The popularity that generative AI solutions are gaining is mindblowing. But how do you build an app like that? Selecting a proper artificial intelligence technology stack for a generative AI app is quite tough. So we’ve put together a simple breakdown of the frameworks, programming languages, and other tools that are commonly used by developers.
Did you know that the generative AI market is forecasted to reach a staggering 1.3 trillion USD in 2032? Generative AI solutions such as Gemini by Google, ChatGPT by OpenAI, and Midjourney are spreading globally at a breakneck speed, contributing to the overall growth of this sector. With this in mind, let’s dive into the world of generative artificial intelligence, explore how it operates, and what to add to the tech stack.
What Does Generative AI Imply?💡
Generative AI is a type of artificial intelligence that leverages certain algorithms to create brand-new data. By using deep learning and neural networks, generative AI produces human-like content, realistic images, and videos resembling real-world footage. Its capabilities go way beyond developing engaging apps. It can be utilized to generate unique designs, write code, create ideas for blogs, and more.
How Does Generative AI Work? 📐
Generative AI operates by utilizing advanced machine learning methods, such as deep learning and neural networks, to generate unique content by crunching up massive datasets and using patterns. Here’s a breakdown of the process in five steps:
1️⃣ Data сollection (gathering extensive datasets relevant to the desired content type)
2️⃣ Model training (using certain neural network architectures to produce new data)
• GANs (Generative Adversarial Networks)
• VAEs (Variational Autoencoders)
• Transformers (such as GPT)
• RNNs (Recurrent Neural Networks)
3️⃣ Pattern recognition (identifying patterns and features within the data to improve content creation)
4️⃣ Content generation (creating new content by applying the learned patterns and features)
5️⃣ Fine-tuning (refining the generated content via human evaluation and feedback)
Generative AI Tech Stack Essentials
If you decide to integrate generative AI into your app, keep in mind that you’ll need to have a grasp of the tools available. Let’s take a sneak peek at the key components of the tech stack:
🟡 Application frameworks offer pre-built components and libraries that speed up development processes. Here are some common generative AI framework options:
➟ Google’s TensorFlow
➟ PyTorch
➟ Keras
➟ MXNet
🟡 Programming languages are essential for integrating algorithms and developing applications. These are a few commonly used languages:
➟ Python
➟ R
➟ Julia
➟ Java
➟ C++
➟ Scala
➟ JavaScript
🟡 Foundation models (FM) serve as the starting point for different AI tasks such as content creation, question answering, translation, and more. FM examples include:
➟ GPT
➟ BERT
➟ DALL-E
And it’s just the tip of the iceberg.
Considering implementing generative AI in your business and eager to know more about the potential tech stack? Browse through this read to see more frameworks and tools that can be of hand during development ⤵
