Generative AI is a type of Artificial Intelligence, used to create a wide range of content and data such as texts, images, videos, audio, code, 3D models, and much more. The creation of this content occurs through learning patterns from existing data.

To understand how Gen AI works we can picture a chef using different ingredients to create a new recipe. Generative AI models will use neural networks to identify the patterns and structure within existing data to generate new and original content.

The hype behind this Artificial Intelligence technique began mainly after the launch of ChatGPT in November 2022, which is a chatbot capable of establishing an interaction with humans in a very natural way. However, the story of Generative AI and the capabilities of AI remote to the sixties.

The early concepts phase of Generative AI was marked by a period of exploration and experimentation, during which the idea of machines generating human-like content began to take shape.

In the first years of study (1950-1980) two primary domains emerged: natural language processing (NLP), where researchers aimed to teach computers not only to understand written and spoken language but also to generate text that processes the fluency and coherence of human communication; and early chatbots, which emerged as a tangible application of these capabilities, providing a glimpse into the potential for interactions between machines and humans.

In the following years, advances brought new systems based on explicitly defining rules and guidelines for generating content. These rules were often crafted by human experts who processed in-depth knowledge of the subject matter. Experts encoded their understanding of language, logic, and domain-specific knowledge into algorithms and systems. These systems, in turn, used predefined rules to generate text and data.

At the beginning of the century, the introduction of probabilistic models made Gen AI improve its efficiency. These models introduced an element of randomness and statistical analytics into content, capturing hidden patterns within data and generating content that exhibited more natural-sounding language. By considering the probabilities of transitioning from one state or word to another, the models improved the coherence and fluency of generated text.

The last decade has turned Generative AI into a fundamental tool in the new era of Artificial Intelligence. This era marked a watershed moment, as AI systems, particularly neural networks, began to exhibit unprecedented capabilities in content generation.

Recurrent Neural Networks (RNNs) play a crucial role in data transformation. Originally designed to process sequential data, they are well-suited for tasks involving language generation, making text coherent and contextually relevant, and improving the fluency and creativity of AI-generated language.

However, it was Generative Adversarial Networks (GANs) and transformers that were the true milestones of change. The GANs have led to the generation of highly realistic content, spanning images, videos, audio, and more. Meanwhile, transformers have allowed models to streamline attention mechanisms and leverage vast datasets to understand and mimic human language patterns. The best example of this is the model GPT, used in ChatGPT.

Deep learning techniques as the ones mentioned above have been game-changers in the past decade, pushing the boundaries of what machines can create. AI-generated content has become an integral part of various industries and business applications.

The possibility of inputting content or data about a particular subject into platforms like ChatGPT, Dall-E, Bard, among others, and requesting the creation of new content, opens a wide range of possibilities and conveniences for society.

Generative AI brings about an almost unlimited, quick, and personalized creation of content. The generated content can be very rich in information that adds value to specific topics, and the speed at which the content is generated is challenging to match by any human professional. However, despite its diversity, the ability to fact-check information and determine if it is correct goes beyond the current capabilities of Artificial Intelligence. Therefore, as much as we can reduce human involvement in some activities, it remains essential for two critical roles: inputting information and correcting the generated output.

Example of Use Cases:

• Translating Text;
• Writing complete and topic-focused text;
• Proofreading text;
• Generating images;
• Creating infographics and figures;
• Editing images;
• Audio transcription;
• Speech and text recognition;
• Text summarization;
• Generating code.

All these applications produce added value for society and specific professionals. But how can they leverage this new technology to improve businesses’ processes?

The truth is that there is a difference between what Generative AI can do for an individual and what it can do for a company.

Businesses cannot simply use AI models and ask them to enhance their customer experience, retain more customers, or make their employees happier. AI models alone, in a business context, aren’t able to do miracles. They need to be integrated into specific software and systems with the right data and information to address the problems companies are trying to solve.

A Conversational AI Platform is already a useful tool for a business that wants to enrich its processes. Put together with the benefits of Generative AI, the results are incredible.

We’ve taken a step forward and now have more capabilities and more possibilities to learn and create new content to help companies and businesses grow and reach their customers even more efficiently. What actual benefits does this new feature bring?

BOTSchool is continuously improving the platform to attend market’s needs. Our team is working hard on more exciting features so that we can bring to our platform the best that AI has to offer.