Franklin Township AI Guidelines

General Definitions of AI

Generative AI, machine learning, deep learning, and expert systems are all different approaches within the broader field of artificial intelligence. Here's a brief overview of each:

1. Generative AI: Generative AI refers to techniques and algorithms that are used to generate new data or content that resembles, but is not necessarily identical to, existing data. These algorithms learn patterns from a dataset and use them to create novel outputs. Examples include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and sequence generation models like Transformers.

2. Machine Learning (ML): Machine learning is a subset of artificial intelligence that focuses on developing algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed to perform a specific task. ML algorithms learn from data, iteratively improving their performance over time. It encompasses various techniques such as supervised learning, unsupervised learning, and reinforcement learning.

3. Deep Learning: Deep learning is a subset of machine learning that involves artificial neural networks with multiple layers (deep neural networks). These networks are capable of learning hierarchical representations of data, enabling them to extract complex features and patterns. Deep learning has been particularly successful in tasks such as image recognition, natural language processing, and speech recognition.

4. Expert Systems: Expert systems are a type of AI that emulates the decision-making ability of a human expert in a specific domain. These systems rely on a knowledge base, which contains facts, rules, and heuristics about the domain, as well as an inference engine, which uses this knowledge to reason and make decisions. Expert systems were popular in the early days of AI for tasks such as medical diagnosis, but have been largely superseded by more advanced machine learning and deep learning techniques.