Any platform developed with artificial intelligence capable of generating content can be considered a generative AI .
They can be texts, images, music or codes, always based on patterns learned from large data sets. Instead of just analyzing data, it generates new information by mimicking the style or characteristics of the training data.
As we will see in this article, there are examples of generative AI for creating the most varied formats. They include templates that produce coherent texts, realistic images or even new designs and products, helping with creative and automatic tasks.
How does generative AI work?
Although Machine Learning exists , a generative AI platform does not develop 100% autonomously. For it to work, human intervention is first necessary, after all, it is for our use that it was created.
Check out the three main steps in the process of developing a generative AI.
Training
An AI needs to be trained so that it can respond to so-called “prompts,” that is, the commands that users enter .
It is necessary to use large volumes of data for this, as well as insert linguistic patterns into the machine so that it can give appropriate feedback.
Adjustment
Even AIs that have already been developed and trained need to undergo adjustments, since, being an intelligence, it cannot stop evolving .
To do this, so-called fine tuning is carried out, in which the AI receives new data, with the aim of improving its response capacity in more elaborate contexts.
Generation, evaluation and adjustment
A generative AI needs constant adjustments and improvements so that it continues to be able to generate updated content and responses .
There are even companies that pay people to help with this process, through remote service provision platforms.
Top Generative AI Models
The best-known examples of generative AI, as we will see later, are ChatGPT and Gemini, which belong to the Large Language Model (LLM) category .
But this is not the only category of AI, as they can also be grouped according to their “firepower”. See below.
Basic models
Generative AIs developed from basic models are capable of generating content or data that serve as input data .
Some of these models are:
- Generative Adversarial Networks (GANs)
- Transformer Models (e.g.: GPT, BERT)
- Variational Autoencoders (VAEs)
- Autoregressive Models
- Diffusion Models
- Normalizing Flows.
Large language models
LLMs are distinguished by having a sophisticated ability to understand spoken and written verbal commands , responding in a similar way to a human being.
They are programmed with billions of parameters, which are a kind of value that is given to each command so that they can understand and process the inserted prompts.
The evolution of generative AI
From what we have seen, generative AI is expected to grow at very high rates in the coming years.
After consolidating itself through the already popular ChatGPT and Gemini, AIs are advancing rapidly to create increasingly realistic content in other formats.
Generative AI has evolved rapidly, transforming from simple text generation models to advanced systems capable of creating images, music and even simulations of virtual environments.
It will come as no surprise when, in a few years, we can watch an entire film made with artificial intelligence.
In the future, we can expect to see even greater integration of these AIs into our everyday lives, with more ethical and personalized systems capable of collaborating more efficiently and safely with humans.
Thus, the trend is for generative AI to become an essential tool for innovation and solving complex problems.
How important are generative AIs?
Although the use of generative AI raises certain controversies, it is undeniable that this is a very useful tool .
Understand now why this is a technology that, from now on, tends to gain more and more strength and relevance.
For society
The evolution of technology shows that, over time, the tendency is for access to previously restricted services and goods to become popular.
A few decades ago, for example, having a computer at home was a luxury for very few people.
Generative AI is another step in this direction, expanding society’s access to a range of possibilities. Check it out.
More access to content creation
If, before generative AI, content such as texts, images and fonts could only be created or improved by designers, today, this is within the reach of anyone with a device connected to the web.
Creative potential unleashed
The generative AI market is growing at a galloping pace, so much so that there are now aggregator sites, such as theresanaiforthat.com, where you can find AIs for all tastes.
Creativity is grateful, since, with so many platforms available, any artist, creative professional and even laymen are able to give free rein to their imagination like never before.
Opens doors to entrepreneurship
As much as it is said that generative AI is taking away jobs , it is also true that it can be a work tool .
If used well, the tools available online can be used to quickly create and scale businesses, helping to build websites and the content included in them.
For companies
It took a while for Google to start being used by companies for commercial purposes .
Something similar should happen with generative AI, but probably at a much faster pace than with the search engine.
Understand from now on how this can happen, considering the relevance of AI in business.
Improve customer experience
One of the characteristics of generative AI is working with immense volumes of data.
For companies, this is a fundamental capability to map their customers’ purchasing behavior .
AIs have already been helping to achieve this, helping to make calculations or suggest new ways to improve the customer experience online and physically.
Increase employee productivity
There are service providers that already use generative AI as a complementary tool in their activities.
This is the case for those that use AI to perform complex calculations or to generate reports based on their sales data.
This allows employees to complete more tasks in less time, increasing overall productivity.
Optimizes business processes
Certain processes can be accelerated or optimized using generative AI.
Those who require calculations certainly benefit the most, since LLM-type AIs are capable of performing complex calculations instantly, including statistics.
Economy of scale
Another benefit that generative AIs provide is that they increase the scalability of a business .
As they help automate a series of activities and tasks, the result is more space in the budget to invest in better products and services.
Not to mention the strategic part, which can also be worked on.