Machine Learning – the beginning of new Era

Machine Learning – the beginning of new Era

Introduction

Isn’t it interesting? The title of this article, which says “Machine Learning - the beginning of new Era”. We are living in the world of machines and humans. We humans have evolved by learned from our past experiences since million of years, while the era of machines and robots have just started. In other words, we are living in the early age of machines while the future of machines is gigantic and literally unimaginable.

At present, we have the robots and machines that are required to be coded or programmed before they perform actions as per our instructions. Now think about a machine that starts learning on its own from its past experience, work like a human, feel like a human, do things more precisely and accurately than a human. I am sure you are fascinated after hearing all these things. But believe me, it is just the beginning of the new era, and there is a lot more in the queue.

Now the question is how machines learn themselves? How they learn from their experiences and examples like we humans do? Well, this is where the concept of Machine Learning comes into picture. Machine learning is the science of making computers learn and behave like humans, and enhance their learning with the course of time without the intervention of human being. The machines begin learning with the help of fed data that is in the form of observations such as examples, experiences or instructions. Computers look at the patterns of data and accordingly make better predictions and decisions in the future on the basis of the examples that are provided by us.

History of Machine Learning

The history of Machine Learning is very interesting. The first computer was developed in the year 1946 with the name ENIAC (Electronic Numerical Integrator And Computer). The meaning of the term "Computer" at that time was a human who was doing mathematical calculations on the paper. ENIAC was called the numeric computing machine during that period, and it was operated by human being who use to connect different parts of the machine together to perform numeric calculations. The idea behind all of this was that human thinking and learning could be logically rendered in such a machine.

Later on, a famous mathematician and computer scientist named Alan Turing came up with a test for measuring the performance of the machine in the year 1950. Turing test was the test to determine whether a machine is capable of learning when a human communicates with it, and exhibit an intelligent behavior such that it cannot be distinguished from that of a human. However, none of the systems could actually pass the Turing test, although multiple interesting systems have been designed and developed so far.

In 1952, Arthur Samuel from IBM developed a machine learning program named ELIZA, this was a game playing program which enables the checkers to acquire enough skills to be able to challenge the world champion. In 1957, an American psychologist and computer scientist developed Perceptron, which is the first neural network for computers. Perceptron accelerates the thought processes of the human brain. The "nearest neighbor" algorithm was written in 1967 which enabled the computers to use the pattern recognition. With the help of this algorithm, a route-map could be prepared for a salesman starting his travel from a random city and covering different cities in his tour.

In 1979, a significant invention was made by the students at the Stanford university which they named "Stanford Cart", this was capable of navigating obstacles within the room on its own. Later in 90s, the machine learning began to shift from a knowledge driven approach to data driven approach. Machine learning scientists started developing programs which could make the computers analyze huge amount of data and learn or draw conclusions based on the results of that data. Machine learning got a breakthrough in the year 1997, when IBM developed a machine learning program named "Deep Blue" and this program defeated the world chess champion.

In 2006, the term "Deep Learning" was introduced by Geoffrey Hinton for explaining the new algorithms which enabled the computers to look and differentiate objects and texts in images and videos. In 2010, Microsoft came with a machine learning program named Kinect which was capable of tracking 20 human features at a brisk rate of 30 times per second enabling people to interact with computers through gestures and movements. Later in the year 2011, Google developed a machine learning program named Brain having deep neural network which could learn to identify and categorize objects in the same way as the cat does.

In 2014, Facebook developed a machine learning program named Deep-face, which could identify individuals in pictures in the same way as the humans do. An year later in 2015, Amazon came up with its own machine learning platform. Recently, a new Robot named Sophia has been given the citizenship of Saudi Arabia. Sophia is a Robot based on artificial intelligence that is conceptually similar to the computer program ELIZA. Machine learning has constantly evolved and shown significant improvements in the recent times and it continues to have a big impact on all aspects of the society.

Advantages of Machine Learning

Machine Learning has certainly revolutionized the modern world of computing. A buzz around big data, artificial intelligence and machine learning has created a curiosity among organizations about the advantages and applications of machine learning in their business. Although Machine Learning is the most heard name in the global IT marketplace, yet it remains unknown to a majority of masses. The recognition of Machine Learning and Artificial Intelligence began with the advent of cloud machine learning platforms of Amazon, Microsoft Azure and Google. The face recognition tool from the Facebook also made a widespread publicity of Machine Learning. ML has evolved over the years and has numerous advantages, few of them are discussed here.

1. Unlimited Data Input

Machine learning can intake a huge amount of data very easily making fast analysis and assessment of that data. This enables the machine to review and adjust its messages based of recent customer interactions and behaviors. Machine Learning analyzes loads of data from various sources, and collect valuable information from that, thus preventing any complicated integrations while focusing on concise and precise data feeds only.

2. Quick Processing and Predictions in Real-Time

Machine Learning algorithms and very fast in operation, which means that machine learning consumes data at a very brisk rate which enables it to tap into flourishing trends and produce real-time data and predictions. For instance, in the departmental stores and grocery stores, customer can get new offers which can be optimized and created by machine learning. To make it simple, customers might find some offer at a particular time that may change an hour later. So, to summarize this all, machine learning has the capability of identifying, processing and creating data based on the predictive analysis.

