1 What is deep learning?
Artificial intelligence has surged into public consciousness, often wrapped in grand promises and dire warnings. This chapter cuts through the noise with clear definitions and a pragmatic view of what deep learning is, what it does well, and where its limits lie—equipping you to separate meaningful progress from hype and to participate in building these systems responsibly.
AI is the broad effort to automate intellectual tasks; historically it began with symbolic, rule-based systems. Machine learning flipped the paradigm: instead of handcrafting rules, it learns them from data by optimizing performance on examples. Central to this is representation learning—finding transformations of data that make a task easier. Deep learning is the modern approach that stacks many such transformations in layers (neural networks), progressively distilling information into features that are highly predictive for the task.
Training a deep network means adjusting millions or billions of parameters (weights) so that the model’s predictions match targets. A loss function measures error; an optimizer uses feedback from that loss to nudge weights in better directions via backpropagation. Repeating this loop over vast datasets yields models that perform remarkably well across perception, language, and decision-making tasks.
Deep learning’s success rests on three pillars: simplicity (it automates feature engineering, replacing complex pipelines with end-to-end models), scalability (it thrives on parallel hardware and massive data), and versatility (models can be continually refined and repurposed—forming the basis of foundation models). Generative AI exemplifies this shift: trained with self-supervised objectives that turn unlabeled data into a learning signal, these systems act like fuzzy knowledge bases that can be steered by prompting.
Results have been striking—fluent chatbots and code assistants, photorealistic image generation, human-level transcription and image classification, improved translation and recommendation, autonomous driving in several cities, and accelerating science (e.g., protein structure prediction). Yet the chapter urges skepticism toward near-term AGI claims: today’s systems are powerful cognitive automation, not autonomous intelligence. History warns that hype cycles can cool; while a severe “AI winter” seems unlikely given tangible value, expectations and investment may need to realign with reality. The pragmatic takeaway: deep learning is transformative and here to stay, but progress is uneven—measure it by delivered capabilities, not forecasts.
1.12 The promise of AI
Although we may have unrealistic short-term expectations for AI, the long-term picture is looking bright. We’re only getting started in applying deep learning to many important problems for which it could prove transformative, from medical diagnoses to digital assistants.
In 2017, in this very book, I wrote:
Right now, it may seem hard to believe that AI could have a large impact on our world, because it isn’t yet widely deployed – much as, back in 1995, it would have been difficult to believe in the future impact of the internet. Back then, most people didn’t see how the internet was relevant to them and how it was going to change their lives. The same is true for deep learning and AI today. But make no mistake: AI is coming. In a not-so-distant future, AI will be your assistant, even your friend; it will answer your questions, help educate your kids, and watch over your health. It will deliver your groceries to your door and drive you from point A to point B. It will be your interface to an increasingly complex and information-intensive world. And, even more important, AI will help humanity as a whole move forward, by assisting human scientists in new breakthrough discoveries across all scientific fields, from genomics to mathematics.
Fast-forward to 2025, most of these things have either come true or are on the verge of coming true – and this is just the beginning.
The AI revolution, once a distant vision, is now rapidly unfolding before our eyes. On the way, we may face a few setbacks – in much the same way the internet industry was overhyped in 1998–1999 and suffered from a crash that dried up investment throughout the early 2000s. But we’ll get there eventually. AI will end up being applied to nearly every process that makes up our society and our daily lives, much like the internet is today.
Don’t believe the short-term hype, but do believe in the long-term vision. It may take a while for AI to be deployed to its true potential – a potential the full extent of which no one has yet dared to dream – but AI is coming, and it will transform our world in a fantastic way.
[1] A. M. Turing, “Computing Machinery and Intelligence,” Mind 59, no. 236 (1950): 433-460.
[2] Although the Turing test has sometimes been interpreted as a literal test – a goal the field of AI should set out to reach – Turing merely meant it as a conceptual device in a philosophical discussion about the nature of cognition.
FAQ
How do AI, machine learning, and deep learning relate to each other?
Artificial intelligence (AI) is the broad effort to automate intellectual tasks normally performed by humans. Machine learning (ML) is a subfield of AI where systems learn rules from data rather than being hardcoded. Deep learning (DL) is a subfield of ML that learns multiple successive layers of increasingly abstract representations, typically using neural networks.How is machine learning different from traditional programming?
In traditional programming, humans write explicit rules that map inputs to outputs. In machine learning, the computer infers the rules by analyzing many input–output examples. The system is trained on data and discovers statistical patterns that let it generalize to new cases.What are the essential ingredients of a machine-learning setup?
- Input data points (for example, images, audio, or text)- Expected outputs or targets (labels, transcripts, tags, etc.)
- A way to measure performance (a loss/metric) that provides feedback to improve the model during training
What does “learning representations” mean, and why does it matter?
A representation is an alternative way of encoding data that makes a task easier (for example, RGB vs. HSV for color). Learning representations means automatically transforming raw inputs into forms that make simple rules sufficient to solve the task. Good representations turn hard problems into easy ones.What does the “deep” in deep learning refer to?
“Deep” refers to models with many successive layers, each learning a more abstract representation than the previous one. Stacking layers creates a hierarchy that distills useful information for the task. Despite the name “neural,” these models are mathematical constructs, not brain simulators.How do neural networks learn in practice?
Each layer has parameters called weights. A loss function measures how far predictions are from targets. An optimizer uses backpropagation to adjust weights in the direction that reduces loss. Starting from random weights, repeating this feedback loop over many examples (epochs) yields a trained model.What makes deep learning especially impactful compared to earlier ML?
- Simplicity: It automates feature engineering, often replacing complex pipelines with end-to-end models.- Scalability: Training parallelizes well on GPUs/accelerators and scales with data and compute.
- Versatility and reusability: Models can be updated with more data and repurposed (fine-tuned) across tasks; large “foundation models” enable broad transfer.
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