Controversial, curious, and undeniably transformative, artificial intelligences have been starring in debates across the tech universe.
With voice command assistants, text generators, and features capable of initiating and maintaining dialogues with real people, these tools are spreading exponentially and revolutionizing life in society. Due to their volume, diversity, and functionalities, many people feel confused about the technologies they are discovering or using. For this reason, we created this article to introduce a bit more about the trend of the moment: Generative Intelligence.
Read on and learn more about the concept, its applications, its benefits, and its drawbacks in this article that the CodeBlog team has prepared for you.

Generative AI: the definition

Generative AI is characterized as an advanced format of predictive texts. In practice, it enables users to write short descriptions of certain content and receive full texts back, optimized for blog posts, social media captions, or even informative articles.

But how does this happen?

To complete information assertively, Generative AI systems process an extensive data set, consult billions of word combinations, and predict the most likely ones to suggest sentence and paragraph combinations. Unlike human comprehension, this feature operates basically like an autocomplete, but certainly much more elaborate and strategic.

For example, if you ask a generative AI bot what the sum of 2+3 is, it will probably answer: “2+3=5”. This does not happen because of an internal algorithm, like a calculator processing requests, but rather by deduction.
That is, through the processing of data available on the internet, it is possible to conclude that the most accurate result for the calculation is, indeed, 5.

Without a doubt, optimized autocomplete can be very useful. After all, the intelligence collects ideas, notes, and drawings from users to produce more complete and attractive content.
In this way, a basic brainstorm becomes a draft, a list of notes turns into an action plan, bullet points are converted into full texts, and much more.
However, as fantastic as these results may seem, they should not be treated as the final product. After all, so far, no Artificial Intelligence possesses the ability to represent human emotions and experiences.

Generative AI: the types

There are several formats of Generative AI. Among the most common are:

NEURAL NETWORKS: based on human brain capacity, they can perform tasks without any kind of intervention, provided they are previously trained and capable of doing so.

EVOLUTIONARY ALGORITHMS: they are based on the natural process of evolution to find new solutions to problems.

PROBABILISTIC METHODS: They use probability theory as a reference to generate new information.

Generative AI: how does it work?

There are several functionalities that operate, generally, by commands. In image creation, for example, Generative AI only needs a command to start working.
In some cases, it is not even necessary to use an image as a base, since the system is able to identify, from a short description, what the user's request is.
You can, for example, ask for the creation of an image with a bus in the middle of a highway, and, in moments, it will be generated.
The realistic nature of the image will depend on countless factors such as the quality of the commands, the tool chosen, and the items that will compose the image.

On the other hand, to compose a text, it is necessary to establish the guidelines and "brief" the tool regarding the topic, the channel where the content will be published, the target audience, and other possible conditions, such as word count, need for subheadings, type of language, etc.

Anyone can use and even work with Generative AI, but to get satisfactory content, one must learn how to use the commands, refine the content, and discover which suggestions and requests are most appropriate. For this, it is important to ensure the training of a team before directing them to Generative AI operations.

Generative AI: applications:

After learning a bit more about how Generative AI works, check out some of its practical functions:

►Creation of images based on a training dataset.

►Image descriptions in natural language.

►Development of realistic 3D models based on 2D sketches.

►Automation of reports through the provided data.

►Microchip design.

►Recommendation of new products or services to users. ►

Prediction of trends, events, and future behaviors.

►Detection of people or objects in digital images or videos.

Generative AI: a summary of pros and cons

Just like any other technology, Generative AI has its positive and also negative points.
It is clear how much this tool facilitates the writing of texts, automates tasks, and assists in understanding data.
On the other hand, the resource can also be used for malicious purposes, such as spreading fake news, plagiarized content, and deceptive advertisements, such as fake reviews of products or services. Therefore, to ensure safe use, the tip is: caution, prevention, and, above all, information. If you plan to implement Generative AI on a large scale, study the topic and always analyze the potential risks.

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