Discover the Differences Between ML and AI

Machine Learning (ML) and Artificial Intelligence are among the most sensational topics of today's world as organizations are leveraging these breakthroughs to create intelligent applications.

Though these terms influence business conversations across the globe, several individuals have difficulty differentiating them, mainly when AI and ML are used interchangeably.

Here we're with yet another blog helping you understand the topic: ML Vs. AI. So, let's go!

What is AI & ML?

a) Artificial Intelligence (AI)

It is the process of conveying information, data, and human intelligence to machines. The main objective of this innovative technology is to create autonomous machines that can think and behave like humans.

AI has the potential to imitate human behavior and execute activities by learning and problem-solving. The best example of an AI-based technology is Amazon Alexa.

Amazon Echo is a smart speaker that employs the virtual assistant AI technology, Alexa. This breakthrough system can play music, voice interaction, play audiobooks, set alarms, news, traffic reports, and weather forecasts.

b) Machine Learning (ML)

It is a domain of computer science that leverages algorithms and analytics to develop predictive models to solve business problems. ML systems access a myriad of data and learn from it using various algorithms and techniques to predict the future.

According to McKinsey & Co., ML technology is based on algorithms that can learn from data without depending on rules-based programming.

Image recognition is the most significant and widespread example of ML in the real world - identifying an object as a digital image based on the pixel intensity in black and white or color pictures.

Different Types of AI and ML

a) AI

b) ML

Application of AI & ML

a) AI

• AI bots like Aibo and Sophia
• Machine Translation like Google Translate
• Speech recognition apps like OK Google and Apple's Siri
• Autonomous cars such as Google Waymo

b) ML

• Product suggestions/recommendations
• Sales forecasting for various products
• Prediction of stock price
• Fraud analysis in banking and finance sector.

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