The generative AI sector is projected to become a $356 billion industry by 2030.
Whatever is on your checklist – saving significant time and costs or reducing reliance on human resources – generative AI models help you achieve it, bringing you closer to your larger business goals.
Want to learn more? Read on as we explore the world of generative AI and its use cases, and see the many ways it can boost your operational efficiency. Let's get started
Implementing Generative AI: A 60-Second Recap
Generative Artificial Intelligence (or Generative AI, as it is commonly known) is an AI technology that uses Natural Language Processing (NLP), machine learning techniques, and mexico number data image processing to identify underlying patterns in existing data and generate responses and new content.
Let us give you an example.
Let's say you've set up a business on the Internet. Everything is set up: the website, the e-commerce store, etc. But just as you were about to get going, you realized that you hadn't created any product descriptions.
This is where generative AI models like ChatGPT, Google Gemini, Claude, or Llama come into play. They just require you to input basic data – such as your product name, its features, cost, etc. – and that’s it. Within seconds, these tools will generate engaging, SEO-friendly product descriptions that reflect your product’s USP, just like any experienced copywriter would do.
In fact, 'texts' aren't everything. Generic AI tools also generate other content, such as audio, video, images, designs, software codes, and even synthetic data. And no, it's not magic.
Generative AI is based on three deep learning models: variational autoencoders (VAEs), generative adversarial networks (GANs), and transformers:
Variational Autoencoders (VAEs): VAEs are the most fundamental of the three models. They use neural networks to learn patterns from training data by compressing it into a simpler form. They then expand the data to generate new data.
Generative Adversarial Networks (GANs): GANs are versatile. They combine two neural networks trained with real-world data to generate highly realistic content, such as audio, video, images, etc.
Transformers: Transformers are primarily used for natural language tasks. They process large amounts of textual data to learn linguistic patterns and contexts that allow them to generate coherent texts.
So, when you need content, any of these three components will do the trick!