  Using de Wit-Nicolai $D=4\;\mathcal{N}=8\;SO(8)$ supergravity as an example,
we show how modern Machine Learning software libraries such as Google's
TensorFlow can be employed to greatly simplify the analysis of high-dimensional
scalar sectors of some M-Theory compactifications.
  We provide detailed information on the location, symmetries, and particle
spectra and charges of 192 critical points on the scalar manifold of SO(8)
supergravity, including one newly discovered $\mathcal{N}=1$ vacuum with
$SO(3)$ residual symmetry, one new potentially stabilizable non-supersymmetric
solution, and examples for "Galois conjugate pairs" of solutions, i.e.
solution-pairs that share the same gauge group embedding into~$SO(8)$ and
minimal polynomials for the cosmological constant. Where feasible, we give
analytic expressions for solution coordinates and cosmological constants.
  As the authors' aspiration is to present the discussion in a form that is
accessible to both the Machine Learning and String Theory communities and
allows adopting our methods towards the study of other models, we provide an
introductory overview over the relevant Physics as well as Machine Learning
concepts. This includes short pedagogical code examples. In particular, we show
how to formulate a requirement for residual Supersymmetry as a Machine Learning
loss function and effectively guide the numerical search towards supersymmetric
critical points. Numerical investigations suggest that there are no further
supersymmetric vacua beyond this newly discovered fifth solution.
