  This paper presents the second release of Pico (Parameters for the Impatient
COsmologist). Pico is a general purpose machine learning code which we have
applied to computing the CMB power spectra and the WMAP likelihood. For this
release, we have made improvements to the algorithm as well as the data sets
used to train Pico, leading to a significant improvement in accuracy. For the 9
parameter nonflat case presented here Pico can on average compute the TT, TE
and EE spectra to better than 1% of cosmic standard deviation for nearly all
$\ell$ values over a large region of parameter space. Performing a cosmological
parameter analysis of current CMB and large scale structure data, we show that
these power spectra give very accurate 1 and 2 dimensional parameter
posteriors. We have extended Pico to allow computation of the tensor power
spectrum and the matter transfer function. Pico runs about 1500 times faster
than CAMB at the default accuracy and about 250,000 times faster at high
accuracy. Training Pico can be done using massively parallel computing
resources, including distributed computing projects such as Cosmology@Home. On
the homepage for Pico, located at http://cosmos.astro.uiuc.edu/pico, we provide
new sets of regression coefficients and make the training code available for
public use.
