Generative AI is able to identify patterns and generate text, video, audio, images, and even programming code in response to any command given by the user.
Understand what generative AI is, learn how this technology works and its types, and see the advantages and disadvantages of its use below.
What is Generative AI?
Generative AI (or Gen AI) is a segment of artificial intelligence with the ability to create content in various formats. The technology is characterized by continuous learning in some cases, as its creation is based on large amounts of existing data, and on new information collected through human interaction.
What are the uses of generative AI?
Generative AI can create text, answer questions, generate images or videos from descriptions, solve mathematical problems, and even develop lines of software code. This is because the technology is based on algorithms that simulate the process of human learning and decision-making.
All of this creative capacity enables generative AI to develop new applications, automate processes, and increase the productivity of companies and people.
How does generative AI work?
The initial process of generative AI occurs with the creation of a base model. At this stage, the algorithm is trained with a large amount of raw data to identify patterns and relationships. This training results in a neural network capable of generating content in response to input or commands.
Next, the developer defines the architecture of the generative model. A Generative Adversarial Network (GAN) can be adopted for applications focused on image generation or a Generative Pre-trained Transformer (GPT) for text generation, for example.
The application of generative AI then begins to generate content in the adopted format. The results are evaluated and adjusted by the developer, based on current data or new information obtained through human interaction.
How does generative AI training work?
Generative AI training begins with a deep learning algorithm that is exposed to a large amount of raw data. Using machine learning techniques, the algorithm begins to identify patterns and is trained to solve problems, fill in gaps, and decipher elements of a sequence.
Next, the generative model adopted for the application starts generating content samples (such as text, images, or audio). The neural network undergoes a series of training and refinements to produce outputs that are tailored and consistent with the input commands, even after it starts working.
What are the types of generative AI?
There are different types of generative AI that can be used for different applications, depending on the purpose. And the main types of Gen AI include:
- Large Language Model (LLM): LLM is a complex generative AI model that can process and generate natural language text from training with a large amount of data;
- Generative Adversarial Networks (GAN): a type of generative AI that is capable of generating new data that is similar to the data used in training;
- Variational autoencoder (VAE): a model similar to (GAN), which learns to compress data and uses this compression technique to generate similar content;
- Transformer: A neural network architecture, such as the Generative Pre-trained Transformer (GPT), that learns context and can transform or transform input sequences into output sequences.
What are some examples of generative AI applications?
Generative AI has been used in a variety of industries, including text, language, audiovisual, and even coding. Examples of applications that use this technology include:
- ChatGPT: an application developed by OpenAI that uses Large Language Models (LLM) to generate text output in response to input commands;
- Google Gemini: a model created by Google that uses large amounts of data to perform tasks in text format;
- Copilot: Microsoft’s generative AI assistant that can perform tasks (including creations for Office Suite applications) from text commands;
- DALL-E: an AI model developed by OpenAI that can generate images from text descriptions;
- Midjourney: a tool similar to DALL-E, capable of creating images and digital art from text descriptions;
- Soundraw: a platform that can generate original and personalized music based on user preferences;
- TabNine: an application used to analyze, predict, or complete lines of development code;
- RunwayML: a platform that uses generative AI to edit, animate, or create videos from text commands.
Do you have to pay to use generative AI?
Not necessarily. ChatGPT, Google Gemini, and Copilot are examples of free generative AI models that may only require a login to use. However, more advanced versions and integrated APIs often require a paid subscription for more complex results and customized applications.
What are the benefits of using generative AI?
The market understands that generative AI can have a positive impact on several sectors of the economy. Some of the benefits of the technology include:
- New content creation: Generative AI can inspire or create content such as text, images, video, and audio;
- Process automation: companies and individuals can use Gen AI to automate everyday processes, such as adopting the use of chatbots for customer service;
- Test environments: Generative AI can create experimental scenarios and environments, which can then use predictive AI for simulations or predictions;
- Application personalization: Gen AI can create content that is targeted to specific groups of people, optimizing the customer experience.
What are the disadvantages of using generative AI?
It is worth mentioning that there are disadvantages to using generative AI applications. Examples of harms caused by the use of technology may include:
- Technical limitations: the application may not produce the expected results if there is not enough data on the subject;
- Comprehensive results: It is possible that artificial intelligence will present unclear or inaccurate results, requiring a search outside the application to confirm the data;
- Data collection: surveys, data, and feedback tend to be collected by the application owner;
- Automation of human labor: companies have automated some tasks with generative AI, reducing human labor in certain professions.
What is the difference between generative AI and predictive AI?
Generative AI focuses on creating content based on large amounts of data. This technology is generally aimed at the creative and artistic sectors, as it is capable of generating text, audio, video, poetry, images, and lines of code.
Predictive AI uses historical data to analyze information and predict future events, such as election results or the sales performance of a particular product. Unsurprisingly, this technology is commonly adopted in the business, financial, and industrial segments.
Is it safe to use generative AI?
The safety of using generative AI depends on several factors of the adopted application, such as the security resources used, data policies, and algorithm bias. In general, the use of generative AI can be safe and bring some benefits, but it is not free from risks to users.
What are the risks of using generative AI?
Generative AI is considered a major technological advancement, but its use also involves sensitive issues. Some of the main risks of the technology include:
- Inaccurate results: Applications may return inaccurate results even though the commands in the generative AI input are detailed;
- Biased algorithms: The results of generative AI applications should not be considered absolute truth, because the data used may only represent a certain view;
- Misinformation and fake news: individuals may report false data and fake news to hinder the application's learning;
- Spreading fraud: Criminals can use generative AI to increase fraud and cyberattacks;
- Copyright infringement: AI-generated works can produce content that is similar to protected works, without proper permission;
- Collection of personal data: Questions, requests for feedback, and sensitive user data are likely to be collected by the application owner.
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