16,912 research outputs found
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)
Inductive Visual Localisation: Factorised Training for Superior Generalisation
End-to-end trained Recurrent Neural Networks (RNNs) have been successfully
applied to numerous problems that require processing sequences, such as image
captioning, machine translation, and text recognition. However, RNNs often
struggle to generalise to sequences longer than the ones encountered during
training. In this work, we propose to optimise neural networks explicitly for
induction. The idea is to first decompose the problem in a sequence of
inductive steps and then to explicitly train the RNN to reproduce such steps.
Generalisation is achieved as the RNN is not allowed to learn an arbitrary
internal state; instead, it is tasked with mimicking the evolution of a valid
state. In particular, the state is restricted to a spatial memory map that
tracks parts of the input image which have been accounted for in previous
steps. The RNN is trained for single inductive steps, where it produces updates
to the memory in addition to the desired output. We evaluate our method on two
different visual recognition problems involving visual sequences: (1) text
spotting, i.e. joint localisation and reading of text in images containing
multiple lines (or a block) of text, and (2) sequential counting of objects in
aerial images. We show that inductive training of recurrent models enhances
their generalisation ability on challenging image datasets.Comment: In BMVC 2018 (spotlight
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
Technology assessment of advanced automation for space missions
Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology
SEPIA: Search for Proofs Using Inferred Automata
This paper describes SEPIA, a tool for automated proof generation in Coq.
SEPIA combines model inference with interactive theorem proving. Existing proof
corpora are modelled using state-based models inferred from tactic sequences.
These can then be traversed automatically to identify proofs. The SEPIA system
is described and its performance evaluated on three Coq datasets. Our results
show that SEPIA provides a useful complement to existing automated tactics in
Coq.Comment: To appear at 25th International Conference on Automated Deductio
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