What Is Generative AI and Why It Is So Popular?

Artificial intelligence (AI) has played a fundamental role in improving processes in various industries thanks to its technological advances. Its capabilities to automate tasks, improve efficiency, make data-based decisions, personalize experiences, and foster innovation make it a crucial tool. These advancements result in operational improvements, informed decision-making, satisfying user experiences, as well as stimulating economic growth through the creation of new products and services.

Generative artificial intelligence boosts productivity by saving time and improving efficiency through task automation, enabling professionals to focus on more important and creative activities. Additionally, this technology has the potential to generate solid and satisfactory results in various professional fields.

Below, we will delve into what generative AI entails exactly and why it has become one of the most widely used technological tools worldwide.

Generative AI Defined

Generative AI is a type of artificial intelligence that uses machine learning algorithms to generate diverse content, such as text, images, or audio, based on a training dataset.

According to ChatGPT, an AI application based on the GPT-3 language model, generative AI distinguishes itself from traditional AI by employing machine learning techniques, like generative neural networks, to autonomously create content.

This is how ChatGPT defines generative AI:

“Generative AI is based on the neural network’s ability to learn from an input dataset and then generate new samples that follow similar patterns. For instance, a generative neural network can learn from a set of flower images and subsequently generate new, authentic-looking flower images, even though they are not actually real.”

The goal of generative AI is to create content that might be furtherused for other purposes, such as analyzing data or helping to control a self-driving car.

How Generative AI Works

Generative AI models use neural networks to identify patterns in large datasets and generate new content. These neural networks consist of interconnected nodes inspired by the neurons in the human brain.

Neural networks are the foundation of machine learning, as they are used to processing vast amounts of data, such as text, code, or images, through complex algorithmic structures. During training, the weights of the neural connections are adjusted to minimize the differences between predicted and desired outputs, allowing the network to learn from its mistakes and make more accurate predictions.

Generative AI Models

To enable artificial intelligence to generate original content, various training models are employed, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion models. These models play a vital role in the learning process of patterns and characteristics through a dataset of inputs.

• Generative Adversarial Networks (GANs) employ two neural networks, a generator and a discriminator, to generate data similar to the training data.
• Variational Autoencoders (VAEs) learn to encode and decode data, making them useful for the generation and compression of multimedia content, such as images and videos.
• Diffusion models work in a two-step process during training: forward diffusion and reverse diffusion. In the forward diffusion step, random noise is gradually added to the training data. This helps the model learn and understand the patterns and characteristics present in the data. While the reverse diffusion step reverses the noise added during the forward diffusion process, enabling the model to reconstruct the original data samples, it also allows the model to generate new and unique data by running the reverse denoising process starting from completely random noise.

These are some examples of the most commonly used text and multimodal models today.

Text models:

1. GPT-3. It is a pre-trained autoregressive model in text that can generate high-quality responses in over 12 languages, as well as perform tasks such as translations or text summaries.
2. LaMDA. It is a pre-trained model in dialogues, allowing it to understand and generate more natural responses in open conversations.
3. LLaMA. It is a new AI language model created by Meta Platforms Inc (NYSE: META) and launched in 2023. Unlike other models, LLaMA is available only for academic and industrial research.

Multimodal models:

1. GPT-4. It is a multimodal model that accepts images and text as input and generates text as output, improving accuracy and fidelity in text generation.
2. DALL-E. It is a multimodal algorithm that creates images or art from text descriptions.
3. Stable Diffusion. Similar to DALL-E, it uses a diffusion process to gradually improve the generated images based on text descriptions.
4. Progen. It is a multimodal model trained on protein samples to generate new proteins with specific characteristics based on input text.

Generative AI Applications

The list of use cases of generative AI is endless, below are only a few applications:

1. Custom music generation. Generative AI can be used to create unique and personalized music, allowing musicians to explore new styles and musical approaches. For example, musician Paul McCartney of The Beatles announced that he had used AI to record an unreleased song emulating the voice of the late artist John Lennon.
2. Simulation in the automotive industry. Generative AI develops 3D virtual environments to test and improve autonomous vehicles, optimizing safety and driving efficiency.
3. Drug discovery. It accelerates the discovery of new drugs by simulating and designing promising molecular structures. There is even talk of generative AI achieving advanced studies to better understand the language and functioning of proteins.
4. Content creation in entertainment. Generative AI streamlines the production of visually appealing content, such as images, avatars, and visual effects, in the entertainment industry.
5. Content generation for social media. It can be used to create original and engaging content for social media by inputting certain text prompts, enhancing productivity for content creators and helping them reach a wider audience.
6. Automation of tasks in the medical field. It can automate tasks in the medical field, such as transcribing medical records or generating clinical reports. By being capable of understanding and processing complex medical information, generative AI streamlines administrative and professional processes.

Pros & Cons of Generative AI

As any technology, generative AI has advantages and limitations.

Benefits offered by generative AI are as follows:

1. Increased productivity due to automating tasks or speeding up their execution.
2. Elimination or reduction of skill or time barriers in content generation and creative applications.
3. Ability to analyze or explore complex data more efficiently.
4. Creation of synthetic data to train and improve other AI systems.

However, there are alos some challenges to consider:

1. Hallucination. Generative AI models can generate incorrect or nonsensical information, causing problems for users.
2. Dependence on data labeling. As mentioned earlier, there are many flaws in the quality and verification of the data used by AI models. For example, ChatGPT only offers information updated until 2021, making it impossible to answer questions about current topics.
3. Difficulties in content moderation. They have limited ability to recognize and filter inappropriate content.
4. Political implications. Generative AI can generate false information, such as photorealistic images or voice recordings of politicians without their consent, allowing malicious users to flood the internet with fake content.
5. Legal and regulatory issues. Despite being the last issue mentioned, it is one of the most important and controversial ones internationally among AI enthusiasts and politicians. The current legal framework cannot adequately address the implications of emerging AI. Currently, several regulatory discussions are taking place to control this problem without stifling the innovation of the technology.

Conclusion

Like any revolutionary technology, generative AI can either help improve user productivity or become a double-edged sword when used for destructive purposes or even to blackmail politicians.

Therefore, it is important for both developers and policymakers responsible for its regulation to consider all the pros and cons that generative AI offers to ensure they address all the challenges it entails. This way, they can mitigate the risks associated with its responsible use.