🎯 Key points for quick navigation: Turing machines lack built-in mechanisms to detect the left end of the tape, but this limitation can be overcome by shifting the input and introducing a special symbol, such as the dollar sign, to mark the left end. Programming Turing machines involves progressively detailing implementation, akin to moving from high-level programming languages to machine code in traditional computing. Turing machines can utilize subroutines, allowing one machine to perform a task that another machine incorporates into a larger computation. Techniques like symbol marking enable complex tasks such as string comparison without altering the original data, expanding the versatility of Turing machines in problem-solving. Made with HARPA AI
The TM shown for the left-end marker technique does not work when the input is the empty string (i.e. the tape only has blanks). Since there is no transition from the start state, the TM would reject all empty inputs, regardless of whether the further computation would have accepted it or not. An easy fix would have been to add a transition _ -> $ to the initial state of the further computation (the same state that D transitions to upon reading a $)
The comparing technique is limited, in that it's construction makes it dependendant on # not being inside w as well as between the two ws to be accurate
At 9:15 you mentioned that decidable languages are a proper subset of context free language but don't you think it should be other way round?
I think that's what he meant.
+Ayush Nenawati yeah, He has his notation right but said the opposite.
Thank you for mentioning that! We all make mistakes at times.
🎯 Key points for quick navigation:
Turing machines lack built-in mechanisms to detect the left end of the tape, but this limitation can be overcome by shifting the input and introducing a special symbol, such as the dollar sign, to mark the left end.
Programming Turing machines involves progressively detailing implementation, akin to moving from high-level programming languages to machine code in traditional computing.
Turing machines can utilize subroutines, allowing one machine to perform a task that another machine incorporates into a larger computation.
Techniques like symbol marking enable complex tasks such as string comparison without altering the original data, expanding the versatility of Turing machines in problem-solving.
Made with HARPA AI
The TM shown for the left-end marker technique does not work when the input is the empty string (i.e. the tape only has blanks). Since there is no transition from the start state, the TM would reject all empty inputs, regardless of whether the further computation would have accepted it or not. An easy fix would have been to add a transition _ -> $ to the initial state of the further computation (the same state that D transitions to upon reading a $)
The comparing technique is limited, in that it's construction makes it dependendant on # not being inside w as well as between the two ws to be accurate
you can always choose a character that is not present in the string