Similarities between Babbage’s difference engine and the Turing machine

What would you consider similarities between the difference engine and the Turing machine? At this point I feel I know how they both function, yet I can’t point out any worthwhile similarities between the two. And how do these two relate to modern phones? Other than the first two being milestones of computational science. I have this task as an assignment.

Similarities between Babbage’s difference engine and the Turing machine

What would you consider similarities between the difference engine and the Turing machine? At this point I feel I know how they both function, yet I can’t point out any worthwhile similarities between the two. And how do these two relate to modern phones? Other than the first two being milestones of computational science. I have this task as an assignment.

Is it possible for a Turing machine to halt without reading the complete input string?

Is it possible for a Turing machine to halt without reading the complete input string. Suppose there is a string “adc” preceded and succeeded by infinite number of blanks. Can a Turing machine halt after reading just “a” and never touching “dc” or any of the blanks. If yes, will this string be accepted or rejected?

How to find out the complement of a Turing machine?

With only using our thinking. What do I have to think about when finding a complement of a Turing machine for example.

L={M∣M is a TM that halts on empty tape after even transition steps} What’s the complement of L would it be:

  1. L={M∣M is a TM that does not halt on empty tape after even transition steps}
  2. L={M∣M is a TM that halts on empty tape after odd transition steps}

Please give me your train of thought when coming up with a complement for an automaton.

Turing: Are the “m-configurations” in his original paper the same as the “means” in his definition of his “computable”?

Are Turing’s “m-configurations” the same as the “means” in his original definition of “computable”?

In the first line of Turing’s paper “On Computable Numbers…”, he defines a “computable” number as follows:

A real number “for which the decimal representation can be calculated by finite means.”

My question is; are “means” to which he refers actually just the number of m-configurations which he proceeds to define shortly after the first line of his paper?

It seems that the “means” he is mentioning are the actual m-configurations, and I’m trying to understand the difference between these, and how the “steps”, “m-configurations”, and “calculations” relate.

As I understanding, it works like this;

A real number R is computable <=> There exists a “machine” with a finite number of “m-configurations” which can be used to print the decimal representation of R, even if the actual quantity of numbers which are printed on the “tape” to represent the decimal value is not finite.

— Therefore, the “means” are the number of m-configurations, and if R is computable, the machine can still perform an infinite number of actions to calculate the decimal representation of R, so long as there are only a finite number of m-configurations which produce the (infinite number) of actions.

Proof by reduction and Turing machines

This is a practice question I have, but I can’t wrap my head around it. …………. Let L = {M | M is a TM that halts with exactly two words on its tape in the form Bw1Bw2B}. B = Blank Position

the problem of deciding whether an arbitrary Turing machine will accept an arbitrary input, is undecidable. Use this result to prove, formally using problem reduction, that given an arbitrary Turing machine M, the problem of deciding if M ∈ L is undecidable. ………… I have no knowledge of proofs. I don’t have a clue how to tackle this question, can someone point me to a tutorial that works with proofs by reduction along with Turing machines.