Understanding how AI systems are trained helps you use them more effectively, spot their limitations, and make better decisions about implementing them. It's like understanding how a car works. You don't need to be a mechanic to drive, but basic knowledge helps you use it better, maintain it properly, and know when to be cautious.
AI learns through a process called training, where it analyzes large amounts of data to identify patterns. For example, to learn what a cat looks like, an AI system might analyze millions of cat photos. It adjusts its internal parameters based on this data, gradually improving its ability to recognize cats. This is similar to how students learn through examples, practice, and feedback, though the underlying mechanism is quite different.
Step 1: Design the system
AI training begins by establishing neural networks, which are computing systems that are modeled after the human brain. They consist of interconnected "neurons" that process and transmit information, called “nodes.” Different architectures are used for different purposes. Large Language Models (LLMs) like ChatGPT and Claude use Transformers for language, while image generating AI tools use Convolutional Neural Networks (CNNs).
Step 2: Determine what the AI system should learn
Similar to teachers developing lesson plans, scientists establish parameters in order to define what AI systems should learn. Parameters are like the "knowledge" or "skills" of an AI model that help it determine what to generate. The more parameters a model has, the more complex patterns it can understand and generate. More parameters can also make the AI model slower and more costly.
For example:
- A model with fewer parameters might be good at simple tasks like identifying basic grammar errors.
- A model with many parameters (like GPT-3 or Claude) can handle more complex tasks like writing essays or coding.
- GPT-3 had 175 billion parameters, whereas GPT-4 had 1.76 trillion parameters and more memory. GPT-3.5 failed the bar exam, while GPT-4 immediately passed and did better than most humans.
The total number of parameters influences how much the model can learn, although it is not the only factor that matters.
Step 3: Translate information so AI systems can understand it
A neural network only processes data in numbers, so all words and images need to be turned into tokens. Tokens provide a digital stand-in in the form of 0s and 1s. They’re the building blocks that the AI model uses to understand and generate text.
When you interact with an AI model, it processes your input as a series of tokens and generates its response token by token. In 2017, there was a massive breakthrough in AI technology when programmers discovered how to give each token a weight, allowing AI models to process information simultaneously. This led to faster processing and a more natural, human-like way of interacting with AI systems, as well as a merger of separate fields into one. Now AI can treat words, images, code, and music as language.
Step 4: Train the AI systems
Neural networks must be trained with large data sets to help an AI system to understand it completely. But just like teaching students, there are different types of training that work together to build a holistic understanding of information.
There are four different types of training:
Supervised Learning
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- Used for initial training to establish basic understanding
- Like teaching with flashcards - clear right and wrong answers
- Helps establish fundamental pattern recognition
Unsupervised Learning
- System finds patterns in unlabeled data
- Builds on supervised learning by discovering new relationships
- Like letting a student explore a library and make connections
- Helps develop deeper understanding of relationships in data
Deep Learning
- Combines supervised and unsupervised approaches with multiple neural network layers
- Each layer learns increasingly complex features
- Earlier layers might learn basic patterns (edges in images, common words)
- Deeper layers learn complex concepts (faces in images, context in language)
Reinforcement Learning
- System learns through trial and error with rewards/penalties
- Often applied after initial training to refine behavior
- Like training a dog - reward good responses, discourage bad ones
- Used to fine-tune responses and align with desired outcomes
By establishing neural networks, determining what you want an AI system to learn, translating information in a way that AI can understand it, and training the AI system in a particular way, you end up with a complex system that can:
- Recognize patterns (from supervised learning)
- Understand relationships (from unsupervised learning)
- Process complex information and generate new content that seems human made (through deep learning)
- Adapt behavior based on feedback (through reinforcement learning)
This interconnected training process is why modern AI systems can handle such a wide range of tasks with such broad implications for so many industries.
Human intelligence is general, adaptable, and includes consciousness, emotional understanding, and true comprehension. We can learn from just a few examples and apply knowledge across very different situations. AI, in contrast, has "narrow" intelligence – it's very good at specific tasks but lacks true understanding. It requires large amounts of data to learn and struggles to apply knowledge outside its training domain.
