How can computer programs can generate diverse types of content like images, music and video?
Thanks to an increasingly popular subfield of Artificial Intelligence (AI) called Generative AI!
This article will take you behind the scenes to explain how these models learn from existing data.
Buckle up and get ready to discover the practical applications of GenAI!
What is Generative AI?
To summarize, Generative AI is a subset of Artificial Intelligence that can create "original content.",
It does so by learning from massive amounts of data, and examples include images and 3D models.
To dos so, GenAI leverages Machine Learning models and Neural Networks (NNs).
The goal is to identify data patterns and structures, allowing it to generate realistic outputs.
These models have a wide range of applications, including Natural Language Processing tasks and promotional content creation.
What sets GenAI apart is its ability to use data to generate content that closely resembles real-world creations.
Whether it's a new melody for a song or a photorealistic image of a landscape:
Generative models can produce results that are often indistinguishable from human-created content!
How Does Generative AI Work?
1. Creation
Neural Networks are complex systems inspired by the human brain— they consist of interconnected nodes known as “artificial neurons.”
These networks are the foundation of GenAI, trained on large datasets to identify patterns and relationships within the data.
Among advanced types are Generative Adversarial Networks (GANs), diffusion models and Transformer-based Models.
In a Neural Network architecture, each layer learns to extract increasingly complex features from the input data.
In image recognition, early layers might detect simple elements like edges, and deeper layers identify complex shapes and objects.
This layered structure allows the network to capture the subtleties and nuances of the data it processes.
Deep Learning (DL) also plays a crucial role, allowing the AI to refine its output through processing layers .
2. Training
GenAI models thrive on extensive training as they're fed on enormous datasets to understand patterns.
The larger and more varied the data, the better the model grasps patterns and generates realistic and diverse outputs.
During training, the Neural Network analyzes the data to identify recurring patterns and statistical distributions.
GenAI models like GANs use two NNs, generator and discriminator. One creates new content and the other improves it.
They go on to encode patterns into internal parameters, essentially compressing the acquired knowledge.
This ater becomes the foundation for generating new content!
3. Generation
Once trained, the model uses its learned knowledge to create new and original content.
First, it samples from the probability distribution it has learned from the training data.
Later on, it generates outputs that are statistically similar but with subtle variations and fresh combinations.
For example, if you show the model thousands of Van Gogh paintings, it can learn his distinctive style, including his brushstrokes, colors, and swirling patterns.
This is similar to how AI tools, such as Freepik AI Image Generator, can learn visual styles to generate new images with similar characteristics.
As a result, it can create "new" paintings that capture his essence.
4. Optimization
To enhance the quality and diversity of its creations, Generative AI models often undergo fine-tuning and optimization.
On the one hand, Fine-tuning involves extra training on a smaller, more specific dataset.
Think of a Language Model specifically made to generate more formal text.
Algorithm optimization, on the other hand, allows to adjust parameters based on feedback or evaluation metrics.
With this process, it guides the model toward producing increasingly impressive results.
This ongoing refinement helps the model generate more realistic, coherent and high-quality outputs.
Advances in ML also ensure that AI-generated content mirrors the nuanced complexity of human-created content.
However, we can't understate the importance of human labor.
GenAI can ease our lives, but it can only provide value as long as there are professionals who harness its power.
At Capicua, we have decided to develop products with Generative AI for multiple reasons. We always strive to be at the forefront of technology and explore the full potential that GenAI offers! This not only allows us to innovate but also to understand better how to apply this technology to solve real problems and bring effective solutions to our customers and users." — Juan Diego Mendez, Technical Lead @ Capicua
Generative AI Applications
GenAI in Finance
Generative AI has emerged as a powerful tool for risk assessment and fraud detection in the financial sector.
AI models can analyze large amounts of historical and real-time transactions to uncover anomalies related to fraudulent activity.
For example, Mastercard is leveraging GenAI to strengthen fraud detection, resulting in better anomaly detection in suspicious transactions.
GenAI can also play a major role in investment management!
Applications include generating synthetic market data, simulating scenarios and analyzing financial instruments.
With these tools, investors can better identify potential opportunities and risks.
JPMorgan Chase is one of the frontrunners in leveraging GenAI for trading strategies and portfolio management.
In customer experience, GenAI chatbots can offer personalized financial advice and assistance.
Other examples include routine inquiries and free up human advisors to focus on more complex needs.
Bank of America's Erica virtual assistant exemplifies these capabilities, providing 24/7 support and personalized financial guidance.+
GenAI in Retail
By analyzing user data, AI models can generate highly targeted marketing campaigns.
This approach leads toboth higher conversion rates and improved customer loyalty.
For example, Stitch Fix, curates personalized clothing recommendations for its clients for higher retention.
In addition, retailers can leverage Generative AI for inventory optimization and supply chain management.
By forecasting demand and predicting disruptions, AI models enable retailers like Walmart to maintain optimal inventory levels.
Further, GenAI is transforming Product Design in the retail sector, as it can generate product concepts and create virtual prototypes.
With this structure, teams and companies can significantly reduce time-to-market.
Nike uses Generative AI in footwear design to refine AI-created variations into prototypes that meet its standards.
Roger Chen, VP of NXT Digital Product Creation, states that creating starting points takes seconds using GenAI.
To summarize, GenAI has the potential to drive innovation in Product Design and Development!
Conclusion
Generative AI is a significant advance in AI for original, realistic content.
Its ability to learn data and generate new outputs has potential for several industries!
As a Product Growth Partner, we're well aware of the value of Gen AI in delivering innovative solutions
To start shaping the future, reach out!
