Major Roles of AI and ML in day-to-day life
We all know that we are in the middle of the revolution being led by Machine Learning applications. There is a huge range of use cases where we have already moved with AI and ML as their core like from Smartphones to the transactions we do. Still AI and ML is covering the world for different aspects.
Before understanding that what AI and ML is doing for us , we need to understand what is AI and ML !!!
What is AI ?
Artificial Intelligence (AI) is a science and a set of computational technologies that are inspired by — but typically operate quite differently from — the ways people use their nervous systems and bodies to sense, learn, reason and take actions.
The definition for AI can be different for different people but the general concept of AI is that — It is a program that can sense , reason, act and adapt.
What is ML ?
Machine Learning is the task of making computers more intelligent without explicitly teaching them how to behave. It does so by identifying patterns in data — especially useful for diverse, high-dimensional data such as images and patient health records. It is also called as the subset of AI or a type of AI.
The above picture telling that “To make a machine AI powered, we need to make it ML Driven” — Simply ML is path to move to the world of AL.
Hence, both the fields are depending on each other. ML driven program will be called as the AL based or powered systems. Lets discuss how and where they became a part of our lives.
Machine Learning Use Cases in day-to-day life :
A). Applications of Smartphones — Machine Learning powered programs :
Voice Assistants are ubiquitous right now. we all uses(or at least heard about ) the popular voice assistants:
- Apple’s siri
- Google Assistant
- Amazon’s Alexa
- Google Duplex
- Microsoft’s Cortana and so on.
The most common thing in all these is that — They are powered by machine learning algorithms! These voice assistants recognize speech(the words we say) through NLP(Natural Language Processing), then convert them into numbers using Machine Learning and generate a resultant response accordingly.
Smartphones Cameras , the incredible images that we click these days are just because of Machine Learning Algorithms. They analyze every pixel in the given image to detect objects, blur the background and much more.
Machine Learning do several things to improve and enhance the picture quality of the smartphone camera:
- object detection , in order to locate and single out the objects (like humans )in the image.
- Filling in the missing parts in a picture.
Face Unlocks , most of us are quite familiar with this feature in our smartphones. We pick our phones and it unlocks itself by recognizing our face. It’s smart, efficient , time-saving and frankly superb.
The application of face recognition are vast and businesses around the world are already taking its benefits :
- Facebook uses it to identify the people in the images
- Government is using it to identify and catch criminals
- Airport authority are using it to verify passengers and crew members and so on.
The usage of face recognition models is only going to increase in coming years.
B). Machine Learning use cases in Transportation
In transport industry , Machine Learning is used in different-different places as per the requirements. Some of the use cases by multiple big companies are :
Transportation and Commuting — UBER , It is using Dynamic Pricing and Optimal route generation using Machine Learning. Lets discuss each of them one by one : —
Imagine you’re starting a ride-hailing business. You need to plan the ride prices for each route in the city in a way that would attract customers but also improve your bottomline . One way to do this is to manually map prices to each route, But it is not an ideal solution. So its solution is the concept of dynamic pricing. It means adjusting the prices of each ride will depend on the factors like location, time of day, weather, overall customer demand , etc.
Dynamic pricing is a thriving practice in various industries , such as travel, hospitality, transportation and logistics , etc.
Google Maps , is a prime use case of Machine Learning. It provide multiple options for multiple requirements and provide optimized solutions, like
- Routes: To go from a point to another.
- Time Estimation: Also estimate the to travel for a specific route.
- Traffic Prediction:Also predict for the traffic on some specific route.
- Explore the nearby places from the current locations of an individual.
Machine Learning is deeply embedded in Google Maps and this is the reason why the routes are getting smarter with each update.
Just like these use cases , there are many places where Machine Learning is helping to enhance and improve the working of that fields like Email Filtering, Google Search , Facebook and LinkedIn recommendations etc.
Since it is the age of Machine Learning and Artificial Intelligence , so there is lot more to come in future.
Thank You !!!!!