Setting the right price for a good or service is an old problem in economic theory. There are a vast amount of pricing strategies that depend on the objective sought. Be it a movie ticket, a plane ticket or cab fares, everything is dynamically priced. In recent years, artificial intelligence has enabled pricing solutions to track buying trends and determine more competitive product prices (Figure 2.20).
How does Uber determine the price of your ride?
Uber’s biggest uses of machine learning comes in the form of surge pricing, a machine learning model nicknamed as “geosurge.” If you are late for a meeting and you need to book an Uber in a crowded area, get ready to pay twice the normal fare. Even for flights, if you are traveling in the festive season, chances are prices will be twice the original price.
FIGURE 2.20 Dynamic pricing.
FIGURE 2.21 Google translate logo.
Remember the time when you traveled to a new place and you find it difficult to communicate with the locals or finding local spots where everything is written in a different language (Figure 2.21).
Well, those days are gone now. Google’s GNMT (Google Neural Machine Translation) is a neural machine learning that works on thousands of languages and dictionaries, uses natural language processing to provide the most accurate translation of any sentence or words. Since the tone of the words also matters, it uses other techniques like POS (point of service) tagging, NER (named entity recognition), and chunking. It is one of the best and most used applications of machine learning.
Online Video Streaming (Netflix)
With over 100 million subscribers, there is no doubt that Netflix is the daddy of the online streaming w'orld. Netflix’s speedy rise has all movie industrialists taken aback—forcing them to ask, “How on earth could one single website take on Hollywood?” The answer is machine learning.
The Netflix algorithm constantly gathers massive amounts of data about users’ activities like:
- • When you pause, rewind, or fast forward
- • What day you watch content (TV shows on weekdays and movies on weekends)
- • The date and time you watch
- • When you pause and leave content (and if you ever come back)
- • The ratings given (about 4 million per day), searches (about 3 million per day)
- • Browsing and scrolling behavior (Figure 2.22).
FIGURE 2.22 The Netflix logo.
And a lot more. They collect this data for each subscriber they have and use their recommender system and a lot of machine learning applications. That’s why they have such a huge customer retention rate.
Experts predict online credit card fraud to soar to a whopping $32 billion in 2020. That’s more than the profit made by Coca Cola and JP Morgan Chase combined. That’s something to worry about. Fraud detection is one of the most necessary applications of machine learning. The number of transactions has increased due to a plethora of payment channels—credit/debit cards, smartphones, numerous wallets, UPI, and much more. At the same time, the amount of criminals have become adept at finding loopholes (Figure 2.23).
Whenever a customer carries out a transaction—the machine learning model thoroughly X-rays their profile searching for suspicious patterns. In machine learning, problems like fraud detection are usually framed as classification problems.
It is another benchmark application of a machine learning approach. Why or how? There is a tremendous increase in the volume of information on the web that can be commonly accessed and utilized by anyone. However, every person has his individual interest or choice. So, to pick or gather a piece of appropriate information becomes a challenge to the users from the ocean of this web (Figure 2.24).
Providing that interesting category of news to the target readers will surely increase the acceptability of news sites. Moreover, readers or users can search for specific news effectively and efficiently.
There are several methods of machine learning in this purpose, i.e., support vector machine, naive bayes, к-nearest neighbor, etc. Moreover, there are several news classification software that is available.
A small video file contains more information compared to text documents and other media files such as audio, images. For this reason, extracting useful information from video, i.e., the automated video surveillance system has become a hot research issue. With this regard, video surveillance is one of the advanced applications of a machine learning approach (Figure 2.25).
The presence of a human in a different frame of a video is a common scenario. In the security-based application, identification of the human from the videos is an important issue. The face pattern is the most widely used parameter to recognize a person .
FIGURE 2.24 News classification.
FIGURE 2.25 Video surveillance.
A system with the ability to gather information about the presence of the same person in a different frame of a video is highly demanding. There are several methods of machine learning algorithm to track the movement of human and identifying them.
Speech recognition is the process of transforming spoken words into text. It is additionally called automatic speech recognition, computer speech recognition, or speech to text. This field benefits from the advancement of machine learning approach and big data (Figure 2.26).
At present, all commercial purpose speech recognition system uses a machine learning approach to recognize the speech. Why? The speech recognition system using machine learning approach outperforms better than the speech recognition system using a traditional method.
A machine learning algorithm is used in a variety of robot control system. For instance, recently several types of research have been working to gain control over stable helicopter flight and helicopter aerobatics (Figure 2.27).
In Darpa-sponsored competition, a robot driving for over one hundred miles within the desert was won by a robot that used machine learning to refine its ability to notice distant objects.
With the rapid growth of the Internet, the illegal use of online messages for inappropriate or illegal purposes has become a major concern for society. For this regard, author identification is required (Figure 2.28).
Author identification also is known as authorship identification. The author identification system may use a variety of fields such as criminal justice, academia, and anthropology. Additionally, organizations like Thorn use author identification to help end the circulation of child sexual abuse material on the web and bring justice to a child.
FIGURE 2.27 Robot control.
FIGURE 2.28 Author identification.