RECENT TRENDS IN MACHINE LEARNING APPLICATIONS
Recently, there has been a dramatic surge of interest in the era of machine learning, and more people become aware of the scope of new applications enabled by the machine learning approach. It builds a road-map to contact with the device and make the device understandable to response to our instructions and commands.
Traffic Alerts (Maps)
Now, Google Maps is probably the most popular app we use whenever we go out and require assistance in directions and traffic. The other day I was traveling to another city and took the expressway, and Maps suggested: “Despite the Heavy Traffic, you are on the fastest route.” But, how does it know that?  (Figure 2.13).
Well, it’s a combination of people currently using the service, historic data of that route collected over time, and few tricks acquired from other companies. Everyone using maps is providing their location, average speed, the route in which they are traveling which in turn helps Google collect massive data about the traffic, which makes them predict the upcoming traffic and adjust your route according to it.
Social Media (Facebook)
One of the most common applications of machine learning is automatic friend tagging suggestions in Facebook or any other social media platform. Facebook uses face detection and image recognition to automatically find the face of the person which matches its database and hence suggests us to tag that person based on DeepFace (Figure 2.14).
Facebook’s deep learning project DeepFace is responsible for the recognition of faces and identifying which person is in the picture. It also provides alt tags (alternative tags) to images already uploaded on Facebook. For example, if we inspect the following image on Facebook, the alt-tag has a description (Figure 2.15).
FIGURE 2.14 Face recognition.
FIGURE 2.15 Image recognition.
FIGURE 2.16 Ubericon.
Transportation and Commuting (Uber)
If you have used an app to book a cab, you are already using machine learning to an extent. It provides a personalized application which is unique to you. It automatically detects your location and provides options to either go home or office or any other frequent place based on your history and patterns (Figure 2.16).
It uses machine learning algorithm layered on top of historic trip data to make a more accurate ETA prediction. With the implementation of machine learning, they saw a 26% accuracy in delivery and pickup.
Suppose you check an item on Amazon, but you do not buy it then and there. But the next day, you’re watching videos on YouTube and suddenly you see an ad for the same item. You switch to Facebook, there also you see the same ad. So how does this happen? (Figure 2.17).
Well, this happens because Google tracks your search history, and recommends ads based on your search history. This is one of the coolest applications of machine learning. In fact, 35% of Amazon’s revenue is generated by product recommendations.
Virtual Personal Assistants
As the name suggests, virtual personal assistants assist in finding useful information, when asked via text or voice. Few of the major applications of machine learning here are:
- • Speech recognition
- • Speech to text conversion
- • Natural language processing
- • Text to speech conversion (Figure 2.18)
All you need to do is ask a simple question like “What is my schedule for tomorrow?” or “Show my upcoming Flights.” For answering, your personal assistant searches for information or recalls your related queries to collect info. Recently, personal assistants are being used in chatbots, which are being implemented in various food ordering apps, online training websites, and also in commuting apps.
Well, here is one of the coolest applications of machine learning. It’s here and people are already using it. Machine learning plays a very important role in selfdriving cars and I’m sure you guys might have heard about Tesla. The leader in this business and their current artificial intelligence is driven by hardware manufacturer NVIDIA, which is based on an unsupervised learning algorithm (Figure 2.19).
NVIDIA stated that they didn’t train their model to detect people or any object as such. The model works on deep learning, and it crowdsources data from all of its vehicles and its drivers. It uses internal and external sensors which are a part of the Internet of Things (IOT). According to the data gathered by McKinsey, the automotive data will hold a tremendous value of $750 billion.
FIGURE 2.18 Assistants logo.
FIGURE 2.19 Tesla logo.