Generative AI: Creating New Content from Existing Data
Generative artificial intelligence (AI) is a type of AI that uses its training data to generate new content, such as text, images, videos, code, data, or 3D renderings. These models are trained on vast amounts of data and then use this data to make predictions and create new output.
Examples of Generative AI
A quintessential example of generative AI is ChatGPT, an AI chatbot that can generate human-like text. Other examples include DALL-E 2, an AI image generator, and Stable Diffusion, an open-source AI image generator.
Current Applications of Generative AI
Generative AI is rapidly gaining popularity due to its potential to automate content creation tasks. It can be used to generate text for marketing materials, create images for social media, or even compose music.
Machine Learning and Generative AI
Machine learning is the subsection of AI that teaches a system to make predictions based on data it’s trained on. While generative AI is a machine-learning framework, not all machine-learning frameworks are generative AI.
Generative AI and Large Language Models (LLMs)
When discussing generative AI models, you often hear the term large language model (LLM) because it is the technology that powers AI chatbots like ChatGPT. LLMs are trained on massive amounts of text data to learn how human language works, enabling them to understand and generate conversational text.
Data Sources for Generative AI Models
Text-based generative AI models are trained on vast amounts of data in a process known as self-supervised learning. This data includes articles, books, websites, and more.
Ethical Concerns with Generative AI Art
One concern with generative AI art models is that many are trained on data from the entirety of the internet. This data includes copyrighted material and information that might not have been shared with the owner’s consent.
Shortcomings of Generative AI
Generative AI models can make predictions and create outputs based on the data they are trained on, however, there are no guarantees the prediction will be correct. Additionally, the responses might incorporate biases inherent in the content the model has ingested from the internet.
Conclusion
Generative AI is a rapidly evolving field with the potential to revolutionize the way we create and consume content. It has the potential to enhance creativity, increase productivity, and make content creation more accessible. However, it is important to be aware of the shortcomings of generative AI and to use it responsibly.
