Each artificial intelligence researcher has their own way of understanding the challenges and opportunities in the field. But generally, they fall into two distinct approaches:symbolic AI and connectionist AI. In symbolic artificial intelligence, mechanisms perform transformations using symbols, letters, numbers or words.
They thus simulate the logical reasoning behind the languages in which human beings communicate with each other. The functioning of our neurons inspires the connectionist approach of AI. Therefore, simulating the mechanisms of the human brain.
An example of a connectionist approach technology is deep learning, the ability of a machine to gain deep understanding by mimicking the neural network of the brain. Some even speak of a third approach, evolutionary AI, which uses algorithms inspired by natural evolution.
It is the simulation of concepts such as environment, phenotype, genotype, perpetuation, selection and death in artificial environments. You saw in the previous section 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 course that companies can select the professionals whose profiles are the most consistent with the job offers offered. Therefore, the candidate must complete the document to please the recruiters and give him an advantage over his competitors.
The number one business value in candidate resumes is certainty of what they are looking for in their career. And this is exposed in the theme of the professional objective. In other words, this part is essential when recruiters choose the professionals who will move on 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 CV will be well evaluated by the HR professionals of the company in which you wish to be part. Then the next step is to move away from common mistakes, which we will list below.
Each artificial intelligence researcher has their own way of understanding the challenges and opportunities in the field.
But generally, they fall into two distinct approaches:symbolic AI and connectionist AI. In symbolic artificial intelligence, mechanisms perform transformations using symbols, letters, numbers or words.
They thus simulate the logical reasoning behind the languages in which human beings communicate with each other.
The functioning of our neurons inspires the connectionist approach of AI. Therefore, simulating the mechanisms of the human brain.
An example of a connectionist approach technology is deep learning, the ability of a machine to gain deep understanding by mimicking the neural network of the brain.
Some even speak of a third approach, evolutionary AI, which uses algorithms inspired by natural evolution.
It 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 spread, new researchers began to take an interest in it. Thus, different perspectives also emerged.
One such contribution has been the differentiation between two types of AI, strong and weak, which we detail below:
Strong Artificial Intelligence
Also known as strong artificial intelligence, such as conversational AI assistant emulates human reasoning with such perfection that it can resolve situations faster and more confidently than a person. No wonder this is a hotly debated issue, as many understand that it is a technology that is enough to be an alternative to the most skilled workforce in companies. Other ethical dilemmas surround this subject, reminiscent of fictional films such as "I, Robot".
Examples of strong artificial intelligence are those that use machine learning and deep learning techniques.
Weak artificial intelligence
Weak artificial intelligence, as its name suggests, does not have such great power to cognitively mimic human reasoning. In practice, he can collaborate in the processing of a large volume of information and even in reporting, but without the self-awareness of the first type.
The big problem is that a weak AI can progress to the strong stage, even if most of the advancements are in the first rank. An example of weak artificial intelligence is natural language processing.
In the areas 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 progress considered relevant to the field has been made in weak artificial intelligence, with little progress made in strong AI.
Examples of the application of artificial intelligence
Artificial Intelligence:Types, Benefits, Examples, Challenges, etc.
Examples of the 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.
