The bottom line is that we’re faced with an ever increasing amount of data, and
our ability to effectively store and process it is failing to keep up. This is the
world of Big Data and it covers the gamut of collecting, storing, cleaning, orga-
nizing, enriching, analyzing and visualizing all this information.
Defining Artificial Intelligence
AI is intelligence exhibited by machines, and is therefore sometimes referred to
as Machine Intelligence (MI). You can contrast this with the natural intelligence
that is exhibited by humans or other organisms. Intelligence can be looked at
from a factual standpoint, and facts can be represented by simple “if:then”
statements. Collect enough of those and you can make a machine look fairly
intelligent.
But another aspect of intelligence is learning: the ability to acquire new or
modify existing factual knowledge, based on new information. This is where it
starts to get interesting. We break down the techniques that enable machines
to learn into two groups — Machine Learning (ML) and Deep Learning (DL).
Defining Machine Learning
We look at ML as computational statistics, or using data to create mathemati-
cal models that are useful for making predictions. Part of a data set is used to
see which models seem to fit best, while the remaining data is used to test the
predictive capability of the model. Once the fit is deemed adequate, new data
can be analyzed with the model and the results can be reasonably acted upon.
Great applications for this approach include predictive maintenance and
detection of security anomalies.
Defining Deep Learning
DL, or hier