Usedprimarily for generating images, GANs consist of two neural networks, a generator and a discriminator, that work together to create realistic data. • Variational Autoencoders (VAEs): These models are used for generating data that follows a certain distribution, often used for creating images orcomplex data structures. • Transformers: Used in natural language processing tasks, transformer-based models like GPT (Generative Pre-trained Transformer) generate human-like text.
While AI is a broad field concerned with creating intelligent systems, Generative AI specifically refers to AI that creates new data or content, mimicking the characteristics of the data it was trained on. History of AI Over the past five decades, state governments have utilized a range of AI technologies to improve operations and service delivery across multiple domains, including public safety, healthcare, education, transportation, and infrastructure management. These technologies have evolved continuously to address the increasing demands and complexities of state governance. Below is an overview of the key AI types employed during this period: 1970s-1980s: Expert Systems • Expert Systems: Early AI applications in state governments were primarily expert systems, which used rule-based algorithms to replicate the decision-making abilities of human experts. These systems were utilized in areas like tax auditing, legal compliance, and resource management.
enables them to generate new content. The systems generally require a user to submit prompts that guide the generation of new content. (Adapted slightly from U.S. Government Accountability Office Science and Tech Spotlight: Generative AI) Artificial Intelligence (AI) is a broad field that encompasses the development of systems and machines with the ability to perform tasks requiring human-like intelligence. These tasks include reasoning, problem-solving, learning, perception, natural language processing, and decision-making. AI can be classified into two main categories: 1. Narrow AI (Weak AI) : AI systems designed to perform a specific task or a limited range of tasks, such as voice recognition, image classification, or recommendation systems. 2. General AI (Strong AI): A theoretical form of AI that would have the ability to perform any intellectual task that a human can
do, showing general intelligence across a wide range of domains. Generative Arti fi cial Intelligence (Generative AI) is a subset of AI focused on creating new content rather than just analyzing or understanding existing data. Generative AI models are trained to generate data, such as text, images, music, or even code, that mimics or is inspired by existing data. The key features of Generative AI include: 1. Content Creation: Generative AI models, like GPT-4, are designed to create new text, images, or other types of content that resemble what humans might produce. 2. Training on Large Datasets: These models are trained on vast datasets and learn patterns, structures, and styles from the data, which they can then use to generate similar outputs. 3. Types of Generative Models: • Generative Adversarial Networks (GANs):
32 – Florida Technology Magazine – 2024 Fall Edition
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