At a time when technology is constantly evolving, it is important to distinguish between terms such as Artificial Intelligence (AI) and Machine Learning (ML).
Although they are often used interchangeably, there are several differences between the two technologies.
In essence, Artificial Intelligence is based on pre-programmed knowledge.
Instead, Machine Learning is based on the experiences and information gained from a continuous learning process.
What does it mean? Let’s delve into the main aspects of how these two new technologies work.
The characteristics of artificial intelligence
Artificial Intelligence is a branch of computer science that aims to create machines that simulate human intelligence. It is also often referred to by the acronym AI, from the English Artificial Intelligence.
AI is used to train machines to answer questions and solve problems autonomously, without having to be programmed for each specific task.
It has a type of learning based on data collection, which is used at the appropriate time to carry out specific operations.
An example of an artificial intelligence system is thevirtual assistant such as Apple’s Siri, or Amazon’s Alexa.
The virtual assistant is programmed to provide answers to questions posed by humans in natural language.
To respond, it uses previously stored information from datasets and databases.
Machine learning algorithms
Machine Learning is a sub-discipline of AI. It focuses on the development of algorithms that enable machines to constantly learn and improve their results, with continuous feedback (reinforcement learning).
Machine Learning thus mimics the neural networks of humans. We can call it a form of deep machine learning (not coincidentally, it is also referred to as deep learning).
Through it, computer applications are able to learn from data and experience.
Not only that, the ML uses unsupervised learning.
That is, machines are programmed to learn continuously, without receiving additional human input, forming what is called an artificial neural network.
A form of learning partly similar to human learning.
One Machine Learning model could be a facial recognition system.
In fact, the facial recognition program learns from the images captured during the identification process and uses them to improve its accuracy in recognizing people.
Fields of application in marketing
The combination of artificial intelligence with machine learning today is changing the daily lives of companies and people.
Above all,natural language processing represents a real revolution in the way humans interact with machines.
By formulating simple written or oral questions, AI today can do big data analysis, write content, make images, etc.
In marketing, ML and AI systems succeed in producing meaningful and original content. They can be used to carry out certain steps of these processes more quickly:
- Drafting an editorial plan for social
- outline for a blog article
- ladder for a landing page
- Construction of an advertising claim;
- realistic images;
- fancy graphics and images, etc.
In two previous articles on the Factory blog, we have discussed AI-generated content and also Google’s position on it.
The accuracy of the AI generated content and its quality depends on the accuracy of the commands that are given by the human operator.
When the AI phenomenon exploded in marketing strategies, many were afraid that the machine would replace humans.
Today, more and more marketers, copywriters and graphic designers are learning to apply this technology to make their work more efficient without sacrificing quality and originality.
After all, marketing is closely related to the emotional sphere: a field where, to date, machines are unable to enter.
Do you want to attract new potential customers?
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