Generative AI

What is Generative AI?

Generative AI is a technology that can produce various types of content, including text, imagery, audio, and synthetic data. Introduced in the 1960s, it has since gained popularity due to its simplicity and ability to create high-quality content in seconds. The introduction of generative adversarial networks (GANs) in 2014 allowed generative AI to create convincingly authentic images, videos, and audio of real people. This has opened up opportunities for better movie dubbing and rich educational content, but also raised concerns about deepfakes and cybersecurity attacks on businesses.

Transformers and breakthrough language models have played a critical role in generative AI becoming mainstream. Transformers enable researchers to train ever-larger models without labelling all data in advance, resulting in more depth and deeper answers. They also enable models to track connections between words across pages, chapters, and books, allowing them to analyse code, proteins, chemicals, and DNA.

Generative AI models can write engaging text, paint photorealistic images, and create entertaining sitcoms on the fly. Innovations in multimodal AI enable teams to generate content across multiple media, including text, graphics, and video, based on tools like Dall-E. Despite initial issues with accuracy, bias, hallucinations, and spitting back weird answers, generative AI's inherent capabilities could fundamentally change enterprise technology and business operations.

How does Generative AI works?

Generative AI models use neural networks to identify patterns in data, generating new content. They can use unsupervised or semi-supervised learning for training, allowing organizations to quickly create foundation models for AI systems. Examples include GPT-3 and Stable Diffusion, which use language to generate various tasks, such as essay generation and photorealistic image generation.

What is LLM’s?

LLM stands for “Large Language Models”. LLMs are language modelling algorithms trained on vast text data, focusing on understanding language patterns for accurate predictions and textual generation. They are the basis for chatbots like OpenAI's ChatGPT and Google's Bard. ChatGPT, or chatbot generative pre-trained transformer, is used by companies and consumers to automate tasks, help with creative ideas, and code software. It uses the GPT large language model (LLM) to process natural language inputs and predict the next word until its answer is complete. LLMs are tied to billions of parameters, making them inaccurate and non-specific for vertical industry use.

What is Prompt Engineering?

Prompt engineering is a process that guides generative artificial intelligence (generative AI) solutions to generate desired outputs. It involves choosing the most appropriate formats, phrases, words, and symbols to guide the AI to interact more meaningfully with users. Prompt engineers use creativity and trial and error to create input texts, ensuring the AI works as expected. While most LLMs are pre-filled with massive amounts of information, prompt engineering can be done by users for specific industry or organizational use. As a nascent and emerging discipline, enterprises are relying on booklets and prompt guides to ensure optimal responses from their AI applications. Marketplaces for prompts are emerging, such as the 100 best prompts for ChatGPT. Prompt engineering is poised to become a vital skill for IT and business professionals, as they will be responsible for creating customized LLMs for business use.