on the internet, we are using natural language processing. Translation apps
must take our voice or our text and analyze the sentence structure to make
meaning. When you type up a paper or a word document,
your word
processor uses natural language processing to sift for grammatical errors
and spelling mistakes.
Despite its popularity, it’s a very complex field of computer science and
artificial intelligence. Being able to interpret
the meaning of the alphabet
arranged in a virtually infinite amount of combinations requires huge
amounts of data for the computer to understand what you are writing or
saying.
In addition to being able to understand what we say and write; computers
can also make strategic decisions based on what they’ve learned from data
in the 1990s. IBM created a computer called Deep Blue that defeated a
world master at chess. It was the first computer to be able to perform such a
task. Because of the simplicity of the rules in chess, computer scientists at
IBM chose to train their computer to play.
But there are thousands of
potential moves and arrangements that the pieces can take once the game
has started. The computer had to learn this using data.
What makes machine learning unique to other types of computer science is
the ability of models to change their methods over time to suit new data.
What separates a machine learning model from a regular line of explicit
code is that machine learning will take in new data and improve itself. It
can also perform tasks which require planning and contain strategic
components. The Deep Blue computer had to be adept at analyzing possible
sequences
of moves, rather than just one move at a time.
The same technologies that enabled a computer to beat a world chess
champion are now making it possible for self-driving vehicles to get a
passenger safely from point A to point B. Compared to the relative
simplicity
of chess, self-driving cars must plan and interpret hundreds of
variables to keep its passenger safe. It goes beyond the two-dimensional
data analysis employed by machines playing chess. Self-driving cars must
master multidimensional data analyses to navigate the everchanging
environment on the road.
The machine learns through trial and error, repeating the task over and over
and learning from failures and successes. These experiences are introduced
as data, and over time, the machine will know its probability of failure or
success for every possible move.
Machine learning models interpret potential states in the environment. For
an
algorithm that plays chess, this is all its possible moves and all its
competitor's possible moves. The algorithm is an amalgamation of goals
and potential actions. By using this data, it creates
a plan to optimize the
likelihood of achieving the goals. It also enables computers to do self-
learning without specific directions through programming.
Trying to get a computer to do all these things sounds simpler in theory than
it is in practice. Most of the functions we’ve just mentioned; from checkers
to self-driving cars require advanced statistical techniques to optimize the
outcome and train a machine that knows how to ‘win’ with a high level of
accuracy.
Machine learning falls under the larger umbrella of artificial intelligence.
Artificial intelligence is a branch of computer
science that includes