Two terms people use interchangeably that are not the same. How they nest, and what actually changed with "deep".
These terms nest inside each other. Artificial intelligence is the widest circle: any technique that makes machines act smart. Machine learning is a circle inside it. Deep learning is a smaller circle inside that.
The confusion comes from the fact that almost every headline-grabbing AI result of the last decade, including large language models, is deep learning. So the words get used as if they mean the same thing. They do not.
In traditional software a person writes explicit rules. In machine learning you instead show the program many examples and it finds the patterns itself. Spam filters, product recommendations, and credit scoring are classic machine learning. Much of it does not use neural networks at all.
Deep learning is machine learning built on neural networks with many layers stacked together. The "deep" just refers to the number of layers. More layers let the model learn its own useful features from raw data, rather than needing a human to hand-engineer them.
Older machine learning often needed experts to decide which features of the data mattered, for example which pixels or which words to measure. Deep learning learns those features on its own, straight from raw input. That is why it scaled to messy data like images, audio, and free text.
A large language model is deep learning: a very large, many-layered neural network (a transformer) trained on text. So it is deep learning, which is machine learning, which is AI. All three labels are technically correct for it.
Think of nested boxes. AI is the big box. Inside it is a machine learning box. Inside that is a deep learning box. Inside that sits the language model. Every inner box is also everything the outer boxes are.
The models Berges AI runs are all deep learning systems. Knowing the hierarchy mostly helps you read the field clearly: when a product says "AI-powered", it usually means one specific deep learning model doing one specific job.
Try Berges AINo. Deep learning shines on large, messy data like images and text, but it needs a lot of data and compute. For smaller, structured problems, simpler machine learning is often faster, cheaper, and easier to interpret.
A neural network with many layers is deep learning. A very shallow one is still a neural network but would not usually be called "deep". The depth is the point.
Generative AI, the models that produce text or images, is a use of deep learning. It sits in the innermost circle alongside language models.