  We use artificial intelligence (AI) to learn and infer the physics of higher
order gravitational wave modes of quasi-circular, spinning, non precessing
binary black hole mergers. We trained AI models using 14 million waveforms,
produced with the surrogate model NRHybSur3dq8, that include modes up to $\ell
\leq 4$ and $(5,5)$, except for $(4,0)$ and $(4,1)$, that describe binaries
with mass-ratios $q\leq8$, individual spins $s^z_{\{1,2\}}\in[-0.8, 0.8]$, and
inclination angle $\theta\in[0,\pi]$.Our probabilistic AI surrogates can
accurately constrain the mass-ratio, individual spins, effective spin, and
inclination angle of numerical relativity waveforms that describe such signal
manifold. We compared the predictions of our AI models with Gaussian process
regression, random forest, k-nearest neighbors, and linear regression, and with
traditional Bayesian inference methods through the PyCBC Inference toolkit,
finding that AI outperforms all these approaches in terms of accuracy, and are
between three to four orders of magnitude faster than traditional Bayesian
inference methods. Our AI surrogates were trained within 3.4 hours using
distributed training on 1,536 NVIDIA V100 GPUs in the Summit supercomputer.
