Apperceptive patterning: Artefaction, extensional beliefs and cognitive scaffolding

In “Psychopower and Ordinary Madness” my ambition, as it relates to Bernard Stiegler’s recent literature, was twofold: 1) critiquing Stiegler’s work on exosomatization and artefactual posthumanism—or, more specifically, nonhumanism—to problematize approaches to media archaeology that rely upon technical exteriorization; 2) challenging how Stiegler engages with Giuseppe Longo and Francis Bailly’s conception of negative entropy. These efforts were directed by a prevalent techno-cultural qualifier: the rise of Synthetic Intelligence (including neural nets, deep learning, predictive processing and Bayesian models of cognition). This paper continues this project but first directs a critical analytic lens at the Derridean practice of the ontologization of grammatization from which Stiegler emerges while also distinguishing how metalanguages operate in relation to object-oriented environmental interaction by way of inferentialism. Stalking continental (Kapp, Simondon, Leroi-Gourhan, etc.) and analytic traditions (e.g., Carnap, Chalmers, Clark, Sutton, Novaes, etc.), we move from artefacts to AI and Predictive Processing so as to link theories related to technicity with philosophy of mind. Simultaneously drawing forth Robert Brandom’s conceptualization of the roles that commitments play in retrospectively reconstructing the social experiences that lead to our endorsement(s) of norms, we compliment this account with Reza Negarestani’s deprivatized account of intelligence while analyzing the equipollent role between language and media (both digital and analog)

Consistent Goal-Directed User Model for Realistic Man-Machine Task-Oriented Spoken Dialogue Simulation

International audienceBecause of the great variability of factors to take into account, designing a spoken dialogue system is still a tailoring task. Rapid design and reusability of previous work is made very difficult. For these reasons, the application of machine learning methods to dia-logue strategy optimization has become a leading subject of re-searches this last decade. Yet, techniques such as reinforcement learning are very demanding in training data while obtaining a substantial amount of data in the particular case of spoken dia-logues is time-consuming and therefore expansive. In order to expand existing data sets, dialogue simulation techniques are be-coming a standard solution. In this paper we describe a user modeling technique for realis-tic simulation of man-machine goal-directed spoken dialogues. This model, based on a stochastic description of man-machine communication, unlike previously proposed models, is consistent along the interaction according to its history and a predefined user goal

Action Selection for Interaction Management: Opportunities and Lessons for Automated Planning

The central problem in automated planning---action selection---is also a primary topic in the dialogue systems research community, however, the nature of research in that community is significantly different from that of planning, with a focus on end-to-end systems and user evaluations. In particular, numerous toolkits are available for developing speech-based dialogue systems that include not only a method for representing states and actions, but also a mechanism for reasoning and selecting the actions, often combined with a technical framework designed to simplify the task of creating end-to-end systems. We contrast this situation with that of automated planning, and argue that the dialogue systems community could benefit from some of the directions adopted by the planning community, and that there also exist opportunities and lessons for automated planning

Machine Learning Methods for Spoken Dialogue Simulation and Optimization

Computers and electronic devices are becoming more and more present in our day-to-day life. This can of course be partly explained by their ability to ease the achievement of complex and boring tasks, the important decrease of prices or the new entertainment styles they offer. Yet, this real incursion in everybody's life would not have been possible without an important improvement of Human-Computer Interfaces (HCI). This is why HCI are now widely studied and become a major trend of research among the scientific community. Designing “user-friendly” interfaces usually requires multidisciplinary skills in fields such as computer science, ergonomics, psychology, signal processing etc. In this chapter, we argue that machine learning methods can help in designing efficient speech-based humancomputer interfaces

Inducing Probabilistic Grammars by Bayesian Model Merging

We describe a framework for inducing probabilistic grammars from corpora of positive samples. First, samples are {\em incorporated} by adding ad-hoc rules to a working grammar; subsequently, elements of the model (such as states or nonterminals) are {\em merged} to achieve generalization and a more compact representation. The choice of what to merge and when to stop is governed by the Bayesian posterior probability of the grammar given the data, which formalizes a trade-off between a close fit to the data and a default preference for simpler models (`Occam's Razor'). The general scheme is illustrated using three types of probabilistic grammars: Hidden Markov models, class-based $n$-grams, and stochastic context-free grammars.Comment: To appear in Grammatical Inference and Applications, Second International Colloquium on Grammatical Inference; Springer Verlag, 1994. 13 page

Learning to ground in spoken dialogue systems

PosterMachine learning methods such as reinforcement learning applied to dialogue strategy optimization has become a leading subject of researches since the mid 90's. Indeed, the great variability of factors to take into account makes the design of a spoken dialogue system a tailoring task and reusability of previous work is very difficult. Yet, techniques such as reinforcement learning are very demanding in training data while obtaining a substantial amount of data in the particular case of spoken dialogues is time-consuming and therefore expansive. In order to expand existing data sets, dialogue simulation techniques are becoming a standard solution. In this paper, we present a user model for realistic spoken dialogue simulation and a method for using this model so as to simulate the grounding process. This allows including grounding subdialogues as actions in the reinforcement learning process and learning adapted strateg

End-to-end optimization of goal-driven and visually grounded dialogue systems

End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning. Yet, most current approaches cast human-machine dialogue management as a supervised learning problem, aiming at predicting the next utterance of a participant given the full history of the dialogue. This vision is too simplistic to render the intrinsic planning problem inherent to dialogue as well as its grounded nature, making the context of a dialogue larger than the sole history. This is why only chit-chat and question answering tasks have been addressed so far using end-to-end architectures. In this paper, we introduce a Deep Reinforcement Learning method to optimize visually grounded task-oriented dialogues, based on the policy gradient algorithm. This approach is tested on a dataset of 120k dialogues collected through Mechanical Turk and provides encouraging results at solving both the problem of generating natural dialogues and the task of discovering a specific object in a complex picture

Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic