What Is Generative AI And How Is It Applied?

Generative AI or generative artificial intelligence is a type of artificial intelligence (AI) technology that can produce new content like text, images, music, audio and videos. Generative AI has been one of the biggest buzzwords in the world of technology for months now not because is brand-new as much as the hype has been driven by the simplicity of new user interfaces and enhanced quality of content generated by this technology.

The technology, as said is not brand-new, in fact Generative AI came in th 1960s in chatbots. It started getting a hot topic with the introduction of a new way to train the neural networks in 2014. This new type of machine learning algorithm was called generative adversial networks or GANs making generative AI more stronger in generate more authentic images, videos and audio of real people.

Generative AI is a technology that can multi-task and perform different tasks, including summarization, Q&A, classification, and more. All this happens thanks to the power a type of models backing Generative AI also known as foundation models (large AI models).

How Does Generative AI Work?

Generative AI works by using an ML model to learn, find and interact with patterns and relationships in a dataset of existing content that could be human-created or AI generated.

There are few ways to to train Generative AI model and the most common way is to train Generative AI models by using supervised learning. With the supervised learning a set of human-created content is given as input to the model with the corresponding labels. The model then learns to generate content that is similar to the human-created content and labeled with the same labels in output.

Where Is Generative AI Applied To?

There are various use cases of Generative AI, because it can processes vast content, creating insights and answers via text, images, and user-friendly formats. These are some examples of how Generative AI can be used to solve tasks.

• In an environment where a user have vast amounts of unstructured data, with Generative AI the exploration is simplified through conversational interfaces and summarizations.

• Optimizing tasks.

• Writing email responses, dating profiles, resumes and term papers.

• Improve customer interactions through enhanced chat and search experiences.

• Generate realistic images from scratch, or modify existing images by adding or removing elements.

• Create synthetic voices that sound human-like, which can be used for voice assistants, audiobooks, or personalized voice messages.

• Compose music by learning from existing compositions and generating new melodies or harmonies.

• Generate synthetic data for training machine learning models, augmenting existing datasets, or creating realistic simulations for testing purposes.

Generative AI Models

With the progress of researches and the continues investing in the innovation in the artificial intelligence (AI) field and subfield more Generative AI models are becoming better into processing texts, audio, video and other type of media. The recent progress made possible the release of Generative AI models such as OpenAI's ChatGPT, Sora, Google's Bard, Meta's Llama, Google DeepMind Gemini models and many more famous AGI models.

What Are Bard, ChatGPT, Llama, Gemini?

Bard. Now called Gemini it's a chatbot based on Generative AI model Gemini Ultra 1.0 released by Google DeepMind. Gemini models are built for multimodality -- process seamlessly text, images, audio, video and code. Gemini represents a significant leap forward in how AI can help improve each humans daily lives. Gemini models are natively multimodal which means users have the potential to transform any type of input into any type of output.

ChatGPT. Came and took the world by storm in November 2022 was built by OpenAI based on the GPTs models. ChatGPT is able to process text and code simulating a real conversation. ChatGPT evolved fast and the latest model currently available is GPT-4 vision, a multimodal GPT model capable of process video, images, text, audio and code. After the incredible run in early 2024 OpenAI CEO Sam Altman anticipated that his team is working on ChatGPT GPT-5 a next-generation Generative AI model, currently in it's training phase. While waiting the release of GPT-5 you can try OpenAI's ChatGPT GPT-3.5 and GPT-4 in our upcoming artificial intelligence (AI) assistant called Draco.

Llama. It's an open source Generative AI model and it was built by Meta AI an artificial intelligence research and development branch company of Facebook parent company Meta. Llama was built in response to OpenAI's ChatGPT GPT models and is able to process text and simulating conversations similarly as OpenAI's ChatGPT. Llama latest model is Llama 2 which has been trained with 40% more data than LLama 1.

Sora. Sora is an Generative AI model announced on February 2024 and built by OpenAI. Sora can create realistic and imaginative scenes from text, images, and videos instructions. The artificial intelligence model understands not only what the user has prompted, but also how the details exist in the physical world making it capable of generate complex scenes with multiple scenarios, specific type of motion and accurate details of the main subject and the background up to a minute long while maintaining visual quality and adherence to the user’s prompt.

What Are The Use Cases Of Generative AI?

Generative AI use cases are countless when it comes to generate any kind of content. Some of the use cases for Generative AI include the following:

• Automated chatbots for customer service and technical support.
• Writing code snippets and debugging.
• Create digital and photorealistic art in different styles.
• Generate commercial videos with cinematic effects.
• Translate from and to multiple languages.
• Writing email responses, dating profiles, resumes and term papers.

What Are The Benefits For Generative AI?

Generative AI make it easier to interpret and understand existing content and automatically generate new content. While developers and researchers are searching new ways to improve Generative AI applications some of the potential benefits of implementing Generative AI could be identified including the following:

• Summarizing complex contents into better understandable and coherent contents.
• Improve aspects in specific areas such as SEO.
• Automating tasks
• Simplifying the process of creating content in a particular style.

What Are The Limitations For Generative AI?

There are many challenges Generative AI have to face, from the legal and ethical aspects to qualitative aspects of the content generated by Generative AI models. Here are some of the limitations to consider when implementing or using a generative AI app:

• It does not always identify or cite the source of the generated content.
• AI generated content can be harmfull and can gloss over bias, prejudice and hatred.
• Large Generative AI models are subject to generating fictitious information, presented as factual or accurate.

What Are The Best Practices For Using Generative AI?

Factors such as accuracy, transparency and ease of use are essential to consider while working with AI. These are some of the most fundamental aspects to consider:

• Generative AI tool's limitation and strenght points
• AI-Generated content quality
• Accuracy of Generated content with the presence of sources of content in text generated content