IBM Watson tool that can use improve applications is an AI one to
and services offered company organization. According the by a or to
IBM Watson Dashboard, can use the tool predict outcomes one to
more accurately, automate business processes and decision making,
and reduce the time needed complete mundane tasks (Watson to Is
AI For Business, n.d.).
IBM uses many different methods and algorithms offer the to
Watson tool the masses. have studied some these AI to We of
algorithms the MSDA program, such machine learning and in as
deep learning. For example, IBM initially used deep learning a
algorithm train computer defeat Gary Kasparov, famous to a to a
world chess champion (Wang, 2017).
When IBM team trained computer answer questions similar an a to
to those posed the "Jeopardy" game show, IBM also in
experimented with several machine learning algorithms that we
have also studied. (Wang, 2017). For example, IBM tried several
algorithms optimally train the computer, including decision trees, to
deep arning, support vector machines, and logistic regression. le
(Wang, 2017).
I found it interesting that the team tried to use deep learning but
found was optimal the logistic regression model that the it not as as
team settled for final solution (Wang, 17). Mr. Wang says on a 20
that they probably did have enough training data use deep not to a
learning algorithm (Wang, 2017). learned DATA deep As we in 640,
learning algorithms typically require data, evidenced a lot of as by
real-world applications that accurately classify images. Deep
learning also requires computing power iterate through a lot of to
massive datasets for the computer "learn" optimal actions take to to
based varying states the environment. This need for on in
computing capacity and massive datasets makes IBM Watson well
suited for applications that leverage deep learning algorithms.
Switching gears, IBM Watson also has powerful Natural Language