3. Practical Situations

The application of machine learning is very crucial in the real world scenarios and applications. Predictive analytics is a key to saving costs and generating revenue. It is also important to understand the impact of predictive analytics in practical situations relating to customer loss or acquisitions.

4. Suitable for Data Mining

Machine Learning automates the process of checking the data from multiple data sources to collect relevant information. Not only it automates the process of analyzing large data, it also cater the original assumptions which can be used for supporting decisions. The information that is obtained from the process is very helpful in multiple domains like banking, retail, health-care, finance etc.

5. Ceaseless Improvements

One of the biggest advantage of machine learning is that it helps in further improvements based on the previous experiences. You must be wondering how? Well, the machine learning algorithms act as agents and help in continuous improvements based on the previous data obtained.

6. Task Automation

Machine Learning systems are capable enough to do the jobs on their own without the intervention of human being. This is all because of its data mining ability along with continuous improvements, which makes machines learn from the regular data patterns and accordingly make better predictions and decisions autonomously. Machine Learning technology has been used by Google for indexing and ranking websites in its search engine.

In addition, Facebook and Google also use proprietary machine learning algorithms for delivering online advertisements. The latest smartphones also have assistants like Apple has Siri and Google has Google Now, these personal assistants make use of machine learning algorithms for answering questions, performing actions and making any recommendations. There are various other examples of automated tasks such as fraud detection, face recognition, processing of loan application, drug discovery or formulation, disease diagnosis in healthcare.

Application Areas of Machine Learning

Machine learning has been identified as most valuable technology by most of the industries that with huge amount of data. By collecting valuable information from this massive data, the companies are able to work with increased efficiency which has helped them to be one step ahead from their rivals. Machine learning is extensively used in various areas and domains like -

1. Finance Industry

Machine learning is broadly used in Banking and Financial services for detection and prevention of fraud along with identifying valuable data insights. These insights are very crucial in the prediction of investment opportunities and helping the investors know the best time for trading. Data mining can also help in recognizing the high-risk profile clients etc.

2. Government Agencies

Machine Learning is also used widely by the government agencies like public safety and utilities. They have various data sources which can be analyzed to collect relevant information and make the job of agencies simple and fast. For instance, the analysis of the data obtained from the sensor helps in identifying ways of increasing efficiency and saving money. Moreover, fraud and identity theft can also be minimized using the machine learning technology.

3. Health & Medicine Industry

Healthcare and medicine is yet another domain where machine learning is extensively used. Obviously, the credit goes to the newly introduced wearable devices and sensors which make assessment of the health of patient in real time by analyzing the data. Machine Learning technology can prove to be very helpful for medical experts in analyzing the data to identify trends or red flags which may result in improved diagnosis and treatment.

4. Sales & Marketing Industry

Machine Learning is used in the domain of sales and marketing. The E-commerce websites and online shopping portals use machine learning to recommend items to the customers that they might be interested in. This is done by collecting the data from their previous shopping and purchasing, and analyzing their purchasing history. The websites not only recommend the items, but they also promote items that you might be looking for. Thus, this industry in making a potential use of machine learning for capturing and analyzing data to personalize the shopping experience or implement any marketing campaign.

5. Oil & Gas Industry

Machine Learning is used in the field of Oil and Gar for identifying new energy resources and analyzing minerals in the ground. Also, it is used for the prediction of failure of refinery sensor. Machine learning is useful in streamlining the distribution of oil for improving its efficiency and reducing the costs. Apart from this, there are various ways in which machine learning is used in the industry and the use cases are increasing day by day.

6. Transport Industry

The transport industry majorly relies on making the routes more efficient and anticipating potential issues to maximize profitability. For that, Machine Learning is used in the transportation industry for analysis of data to identify patterns and trends. public transportation, delivery companies and other transportation organizations heavily depend on the data analysis and modeling aspects of machine learning, which are considered as their important tools.

Conquering the future

Machine Learning is gaining popularity day by day. With companies like Google, Microsoft, Amazon etc introducing their own cloud based machine learning platforms, there has been a sudden increase in the popularity of machine learning in the global arena. As per the report of a research, around 12.5% of the time of an employee is consumed in collating the data, which is approximately 11 days in a month. With machine learning, this can be done within few minutes. Sounds fascinating, right? Ofcourse, it is indeed very useful and saves a lot of time, and that is the reason most of the industries have adopted machine learning technology to increase their efficiency, productivity and minimize costs. Moreover, Practica says that it is predicted that the growth of machine learning in the automotive industry will be 48% i.e. $14 billion by 2025. According to Forbes, the cost saving due to machine learning in the health-care industry will increase to €150 billion by 2025. Gartner says that the by 2020, about 85% of the customer service interactions will be handled by chatbots. So, this clearly shows how machine learning is spreading its wings and it will soar to greatest heights in the coming future.

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