AI To ML….

What is Artificial Intelligence?

Well as the name suggests, artificial intelligence commonly known as AI is a way of artificially making a computer intelligent. Can you think of a computer or maybe a robot which can do various tasks similar to humans? It is a branch of computer science in which you programmatically code the machine to become intelligent.

According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”.

Your brain is wired in such a way that the Neocortical Memory is responsible for storing not images, but instead patterns (both temporal and spatial patterns). When these patterns or memories gets recalled the brain predicts what it should see or hear and what it encounters. And if that prediction is correct, the human feels usual, but a sense of surprise is evoked when the prediction is incorrect.

According to Wikipedia, “Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science AI research is defined as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.”

Machine Learning

In the most basic sense, Machine Learning (ML) is a way to implement artificial intelligence. Similar to AI, machine learning is a branch of computer science in which you devise or study the design of algorithms that can learn.

There are various machine learning algorithms like

• Decision trees,
• Naive Bayes,
• Random forest
• Support vector machine
• K-nearest neighbor,
• K-means clustering,
• Gaussian mixture model,
• Hidden Markov model etc.

For now, understand that in machine learning you use one of the algorithms as mentioned above which provides the computer the ability to automatically learn and understand without being programmed time and again.

Supervised Learning: Supervised as the name suggests, is a learning technique wherein the whole learning process is governed. These learning algorithms main goal is to predict the outcome given a set of training samples along with the training labels, also known as classifying a data point. Since, you tell the algorithm at training time, what it should predict, hence it’s called supervised learning.

• Unsupervised Learning: Unlike supervised learning, you have no training labels for the training samples. The algorithms are formulated in such a way that they can find suitable structure and patterns in the data. Once, these consistent patterns become apparent, the similar data points can be clustered together, and different data points will be in different clusters. It is mostly used to project high-dimensional data into low-dimension for visualization or analysis purposes.

Reinforcement Learning: It is a type of machine learning that has an agent (like a robot) that learns how to behave in an environment by taking actions and quantifying the results. If the agent makes a correct response, it gets a reward point, which boosts up the agent’s confidence to take more such actions. It works on Markov Decision Process also known as (MDP).