Artificial Intelligence in the Creative Industries: A Review

This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the `creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity

Generative Temporal Models with Spatial Memory for Partially Observed Environments

In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations during training or via use as part of an explicit planning mechanism. However, their application in practice has been limited to simplistic environments, due to the difficulty of training such models in larger, potentially partially-observed and 3D environments. In this work we introduce a novel action-conditioned generative model of such challenging environments. The model features a non-parametric spatial memory system in which we store learned, disentangled representations of the environment. Low-dimensional spatial updates are computed using a state-space model that makes use of knowledge on the prior dynamics of the moving agent, and high-dimensional visual observations are modelled with a Variational Auto-Encoder. The result is a scalable architecture capable of performing coherent predictions over hundreds of time steps across a range of partially observed 2D and 3D environments.Comment: ICML 201

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

O passado revolucionário: descolonizando o direito e os direitos humanos

Combining a radical revision of the historical formation of occidental law with perspectives derived from decolonial thought, this paper advances a deconstruction of occidental law. That deconstruction is then brought to bear on human rights. Although occidental law and human rights are shown in this way to be imperial in orientation, that same deconstruction reveals resistant elements in law and in human rights. These are elements which the decolonial can draw on in its commitment to intercultural transformation

Amergent Music: behavior and becoming in technoetic & media arts

Merged with duplicate records 10026.1/1082 and 10026.1/2612 on 15.02.2017 by CS (TIS)Technoetic and media arts are environments of mediated interaction and emergence, where meaning is negotiated by individuals through a personal examination and experience—or becoming—within the mediated space. This thesis examines these environments from a musical perspective and considers how sound functions as an analog to this becoming. Five distinct, original musical works explore the possibilities as to how the emergent dynamics of mediated, interactive exchange can be leveraged towards the construction of musical sound. In the context of this research, becoming can be understood relative to Henri Bergson’s description of the appearance of reality—something that is making or unmaking but is never made. Music conceived of a linear model is essentially fixed in time. It is unable to recognize or respond to the becoming of interactive exchange, which is marked by frequent and unpredictable transformation. This research abandons linear musical approaches and looks to generative music as a way to reconcile the dynamics of mediated interaction with a musical listening experience. The specifics of this relationship are conceptualized in the structaural coupling model, which borrows from Maturana & Varela’s “structural coupling.” The person interacting and the generative musical system are compared to autopoietic unities, with each responding to mutual perturbations while maintaining independence and autonomy. Musical autonomy is sustained through generative techniques and organized within a psychogeographical framework. In the way that cities invite use and communicate boundaries, the individual sounds of a musical work create an aural context that is legible to the listener, rendering the consequences or implications of any choice audible. This arrangement of sound, as it relates to human presence in a technoetic environment, challenges many existing assumptions, including the idea “the sound changes.” Change can be viewed as a movement predicated by behavior. Amergent music is brought forth through kinds of change or sonic movement more robustly explored as a dimension of musical behavior. Listeners hear change, but it is the result of behavior that arises from within an autonomous musical system relative to the perturbations sensed within its environment. Amergence propagates through the effects of emergent dynamics coupled to the affective experience of continuous sonic transformation.Rutland Port Authoritie