The first definition is the informal one, but at the same time seems rather convoluted to me.
I'd prefer: X and Y are conditionally independent with respect to a given Z iff
$P(X \; Y | Z) = P(X | Z ) P(Y | Z)$
Recall that conditioning one (or several) variables on the value of another, is (informally) the same as restricting the whole universe to a part of it.
Then, if you are given the value of $Z$, you can think as if you are defining new variables that are the same as the unconditioned but that are restricted to our new (smaller universe) $X' \equiv X | Z$ $Y' \equiv Y | Z$
The above formula simply states that $X'$ and $Y'$ are independent.
The first definition says the same, but applying (in words) the property that two variables are independent iff their conditioned probabilities are the same as the unconditioned : $A$ indep $B$ iff $P(A | B ) = P (A)$
Events $B_1,B_2,\ldots,B_n$ are mutually independent provided
$$
P[\cap_{k=1}^n B_k^*]=\prod_{k=1}^n P[B^*_k]
$$
for all possible choices of each $B_k^*$ as either $B_k$ or $B_k^c$. This entail $2^n$ identities that must hold.
Notice that if $B_1,B_2,B_3$ are independent in this sense, then
$\begin{align*}P[B_1\cap B_2]
&=P[B_1\cap B_2\cap B_3]+P[B_1\cap B_2\cap B_3^c]\\
&=P[B_1]P[B_2]P[B_3]+P[B_1]P[B_2]P[B_3^c]\\
&=P[B_1]P[B_2]\left\{P[B_3]+ P[B_3^c]\right\}\\
&=P[B_1]P[B_2]\end{align*}
$
etc., so that $B_1$ and $B_2$ are independent, yielding the subsequence property you mention.
Also, events $B_1,B_2,\ldots,B_n$ are mutually independent in the above sense if and only if the $\sigma$-algebras $\mathcal B_1,\ldots,\mathcal B_n$ are independent, where $\mathcal B_k=\{\emptyset,\Omega,B_k,B_k^c\}$.
Best Answer
The quote appears to be from Wikipedia: https://en.wikipedia.org/wiki/Pi-system#Independent_random_variables
Looking in earlier sections, we see $\mathcal{I}_f$ is defined under Examples
So in words, given a real-valued random variable $X$ on probability space $(\Omega, \mathcal{F}, P)$, each subset of $\Omega$ which is in the $\pi$-system $\mathcal{I}_X$ is the set of all outcomes which give a value of $X$ not larger than a specific upper bound.