AI Fundamentals: A Guide to the Basics

AI Fundamentals: A Guide to the Basics

Whether we like artificial intelligence (AI) or not, this tech is in our lives and is here to stay.

You most likely already use AI in your day to day, whether it’s searching on Google or using a system to help your practice be more efficient.

So, we know this tech is in our lives and we know we use it, but what is AI and how does it work?

Let us help guide you through the basics of AI so we can all understand.

AI is a broad field of computer science that focuses on creating machines that can:

• Learn: Improve their performance over time based on data.
• Reason: Solve problems and make decisions.
• Perceive: Understand information from the world (like images or sounds).
• Understand Language: Communicate with humans.

In essence, AI is about building intelligent agents that can take actions to achieve goals.

AI Functions With 3 Parts: Data, Algorithms, and Models

1. Data

AI systems learn from information. This “information” is data.

It can be anything:

• Numbers: sales figures, temperatures
• Text: books, emails, social media posts
• Images: photos of cats, X-rays, satellite images
• Audio: speech recordings, music

The more data an AI has, and the better quality that data is, the smarter and more accurate it can become. Without data, AI can’t learn.

2. Algorithms

Algorithms are step-by-step instructions or rules that an AI system follows to process data and perform tasks. They are the “recipes” that tell the AI how to learn and what to do with the data.

• Examples:
  • An algorithm to sort a list of numbers from smallest to largest
  • An algorithm to detect if an email is spam

3. Models + Machine Learning

After an AI has data and applies algorithms, the AI creates a model. This model is the learned knowledge, the patterns it has identified, and the rules it has figured out.

Once a model is “trained,” you can give it new data it’s never seen before, and it will use its learned knowledge to make predictions or decisions.

• Example: You train an AI model on thousands of pictures of hearing aids. The model learns what a hearing aid looks like. Now, if you show the AI a new picture, it can tell you if it’s a hearing aid.

Machine learning models:

• Machine learning is how computers learn from data to improve performance over time without being explicitly programmed to do so. Without telling the machine what to do line by line, we feed a lot of data and let the AI figure out patterns on its own.

• AI systems are built on reflex-based models called predictors. These are systems that take input, such as an image or a sentence, and use learned patterns to predict an output.

Now that you have an understanding of how AI generally works, we encourage our members to further educate themselves on how AI can be implemented to save time, and to focus on the most important thing: being an audiologist!