$AIC$ for model $i$ of an a priori model set can be recaled to $\mathsf{\Delta}_i=AIC_i-minAIC$ where the best model of the model set will have $\mathsf{\Delta}=0$. We can use the $\mathsf{\Delta}_i$ values to estimate strength of evidence ($w_i$) for the all models in the model set where:
$$
w_i = \frac{e^{(-0.5\mathsf{\Delta}_i)}}{\sum_{r=1}^Re^{(-0.5\mathsf{\Delta}_i)}}.
$$
This is often refered to as the "weight of evidence" for model $i$ given the a priori model set. As $\mathsf{\Delta}_i$ increases, $w_i$ decreases suggesting model $i$ is less plausible. These $w_i$ values can be interpreted as the probability that model $i$ is the best model given the a priori model set. We could also calculate the relative likelihood of model $i$ versus model $j$ as $w_i/w_j$. For example, if $w_i = 0.8$ and $w_j = 0.1$ then we could say model $i$ is 8 times more likely than model $j$.
Note, $w_1/w_2 = e^{0.5\Delta_2}$ when model 1 is the best model (smallest $AIC$). Burnham and Anderson (2002) term this as the evidence ratio. This table shows how the evidence ratio changes with respect to the best model.
Information Loss (Delta) Evidence Ratio
0 1.0
2 2.7
4 7.4
8 54.6
10 148.4
12 403.4
15 1808.0
Reference
Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. Second edition. Springer, New York, USA.
Anderson, D. R. 2008. Model based inference in the life sciences: a primer on evidence. Springer, New York, USA.
If you are using cross validation, there's usually no need to compute the AIC or the BIC. The goal of using AIC or BIC is to find the model that will do the best on future data. But cross-validation already gives you a pretty good idea of which models do well on future data (namely those with a low cross-validation error). So you can just pick your model by cross-validation, and not worry about the AIC or BIC.
Best Answer
As a reminder:
$$AIC = - 2 \log \mathcal{L}(\hat{\theta}|X)+2k $$
$$BIC = - 2 \log \mathcal{L}(\hat{\theta}|X)+k \ln(n)$$
So for what values of $n$ is $2 = \ln(n)$?