Artificial Intelligence and Machine Learning Basics

Introduction

In recent years, the terms of artificial intelligence and machine learning have started to appear frequently in news and technology websites.

Often the two are used as synonyms, but many experts claim to have subtle but real differences.

And of course, experts sometimes disagree with each other about what these differences are.

In general, however, two things seem clear: first, the term artificial intelligence (AI) is older than the term machine learning (ML),

and second,

most people consider learning as a subset of artificial intelligence.

Artificial intelligence vs. Machine Learning

Although artificial intelligence is defined in many ways,

the most widely accepted definition is “the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence,

such as learning, problem-solving and model recognition.

“In essence, it is the idea that machines can possess intelligence.

The core of a system based on Artificial Intelligence is its model.

A model is nothing but a program that improves its knowledge through a learning process by making observations on its environment.

This type of learning-based model is grouped under Supervised Learning.

There are other models that fall into the category of unsupervised learning models.

Even the phrase “machine learning” dates back to the middle of the last century.

In 1959, Arthur Samuel defined ML as “the ability to learn without being explicitly programmed”.

And he went on to create a computer control application that was one of the first programs to learn from his mistakes and improve his performance over time.

Like AI research, ML has been out of fashion for a long time but has become popular again when the concept of data mining started to take off in the 1990s.

Data mining uses algorithms to search for models in a given set of information.

ML does the same thing but then goes one step further –

changes the behavior of your program based on what you learn.

An ML application that has become very popular recently is image recognition.

These applications must first be trained –

In other words, humans have to look at a bunch of images and tell the system what’s in the picture.

After thousands and thousands of repetitions, the software learns which pixel patterns are generally associated with horses,

dogs, cats, flowers, trees, houses, etc.

And it can make a good hypothesis about the content of the images.

Many web-based companies also use ML to power their recommendation engines.

For example,

when Facebook decides what to show in your newsfeed when Amazon highlights the products you may want to purchase and when Netflix suggests the movies you may want to watch,

all these recommendations are based on forecasts that derive from models in their existing data.

Frontiers of Artificial Intelligence and Automatic Learning:

Deep Learning, Neural Networks, and Cognitive Calculus

Of course, “ML” and “AI” are not the only terms associated with this field of computing.

IBM frequently uses the term “cognitive calculation”,

which is more or less synonymous with artificial intelligence.

However, some of the other terms have really unique meanings.

For example, an artificial neural network or neural network is a system that is designed to process information in ways similar to the ways in which biological brains work.

Things can get confused because neural networks tend to ,

be particularly suited to machine learning, so these two terms are sometimes merged.

Furthermore, neural networks provide the foundation for deep learning, which is a special kind of machine learning.

In-depth learning uses a certain set of machine learning algorithms executed on multiple levels.

It is made possible, in part, by systems that use GPUs to process a large amount of data at the same time.

If you are confused by all these different terms, you are not alone.

Computer scientists continue to discuss their exact definitions and will probably still do so for some time.

And as companies continue to pour money into artificial intelligence and research into machine learning,

it is likely that some terms still emerge to add even more complexity to problems.

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