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What is Artificial Intelligence?

What is Artificial Intelligence?

Artificial Intelligence (AI), a term introduced by the esteemed Stanford Professor John McCarthy in 1955, was originally described as "the science and engineering of making intelligent machines." Initially, AI research centered on programming machines to demonstrate certain behaviors, like playing games. However, the current focus is on crafting machines with the capacity to learn, resembling aspects of human learning processes.

Andresen, S. L. (2002). John McCarthy: father of AI. IEEE Intelligent Systems, 17(5), 84-85.

What is Generative Artificial Intelligence?

Generative Artificial Intelligence (AI) refers to a branch of AI that focuses on creating models and systems capable of generating new and original content, such as images, text, music, and even entire virtual environments. Unlike traditional AI models that rely on predefined rules and patterns, generative AI models have the ability to learn from vast amounts of data and then generate new content that resembles the patterns and characteristics found in the training data. - ChatGPT 

                                                                                                  Source: Baker College Research Guide

Generative AI TAPE

AI Glossary

  • Algorithm: In computing, an algorithm is a precise list of operations that a Turing machine could do. For the purpose of computing, algorithms are written in pseudocode, flow charts, or programming languages.  https://simple.m.wikipedia.org/wiki/Algorithm
  • Artificial Intelligence: The theory and development of computer systems able to perform tasks that usually require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. https://www.lexico.com/en/definition/artificial%20intelligence
  • Deep Learning: In practical terms, deep learning is just a subset of machine learning. In fact, deep learning technology is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged). However, its capabilities are different. While basic machine learning models do become progressively better at whatever their function is, they still need some guidance. If an AI algorithm returns an inaccurate prediction, then an engineer has to step in and make adjustments. With a deep learning model, an algorithm can determine on its own if a prediction is accurate or not through its own neural network. https://www.zendesk.com/blog/machine-learning-and-deep-learning/
  • Imitation Learning: Generally, imitation learning is useful when it is easier for an expert to demonstrate the desired behavior rather than to specify a reward function that would generate the same behavior or to directly learn the policy. The main component of IL is the environment, which is essentially a Markov Decision Process (MDP).  https://smartlabai.medium.com/a-brief-overview-of-imitation-learning-8a8a75c44a9c
  • Machine Learning: Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it... learn for themselves.  https://expertsystem.com/machine-learning-definition/
  • Neural Network in AI: A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. It creates an adaptive system that computers use to learn from their mistakes and improve continuously. Thus, artificial neural networks attempt to solve complicated problems, like summarizing documents or recognizing faces, with greater accuracy.  https://aws.amazon.com/what-is/neural-network/
  • Reinforcement Learning: In this kind of machine learning, AI agents are attempting to find the optimal way to accomplish a particular goal, or improve performance on a specific task. As the agent takes action that goes toward the goal, it receives a reward. The overall aim: predict the best next step to take to earn the biggest final reward. https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning/
  • Supervised and Unsupervised Learning: In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own. https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning/

Source: FIU Libraries