AI in Public Works

AI is when computers and machines are designed to think, learn, and make decisions like humans.

Machine learning is when computers use data to extract patterns and use those patterns to make predictions or decisions on their own.

An AI engine is a subset of machine learning that processes and analyzes vast amounts of data, recognizing patterns and making decisions or predictions. It allows the system to learn and adapt to varying conditions and data and provide an output for the user.

Deep learning is a type of machine learning that uses layered networks (called neural networks) to learn complex patterns from large amounts of data, similar to how human brains might learn.

Generative AI is a type of AI that can create new content such as text, images, audio, video, and code.

A large language model (LLM) is a type of AI that is designed and trained on vast amounts of text data to understand, generate, and work with human language. It uses patterns in the data to perform tasks like answering questions, writing content, translating languages, and summarizing information. LLMs are based on machine learning techniques, especially deep learning using neural networks.

Transformers are the underlying architecture of what makes LLMs work effectively. Transformers can look at all data at once, allowing it to order data, understand context, and put meaning behind what it produces.

Natural language processing is a type of AI that can understand, interpret, and generate human language like English, Spanish, or any other spoken or written language.

Computer vision is a type of AI that allows computers and machines to interpret and make sense of visual data such as pictures and videos just as humans do.

A hallucination in AI is when the AI system, typically generative AI, confidently produces an incorrect or non-sensical output.

AI is not immune to bias. AI systems can produce biased results if the data it’s learning from is unbalanced or has been overly influenced.

AI often relies on large amounts of data, including customer or employee information. If data isn't properly secured, it can be exposed, stolen, or misused. Insecure AI systems can be accessed by outsiders, leading to data breaches or system manipulation.

Transparent use of AI means disclosing the types of AI systems that are being employed, the data being shared, the processes and decisions that are being influenced by AI, and the human oversight of the AI system.