Introduction
Artificial Intelligence is a powerful and often misunderstood field. But what is Artificial Intelligence? How does it relate to Machine Learning, Deep Learning, and Data Science — terms that you may have heard with increasing frequency. This article will demystify these terms and is the first in a series we’ll be releasing to demonstrate the power of AI in the wider economy and to help boost your business in particular.
What’s the difference between Artificial Intelligence and Machine Learning? What is Deep Learning?
Artificial Intelligence (AI) and Machine Learning (ML) are often used interchangeably. However, there is a difference. AI is any program that has the ability to make conclusions. ML is a subset of AI, encompassing programs that set parameters and “learn” without being programmed with explicit instructions. Deep Learning is an approach within Machine Learning that utilizes an architecture called a neural network to “learn” from large amounts of data.
To help visualize these relationships let’s look at a diagram:
How Does AI Help Me?
AI can process information beyond human quantities and speeds. A person is fully able to understand and anticipate the needs of 10 or so customers. AI can be made to understand and anticipate the purchasing patterns of 10 million people. AI has the power to free us from repetitive tasks and provide insights on an otherwise impossible scale. In future posts, we will explore and expand on what AI provides the world currently and what it is capable of providing in the future.
AI and Data Science.
Data science is a broad term that applies to combining programming, statistics, and business knowledge to extract insights and information from data. This usually includes using ML to produce insights or analyze large quantities of data.
What is Data Engineering and Data Analytics?
You may have probably heard the terms Data Analytics or Data Engineering and might be wondering, how do these differ from data science? These terms refer to aspects or steps in the data science process.
Data science is often used to refer to the entire operation of a project, starting with data analysis, moving through data engineering and beyond, such as creating a statistical model or building a machine learning algorithm to create predictions. Data analytics is often used to refer to the early steps of a project including cleaning data, refining the question into a form that a computer can understand, and reviewing trends in the data.
Data engineering describes a step after analyzing data involving developing and manipulating data into forms that can be used in ML programs, as well as performing additional operations needed for data analysis.
Overall, data science is focused on establishing potential trends based on existing data, as well as realizing better ways to analyze and model data to provide insights that are not obvious from direct observation.
It’s important to forget about viewing data science, data analytics and data engineering as separate pieces. Instead they should be viewed as parts of a whole that aid understanding and analysis of data.
Conclusion
So, what does this all mean? AI can provide your business with powerful tools for a variety of business applications. Understanding the concepts of AI / ML, data science, and data analytics is the first step in any business’s journey to use data in order to drive business value. In our next post we cover the different types of machine learning and their corresponding use cases. You can find that post here.
We at Lumio are giving businesses data superpowers. If you have questions about AI, data science, and how it can apply to your business get in touch with us at hello@lumio.ai, or by visiting our website at lumio.ai.
