Confused by AI Buzzwords? Let’s Break Down Neural Nets, Transformers, and LLMs
Decode the jargon and see how it all connects under the hood of AI
Everyone’s Talking About AI. But What Does It All Mean?
Neural networks. Deep learning. Transformers. Large Language Models.
If you’re just getting started in tech or robotics, it’s easy to feel like you’ve missed the memo. The terms get thrown around everywhere, but no one stops to explain how they actually fit together.
This post is meant to change that.
Let’s cut through the noise and map the landscape—so you can see where each idea belongs and how they build on each other.
From Expert Systems to Machine Learning: A Brief History of Intelligence in Machines
Artificial Intelligence sits at the top of the hierarchy. It’s the broad umbrella that includes any system—robotic or digital—that exhibits intelligence. The term was coined in the 1950s, during a wave of optimism. People imagined robot butlers, automated reasoning machines, and intelligent agents navigating the world.
That wave crashed. The tech simply wasn’t ready. By the late 1990s, researchers had stopped using “AI” in funding proposals—it had become a red flag. But underneath the surface, progress continued.
One such effort was expert systems, which dominated the 1980s. These were rule-based engines—if X is true, and Y is also true, then do Z. Hardcoded logic, decision trees, and fuzzy rules drove them. Surprisingly, some still work today. One example is the Tetracorder, a mineral analysis tool used by geologists that originated from spectral data research. (Yes, it’s named after the device from Star Trek.)
Then came a shift.
Instead of programming intelligence explicitly, what if machines could learn patterns from data?
That question gave rise to machine learning. Techniques like Support Vector Machines (SVMs) and decision forests became common. But back then, datasets were small—hundreds of rows, maybe thousands. The algorithms were clever, but constrained.
Neural Networks, Deep Learning, and the GPU Explosion
Neural networks have been around since the 1980s. They mimic a simplified version of the human brain, with interconnected “neurons” passing signals. The idea was powerful, but it didn’t scale. Until it did.
The turning point came in 2012, when a deep convolutional neural network called AlexNet blew past previous records in an image classification competition called ImageNet. The key? GPUs. Instead of using CPUs, the team trained the model on graphics processors, which are orders of magnitude faster for the types of computations neural nets require.
This was the start of the deep learning era.
Deep learning simply refers to neural networks with many layers—“deep” in structure. The more layers, the more abstract patterns a network can learn. But with depth comes complexity. During training, each layer must pass learned adjustments backward, a process called backpropagation. Managing this became its own technical art.
Still, the results were astonishing. Vision, speech, and even robotic control systems started seeing breakthroughs.
Transformers and the Rise of Large Language Models
Then came the transformer.
In 2017, researchers at Google published a paper titled “Attention Is All You Need.”
They introduced a radically new architecture that ditched the older sequence models like RNNs and LSTMs. Instead, they leaned entirely on a mechanism called attention, which allowed the model to focus on relevant parts of a sequence dynamically. Transformers were faster to train, more accurate, and far more scalable.
But they had a catch: they needed huge amounts of data.
Fortunately, companies like Google had it. And they leaned in.
This architecture became the foundation for Large Language Models (LLMs)—systems trained on massive corpora of human text to generate, summarize, translate, and reason about language. Previous attempts at language AI relied on rules and grammar trees. But LLMs learned language statistically, through patterns in real-world usage.
The results surprised everyone.
Early models struggled with long-range coherence, but transformers solved that too. With enough data and computing power, LLMs began to write code, explain jokes, translate poetry. The leap in capability was dramatic—and real.
Why This Matters for Robotics
Every robot that needs to see, hear, understand, or make decisions is part of this story.
A robot navigating a home may use deep learning for vision, reinforcement learning for control, and a transformer-based model to interpret human commands. These aren’t just buzzwords. They are building blocks that robotics engineers stitch together.
Understanding where each piece fits helps you reason about what’s possible—and what’s still hard.
If you’re building robots, you’re also building on top of decades of AI research. The tools are more powerful than ever.
But to wield them well, you need to see the full map.
Want more posts like this?
Subscribe to BuildRobotz and get insights that demystify robotics and AI—straight to your inbox.