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Artificial Intelligence and Big Data: A Perfect Match

Artificial Intelligence has been around for decades. However, recently with the arrival of "Big Data," it's been getting more attention. For reference, Wikipedia says this with respect to Artificial Intelligence:

"In computer science, the field of Artificial Intelligence research defines itself as the study of 'intelligent agents AI and Big Data: A Perfect Match': any device that perceives its environment and takes actions that boost its chance of success at some goal."

And they define Big Data as follows:

"Big data is a term for that are so vast or complex that traditional data processing application software is inadequate to deal with them."

Computers have turned out to be so powerful that we are able to now store millions of records per second. Unfortunately, our capacity to analyze that data can be an obstruction. It is difficult to keep up using traditional methods.

AI and Big Data: A Perfect Match

So why has Big Data led attention to AI? The answer is just that Artificial Intelligence, a focus area for any leading AI Development Company, can deal with vast and complex data sets in ways that traditional data processing-or humans-cannot.

Let's use a banking application for a reference. The app streams a large number of records a second, and we want it to send an alert if an irregular action happens, like a fraud condition or theft. In this circumstance, people can't possibly process or analyze more than a small part of this volume of data, second-by-second, to prevent or stop a crime. Even with several people tasked with analyzing possible fraud conditions, the sheer volume of data basically overpowers human decision-making capabilities.

Then what about traditional data processing systems? The issue is that they are algorithmic — bound to pursue the same logic again and again. When searching for anomalies — not something we expect — adaptability is required, something traditional approaches are not good at.

Now enter AI. These systems work with fuzziness. They predict. They will think about a way but can abandon it if new data negates a line of reasoning — then start looking at a new direction. Since AI systems get smarter as more data is given them, they are appropriate for identifying anomalies over time.

Let's now look at some of the AI technologies utilized with Big Data. Examples of practical business use for each technology will also be given.

Artificial Intelligence Technologies Being Used with Big Data


Extrapolation is the way toward evaluating, beyond the original observation range, the value of a variable dependent on its relationship with other variables. For instance, let's assume some data is exhibiting a pattern. Executives at the organization want to know: where will the company be in three months if this pattern continues? Extrapolation can decide this. Remember that not all patterns are linear. Linear patterns are simple; a simple line chart will suffice. Non-linear patterns are much more included and that is where extrapolation functions help. These algorithms depend on polynomial, conic, or curve equations.

Anomaly Detection

Anomaly detection is also referred to as outlier detection. It includes identifying items, events or observations which do not obey an expected pattern, or other items in a dataset. Anomaly detection can find events such as bank fraud (an application of AI previously mentioned). It also is appropriate to several other domains including (but not limited to): fault detection, system wellbeing monitoring, sensor networks, and eco-system disturbances.

Bayes Theorem

In probability theory and insights, Bayes Theorem portrays the probability of an event based on prior knowledge of conditions that may be related to the event. It's a method for predicting the future dependent on past events. For instance, let's assume a company wishes to know which customers they are a possibility of losing (churn). Utilizing Bayes, historical data of dissatisfied customers can be gathered and used to predict customers likely to be lost in the future. This is a fabulous fit for Big Data because as more historical data is fed to a Bayes algorithm, the more perfect its predictive results become.

Automating Computationally Intensive Human Behavior

In some circumstances, it might be possible for a human being to analyze a lot of data, but it proves exhausting after some time. AI can help. Principle-based systems can be utilized to extract, store, and manipulate knowledge from humans for the purpose of interpreting data in useful ways. Practically speaking, rules are gotten from human experiences and represented as a set of "if-then" statements that use a set of the declaration, on which rules on how to act upon those declarations are created. Principle-based systems can be used to create software that provides answers to a problem instead of a human expert. These systems might also be called expert systems. Think about a company that has a human expert capable of analyzing data for a particular objective. However, the task is monotonous and repetitive. A principle-based system can catch and automate this expertise.

Graph Theory

In mathematics, graph theory is the study of mathematical structures used to display pairwise relations between objects. A graph in this context is made up of vertices, nodes, or points connected by edges, arcs, or lines, and can be quite complex and broad. With graph theory, insights into relationships between data can be successfully obtained. For instance, consider a complex network of computers. Graph theory can provide insights into how an obstruction in the network will cause other problems as well as the root cause of a particular obstruction.

Pattern Recognition

As its name suggests, pattern recognition is used to detect patterns and consistency in data and is a type of machine learning. Pattern recognition systems are instructed with training data, and this procedure is called supervised learning. They also can be utilized to find previously unknown data patterns with a procedure called unsupervised learning. Unlike abnormality detection, which screens potential abnormalities depend on a single type of data, pattern recognition can discover previously unknown patterns in several pieces of data and take into consideration the patterns (or relationships) among the data. A company (of any industry) may be interested in knowing when something out of the ordinary start to occur, such as if customers all of a sudden begin purchasing one item to go with another item. This pattern might bear some importance to a business.


In summary, AI is an approach to explore and gather insights into the world of Big Data.

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