When you think of AI, what’s the first thing that comes to mind? Many people think of data and information generators, autonomous developers, or fast automation. However, it’s unlikely that the first thing you think of is Gen-AI.
Even though we are already using Generative AI models today, there’s still much to learn in order to fully understand them—whether or not you need to know exactly how they work.
According to Microsoft, this type of AI uses deep learning to create content based on prompts or instructions, delivering innovative results wherever it is applied.
What makes it different from other AI models is that it generates results by studying and learning from existing patterns, structures, and data—allowing it to produce analyses and outputs in multimedia formats, or even code.
How it works
As mentioned earlier, unlike rule-based systems or predictive AI, Generative AI (Gen-AI) relies on deep learning as its main tool to:
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Recognize patterns in lakehouses.
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Generate coherent and creative content based on what it has learned.
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Adapt and improve continuously as it learns from new data.
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Respond to user prompts effectively.
While all AI models can deliver useful results for users, it’s important to note the distinction: predictive AI analyzes historical data to anticipate outcomes; conversational AI focuses on natural dialogue without creating new complex content; while generative AI creates original content with minimal human intervention, based on the data it is trained on.
What does generative AI do with data?
As mentioned, data is the main input that fuels these systems. When the data is clear, it enables the construction of “networks” for analysis and processing, resulting in outputs such as:
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Natural language text
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Images
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Audio
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Code or sequential data
For this reason, it is widely used in creative industries, healthcare, and finance, offering multiple applications through two main model types:
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Generative Adversarial Networks (GANs): Two networks (a generator and a discriminator) compete to produce increasingly realistic data.
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Generative Pre-trained Transformers (GPT) and other multimodal models such as GPT-4o, capable of processing text, images, and audio simultaneously.
Advantages and benefits
As we’ve already noted, and as many already know, Gen-AI offers countless advantages across different areas (home, school, or work). The most notable benefits are:
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Innovative and effective solutions
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Acceleration of creative tasks
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Reduction of manual activities
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Personalization of responses, adapting to users and context
What’s next for Gen-AI?
It’s clear that Gen-AI is evolving rapidly, expanding its use across industries. These technologies are increasingly being integrated into business processes, continuing to deliver transformative, sophisticated, and realistic solutions for all.
Generative AI represents a radical shift in how solutions are created. Beyond producing text, images, or code, these models bring innovation to the process itself.
Of course, while it provides enormous advantages in innovation and efficiency, it also raises important ethical and legal challenges that must be addressed.
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