Machine learning (ML) is a complicated field. A “simple introduction” can seem like an impossible thing. In fact, it’s also a subfield of artificial intelligence (AI). The two have become closely entangled in recent years, though, so unweaving them can be a bit challenging.
If you ask experts what machine learning is, you’ll get
several different answers. It’s a field and a science and a form of data
analysis all in one. It’s the study and application of algorithms that operate
within computer systems to carry out tasks using patterns, probability and
insight.
A simplified definition is still confusing, so breaking ML
down is necessary to understand it. This introduction to machine learning will
explain the past, present and future of the field.
Machine learning is not new. It’s been around for about 60 years and has been evolving
ever since.
In 1955, researcher John McCarthy created the term
artificial intelligence in reference to the new, developing area of computer
science. The goal during this period was to develop computers that could
observe the world and turns those observations into the basis for making
decisions.
Four years later, in 1959, Arthur Samuel coined the term machine
learning. It signified computers that could learn without overt
programming.
The two fields are directly related, but machine learning
is a branch of AI. The two have become synonymous in recent years, but
nevertheless have differences. ML allows a system to learn from data, whereas
AI lets technology make decisions.
Machine learning has come a long way from where it
started. It shows up in many aspects of everyday lives and tasks. From laptops
to the workplace, machine learning is everywhere you look.
Some of these machines may seem simple, but a significant
amount of work goes into each process.
ML requires three elements: data, variables and
algorithms.
The data is the base from which it learns and analyzes. Then it puts that information into context with variables like stock prices, recommendations or demographics. The algorithms help it solve and learn, evolving with each decision and result.
This process provides you with things like product
recommendations on Amazon or automated customer support.
You may notice that ML involves a foundation of
probability. After analyzing the base data, it tries to predict what to suggest
or how to respond. For instance, if you just watched an action movie that stars
a certain actor on Netflix, the predictive features may recommend a similar
film with the same actor.
As you provide feedback, ML learns your preferences and
adapts.
Classifications of ML learning also play a role in
performance. They include supervised, unsupervised, reinforcement and
semi-supervised. These kinds of learning speak to how much guidance or
instruction the ML algorithms receive.
A supervised learning algorithm receives data and derives
rules or concepts from it. Unsupervised data entails the algorithm learning and
forging its own path, reformatting information as it progresses. Reinforcement
algorithms learn from feedback, both positive and negative.
For example, if you were to reject that Netflix movie
recommendation, the algorithm would relearn and reconfigure some of its
processes.
These innovations are evolving every day, but they are
only the beginning of what’s possible.
Machine learning is currently affecting the majority of
technology.
Speech and image recognition both feature ML algorithms.
Siri is a good example of how it learns preferences and adapts to them.
Decision making and reasoning are other key factors in ML and AI.
Experts and consumers have recently started to realize the
field’s full potential. Some fear, though, that ML and AI are going to take
over our lives, but other people assure that ML is changing the game and not necessarily
pushing humans out.
Regardless, ML has undeniably improved efficiency and
simplified everyday tasks, paving the road toward an innovative way of life.
