How AI Learns
Train a neural network yourself
Train a neural network yourself
AI is inspired by your brain! Both biological neurons and artificial neurons receive inputs, process them, and produce an output. Let's compare them.
The first artificial neuron was created in 1943! Called the "McCulloch-Pitts neuron," it was inspired by how brain cells work.
Each input to a neuron has a weight - a number that says how important that input is. Adjust the weights below to see how they affect the output!
Output = sigmoid(input1 × weight1 + input2 × weight2 + input3 × weight3)
The sigmoid function squashes any number to between 0 and 1.
GPT-4 has about 1.76 TRILLION weights! Training means finding the right value for each one.
AI learns by seeing examples! Below are simple 5×5 patterns. Click "Train" to teach the network to recognize each shape, then test it by clicking on patterns.
Training GPT-4 took months on thousands of expensive GPUs and cost over $100 million!
AI isn't perfect! Here are common ways neural networks fail. Understanding these helps us build better, safer AI systems.
AI is used in healthcare, hiring, and criminal justice. Understanding its limitations helps us use it responsibly and know when human judgment is needed.
AI learns from data - but what if the data is limited or biased? See how training data affects what the AI learns.
If we only show it apples...
Then we test it on other fruits...
ImageNet, a famous AI training dataset, contains 14 million images labeled by humans. It took years to create!
Draw a simple shape and watch the AI try to recognize it! The AI measures the distance from the center to each part of your drawing to figure out what shape it is.
The AI divides your drawing into 36 slices (like a pizza!) and measures how far each slice extends from the center:
Tip: Draw shapes with clear, distinct features for better recognition!
Here's a complete neural network! Watch data flow through layers as it processes inputs and produces outputs. Click neurons to see their values.
The human brain has about 100 trillion connections between neurons - far more than any AI! But AI can process numbers much faster.
From neurons to networks
AI is inspired by the brain. Artificial neurons take inputs, apply weights, and produce outputs.
The "knowledge" of a neural network is stored in millions or billions of weights that are adjusted during training.
AI learns by seeing many examples and adjusting weights to reduce errors.
AI can be fooled, biased, or overconfident. Understanding limits helps us use AI responsibly.
Put your new knowledge into practice!