Artificial intelligence (AI) can feel like an elusive technology from the future, but businesses across all industries are utilizing machine learning technology in various ways. From chatbots to streaming service recommendations, AI can automate certain processes in your company while providing your audience with a customized and streamlined experience.

With businesses exploring how this revolutionary tool can elevate their practices, many professionals wonder how they can learn about generative AI. Generative AI training can equip your team with skills to bring your business’s visions to life.

AI training is the process of teaching your AI technology to interpret data and learn from it accurately. With this knowledge, the AI should be able to perform a specified task with greater accuracy. This can take time because, depending on the task, your AI may need to learn and interpret a large amount of data to perform accurately for your business needs.


What Is Generative AI?

Generative AI refers to a type of AI that is capable of generating content, including:

  • Text
  • Images
  • Sound or audio files
  • 3D models
  • Video

This technology uses generative models, using the patterns and structure in their input training data to create new data. The resulting data or media should mirror similar characteristics to the data used to train the AI.


AI Training: Explained

Training a generative AI tool requires a large amount of high-quality data, a well-designed AI model, and a computing platform to support it all. Depending on the tool’s intended use in your organization, this process can be incredibly demanding.

The key to accurate and unique results is the data. While the exact amount of data necessary will fluctuate from project to project, every generative AI tool requires accurate, high-quality data to perform as intended. 


How Does It Work?

A good rule to live by when training a generative AI model is that ‘your results are only as good as your datasets.’ This means that if you pull data from incorrectly tagged data or low-quality data sources, your resulting project will represent that level of quality.

Even if you have a data science, programming, or development background, AI training can take years to create a singular, new AI model. This is because training an AI model for an enterprise is difficult. Most of these models are used to perform more complicated tasks, such as language translation or autonomous navigation.

To ensure your AI model performs to the level you need, you will need to follow three main steps:


Step One: Train

In this first step, your AI model is introduced to its first training data set. It is then asked to make certain decisions based on the information presented, which may take some time or yield inaccurate results. 

Programmers and developers dub this the ‘toddler’ stage for the model because it is just learning how to perform as intended, the same way a toddler stumbles when learning to walk. When you spot these inaccuracies or mistakes, you adjust accordingly to train the AI on accuracy. 

An issue many run into during this stage is ‘overfitting.’ Overfitting refers to aligning your AI model too closely to a specific dataset so that it has memorized the set rather than learned how to interpret it. This can skew your model’s ability to analyze new data once it is introduced.


Step Two: Validate

After several rounds of trial and error, you must validate any assumptions you’ve made about how well the AI may perform. To do this, you must introduce it to a new data set. Some questions used during this phase include:

  • Is the AI suffering from overfitting?
  • Does the AI account for additional variables, and does it need to?
  • Is it performing as expected?

During this phase, you must evaluate the results as you validate. This phase can seem similar to the training phase, but the key here is to evaluate the model based on increased expectations.


Step Three: Test

This is where you introduce your AI model to a novel dataset without tags and targets. Tags and targets act as training wheels for your model, but the testing should emulate how your AI will perform when put to actual use.

Take note of whether or not your AI model can make accurate decisions based on this new, unstructured information. If it performs as intended, your training has been successful. Consider any adjustments that need to be made and repeat the training process until the desired results are achieved.


Learn Generative AI With ONLC

ONLC is the leading training solution for professionals learning generative AI. ONLC offers instructor-led AI training that is hands-on and collaborative. To see your options for AI training, check out ONLC’s available courses!

About The Author

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>