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Our book "The Reality of Multi-Lingual Machine Translation" discusses thebenefits and perils of using more than two languages in machine translationsystems. While focused on the particular task of sequence-to-sequenceprocessing and multi-task learning, the book targets somewhat beyond the areaof natural language processing. Machine translation is for us a prime exampleof deep learning applications where human skills and learning capabilities aretaken as a benchmark that many try to match and surpass. We document that someof the gains observed in multi-lingual translation may result from simplereffects than the assumed cross-lingual transfer of knowledge.  In the first, rather general part, the book will lead you through themotivation for multi-linguality, the versatility of deep neural networksespecially in sequence-to-sequence tasks to complications of this learning. Weconclude the general part with warnings against too optimistic and unjustifiedexplanations of the gains that neural networks demonstrate.  In the second part, we fully delve into multi-lingual models, with aparticularly careful examination of transfer learning as one of the morestraightforward approaches utilizing additional languages. The recentmulti-lingual techniques, including massive models, are surveyed and practicalaspects of deploying systems for many languages are discussed. The conclusionhighlights the open problem of machine understanding and reminds of two ethicalaspects of building large-scale models: the inclusivity of research and itsecological trace.