Each artificial intelligence researcher has their way of understanding the challenges and opportunities in the field. But generally, they split into two distinct approaches: symbolic AI and connectionist AI. In symbolic artificial Intelligence, mechanisms effect transformations using symbols, letters, numbers, or words.
They thus simulate the logical reasoning behind the languages in which human beings communicate with one another. The functioning of our neurons inspires the connectionist AI approach. Therefore, simulating the mechanisms of the human brain.
An example of connectionist approach technology is deep learning, the ability of a machine to acquire deep understanding by mimicking the brain’s neural network—some even talk of a third approach, evolutionary AI, which uses algorithms inspired by natural evolution.
That is the simulation of concepts such as environment, phenotype, genotype, perpetuation, selection, and death in artificial environments. You saw in the previous topic a step-by-step to define the professional objective. But there are a few tricks that get you closer to the ideal build.
As you know, it is through the curriculum that companies can select professionals with profiles that are most consistent with the vacancies offered. Therefore, the candidate needs to fill out the document to please recruiters and put him at an advantage over competitors.
Companies’ first value in candidates’ resumes is the certainty of what they are looking for in their careers. And this is exposed in the topic of the professional objective. In other words, this part is essential when recruiters choose which professionals will go to the next phase of the selection process.
Companies expect candidates to specify their expectations in the profession and the organization to which they are sending the resume. Although the space seems small, you need to be transparent and show where you want to go.
With a well-defined objective, your resume will be well evaluated by the HR professionals of the company you want to be part of. Then, the next step is to get away from the common mistakes, which we’ll list below.
Each artificial intelligence researcher has their way of understanding the challenges and opportunities in the field.
But generally, they split into two distinct approaches: symbolic AI and connectionist AI. In symbolic artificial Intelligence, mechanisms effect transformations using symbols, letters, numbers, or words.
They thus simulate the logical reasoning behind the languages in which human beings communicate with one another.
The functioning of our neurons inspires the connectionist AI approach. Therefore, simulating the mechanisms of the human brain.
An example of connectionist approach technology is deep learning, the ability of a machine to acquire deep understanding by mimicking the brain’s neural network.
Some even talk of a third approach, evolutionary AI, which uses algorithms inspired by natural evolution.
That is the simulation of concepts such as environment, phenotype, genotype, perpetuation, selection, and death in artificial environments.
Types of Artificial Intelligence
As the concept of artificial intelligence became more widespread, new scholars began to focus on it. Thus, different perspectives also emerged.
One of these contributions was the differentiation between two types of AI, strong and weak, which we detail below:
Strong Artificial Intelligence
Also known as self-aware, Strong Artificial Intelligence such as conversational AI assistant emulates human reasoning with such perfection that it can solve situations faster and more assertively than a person. No wonder it is a very controversial issue, as many understand that it is a technology that is enough to be an alternative to the most qualified workforce of companies. Other ethical dilemmas surround this subject, reminiscent of fiction films such as “Me, Robot.”
Examples of Strong Artificial Intelligence are those that make use of machine learning and deep learning techniques.
Weak Artificial Intelligence
Weak Artificial Intelligence, as the name suggests, does not have such great power to imitate human reasoning cognitively. In practice, it can collaborate in processing a large volume of information and even reporting, but without the self-awareness of the former kind.
The big thing is that a weak AI can develop into the strong stage, even though most advances are in the first rank. An example of Weak Artificial Intelligence is Natural Language Processing.
Within the fields of Weak Artificial Intelligence is Natural Language Processing, which we have just seen. In this case, the machines use software and algorithms created for specific purposes, such as simulating a human conversation.
Currently, most of the advances considered relevant to the area have been made in Weak Artificial Intelligence, with little progress being made in Strong AI.
Examples of Application of Artificial Intelligence
Artificial Intelligence: types, benefits, examples, challenges, and more
Examples of Application of Artificial Intelligence
Artificial Intelligence such as a conversational AI assistant is no longer a thing of the future. It is already applied in various segments of the economy.
