Guides @ UF: Artificial Intelligence (AI): Machine Learning

Trendy AI Terms

Durante, Zane, et al. "Agent AI: Surveying the horizons of multimodal interaction." arXiv preprint arXiv:2401.03568 (2024). Agent AI is a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally grounded data, and can produce meaningful embodied actions.

Retrieval-Augmented Generation

(Lewis, Patrick et al. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” arXiv.org (2021): n. pag. Print.): A general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) -- models which combine pre-trained parametric and non-parametric memory for language generation.

Artificial General Intelligence

(Bubeck, Sébastien et al. “Sparks of Artificial General Intelligence: Early Experiments with GPT-4.” arXiv.org (2023): n. pag. Print.): an early version of GPT-4 which can solve some novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting.

Oversimplified steps to prepare your project using machine learning

1, making sure machine learning method(s) is actually needed for your research project is the important first step. Checking with your professor first is highly recommended. I am happy to meet you if you'd like to discuss machine learning methods for your project idea. (Email Borui)

2, many machine learning modeling algorithms are out there, you need to find the right ones for you (or create the ones by yourself -- Go Gator data scientist!) You will need to review a certain amount of literature and learn some AI theories and coding for being able to implement a model. Fortunately, UF offers many AI training opportunities -- UF Research Computing offers different levels of AI training. You can also check if machine learning courses are offered at your college. Learn more about research computing policies on research data at UF: https://www.rc.ufl.edu/documentation/policies/.

3, depending on the size of your data and the complexity of the algorithms, you might able be able to run machine learning experiments on your own laptop locally or using cloud computing resources such as Google Colab. You must know that UF has the most academically powerful research computing resource - HiperGator. You may apply for the Library HiPerGator Sponsorship to use it for your project. Research Computing also offers 3-month trial for new users (learn more).

Deepening your understanding of AI

Artificial Intelligence by Stuart Russell; Peter Norvig
ISBN: 0134610997

Publication Date: 2020-04-28

The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
ISBN: 9781492032649

Publication Date: 2019-10-22

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow--author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets

Artificial Intelligence from Stanford Encyclopedia of Philosophy
An excellent resource details AI history, approaches ethics and other related issues from a philosophical perspective.

Wikipedia Glossary of Artificial Intelligence