What is Generative AI and how does it differ from other AI techniques?

What is generative AI?

Generative AI has emerged as a champion in the domain of artificial intelligence by generating innovative content within seconds. Unlike traditional AI that spots patterns and predicts, generative AI takes the lead by learning from data and crafting diverse outputs like audio, text, code, video, and images. 

Imagine asking a computer for a story, and it not only writes it but paints a visual to go along. That's the magic of generative AI, seen in marvels like ChatGPT, Bard, DALL-E, Midjourney, and DeepMind. 

From sparking creativity in writing to aiding scientific research, it seamlessly guides various mediums, making its mark in industries from automotive to healthcare. It's not just predicting the future; it's creating it.

How does generative AI work?

Are you tired of predictable content and seeking a touch of innovation? Then this post is for you. Generative AI is like a storyteller with weighted dice, where the training data shapes the probabilities. 

The AI can create a listicle on "best winter getaways for tech enthusiasts" by analyzing its training on winter getaways, and tech-related topics. It breaks the input text into tokens like "best," "winter," "getaways," and "tech enthusiasts."These tokens transform vectors, capturing the essence of each word. 

The model uses positional encoding to retain word order and attention mechanisms to establish relevant connections. The model creates original content by combining learned contexts to predict the next word based on associations.

What are the challenges of generative AI?

Are you aware of the potential risks of Generative AI? While it has the power to generate impressive results, experimenting with AI, especially GPT models, comes with a set of challenges. 

The lack of "common sense" comprehension and its inability to detect sarcasm, humor, or lies reveals the gap in understanding human interactions. While these models store vast amounts of information, they can produce technically correct yet contextually irrelevant outputs. 

Additionally, privacy concerns loom large, especially in applications that handle sensitive data, raising questions about ownership and intellectual property rights. The syntactical correctness of AI-generated code does not guarantee functionality and often leads to flawed outcomes. 

What are the applications of generative AI?

Have you ever thought about how generative AI is revolutionizing multiple industries in ways that nobody really talks about? In the automotive industry, AI is creating data that helps vehicles learn to drive themselves. 

Healthcare and science are making groundbreaking strides in using AI to help with things like creating drug compounds and analyzing medical images. The media and entertainment industries are using AI to create content quickly and cost-effectively. 

Climate science benefits from AI's ability to simulate disasters and weather patterns. Education is being revolutionized by AI-powered chatbots that offer personalized tutoring. 

Even government departments are finding ways to incorporate AI into their work in exciting ways.

What are the domains of generative AI?

Generative AI has become known through the massive use of ChatGPT. However, it has actually been used in various applications for a long time. Generative Adversarial Networks (GANs) were the pioneers in creating images, while Generative CAD design used complex mathematical algorithms and machine learning to design intricate models. 

Chatbots, which existed long before ChatGPT, were already capable of understanding context, learning questions, and providing human-like responses. Natural Language Text Generation models also made significant progress in producing diverse content, ranging from blogs to scripts. DALL-E, another creation of OpenAI, was a fusion of language and image generation that came before ChatGPT.