Why do the definitions of non-deterministic Turing machines look strange? I read some definitions of the NDTM in several books. Something makes me confused.

Some definitions say that the NDTM $M$ makes an arbitrary choice as to which of its transition functions to apply.

So, if $x \in L(M)$, then $M$ can always guess a branch which leads to $q_{accept}$. And if $x \not\in L(M)$, then $M$ just chooses an arbitrary branch until it enters $q_{reject}$. It seems that $M$ knows the result after reading $x$ because the rule which tells $M$ how to guess depends on whether $x$ is in $L(M)$.

It looks so strange! 

Another definitions say that $M$ executes all branches in parallel.

That looks better. And it shows that if $x \in L(M)$, then $M$ halts when it enters $q_{accept}$ at the first time. However, the running time of NDTM is defined as the maximum number of steps that $M$ uses on any branch of its computation on any input. I am not sure if the definitions of the running time of NDTM are equivalent.
Basically, a NDTM on input $x$ may have more than one computation branches. If at least one of these branches accepts the input then we say the NDTM accepts the input $x$, otherwise it rejects.

You can consider a NDTM as a finite number computation branches such that branch on input$x$ may halt with accept or reject, or run forever. If at least one branch halts with accept then the NDTM accepts that input no matter other branches halts or run forever. If no branch accepts (halts with in ACCEPT state) then we say the NDTM does not accept the input. And if every branch halts and rejects then we say that NDTM rejects the input.

Do not try look for a real life model of a NDTM. It is just a conceptual computation model which allows to define a complexity class known as $\mathrm{\mathbf{NP}}$ class.

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