Understand the Difference: Artificial Intelligence Vs Machine Learning

January 11, 2019
artificial intelligence

Machine Learning and Artificial Intelligence are now considered the greatest areas of innovation since the microchip. Almost all the tech giants like Facebook, Google, IBM and more have bet big on Artificial Intelligence and Machine Learning, and are using it in their products right now. To understand the impact of AI when it comes to generating jobs, let us look at some numbers below.

In businesses today, almost one-tenth of the company’s digital budget goes towards Artificial Intelligence, which is why it’s no wonder that in 2018, six out of 15 jobs that emerged were related to Artificial Intelligence. In fact, Machine Learning and Deep Learning have been cited as in-demand skills with the requirement growing by 35x from 2015 till 2017.

Over the next few years, we are about to witness the social, corporate and even political world using AI and ML capabilities in one way or another. As facial and voice recognition continues to evolve, so will the learning of these algorithms towards a smarter solution.

That being said, both AI and ML seem to be used interchangeably as they crop up simultaneously whenever topics such as analytics, Big Data and the broad waves of technology are being discussed. This article would be helpful to those wanting to understand the difference between the two.

Visualizing the Distinction

It is important to clearly understand that just like Deep Learning is a subset of Machine Learning, Machine Learning is a subset of Artificial Intelligence. This was visually represented by Andrey Bulezyuk, a German computer expert who meant it for practitioners in these fields to clearly articulate the distinction between the three closely-related terms.

This essentially means that AI is an umbrella concept which was followed by Machine Learning, and finally, Deep Learning which promises to catapult the advances of Artificial Intelligence to a new level.
Tip: Artificial Intelligence is a broad concept of machines being able to carry out tasks in a “smart” way. Machine learning is the application of AI based on the idea that machines should be able to access data and learn to complete tasks on their own.

What is Artificial Intelligence?

Artificial Intelligence is the study of how one can train computer systems to work as well as humans do. It’s important to note that many confuse AI to be a system when it’s not. AI is integrated into the system so that the computer can mimic the cognitive functions of human beings. AI’s progress is measured through the ability of machines carrying out tasks in an intelligent manner.

Sharing of knowledge is an important part of understanding AI. The training of computers to think like us humans are achieved partially through the use of neural networks – a series of algorithms modeled after the human brain. Neural networks help the computers categorize and classify information to make sense of it. Any AI bot must have access to categories, properties, objects and the relation between them to initiate common sense, solving problems and reasoning analytically. Our human brain has also been trained to make sense of the world in a similar way.

Artificial Intelligence has been classified into two fundamental groups – applied and general. Applied AI is the most common form of AI which includes everything from automated driving to intelligent stock-trading systems. Whereas, generalized AI are devices or systems that can handle any task, in theory. This subset of AI has led to the development of Machine learning in the modern era.

What is Machine Learning?

Arthur Samuel, the pioneer in the field of artificial intelligence and computer gaming coined the term Machine Learning in 1959. He referred to ML as the ability to understand and learn without being overtly programmed. Simply put, Machine Learning is the science of design and application of algorithms that learn from past use-cases. If an event has taken place in the past, it is possible to predict the same, lest it happens again, which means that to create an Artificially intelligent robot, you would need millions of lines of code, complex decision trees and rules to define the purpose of the robot.

Using complex algorithms iterating constantly over huge data sets, Machine Learning is the method to analyze new patterns in data and helping machines figure out how to respond to a given situation. This way, machines are able to learn from their history and produce trustworthy results.

The emergence of the internet has created a vast online repository of digital information that is being stored, generated and sorted for various types of analysis. This has been a very big driving factor for teaching computers and machines understanding how human beings do everything. Think about how much online training have made the process of knowledge sharing possible across geographies today. With efficient codes, machine learning allows engineers to design solutions that no only use the power of the internet to learn, improve and deliver but also predict the future based on past experiences.

Data at the Center
Any algorithm, artificially intelligent system or a machine learning tool is inefficient if the data is flawed. Because if the data is incorrect or too small, then the insights and information extracted will be flawed as well. Data scientists often spend a majority of their time spending in Data Cleansing – a process least liked about their job. This job, however, is extremely crucial as only by detecting, correcting or removing corrupt data from the database, can you replace, modify or get rid of the useless data. More importantly, as it is algorithms driving AI tools and Machine Learning, the data needs to be thorough and of high quality.

Jobs in Demand
The overall employment landscape in the IT sector is undergoing radical changes and will continue to do so over the next few years. Data suggests that by 2020 more than 2 million jobs will get added in the areas involving computers, mathematics, architecture, manufacturing and engineering fields – of which 13 percent increase will be in India. In fact, the rise of online training materials and professional skilling platforms have emerged as a way to educate the workforce in organizations for upskilling.

The demand of data analytics sector will contribute to an increase in employment opportunities by 25 percent, which means that 2019 is a huge year for individuals looking to increase their proficiency in Artificial Intelligence and Machine Learning.

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