The Turing machine is more powerful than both finite state machines (FSMs) and pushdown automata (PDAs) because it can simulate both and go beyond their computational capabilities. FSMs have no memory beyond their current state, so they can only recognise regular languages. PDAs introduce a stack, allowing them to handle some context-free languages, like balanced brackets, but are still limited by having only one stack with last-in, first-out access. In contrast, Turing machines use an infinite tape as memory, allowing for both reading and writing in both directions. This tape acts like a limitless, random-access storage medium, meaning Turing machines can track multiple variables, simulate loops and recursion, and maintain arbitrary amounts of state. They can implement any algorithm that a real computer can execute in theory. Because of this, the Turing machine is used as the standard for what is considered "computable", while FSMs and PDAs serve narrower, specific language classes within the Chomsky hierarchy.

In the standard definition of a Turing machine, the tape is either infinite in one direction (usually to the right) or in both directions. If it is infinite only to the right, then the machine cannot move left indefinitely. In this model, the machine reaches the leftmost end of the tape and cannot move further left—attempting to do so may result in a machine halting or behaving as undefined, depending on the transition function. However, many Turing machines are defined with tapes that are infinite in both directions to avoid this limitation, which makes them more flexible. In that case, the machine can move left as far as needed. For exam purposes, it's usually assumed that the Turing machine's tape is either infinite to the right or bi-infinite, and any movement off the edge must be accounted for in the machine's transition rules. If not handled, the machine may crash or halt due to lack of defined behaviour.

A Turing machine is a theoretical model with simplified assumptions, while a real-world computer is a physical machine built with practical constraints. The Turing machine has an infinite tape, which gives it limitless memory, whereas real computers have finite RAM and storage. The Turing machine operates with a single read-write head and follows one instruction at a time based on a transition function, making it inherently sequential and slow. In contrast, modern computers are capable of parallel processing, conditional branching, and can handle multiple instructions simultaneously. Turing machines lack concepts such as input/output devices, user interfaces, networking, and power management, which are all crucial in real computing systems. Despite this, any function performed by a computer program can theoretically be modelled by a Turing machine, as long as enough tape and time are provided. The differences lie in efficiency and implementation, not in computational capability.

The blank symbol, typically represented as □ or _, plays a crucial role in the Turing machine’s operation. It acts as the default or “empty” value on the tape and fills all unused squares. When the machine begins, the input is written on a section of the tape, and all remaining squares (both before and after the input) contain blank symbols. During computation, blank symbols help indicate boundaries, signal the end of input, and allow the machine to detect when it has reached unvisited or unmodified parts of the tape. For example, many machines halt when the read head encounters a blank, which can be used to detect the end of a string. Blanks also serve as a neutral symbol that can be written over without interfering with meaningful data. The transition function often includes specific behaviour for encountering a blank, making it a critical tool for managing flow control and ensuring proper termination of processes.

Yes, but not in the same way or with the same efficiency as modern software. A Turing machine can recognise patterns and perform comparisons, but it must do so using only its finite states, transition rules, and the read-write head moving across the tape. For example, to compare two binary numbers stored on the tape, the machine must sequentially scan the digits, use state changes to track differences, and mark positions on the tape to remember key values. Pattern recognition involves encoding the desired structure into the transition rules—each rule carefully crafted to identify whether the input matches the pattern. This is a much slower and more manual process than in modern programming, where you might write a simple function or use built-in tools. Nonetheless, anything that can be expressed algorithmically, including pattern matching and comparisons, can be performed by a Turing machine given enough time and tape. The process is less efficient but theoretically complete.