Tacit knowledge

Summary

Tacit knowledge or implicit knowledge—as opposed to formal, codified or explicit knowledge—is knowledge that is difficult to express or extract; therefore it is more difficult to transfer to others by means of writing it down or verbalizing it. This can include motor skills, personal wisdom, experience, insight, and intuition. For example, knowing that London is in the United Kingdom is a piece of explicit knowledge; it can be written down, transmitted, and understood by a recipient. In contrast, the ability to speak a language, ride a bicycle, knead dough, play a musical instrument, or design and use complex equipment requires all sorts of knowledge which is not always known explicitly, even by expert practitioners, and which is difficult or impossible to explicitly transfer to other people. Overview Origin The term tacit knowing is attributed to Michael Polanyi's Personal Knowledge (1958). In his later work, The Tacit Dimension (1966), Polanyi

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A collaborative demand forecasting process with event-based fuzzy judgements

Omar Ayadi, Naoufel Cheikhrouhou, Philippe Wieser

Mathematical forecasting approaches can lead to reliable demand forecast in some environments by extrapolating regular patterns in time-series. However, unpredictable events that do not appear in historical data can reduce the usefulness of mathematical forecasts for demand planning purposes. Since forecasters have partial knowledge of the context and of future events, grouping and structuring the fragmented implicit knowledge, in order to be easily and fully integrated in final demand forecasts is the objective of this work. This paper presents a judgemental collaborative approach for demand forecasting in which the mathematical forecasts, considered as the basis, are adjusted by the structured and combined knowledge from different forecasters. The approach is based on the identification and classification of four types of particular events. Factors corresponding to these events are evaluated through a fuzzy inference system to ensure the coherence of the results. To validate the approach, two case studies were developed with forecasters from a plastic bag manufacturer and a distributor belonging to the food retailing industry. The results show that by structuring and combining the judgements of different forecasters to identify and assess future events, companies can experience a high improvement in demand forecast accuracy.

Elsevier2011

Humans are able to learn and compose complex, yet beautiful, pieces of music as seen in e.g. the highly complicated works of J.S. Bach. However, how our brain is able to store and produce these very long temporal sequences is still an open question. Long short-term memory (LSTM) artificial neural networks have been shown to be efficient in sequence learning tasks thanks to their inherent ability to bridge long time lags between input events and their target signals. Here, I investigate the possibility of training LSTM networks to learn and reproduce musical sequences and eventually better understand some of the mechanisms neural networks deploy to learn and compose long time scale structures. To be able to learn music with LSTM networks requires representing musical sequences in these networks. The musical representation developed for this work is inspired by the tonotopic representation of sounds in the auditory system. It is shown that LSTM networks are able to learn each note transitions of the monophonic and polyphonic versions of a simple song using a particular network architecture where both input and output of LSTM networks are musical notes in the developed network representation. However, this architecture for LSTM networks fail to learn longer and more complex musical sequences (e.g. the J.S. Bach cello suites). To solve this problem, I introduce the separation of time scales model, which consists in two connected LSTM networks, operating on different time scales. On one hand, trained slow time scale LSTM networks produce transitions between unique identifiers of musical patterns, which resemble a compressed memory of the pattern akin to neural memory. This gives the long time structure of music. On the other hand, trained fast time scale LSTM networks are producing the note-to-note transitions of each musical patterns. The latter receives as additional inputs the identifiers from slow time scale networks, akin to feed- forward input from memory regions of the brain. These unique identifiers bias fast time scale LSTM networks toward the production of the corresponding musical pattern. The most efficient identifiers of musical patterns are found to be a representation of how similar patterns are from one another. Finally, when unlearned pattern identifiers are given to trained fast time scale networks, novel musical patterns are created from the learned production rules. I show that the introduction of a separation of time scales greatly improves the capacity of LSTM networks to learn a larger body of musical sequences. Finally, I demonstrate that previously unseen input biases can be used to induce the network into the generation of new musical sequences, akin but not similar to known patterns. This presents a possible first step towards the generalization of previously learnt musical knowledge to the creation and composition of new music by artificial neural networks.

2015

Musical Garden Paths: Evidence for Syntactic Revision Beyond the Linguistic Domain

Gabriele Cecchetti, Steffen Alexander Herff, Martin Alois Rohrmeier

While theoretical and empirical insights suggest that the capacity to represent and process complex syntax is crucial in language as well as other domains, it is still unclear whether specific parsing mechanisms are also shared across domains. Focusing on the musical domain, we developed a novel behavioral paradigm to investigate whether a phenomenon of syntactic revision occurs in the processing of tonal melodies under analogous conditions as in language. We present the first proof-of-existence for syntactic revision in a set of tonally ambiguous melodies, supporting the relevance of syntactic representations and parsing with language-like characteristics in a nonlinguistic domain. Furthermore, we find no evidence for a modulatory effect of musical training, suggesting that a general cognitive capacity, rather than explicit knowledge and strategies, may underlie the observed phenomenon in music.

Wiley2022

2015