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