Big Data Is Not Artificial Intelligence
Oftentimes, in discussions about big data, I hear other terminology used and grouped in to how big data will change the way of business. While it may be true, the phrasing used is often incorrect within the context it is used and for what purpose. Normally, this would not be a big deal, but I think it is important to distinguish the following terms and explore how they relate to one another: big data, artificial intelligence, machine learning, and deep learning.
Big Data: We should have a pretty good concept of big data by now from the progressing posts. Big data refers to the massive data sets brought in by new technologies and the Internet of Things (IoT). Typically, these data sets are very large and complex, so different tools are used in data mining and companies have quickly emerged that specialize in housing, mining, and modeling big data. Competing in big data as a business is sometimes colloquially called “the game of picks and shovels” to capture how data is drilled down, examined, and resurfaced with relevant information that can drive firms to make more substantiated decisions. It is important to note that people are the data scientists that make bid data analytics possible and useful in business application.
Artificial Intelligence: AI is concept that includes when computers can begin to process data, with little to no human oversight, to make connections in complex relationships between data sets. AI technologies can preform some tasks faster and with more accuracy than human can. It is a popular topic in science fiction and is starting to confirms it’s place in today’s world as well. But what mechanisms can AI use that drives its functionality? This is where we get into machine learning.
Machine Learning: In its most basic form, machine learning deals with using algorithms to parse data, learn from it, and use it to make a prediction or a conclusion. In this iteration, machines are trained using large amounts of data to accomplish a particular task and the machine will learn over time how to complete the task as it increases in complexity. This concept was derived from that same AI crowd and has evolved to include logic programming and network clustering to achieve the general structure of AI. Machine learning approaches have served as the backbone for AI and are continually used for deep learning methodologies.
Deep Learning: Deep learning is another algorithmic approach in which the machine will take unstructured big data and use more cognitive-based computing to learn more over time, based on experience. Deep learning has certainly enabled more practical applications of machine learning and made great contributions to the overall field of AI.
As the different capabilities are explored, we see the wave of AI quickly following big data. For the first time, companies can use big data to position products and respond to customer needs. They know their methods are working because they have data that proves it. But, if companies want to stay competitive as data growth continues to skyrocket, they will need to scale up a platform using some type of artificial intelligence.
AI will help add a layer of understanding to big data to complete extremely complex analytical task much faster than human.
Big data is not artificial intelligence. Big data is used in artificial intelligence. Data labs will continue to develop amazing technologies using these concepts, but it will take the imaginative nature of people in real-world applications to help turn those theories that will change our professional and personal lives for the better.
