I argue that the contemporary interplay of cosmology and particle physics in their joint effort to understand the processes at work during the first moments of the big bang has important implications for understanding the nature of lawhood. I focus on the phenomenon of spontaneous symmetry breaking responsible for generating the masses of certain particles. This phenomenon presents problems for the currently fashionable Dretske-Tooley-Armstrong theory and strongly favors a rival nomic ontology of causal powers.

The success of deep learning in natural language processing raises intriguing questions about the nature of linguistic meaning and ways in which it can be processed by natural and artificial systems. One such question has to do with subword segmentation algorithms widely employed in language modeling, machine translation, and other tasks since 2016. These algorithms often cut words into semantically opaque pieces, such as ‘period’, ‘on’, ‘t’, and ‘ist’ in ‘period|on|t|ist’. The system then represents the resulting segments in a dense (...) vector space, which is expected to model grammatical relations among them. This representation may in turn be used to map ‘period|on|t|ist’ (English) to ‘par|od|ont|iste’ (French). Thus, instead of being modeled at the lexical level, translation is reformulated more generally as the task of learning the best bilingual mapping between the sequences of subword segments of two languages; and sometimes even between pure character sequences: ‘p|e|r|i|o|d|o|n|t|i|s|t’ → ‘p|a|r|o|d|o|n|t|i|s|t|e’. Such subword segmentations and alignments are at work in highly efficient end-to-end machine translation systems, despite their allegedly opaque nature. The computational value of such processes is unquestionable. But do they have any linguistic or philosophical plausibility? I attempt to cast light on this question by reviewing the relevant details of the subword segmentation algorithms and by relating them to important philosophical and linguistic debates, in the spirit of making artificial intelligence more transparent and explainable. (shrink)

Neural machine translation (NMT) is one of the success stories of deep learning and artificial intelligence. Revolutionary innovations in the computational architectures made in 2015–2017 have led to dramatic improvements in the quality of machine translation (MT) and changed the field forever. Some professional translators welcome these changes with enthusiasm, others less so. But everyone has to deal with them. Historically, the relationship between human translation and MT has been uneasy and complicated, but an increasing number of players in both (...) fields are now coming to view it as synergistic. -/- OPUS-CAT is a collection of machine translation software based on a Windows implementation of Marian NMT—a stable production-ready neural machine translation framework used in many industrial and research applications. It can be installed on your PC, fine-tuned to specialized translation domains, and integrated into CAT tools. (shrink)