Artificial Intelligence vs Machine Learning: Know the Difference
Machine Learning and Artificial Intelligence; these two words have created a buzz in all economic sectors due to how they notch up your business and also because of their unveiled potential. Since they are used so indiscriminately, we need to know that they do not correspond to the same concept. Here’s a guide for you to sound smarter in your tech conversations as you understand the difference between AI and ML through this quick read.
To begin with, Artificial Intelligence (AI) has been defined as “the process of imparting data, information, and human intelligence to machines”. The fundamental purpose of these machines is to acquire knowledge and resolve issues to replicate human behaviour and accomplish goals. There are 4 types of AI namely Reactive Machines, Limited Memory, Theory of Mind, and Self-Awareness. For example, Google Translator, Siri, and OK Google.
While AI is about developing smart and intelligent technologies, Machine Learning (ML) is a branch of artificial intelligence that enables developers to construct AI-driven programs. Machine Learning is a subfield of computer science that employs computer algorithms and data analytics to create forecasting templates for addressing industrial challenges. It learns from large volumes of data (both structured and unstructured) to estimate the future. Examples, fraud analysis in banking, product recommendations, and stock price prediction.
There are three types of ML:
- Supervised Learning:
Since the information has already been categorised, you immediately recognize the target variable. Programs can forecast prospective consequences by analysing past data using Machine Learning. It is crucial to provide the system with at minimum one input and output variable for it to be trained.
- Unsupervised Learning:
Unlabeled data is used by algorithms to uncover patterns on their own. The programs are capable of detecting underlying elements in the input data. The connections and commonalities become increasingly apparent once the data is more intelligible.
- Reinforcement Learning:
Reinforcement learning aims to teach an agent how to accomplish the job in an unpredictably changing circumstance. The environment provides the agent with facts and an incentive, and the agent responds by sending actions to the environment. The incentive indicates how successful the action was in accomplishing the assignment objective.
Face recognition features on smartphones, tailored online shopping experiences, automation tools in homes, and even disease assessment are all made possible by these technologies. The need for these technologies, as well as personnel who are knowledgeable about them, is skyrocketing. The estimated value of AI projects in existence in an industry is expected to more than triple over the next two years, as per research by Gartner. This is why Mark Cuban, American tycoon, and telly-star quoted “Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise, you’re going to be a dinosaur within 3 years.”
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