Robot representing AI is reading books labeled machine learning.

How do you train artificial intelligence?

Businesses in all industries are developing innovative artificial intelligence (AI) applications, tapping into the technology for diverse use cases ranging from virtual assistants to medical-grade diagnostic algorithms. In fact, research firm Omdia has predicted that the AI-based software market will be a $99 billion industry by the middle of the decade.

But with any AI and machine learning project, there needs to be an emphasis on 'learning' in order for it to be successful. Here's a look at what AI training is, how it works and what is required to do it well.

What is AI training?

When you train AI, you're teaching it to properly interpret data and learn from it in order to perform a task with accuracy. Just like with humans, this takes time and patience (just consider all of those worksheets you had to complete when learning your multiplication tables back in grade school). Only by training AI to correctly perceive information and make accurate decisions based on the information provided, can you ensure your AI will perform the way it's intended.

How does AI training work?

First and foremost, AI training starts with data. While the actual size of the dataset needed is dependent on the project, all machine learning projects require high-quality, well-annotated data in order to be successful. It's the old GIGO rule of computer science — garbage in, garbage out. If you train your AI using poor-quality or incorrectly tagged data, you'll end up with poor-quality AI.

Once the quality assurance phase is complete, the AI training process has three key stages:

1. Training

In the initial training step, an AI model is given a set of training data and asked to make decisions based on that information. Don't be surprised if the AI stumbles at this point — like an infant, it is just beginning to learn how to walk. As you spot these mistakes, you can make adjustments that help the AI become more accurate.

One issue to try and avoid is overfitting. That's the name of a common problem that happens when you've aligned your machine learning model so closely with a specific dataset that it's memorized it rather than learned from it. In this instance, the AI would be unlikely to interpret new data correctly.

2. Validation

Once your AI has completed basic training, it can graduate to the next stage: validation. In this phase, you will validate your assumptions about how well the AI will perform using a new set of data.

As with the training phase, you will want to make sure to evaluate the results so you can confirm the AI is behaving as expected, and account for any new variables that you may not have considered previously. Any issues with overfitting will likely become evident during the validation stage.

3. Testing

Now the training wheels come off and it's time to conduct a real world test. Give the AI a dataset that does not include any tags or targets (those would be the training wheels that help it interpret the data). If your AI can make accurate decisions based on this unstructured information, it is ready to go live! But if not, it's back to the training stage and the process is repeated until you're happy with the outcome and the AI is performing as expected.

Keys to successful AI training

You need three ingredients to train AI well: high-quality data, accurate data annotation and a culture of experimentation.

High-quality data

You will require lots of high-quality data for any AI project you want to pursue. If you use a dataset that isn't relevant or includes even a small amount of poor-quality data, you'll quickly run into trouble. Bad data skews AI's judgment and produces undesirable results. It can even create AI that is bias.

Accurate data annotation

Not only do you need to have plenty of high-quality data, but you must also accurately annotate it. Otherwise, your AI will have no contextual guidance to help it properly interpret the data, let alone learn from it. For example, correctly annotated images can help teach AI programs to tell the difference between suspected skin cancer and benign birth marks.

two people looking at data on a computer screen

AI starts with data: Facing the challenges of data collection & annotation

Discover useful insights into the challenges of data preparation to ensure that your next artificial intelligence project is a success.

Only humans can perform the painstaking work of data annotation — particularly if the data is specialized or requires subject matter expertise. You can set your AI project up for success by working with an experienced partner that has the necessary knowledge to complete this critical preparatory step.

A culture of experimentation

Expect your AI to make mistakes during training. Errors are actually a valuable and normal part of the AI training process. When your AI doesn't properly interpret data during a training project, the key is to analyze the results for insights that will help you get to the bottom of what happened and why. The knowledge you gain will help to create AI that is even better and more innovative for your business moving forward.

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