What is Machine Learning and Artificial Intelligence?



The name machine learning was coined in 1959 by Arthur Samuel.

Machine Learning (ML) is the scientific study of algorithms, statistical models which computers use to perform a specific task without the use of any detailed instructions, relying on patterns and inference instead. Machine Learning is sometimes seen as a subset of Artificial Intelligence (AI). 

It is an application of AI which provides systems the ability to automatically learn without being explicitly programmed. It can learn new things and improve from experience. Machine learning is similar to the human brain, It learns itself, improves its performance itself.

Machine learning algorithms are used in a wide variety of applications, for example, email filtering, computer vision, social media, online customer support and more, ML is implemented where it is difficult to develop a conventional algorithm for effectively performing the task.  It is closely linked to computational statistics that focus on making predictions using computers.  

The process of learning starts with observations by the computer or by feeding the data, for example, direct experience or instruction, to search for the patterns in data and make better decisions in the future based on the examples/observation that we provide.

Often the data isn’t provided, the computer is allowed to learn automatically without human intervention or assistance and adjust accordingly. 

Methods of Machine Learning

If we look in-depth, there are perhaps 14 types of Machine Learning that you must be familiar with but we aren’t discussing all 14 types here. 

Machine Learning algorithms are often categorized as supervised or unsupervised:

  1. Supervised Learning:
  2. Unsupervised Learning
  3. Reinforcement Learning

1. Supervised Learning:

It is a process where one can apply what has been learned in the past to new data using labeled examples to predict future events. It is a task of learning a function that maps an input to an output based on the example provided.  In this model of learning, each example is a pair consisting of an input and its desired output.

2. Unsupervised Learning:

It looks for the previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision. When the information that is used to train is neither classified nor labeled this method is used. This system doesn’t figure out the correct output, but it explores the data and can draw inferences from datasets to describe hidden structure from unlabeled data. 

Principal component and cluster analysis are the two main methods used in unsupervised learning.

3. Reinforcement Learning:

It is an area concerned with how a software agent ought to take actions in an environment to maximize the notion of cumulative reward.  It is used in various software and machine to find the possible behavior or path it should take in a specific event. 

You might have heard of Semi-Supervised Learning, This learning falls between unsupervised and supervised learning. Semi-supervised learning is a machine learning process which combines a small labeled data with large unlabeled data during training. If an unlabeled data is used in conjunction with a small labeled data, it can produce a considerable improvement in learning accuracy.

Artificial Intelligence (AI)

AI is sometimes referred to as machine intelligence.  It is a technology using which we can create intelligent systems. It is implemented in a system to train the computers so that computers can do things that humans can do.  AI requires no pre-programming; they use such algorithms that can work with their intelligence.  AI uses ML algorithms (reinforcement learning and deep learning neural networks). Some of the best examples of AI for now are, Siri, Google’s AlphaGo, Sophia world’s first AI humanoid, chess game.

The key difference between ML and AI are:

  1. ML focus on accuracy, AI focus on success.
  2. The goal of ML is to learn from data, the goal of AI is to simulate natural intelligence. 
  3. Deep learning is a main subset of ML, whereas ML and deep learning are two main subsets of AI.
  4. ML has a limited scope, AI has a vast scope.
  5. ML is working on creating machines that can perform a specific task, AI is working to create an intelligent system that will be capable to perform complex tasks. 
  6. Facebook auto friend tagging suggestion is one of a good example of ML, Siri is an example of AI.

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