Today, the terms – Artificial Intelligence (AI) and Machine Learning (ML) are often used interchangeably. While these terms are related, they mean different things. There’s much confusion within the public and the media regarding what genuinely is artificial intelligence and what exactly is machine learning. Often the terms are being used as synonyms. In other cases, these are being used as discrete, parallel advancements, while others are taking advantage of the trend to create hype and excitement, as to increase sales and revenue. AI refers to the concept of machines mimicking human cognition. To reference artificial intelligence is to allude to machines performing tasks that only seemed plausible with human thinking and logic. Machine learning (ML) is a branch of artificial intelligence, and as defined by Computer Scientist and machine learning pioneer Tom M. Mitchell: “Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience.” — ML is one of the ways we expect to achieve AI. Machine learning relies on working with small to large datasets by examining and comparing the data to find common patterns and explore nuances. In this edition of the StartupJam, we map out how they all relate to one another, so your team can find the best candidates, best approaches and best frameworks as you embark upon your AI journey.