Since you are reading this, I assume you are aware of or at least have heard about Machine Learning and Artificial Intelligence. Being two of the hottest buzzwords in the industry right now, these are often used interchangeably leading to some confusion. However, these two have different meanings and applications. The two terms are very strongly related though, as they share a containership relationship between them where the former is a subset of the later. Let’s dive deep into these topics and try to find the reason for this confusion and related solutions.

Why this confusion?

The main culprit behind this confusion is the interchangeable use of these two terms and the limited knowledge of the subject among the developer as well as the user community. Artificial intelligence is heavily dependent on machine learning, which has led to the perception that both terms refer to the same thing. This confusion has spread like wildfire in the industry and only people who are experts in this field, know the clear distinction among these terms.

Artificial Intelligence-The Big Brother

Artificial Intelligence is the intelligence demonstrated by machines that emulate human-like thinking and behavior, allowing them to make their own decisions in real-life situations. Going by the computer science definition, AI is referred to as the study of intelligent agents, which are devices that perceive their environment and take actions accordingly in order to maximize the fulfillment of their goals. These agents mimic certain cognitive functions, which humans relate to the human mind, like problem-solving and learning. AI, traditionally, attempts to solve problems such as Reasoning, Knowledge Representation, Learning, Planning, Natural Language Processing, etc. Generating an intelligent agent that can think like humans are the long-term goal since it makes use of all the former techniques mentioned.

Now, there are two ways in which the intelligent agents can achieve this, the first one being by using a set of if/else statements that provide the answer for each problem statement. This is a very orthodox, ineffective, and tedious way of doing this. Another way is to use Machine Learning which is more flexible & dynamic, being able to learn from the data it processes and improve upon the results incrementally in real-time.

Due to disagreements on any established paradigm to be used in machine learning, there is still no fixed approach that works effectively. Some of the popular approaches are as follows:

• Cybernetics and brain simulation
• Cognitive Simulation
• Statistical Learning

Machine Learning-An Overview

Machine Learning is a field of artificial intelligence that makes use of statistical techniques and functions to give a computer the ability to learn itself from the provided data, without the need for any explicit programming. This act of the machine system learning by itself progressively improves its performance for a specific task. The heart of machine learning models lies in the dataset which is being used to train the model as well as the algorithms which operate on the data. These algorithms learn from the provide data and make predictions, overcoming static and fixed programming instructions by employing data-driven decisions through a model which it builds from the training data given.

There are two learning types in machine learning- supervised learning and unsupervised learning. The former takes a well-labeled data set to operate upon and makes deductions based on it. The later one takes in raw, unlabeled data and finds patterns in the data based on which new data predictions are made.
Closely related to the field of Computational Statistics, machine learning also focuses on making predictions through the use of computers. Mathematical optimization is also tightly coupled with ML since it provides the theory, methods, and application domains to the field. Machine Learning is also used in the field of Data Analytics to generate complex models and algorithms which lend themselves to prediction; commercially this is referred to as Predictive Analytics. These models help data scientists, researchers, engineers, and experts, to produce reliable results, repeatedly and uncover hidden insights within the data

There is an N number of algorithms present that can be utilized to solve your machine learning problem. Each has its own strengths and weaknesses and its fit depends completely on the dataset and the use case. Some of the popular algorithms/methods being used are Decision tree learning, Artificial Neural networks, Inductive Logic Programming, Deep Learning, Bayesian Networks, and many more.

Conclusion

Well, this confusion, however small it is, must be cleared since this might lead to problems down the line as the scale of artificial intelligence and machine learning increases. In large and critical implementations, using them interchangeably should be unacceptable and the community should be made more aware of these concepts and their differences. You can reach us for all your AI needs since we, being the AI & ML experts, can help you design a custom solution for your machine learning problem.