How a “boring theory subject” ended up shaping my entire AI career
When I was doing my Master’s in computer engineering at the University of Padova, there was one subject everyone whispered about:
Automata Theory & Computation.
Not because it was exciting…
…but because most students wanted to survive it.
I remember sitting in the lecture hall asking myself:
“Why are we learning about an imaginary tape machine in 2024?
I want to build AI systems, not decode puzzles from 1936.”
What I didn’t know was that this single subject—the one we all underestimated—would quietly reshape the way I think about AI, computation, and even my day-to-day engineering work.
Let me tell you how.
The Day I Realized a Neural Network Is Not Magic
Months later, when I was working on high-speed machine vision projects (with 1ms deadlines), something struck me:
Everything I was building, every pipeline, every RL loop, every segmentation model could be reduced to:
State -> Transition -> New State
Exactly like the thing I thought was useless in university.
Suddenly, the Turing Machine wasn’t a historical artifact.
It was a mirror showing me the essence of modern AI.
A lot of students think the Turing Machine is just a boring theoretical device.
But in reality, it answers two of the most important questions in modern AI:
- What can be computed?
- What cannot be computed by ANY machine — even GPT-50?
No matter how big or advanced a neural network becomes, it still cannot solve anything beyond what a Turing Machine can solve.
This means AI is still bound by:
- undecidable problems
- halting limitations
- computational complexity
Modern AI might look magical, but it does not break the laws established in 1936.
The Classic Turing Machine We All Ignored.
Back then, it was just this.
A tape.
Some states.
A transition function.
But what I didn’t understand was:
This is literally the foundation of all computation including today’s AI.
Modern AI Model vs. Turing Machine
At a conceptual level:
A Transformer is a sophisticated state machine
built on a theory created in the 1930s.
Mind = blown.
Mine definitely was.
The Hidden Superpower of Automata Theory
Once you get it, something changes:
You stop thinking like a coder.
You start thinking like a computational architect.
Automata teaches you:
- how to break problems down into minimal logic
- how to reason in sequences (vital for NLP + RL)
- why some problems are inherently slow
- why some optimizations are impossible
- how systems transition, not just compute
Most importantly:
It gives you a mental model so strong
that AI becomes less of a black box and more of a predictable system.
Every pipeline is a giant state machine.
Even the most advanced RLHF systems.
Even computer vision.
Even GPT.
This is what separates AI “users” from AI “engineers.”
Final Message to New AI Learners
If you’re entering AI today…
Don’t skip the fundamentals.
Don’t choose short-term speed over long-term mastery.
And don’t underestimate the Turing Machine the way I did.
Because when the hype fades and trust me, it will,
the engineers who understand theory
are the ones who keep building the future.
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