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Big Data: Definition, Examples, and Types

Big Data: Definition, Examples, and Types

How big is really big?
por Devin Pickell
CX Tech: Artificial and Intelligent

CX Tech: Artificial and Intelligent

Of all technologies that impact customer experience (CX), few have as immediate or obvious an effect as artificial intelligence (AI).
por Marshall Lager
What Is Statistical Modeling? When and Where to Use It

What Is Statistical Modeling? When and Where to Use It

You can interpret data in multiple ways.
por Sagar Joshi
An Exhaustive History of Robots and Robotics

An Exhaustive History of Robots and Robotics

The idea of artificial beings is nothing new to human civilization.
por Piper Thomson
80+ Cyber Security Terms (A-Z): A Complete Glossary

80+ Cyber Security Terms (A-Z): A Complete Glossary

Those who believe using a long password is all they need to keep their data secure have a lot to learn.
por Mara Calvello
AI Trends in 2019: MLaaS, RPA, and Big Data

AI Trends in 2019: MLaaS, RPA, and Big Data

Over the past year, there have been a number of trends that have impacted digital transformation, including artificial intelligence (AI), big data, and robotic process automation (RPA), among many others.
por Rob Light
The Data Toolbox: The Expanding Domain of AI & Analytics

The Data Toolbox: The Expanding Domain of AI & Analytics

Killer robots. Threatening humanoids. Robo-apocalypses and evil robots taking over the world. (Just kidding.)
por Matthew Miller
What Is Predictive Analytics? Examples, Model Types, and Uses

What Is Predictive Analytics? Examples, Model Types, and Uses

Every business wants to increase their bottom line.
por Mara Calvello
What Is Data Mining? How It Works, Techniques, and Examples

What Is Data Mining? How It Works, Techniques, and Examples

Brittany Kaiser, former Director of Business Development for Cambridge Analytica, stated in Netflix’s The Great Hack that data is now more valuable than oil.
por Mara Calvello
Parallel Processing

Parallel Processing

What is parallel processing? Parallel processing is defined as an architecture where processes are split into separate parts and each part is run simultaneously. By running the processes on multiple processor cores instead of a single one, the time taken to execute tasks is much lower. The main goal of parallel computing is to ensure that complex tasks are broken into simpler steps for easier processing driving better performance and problem-solving capabilities.
por Preethica Furtado