Sundar Pichai, the CEO of software giant Google, on being asked what is the next thing at the company, said “I can’t quite tell exactly but advances in AI and Machine Learning, we are making a big bet on that. Advances in machine learning will bring a difference in many many fields.“ while interacting with students at his alma mater IIT-Kharagpur.

Earlier other leaders in the field of Information Technology had also mentioned the rising importance of AI and machine learning and how these will replace the jobs of many across different sectors.

Artificial Intelligence and Machine Learning (ML) seems to be the buzzwords now. Often these words are interchanged, but in reality these two differ. It is important to understand the meaning of the two and the difference between them to stay abreast with this next big thing.

Artificial Intelligence is the intelligence that machines exhibit. According to computer science an intelligent machine acts as a ‘flexible, rational agent’ that can perceive the environment and take action that will maximize chances of success. It is a broad concept where machines can perform tasks that would be considered smart.

Wikipedia defines Machine Learning as “gives computers the ability to learn without being explicitly programmed“. It is a subfield of computer science and it is related to computational statistics and emphases on predicting with the use of computers. Machine Learning is based on AI and it provides machines the ability to access data and learn it themselves.

Know about Artificial Intelligence

Artificial Intelligence is nothing new. Though the term was coined in 1956, at New Hampshire, it dates back to Greek myths where there is mention of mechanical men or automatons who were capable of mimicking human behavior. The early computers in Europe were considered logical machines that reproduced the capability of basic arithmetic. Then the engineers conceived their job as to create mechanical brain.

With time the concept of Artificial Intelligence has changed increasingly, it now aims at accomplishing tasks in a human way. Devices empowered by AI are of two types- Applied AI and General AI. Applied AI is commonly used for systems dealing with trading shares and stocks, or maneuvering automatic vehicles. Generalized AI includes devices that handle less common tasks. This is a subset of Artificial Intelligence, major advancements have occurred in this area and has led to the development of Machine Learning.

Introduction to machine learning

Important breakthroughs leading to the emergence of Machine Learning are- invention by Arthur Samuel and the advent of internet. In 1959, Arthur Samuel realized that instead of teaching computers, it is wise to teach these machines to learn themselves. With the emergence of internet, there is an increase in the generation of digital information. These innovations, led engineers to realize that it will be effective to code machines or computers to think like human beings and connecting them to the internet which would provide them access to unbound information.

However, ML now has evolved from what it used to be earlier. Pattern recognition by machines and their ability to learn as how to perform tasks without programming lies to the core of machine learning. Many ML algorithms do exist, but the capability of machines to apply complex mathematical calculations to big data is a recent development.

Source: education infographics 

Why machine learning is important

With ML, businesses can perform better decision making without any human intervention. It helps businesses in solving problems and predicting user behavior, both these are instrumental to business growth. Data is the most important asset that fuels the growth of any business. Information about customer demographics, inventory, pricing, delivery systems and web analytics, impact customer relationship and specifically the organizational growth.

Machine learning has improved the process of data analysis and the prediction model. There are different types of machine learning methods like Supervised learning, Semisupervised learning, Unsupervised learning and Reinforcement learning. For an effective machine learning system, few important requirements are:

• Capability for data preparation
• Advanced Algorithm
• Scalability
• Iterative and automation processes
• Ensemble modeling

Understanding machine learning, its benefits and implementing it correctly will help businesses to work efficiently and grow.

Machine learning uses and reasons

Industries dealing with large volumes of data, now opt for machine learning technology to get an advantage over their competitors. The widespread use of machine learning includes- fraud detection, filtering of email spam, image recognition, sentiment analysis, detection of network intrusion, best offers and real-time ads on mobile devices. It can benefit businesses exponentially:

• Machine Learning relies on an algorithm that helps in data analysis for various combinations available.
• Innovation takes the driving seat as it can redefine the benchmarked rules.
• The best model that combines human behavior with science.
• Predictive analysis gives business insight about performance, engagement and loyalty.
• Identify the hidden patterns, anomalies if any, and generate reports based on it.
• Adaptability is yet another important benefit.

Owing to these benefits, various industries have implemented machine learning technology in their daily activities. Some of these industries are

• Finance– In finance industry, businesses like banks use this technology for identifying data insights and prevention of fraud. These insights help in identifying investment opportunities.
• Government– For government agencies machine learning is important as there are multiple sources of data that need to be mined for increasing efficiency and effective use of infrastructure.
• Transportation– Identifying trends and patterns is key to success in the transportation industry. Predictive model and data analysis are two important components of machine learning that is beneficial for transportation companies.
• Marketing– Machine Learning will help in capturing data, analyzing the buying history of customers and promote customer experience.
• Healthcare– With the advent of technology, medical practitioners can now get real-time insight about the patient’s health condition. This technology improves treatment and diagnoses by identifying the red flags fast.

Pattern recognition is a popular use across different industries. Experts are of the belief that Algorithms of Machine Learning have a great scope of improvement. Technology companies now concentrate on improving machine learning to make it more intelligent, so that businesses can reap high benefits.

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