AI and Generative AI: How They Work

1. Overview of AI

Artificial Intelligence (AI) refers to systems that perform tasks requiring human-like intelligence. It spans rule-based programs, decision trees, and—most effectively today—machine learning.

2. Machine Learning and Neural Networks

Machine Learning (ML): Algorithms learn patterns from data instead of following explicit rules.
Neural Networks: Inspired by the brain, they stack layers of “neurons” that transform inputs into outputs via weights and activation functions.

3. Training vs Inference

Training: Adjust model weights by minimizing loss on large datasets. It’s compute-intensive and done once.
Inference: Use a trained model to make predictions. It’s faster but still benefits from hardware acceleration.

4. Role of GPUs

CPUs handle sequential tasks; GPUs excel at parallel operations. Training neural nets involves matrix multiplications on huge tensors—GPUs (and their tensor cores) crunch these in parallel, cutting training time from months to days.

5. Generative AI Models

5.1 Language Models

Example: GPT: Trained to predict the next word. Given “The cat sat on the”, it might output “mat.” It uses transformers—attention mechanisms that weigh relationships between tokens.

5.2 Image Generation

Example: Stable Diffusion: Learns to iteratively denoise random noise into coherent images. Conditioned on text, it shapes noise into art.

6. Example Workflow

Data Prep: Collect and preprocess text/images.
Model Definition: Choose architecture (e.g., transformer).
Training: Run on GPU clusters; monitor loss.
Validation: Check performance on held-out data.
Deployment: Export model; use optimized runtime for inference.

7. Why GPUs Matter

Parallelism: Thousands of cores work simultaneously.
Tensor Cores: Specialized units accelerate matrix maths.
Memory Bandwidth: Handle large tensors without bottlenecks.

8. Sample PyTorch Code (on GPU)

import torch
from torch import nn, optim

# Check for GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Simple feedforward network
model = nn.Sequential(
    nn.Linear(784, 256),
    nn.ReLU(),
    nn.Linear(256, 10)
).to(device)

# Dummy data and labels
inputs = torch.randn(64, 784).to(device)
labels = torch.randint(0, 10, (64,)).to(device)

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Training step
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()

print(f"Loss: {loss.item():.4f}")  # Should run on GPU

9. Conclusion

AI and generative AI rely on data, models, and hardware. GPUs turn theoretical architectures into practical systems by speeding up both training and real-time inference.