Artificial Intelligence at NYU
For many years, NYU has been active in areas of Artificial Intelligence (AI) and machine learning for experimentation and research. The recent swell in publicly available toolsets and their ease of use has brought visibility to generative AI. For privacy and data protection purposes, careful consideration should be exercised by our NYU community when selecting and using these new tools.
Generative AI can create content, such as images, videos, and text. Well-known applications include ChatGPT (both a free and commercial application), Google’s Bard, and Microsoft’s Bing, as well as open source tools like Vicuna and Llama. These applications have potential uses that are now being explored by the general population, educators, corporations, scientists, and clinicians. For privacy and data protection purposes, they require careful consideration before selecting and using the tool that best fits the specific circumstances. This technology has the potential for transformative change across a wide range of areas, including operations, data and text analysis, research, teaching and learning, and administration. (Adapted from NYU Langone’s Predictive Analytics & Machine Learning.)
The NYU Office of the Provost is hosting teaching and learning workshops throughout the fall and provides extensive guidance and suggestions on teaching and learning with generative AI.
It may be helpful to explore some general terminology if you consider potential personal experimentation or professional use. (Adapted from this Glossary of Artificial Intelligence Terms for Educators)
Artificial Intelligence (AI)
AI is a branch of computer science. AI systems use hardware, algorithms, and data to create “intelligence” to do things like make decisions, discover patterns, and perform some sort of action. AI is a general term and there are more specific terms used in the field of AI. AI systems can be built in different ways. Two of the primary ways are:
- through the use of rules provided by a human (rule-based systems); and
- with machine learning algorithms. Many newer AI systems use machine learning (see definition of machine learning below).
Chatbots such as ChatGPT, Microsoft Bing, and Google Bard use generative AI and natural language processing to simulate human-like conversations in a chat window where the user can ask the bot to help with a variety of tasks, including editing or writing emails, essays, code, and more.
Generative AI (GenAI)
Generative AI is a type of AI system capable of generating text, images, or other media in response to prompts. One example of this is chat-based generative pre-trained transformer (ChatGPT): a system built with a neural network transformer type of AI model that works well in natural language processing tasks. In this case, the model:
- can generate responses to questions (generative);
- was trained in advance on a large amount of the written material available on the web (pre-trained); and
- can process sentences differently than other types of models (transformer).
Machine Learning (ML)
Machine learning is a field of study with a range of approaches to developing algorithms that can be used in AI systems. AI is a more general term. In ML, an algorithm will identify rules and patterns in the data without a human specifying those rules and patterns. These algorithms build a model for decision making as they go through data. (You will sometimes hear the term machine learning model.) Because they discover their own rules in the data they are given, ML systems can perpetuate biases. Algorithms used in machine learning require massive amounts of data to be trained to make decisions.
Natural Language Processing (NLP)
Natural Language Processing (NLP) is the field of AI where computer science meets linguistics to allow computers to understand and process human language.