ML is technically a subset of AI.
Essentially, ML provides systems the ability to automatically learn
and improve from experience without being explicitly programmed;
focusing on the development of computer programs that can access data
and use it learn for themselves.
In other words, Machine
Learning Development
Service relies on processing big datasets, while
detecting trends and patterns within that data and essentially
“learning” about these trends along the way.
Like people, machines have the ability to
“learn,” acquiring knowledge and/or skills through their unique
experiences. For example, say you have an ML program with lots of
images of skin conditions, along with what those conditions mean.
The machine
learning algorithm
examines the images and identifies patterns, allowing it to analyze
and predict skin conditions in the future.
When the machine
learning algorithm
is given a new, unknown skin image, it will compare the pattern in
the current image to the pattern it learned from analyzing past
images. In the instance of a new skin condition, however, or if an
existing pattern of skin conditions changes, the algorithm will not
predict those conditions correctly.
This is because one must feed in all the
new data so that the algorithm can continue to predict skin
conditions accurately.
Unlike machine
learning services,
AI learns by acquiring and then applying knowledge. The goal of AI is
to find the most optimal solution possible, by training computers a
response mechanism equal to or better than that of a human being.
In the instance of adaptation in new
scenarios, Artificial
Intelligence services
is perhaps the most ideal.
Let’s take a simple video game, for
example, where the goal is to move through a minefield using a
self-driving car. Initially, the car does not know which path to take
in order to avoid the landmines.
After enough simulated runs, large
amounts of data are generated concluding which path works and which
paths do not. When we feed this data to the machine
learning algorithm, it is able to learn from the past driving
experience and navigate the car safely.
But, what if the location of the
landmines has changed? The machine
learning
algorithm does not know these individual landmines exist,
rather, it only exclusively knows the all it knows the pattern
resulting from the initial data.
Unless we feed the algorithm the new data
so it can continue learning, it will continue to guide along that
(now incorrect) path.
Enter, AI – capable of analyzing new
data in an algorithm to determine multiple factors; answering
questions like, why did the paths change? Which direction is most
ideal, given the new circumstances, and where are the new hot-spots?
It will then codify rules of those hot-spots where the land mines
exist.
Slowly, AI will begin to avoid them
altogether by following the new trails – just like people, learning
and adapting to new boundaries and environmental challenges.
The
Future is Now with AI and ML
So, by now, you’ve learned the basic
differentiating factors between ML and AI. Machine learning uses past
experiences to look for learned patterns, while Artificial
Intelligence services
uses the experiences to acquire knowledge and skills, then applies
that knowledge to new scenarios.
It’s clear that both AI and machine
learning have valuable business
applications, empowering companies to respond quickly and
accurately to changes in customer behavior and solve critical
business problems.
As the adoption of AI and ML become more
commonplace, namely predictive analytics and data science will see a
massive uptake in virtually all industries across the marketplace.
For
More Information, Please visit our website:
No comments:
Post a Comment