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MACHINE LEARNING AI

Machine learning (ML) refers to a wide range of techniques which automate the learning process
of algorithms. In ML, the algorithm usually improves by training itself on data. This is why Machine
Learning AI is also called data-driven AI.

Rather than having their knowledge provided
by humans in the form of hard-programmed
rules, Machine Learning systems generate
their own rules. For Machine Learning systems,
humans provide the system training data. By
running a human-generated algorithm on the
training dataset, the Machine Learning system
generates the rules such that it can receive

input x and provide correct output y.
Data is the main raw material out of which high-performing Machine Learning AI systems are built.
The quality, quantity, representation, and diversity of data will directly impact the operational
performance of the ML system. Hence, having the right data tends to be the key. While it is true

that Machine Learning systems program themselves, humans are still critical in guiding this learning
process. Humans choose algorithms and datasets, format data, set learning parameters, and
troubleshoot problems.

FACTORS ENCOURAGING ML
The factors that make Machine Learning AI the more relevant approach towards AI are:

Massive Datasets
Machine Learning algorithms tend to require large quantities of training data in order to produce
high performance AI models. When Machine Learning was first developed decades ago, there were
very few applications where sufficiently large training data was available to build high performance
systems. Today, an enormous number of computers and digital devices and sensors are connected

to the internet, where they are constantly producing
and storing large volumes of data, whether in the
form of text, numbers, images, audio, or other
sensor data files. Of course, more data only helps
if the data is relevant to your desired application. In
general, training data needs to match the real-world
operational data very, very closely to train a high-

performing AI model.
Increased Computing Power
Machine Learning AI systems require a lot of computing power to process and store the large volumes
of datasets. During the early 21st century, computing hardware started getting powerful enough and
cheap enough that it was possible to run Machine Learning algorithms on massive datasets using
commodity hardware.

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