the program itself if this information is provided from the beginning of testing and exposure.
How does Apple implement machine learning in its products?
Apple marketers/researchers frequently uses the reference of machine learning when promoting features like image recognition and text analysis in their current products. Although they often omit many technical facts. Comparing this to Google, which emphasizes AI's more advantageous elements while ignoring any suggestion that it might be unpleasant in order to engage the public.
To maximize the longevity of the device battery, machine learning is employed to monitor usage patterns. This software keeps track and logs user interactions with the iPad, including stylus usefulness, usage patterns, and charging times.
Internal components of the laptop recognize abnormal patterns associated with these inclinations and identify potential problems that may affect future operations.
Information based on prior analysis is provided to the components inside the machine.
For instance, if a dramatic increase in battery consumption is detected early enough, then the laptop's internal components have a better chance of identifying and fixing any reported anomalies. And this can be done before they cause more serious issues later on.
Software developers use these algorithms to create suggestions in the App Store. It helps ensure the product managers are aware of the apps that would be most appropriate for your requirements and interests.
Top 5 ML Innovations of Apple
1. Machine Learning in voice assistance:-
Siri, the first virtual personal assistant, was developed through cooperation between SRI International and the Swiss Federal Institute of Technology (EPFL). It is a part of the Cognitive Assistant that Learns and Organizes (CALO) program's DARPA Personalized Assistant that Learns (PAL) project, which is run by SRI. This is what you might call "the largest known artificial intelligence development project in history!"
Siri is one of the most famous and developed voice assistants, which is used worldwide. Natural Language Processing (NLP) and speech recognition are combined in Siri's approaches for large-scale machine learning.
Speech recognition converts spoken words into their equivalents in written language. NLP algorithms decipher the intent of what a user says to provide more robust quality in search, context, and relevance for users. Additionally, it helps in more effective and relevant marketing content.
2. Image sorting and emojis:-
Knowledgeable Apple supporters will note that the corporation has fast surpassed all competitors in offering clients the newest advances in machine learning.
For instance, thanks to the recent addition of animated emoticons to iPhones, users now have more ways to express their thoughts when chatting or texting.
Additionally, iPhone users might find that the Photos app uses machine learning to automatically group photos into pre-made galleries or to display a photo of a friend when their name is typed into the app's search bar.
3. ML in clicking images:-
As long as you retain your finger on the shutter button, machine learning enables rapid photo-taking experiences in which the application shoots numerous photographs quickly instead of one at a time.
Then, an ML-trained algorithm learns to analyze each photograph, combines them into one fantastic photo, and it's immediately available for your next shoot!
4. ML in Apple Pencil:-
The Apple pencil taught the iPad a helpful trick. When it detects your palm touching the screen, the program is used to rebuild pencil strokes precisely and lowers the screen resolution; it also uses pressure-sensitive touchscreen automation.
This is possible because your phone's touch screen sensors can determine how you're tapping the screen and modify some settings to save battery life.
5. ML in Apple watches:-
Thanks to the Apple Watch, keeping track of your sleep schedule is simpler than ever. The Apple Watch not only records your daily activity and calorie expenditure but also supports you in having the best possible sleep patterns.
It becomes very crucial in maintaining good health. To discover more about your sleeping patterns over time, sync the Apple Watch with your phone.
You will receive reminders directly on your wrist when it is time for bed. In addition, keep a running schedule of your sleep so that you don't put off going to bed longer than a healthy requirement.
Like an app, these sleep periods can be determined based on when the device senses a user's wrist has become still or motionless for a moment.
Then, accelerometer measurements from the device show deficient activity levels. These motion detection capabilities allow it to measure how well someone slept (i.e., when was REM? When were they awake? How many times did they wake up?)
Apple uses machine learning in various ways, but these are just a few examples that will give you a better understanding of how it is used.
It is clear from this that Apple has achieved its current position in the global IT industry because of continuous technological advancement in the field of ML. This article is an overview of the technologies such as Core ML and Create ML, which are getting advancements with the recent technological growth, helping in enhancing the apps with intelligent features, or building new apps that require AI capabilities.
Please check out our Artificial Intelligence and Machine Learning course to learn more about machine learning and AI-based technologies.