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T is for Training


From A to I to Z: Jaid’s Guide to Artificial Intelligence

Training is how AI masters a skill. 

During training, the AI is fed a large amount of data. The AI learns how to recognize patterns in that data and take appropriate action based on those patterns.

AI is typically trained using one of three types of data:

1. Structured

Ever did a crossword, then flipped the newspaper over to check if you got the answers right?

Trainers provide AI with structured data for the same reason. The AI can try its hand at sorting and labeling the data, then compare its output to the original data set to confirm whether it did its job correctly. 

This process is called supervised learning. 

2. Unstructured

Here, the AI works with raw data and figures out patterns on its own. This process is called unsupervised learning. 

3. Partly structured and partly unstructured

Here, the AI works with the raw data, then compares its results to the structured data. 

This is called semi-supervised learning. Its main benefit is that it gives AI a reference point, but isn’t as time-consuming and expensive as supervised learning, because trainers need to structure a smaller data set. 

In some instances, AI is trained using trial and error — a process called reinforcement learning. Here, the AI is rewarded for performing the correct action and penalized when they get it wrong. 

Reinforcement learning is useful for training AI to act on incomplete information, for example deciding the next move it should make while playing a game.

Regardless of how AI is trained, the ultimate goal is for the AI to become sufficiently good at the task to be able to perform it with reliable accuracy on new, previously unseen data. 

For this reason, training takes place in several stages.

This isn’t too different to how we humans master a skill. 

If you wanted to learn how to play a song on the guitar, you’d practice it over and over until the hand movements came naturally to you. Similarly, in training, AI is exposed to the same data multiple times so it can get better at performing the task it’s being trained to do — whether it’s sorting the data, recognizing patterns, or taking action based on the patterns it has recognized. 

Its performance is then validated on previously unseen data to make sure it’s ready to be used in the real world. 

Some facts

During the training process, trainers use several techniques to help the AI master a skill quickly and efficiently. These include:

Data Augmentation

When trainers have limited data to work with, they increase the data set by modifying some of it. For example, if the data consists of images, they might crop or rotate some of them. Or, if the data is text, they might rephrase or rework some of it. 

The idea is to give the AI more data to work with, because the more data it’s exposed to, the better it gets at performing the particular skill it’s being trained to do.

Feature Extraction

If the data being fed to the AI is very complex, trainers may simplify it using a technique called feature extraction. Here, the data is broken down into smaller parts and sorted. 

If the AI is being trained to recognize a text’s sentiment, for instance, the words in the text the AI is being trained on might be pre-labelled based on their grammatical function, or their role in the text.

Hyperparameter Tuning 

Hyperparameters are the settings that determine how AI performs during training, so getting them right is crucial.

Imagine you wanted to play a driving rock song on the guitar. To do this, you’d crank up your amplifier’s volume and experiment with your overdrive setting to get the right sound. 

Similarly, hyperparameters determine the AI’s level of accuracy, so trainers try out several different combinations during training to find out which one delivers the best results.

  • While the goal of training is to teach AI to act appropriately without the need for human intervention, human input is critical. One type of training process, called active learning, involves humans giving the AI feedback to help it learn from its mistakes and improve its output. 
  • Training AI is extremely (and increasingly) resource-intensive. OpenAI, the company behind ChatGPT, reckons that, since 2012, the amount of processing power required to train AI has been doubling every three months. 

Training large AI models like GPT requires thousands of specialized processors called Graphic Processing Units working in sync. 

Want to know more?

This article covers the basics of training AI in plain, easy-to-understand language.

If you have some basic coding experience, this free course teaches you how to build and train a deep learning model.

Jaid’s perspective

Training is the most critical stage in AI development, because it’s how the AI masters the skills it needs to be useful in a real-world setting. The unique Jaid Gym labelling and training tool allows us to train models quickly and efficiently with high accuracy. Jaid engineers can efficiently and consistently label raw data removing the need for clients to perform the mundane and error prone activity themselves. When an AI is properly trained, it can home in on patterns quickly — even those that aren’t readily visible to the human eye — and make reliably appropriate decisions without the need for human intervention. In a customer service setting, this means using AI can free you up to focus on what matters most. AI can handle inquiries quickly and efficiently, while your customer service team can work on ensuring customers have the best experience possible whenever they interact with your firm.

Discover how Jaid’s AI-powered platform is increasing resolution rates and customer satisfaction via model training – contact us today for a free demo!