Definition
Generative AI is a type of artificial intelligence that creates new content, including text, audio and imagery using machine learning (ML) algorithms. It has many use cases including music composition, text generation and video and image production.
A Brief History of Generative AI
- 1964: Joseph Weizebaum, an MIT computer scientist, designs ELIZA, the text-based bot powered by pattern-matching scripts for text-based answers.
- 1999: Nvidia introduces the first graphical processing unit (GPU), GeForce. While GPUs were developed to provide smooth motion graphics for video games, they have become the foundation for developing AI models and mining cryptocurrencies.
- 2004: Google introduces an autocomplete function that gives users suggestions for the words they are searching for based on the first few letters of their query.
- 2013: The first variational autoencoders (VAEs) debuts.
- 2014: Both generative adversarial networks (GANs) and the diffusion models are developed.
- 2017: Ashish Vaswani and a team from the University of Toronto published a paper titled ‘Attention is All You Need’, which documents the principles of transformer models. It is acknowledged as the enabler of modern generative AI models.
- 2019-2020: OpenAI debuts GPT (Generative Pretrained Transformer) language model – GPT-2 and GPT-3.
- 2022: OpenAI announces ChatGPT, a GPT-3 powered platform which simulates human conversation and conversation-like answers for user input.
How Generative AI Works
- It uses machine learning to analyse large datasets and identify patterns which it uses to generate new content.
- The first step involves training the generative AI model with the extensive data set. The information can include images, music, text or video.
- The AI model analyses relationships and identifies patterns within the dataset, such as which words appear most.
- Based on the learned patterns, the AI model generates new content that resembles the training datasets.
Examples of Generative AI
- Text generation: Used to generate text based on training materials such as text from social content, books and journals.
- Creating video: Generating new videos for TV or virtual reality games.
- Speech synthesis: Using the training data, generative AI can recreate new speech used in voice assistants, synthetic voiceovers and text-to-speech.
Examples of Generative AI Tools
- ChatGPT: A language model designed by OpenAI that generates human-like text responses to queries and prompts.
- StyleGAN: Used to generate images based on training datasets.
- Magenta: Generates music and art.
Challenges of Generative AI
- Costly: Generative AI require vast compute resources, technical expertise and significant capital investment to develop and maintain generative models.
- Slow sampling speed: Responses from generative models may have latency problems. This is a huge concern in real-life applications such as chatbots and customer service, where prompt responses are vital. As diffusion models has become popular, their sampling speed has slowed significantly.
- Lack of quality data: The AI generative models need authentic and unbiased data for its efficient and proper operation. However, some domains lack sufficient data to train AI models.
- Data licensing: It is difficult to get a commercial license to build new data bases or use existing ones to tarin generative models. This is a crucial step to avoid intellectual property infringement problems.
Benefits of Generative AI
- Create original content: Generative AI can help to create new and original content such as videos, images and text that is almost similar to the content human creates. This has many use cases like advertising, entertainment and creative arts.
- Enhancing AI systems: Generating AI can supplement or improve the efficiency and accuracy of present AI systems like computer vision or natural language processing.
- Uncovering hidden patterns: Generative AI provide new ways to explore and analyse complex data sets, allowing researchers and businesses to uncover hidden trends and patterns.
- Automation and efficiency: Generative AI can automate and accelerate various processes and tasks, saving businesses and organisations time and resources.
Generative AI Use Cases
- Customer experience: Marketing organisations can use AI tools to save up time and improve their content production. For example, AI can be used to power chatbots and virtual agents to give customers prompt and personalized responses, thus improving customer experience.
- Software development and application modernization: AI code generation tools can automate and accelerate the process of writing new code. Also, they can fast-track application modernisation by automating repetitive codes.
- Digital labour: AI can expedite processes such as drafting or revising contracts, bills, invoices and other digital or physical paperwork, enabling the employees who used to manage them to concentrate on high-level tasks.
- Science, engineering and research: Generative AI helps scientists, engineers and other experts in solving complex problems.