Generative AI
Empowering businesses by turning data into creativity, generating unique content and insightful predictions that shape the future.
What is Generative AI?
Generative AI represents the cutting-edge of machine learning technology, offering transformative potential for businesses across industries. At its core, Generative AI is a subset of artificial intelligence that leverages deep learning models to create new data instances that resemble your training data.
It can be used to generate realistic financial scenarios for risk assessment and strategic planning, create high-quality synthetic data for robust system testing, or even automate the generation of complex legal or technical documents.
Generative AI is not just about automation, it’s about leveraging technology to augment human capabilities, drive innovation, and propel strategic growth in an increasingly digital business landscape.
Use Cases
- Automated Journalism
- Photorealistic Image Generation
- Bioinformatics and Drug Design
- Synthetic Data Production
- Speech Synthesis for AI Assistants:
- Algorithmic Music Composition
- High-Definition Video Rendering
- Innovative Fashion Prototyping
- Architectural Visualization
How GenAI Works
Data Collection
The first step in Generative AI is to collect and preprocess a large amount of data. This data serves as the training set for the AI model. The type of data collected depends on the desired output. For instance, if the goal is to generate human-like text, the training data might consist of large text corpora. Preprocessing involves cleaning the data, removing irrelevant information, and transforming the data into a format that can be fed into a machine learning model.
Model Training
The second step involves training a generative model on the preprocessed data. There are several types of generative models, including but not limited to, Generative Adversarial Networks (GANs), Variational Auto-encoders (VAEs), and Long Short-Term Memory Networks (LSTMs).
These models learn the underlying patterns and distributions of the training data. For instance, in a GAN, two neural networks – a generator and a discriminator – are trained simultaneously. The generator tries to create fake data to fool the discriminator, while the discriminator tries to distinguish between real and fake data. Through this adversarial process, the generator learns to produce data that closely mimics the real data.
Data Generation
Once the model is adequately trained, it can generate new data. This is done by feeding the model a random seed or input, and the model outputs data that mimic the patterns and structures it learned during training. For example, a GAN trained on images of faces can generate new images of faces that do not exist but look convincingly real. Similarly, a LSTM trained on text can generate human-like sentences or even entire articles.
Companies we've worked with
