This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices. The embedded inference is fast and accurate while enforcing privacy by design, as no personal user data is ever collected. Focusing on Automatic Speech Recognition and Natural Language Understanding, we detail our approach to training high-performance Machine Learning models that are small enough to run in real-time on small devices. Additionally, we describe a data generation procedure that provides sufficient, high-quality training data without compromising user privacy.1 https://www.voicebot.ai/2018/03/07/new-voicebot-report-says-nearly-20-u-sadults-smart-speakers/ 2 In French: https://www.cnil.fr/fr/enceintes-intelligentes-des-assistants-vocauxconnectes-votre-vie-privee 3 https://www.eugdpr.org/

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The first lecture contains an introduction to the replica method, along with a concrete application to the computation of the eigenvalue distribution of random matrices in the GOE. In the second lecture, the solution of the SK model is derived, along with the phenomenon of replica symmetry breaking (RSB). In the third part, the physical meaning of the RSB is explained. The ultrametricity of the space of pure states emerges as a consequence of the hierarchical RSB scheme. Moreover, it is shown how some low temperature properties of physical observables can be derived by invoking the stochastic stability principle. Lecture four contains some rigorous results on the SK model: the existence of the thermodynamic limit, and the proof of the exactness of the hierarchical RSB solution.

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