The world of AI is not easy to understand. There are dozens of terms that are thrown
around and it can be hard to know what something means. I'm going to try to help
people who may not have a strong AI background understand what these terms
mean and how they fit together. I am going to cover topics like:
Artificial Neural Networks, Deep Learning, Convolutional Neural Networks, Recurrent
Neural Networks, Natural Language Processing, and, hopefully, Singularity!
This is going to be a big project and you are going to see every wild theory created by
people on the internet which may have the potential to not make sense at all.
Deep learning is a type of machine learning and artificial intelligence (AI) that
mimics how humans acquire specific types of knowledge. Deep learning is a critical
component of data science, which also includes statistics and predictive modeling.
Deep learning is extremely beneficial to data scientists who are tasked with
collecting, analyzing, and interpreting large amounts of data; deep learning speeds
up and simplifies this process.
Consider a toddler whose first word is "water." By pointing to objects and saying the
word "water," the toddler learns what water is and is not. "Yes, that is water," or "No,
that is not water," the parent says. As the toddler continues to point to objects, he
becomes more aware of the characteristics that all dogs share. Without realizing it,
the toddler clarifies a complex abstraction of the concept of water by constructing a
hierarchy in which each level of abstraction is created with knowledge gained from
the previous layer of the hierarchy.
Working on Deep Learning
Computer programs that use deep learning go through much the same process as
the toddler learning to identify water. Each algorithm in the hierarchy applies a
nonlinear transformation to its input and uses what it learns to create a statistical
model as output. Iterations continue until the output has reached an acceptable
level of accuracy. The number of processing layers through which data must pass is
what inspired the label deep.
The learning process in typical machine learning is supervised, and the programmer
must be exceedingly detailed when instructing the computer on what types of
things it should look for to determine whether an image contains or does not
contain a dog. This is a time-consuming procedure known as feature extraction, and
the computer's success rate is totally dependent on the programmer's ability to
precisely define a feature set for. The benefit of deep learning is that the software
builds the feature set without supervision. Unsupervised learning is not only faster,
but it is also more accurate in most cases.