Deep learning is the breakthrough that unlocked modern AI and this IBM explainer covers it without the jargon spiral

People use the terms AI, machine learning and deep learning interchangeably in most conversations. IBM's deep learning explainer https://www.ibm.com/think/topics/deep-learning draws the distinctions clearly enough to understand which capability is responsible for which class of AI achievement.

The layered neural network structure is what makes deep learning different from earlier ML approaches. Having multiple layers allows the model to learn progressively more abstract representations of the input data rather than relying on human-designed features. That is why deep learning was the breakthrough for speech recognition, image recognition, natural language processing and everything that followed.

The practical implication worth understanding: the quality leap in AI capability that happened roughly between 2012 and 2020 was primarily a deep learning story. The transformer architecture that powers LLMs is deep learning. The image generation models are deep learning. Computer vision is deep learning. Understanding this history changes how you evaluate claims about future AI breakthroughs.

The article covers use cases across computer vision, NLP, speech, recommendation systems, fraud detection and healthcare in enough breadth to show where the same underlying technology produces different applications.

Which specific deep learning application has changed daily life the most, either for better or worse, and is it one most people recognise as AI?