How can machine learning and AI be effective in your business?

Going from a computer that plays chess to self-driving cars, technology keeps reinventing itself. Today AI, machine learning and deep learning are everywhere!

Going from a computer that plays chess to self-driving cars, technology keeps reinventing itself. Today artificial intelligence (AI), machine learning (ML) and deep learning (DL) are present in all sectors and at all levels. These technologies are inevitable and make it possible to predict equipment failure or even via a chatbot to interact with customers for commercial acts, for example.

What is the history of artificial intelligence?

The origins of the concept artificial intelligence (AI) date back to antiquity and have existed for centuries. AI as we know it today has more recent roots. In 1950, mathematician Alan Turing published “Computing Machinery and Intelligence”, which answers the question “Can machines think?” “. This gave rise to the ” Turing-test which is a method testing the intelligence of a machine.

Since the time of Turing, advances in the field of AI have continued to be made, especially with the development of a computer program that is able to play chess against a human. Computer scientist Arthur Samuel created this program which is able to record all the opponent’s previous movements and create his strategy. In summary, the computer learns from past mistakes and plays at a higher, so-called intelligent level at every step. Samuel continually improved and developed this program and in 1952 coined the term machine learning (machine learning).

From the 1960s to the 1990s, AI achieved international recognition through the making of popular films such as “2001: A Space Odyssey” and “Star Wars” and “Electric Dreams”. Since that time, AI has been making great strides and rivaling the big screen. It was in the 2000s that AI officially became widespread, appearing everywhere. Although less well known to the general public, machine learning ML has also continued to evolve and is now one of the most common applications of AI.

Definition of artificial intelligence (AI)

There is no universally known definition of artificial intelligence, however, it can be defined as “a branch of computer science that focuses on the construction of intelligent machines capable of performing tasks that generally require intelligence. human”.

What is Narrow AI and General AI?

Narrow AI, also known asWeak AI, is used to describe AI systems that deal with a particular task that would have required human intelligence. Narrow AI is only used to accomplish a so-called limited task, or a single task at a time.

Narrow AI is the form we find everywhere because it is the most common form of artificial intelligence: from intelligent assistants to facial recognition systems or the recommendations that search engines make. or predictive maintenance models.

Regarding the general AI, also called the strong AI, reproduces and accomplishes the same intellectual tasks that the human being could do. According to TechTalks, she is able to mimic “common sense, basic knowledge,transfer learningabstraction and causality”. General AI is still theoretical in nature. That being said, some of its applications – such as emotional analysis – rely on natural language analysis to record emotion in a text – which represents the first stage of development of this technology.

Definition of machine learning?

Machine learning is part of a branch of AI that allows computers to learn, to use a multitude of data and toalgorithms structured to identify several models and then make predictions. The most telling example of ML is Google Maps, which analyzes several data models: past traffic and present traffic and which recommends the fastest route to its user.

Where Machine Learning gets really exciting is with the deep learning. It is a subset of machine learning that uses artificial neural networks – computer systems inspired by the human brain – to ingest and learn from structured and unstructured data. An example of deep learning in action is driverless cars, which inherently understand the rules of the road and can react in real time to things like a stop sign or a person crossing the street.

Deep learning is the most exciting example of machine learning. It is a subset of ML that will use a kind of artificial neural networks (more precisely several computer systems that are inspired by the brain), in order to integrate and learn data that is structured and unstructured. The simplest example of deep learning are connected, driverless cars, being created to understand the rules of the road and react with the elements in real time: a person crossing, a red light etc…

AI vs machine learning vs deep learning: what are the main differences?

Although deep learning AI and machine learning belong to the same family, they have different and unique qualities and applications.

Below, the diagram helps you to understand the main differences:

© Hitachi Solutions

We want to thank the author of this article for this incredible web content

How can machine learning and AI be effective in your business?

You can find our social media pages here and other related pages here