We listen to the possibilities AI (Artificial Intelligence) can bring to us each day. We are already using some of its applications, such as face recognition (face tag) in Facebook photos, Speech Recognition for various applications and Autonomous cars, to name a few. AI is in the beginning stage for some of us, while others think we are ready to reap its benefits. On one side, Researchers invent new algorithms or optimize them to use these algorithms on big data each day. On the other end, the management and the end-users equate AI possibilities with science fiction movies such as C3PO from Star Wars or Terminator posing an existential threat to humanity. As we are riding the fourth industrial revolution driven by Artificial Intelligence, it is paramount to understand what AI can and can not deliver.
This series of articles will cover different aspects of building an AI-driven business. Let us first understand the most confusing core terms of this domain; Artificial Intelligence vs Data Science vs Machine Learning. These terms overlap and sometimes lead to confusion as we use them interchangeably despite having slightly different meanings.
Artificial Intelligence: We can define AI as the intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals. To simplify, we can think of an AI-powered machine as a device that can perceive its environment and take action on its own to achieve its goals. These devices/machines mimic the human mind’s cognitive function (learning and problem-solving) as its core capability. The best example of such a device is an Autonomous vehicle.
Data Science: Data Science is the field of study where organizations study the data they have captured to drive business value out of it. Data science is all about data at its core, including data cleaning, understanding, engineering, visualizing, finding patterns, and extracting business value. When we examine this data, we get valuable information about business or market patterns that helps the business have the edge over competitors. This data can be in structured or unstructured form.
Machine Learning: We can define machine learning as studying computer algorithms that automatically improve through the experience without explicit programming. Machine learning algorithms take data to learn patterns and make predictions without explicitly being programmed—for example, Decision Tree and CNN.
Most of the recent advancements and practical applications such as Face Detection and voice understanding arise from the development in the field of machine learning algorithms together with the cheaper storage and processing power provided by cloud technologies.
In the following article, we will look at the framework for identifying good use cases to apply machine learning and drive business value. To make AI successful, this is an essential part of selecting functionalities that can genuinely transform a business in one way or other.
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