Suppose likelihood is $L(X; \theta)$, log likelihood is $l(X; \theta)$, then

(1) Fisher Information is second moment (and variance) of the gradient of log likelihood

Fisher information $I(\theta)= \mathbb{E}[(\frac{\mathrm{d}l}{\mathrm{d}\theta})^2| \theta]  = Var(\frac{\mathrm{d}l}{\mathrm{d}\tilde{\theta}}| \theta)$

since $\mathbb{E}(\frac{\mathrm{d}l}{\mathrm{d}\theta}| \theta)=0$ (Proof)

(2) Fisher information is related to asymptotic distribution of MLE $\hat{\theta}_{MLE}$

By CLT and slutsky theorem, we can conclude that $$\sqrt{n}(\hat{\theta}_{MLE}-\theta) \overset{p}{\to} N(0, I(\theta)^{-1})$$

Applications: Cramer Rao Bound

Under regularity conditions, the variance of any unbiased estimator ${\hat{\theta }}$ of  $\theta$  is then bounded by inverse of Fisher information $I(\theta)$ with 

$$Var(\hat{\theta}) \geq \frac{1}{I(\theta)}$$

Note that this CR lower bound is just a "theoretical" lower bound, which means that it may not be applicable (i.e. fail to satisfy regularity conditions) or attainable (can't reach lower bound)

e.g.

CR applicable but not attainable for estimating $\sigma^2$ when $X \overset{i.i.d} \sim N(\mu, \sigma^2)$ since $var(s^2)= \frac{2\sigma^4}{n-1} > \frac{2\sigma^4}{n} = $CR bound.

Reference: 

Sinho Chewi's Theoretical Statistics Note

Fisher Information