When we think about the concept of an algorithm, we are essentially talking about something that can be interpreted as a Turing Machine, and vice versa.
A Turing Machine is a formal and abstract model that represents a computer in its most primitive form. It consists of three main elements:
- A set of states, which defines the different steps in the processing.
- A tape (or memory), which can be understood as an infinite list where data is read, written, and processed.
- Transition functions, which determine how the machine changes states and interacts with the tape (by reading or writing data and moving left or right).
With these elements, we can create algorithms using a Turing Machine to interpret and process data based on an initial input.
The most fascinating thing is that, although modern computers are incredibly advanced, they can all be conceptually reduced to a simple Turing Machine. Of course, the processing would be extremely slow, but the principle remains valid.
This model was created by Alan Turing in 1936 and remains, to this day, the theoretical foundation of computing. Turing's work demonstrated that a Turing Machine can solve any computational problem that is computable, as long as there is unlimited time and memory.
Recently, I had to study this topic during my master’s program, and it has completely transformed my understanding of algorithms. Not only did it enhance my logical reasoning, but it also helped me gain a deeper understanding of how code is actually processed.
I asked to Chat GPT to create a Turing Machine image, and he gave me this back 🤔
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