Generative AI
Generative AI refers to a branch of artificial intelligence that focuses on creating new content, such as text, images, music, and more, based on learned patterns from large datasets. This type of AI uses various machine learning models, including large language models (LLMs), generative adversarial networks (GANs), and transformers, which analyze extensive data to generate outputs that can mimic human-like creations. Notably, generative AI systems, such as ChatGPT for text and DALL-E for images, have been developed to produce content that can be practically indistinguishable from that created by humans (Coursera) (Caltech Science Exchange) (Cornell Teaching).
These AI models are particularly useful because they can automate and enhance creative processes across different fields, including education, content creation, and more. For example, in educational settings, generative AI can assist in creating customized learning materials and assessments. However, it is essential to approach the use of generative AI with a consideration of its potential to reproduce or amplify biases present in the training data and the ethical implications of its outputs (Coursera) (Cornell Teaching).
Other types of AI
Reactive Machines: These AI systems are designed to respond to specific situations based on the data available at the moment without past memory. They are primarily used for tasks that involve straightforward decision-making based on static rules. Examples include IBM’s Deep Blue and basic recommendation systems like those used by Netflix to suggest content based on current viewing patterns (IBM - United States).
Limited Memory AI: This type of AI can use both pre-programmed knowledge and past data collected over a short period to make decisions. Unlike reactive machines, they can adjust their actions by learning from recent past experiences. This type is commonly seen in more dynamic systems like self-driving cars, which continuously collect and analyze data such as distances and speeds of nearby objects to make driving decisions (IBM - United States) (Coursera).
Generative AI typically falls under the category of Limited Memory AI. While it does not retain specific past interactions, it uses massive amounts of training data to generate responses and content. Generative AI systems like ChatGPT or DALL-E analyze patterns in data to produce outputs (such as text or images) that are coherent and contextually appropriate based on the input they receive. These systems are trained on vast datasets and use this information to predict and generate outputs that are not pre-defined but constructed in real-time based on learned patterns (IBM - United States).
The definitions and descriptions are based on a synthesis of the presented sources, providing a comprehensive understanding of the current landscape of practical AI applications (IBM - United States) (Coursera).
*OpenAI. (2024). ChatGPT4 (April 18 version) [Large language model]. https://chat.openai.com/chat