The Illusion of Internal Joy

International audienceJ. Schmidhuber proposes a "theory of fun & intrinsic motivation & creativity" that he has developed over the last two decades. This theory is precise enough to allow the programming of artificial agents exhibiting the requested behaviors. Schmidhuber's theory relies on an explicit 'internal joy drive' implemented by an 'information compression indicator'. In this paper, we show that this indicator is not necessary as soon as the 'brain' implementation involves associative memories, i.e., hierarchical cortical maps. The 'compression factor' is replaced by the 'smallest common activation pattern' in our framework, with the advantage of an immediate and plausible neural implementation. Our conclusion states that the 'internal joy' is an illusion. This remind us of the eliminative materialism position which claims that 'free-will' is also an illusion

Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes

I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful. Curiosity is the desire to create or discover more non-random, non-arbitrary, regular data that is novel and surprising not in the traditional sense of Boltzmann and Shannon but in the sense that it allows for compression progress because its regularity was not yet known. This drive maximizes interestingness, the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and (since 1990) artificial systems.Comment: 35 pages, 3 figures, based on KES 2008 keynote and ALT 2007 / DS 2007 joint invited lectur

VIME: Variational Information Maximizing Exploration

Scalable and effective exploration remains a key challenge in reinforcement learning (RL). While there are methods with optimality guarantees in the setting of discrete state and action spaces, these methods cannot be applied in high-dimensional deep RL scenarios. As such, most contemporary RL relies on simple heuristics such as epsilon-greedy exploration or adding Gaussian noise to the controls. This paper introduces Variational Information Maximizing Exploration (VIME), an exploration strategy based on maximization of information gain about the agent's belief of environment dynamics. We propose a practical implementation, using variational inference in Bayesian neural networks which efficiently handles continuous state and action spaces. VIME modifies the MDP reward function, and can be applied with several different underlying RL algorithms. We demonstrate that VIME achieves significantly better performance compared to heuristic exploration methods across a variety of continuous control tasks and algorithms, including tasks with very sparse rewards.Comment: Published in Advances in Neural Information Processing Systems 29 (NIPS), pages 1109-111

Can intelligence explode?

The technological singularity refers to a hypothetical scenario in which technological advances virtually explode. The most popular scenario is the creation of super-intelligent algorithms that recursively create ever higher intelligences. It took many decades for these ideas to spread from science fiction to popular science magazines and finally to attract the attention of serious philosophers. David Chalmers' (JCS, 2010) article is the first comprehensive philosophical analysis of the singularity in a respected philosophy journal. The motivation of my article is to augment Chalmers' and to discuss some issues not addressed by him, in particular what it could mean for intelligence to explode. In this course, I will (have to) provide a more careful treatment of what intelligence actually is, separate speed from intelligence explosion, compare what super-intelligent participants and classical human observers might experience and do, discuss immediate implications for the diversity and value of life, consider possible bounds on intelligence, and contemplate intelligences right at the singularity

Transforming exploratory creativity with DeLeNoX

We introduce DeLeNoX (Deep Learning Novelty Explorer), a system that autonomously creates artifacts in constrained spaces according to its own evolving interestingness criterion. DeLeNoX proceeds in alternating phases of exploration and transformation. In the exploration phases, a version of novelty search augmented with constraint handling searches for maximally diverse artifacts using a given distance function. In the transformation phases, a deep learning autoencoder learns to compress the variation between the found artifacts into a lower-dimensional space. The newly trained encoder is then used as the basis for a new distance function, transforming the criteria for the next exploration phase. In the current paper, we apply DeLeNoX to the creation of spaceships suitable for use in two-dimensional arcade-style computer games, a representative problem in procedural content generation in games. We also situate DeLeNoX in relation to the distinction between exploratory and transformational creativity, and in relation to Schmidhuber’s theory of creativity through the drive for compression progress.peer-reviewe

Active Fovea-Based Vision Through Computationally-Effective Model-Based Prediction

What motivates an action in the absence of a definite reward? Taking the case of visuomotor control, we consider a minimal control problem that is how select the next saccade, in a sequence of discrete eye movements, when the final objective is to better interpret the current visual scene. The visual scene is modeled here as a partially-observed environment, with a generative model explaining how the visual data is shaped by action. This allows to interpret different action selection metrics proposed in the literature, including the Salience, the Infomax and the Variational Free Energy, under a single information theoretic construct, namely the view-based Information Gain. Pursuing this analytic track, two original action selection metrics named the Information Gain Lower Bound (IGLB) and the Information Gain Upper Bound (IGUB) are then proposed. Showing either a conservative or an optimistic bias regarding the Information Gain, they strongly simplify its calculation. An original fovea-based visual scene decoding setup is then proposed, with numerical experiments highlighting different facets of artificial fovea-based vision. A first and principal result is that state-of-the-art recognition rates are obtained with fovea-based saccadic exploration, using less than 10% of the original image's data. Those satisfactory results illustrate the advantage of mixing predictive control with accurate state-of-the-art predictors, namely a deep neural network. A second result is the sub-optimality of some classical action-selection metrics widely used in the literature, that is not manifest with finely-tuned inference models, but becomes patent when coarse or faulty models are used. Last, a computationally-effective predictive model is developed using the IGLB objective, with pre-processed visual scan-path read-out from memory, bypassing computationally-demanding predictive calculations. This last simplified setting is shown effective in our case, showing both a competing accuracy and a good robustness to model flaws

A formal descriptive theory of software-based creative practice

PhDCreative artefacts, from concert posters to architectural plans, are often created in entirely software-based workflows. Software tools can be easily made to record all user interactions, thereby capturing the observable part of creative practice. Although recording software-based creative practice is easy, analysing it is much harder. This is especially true if one wishes to analyse the cognitive process that underlies the recorded creative practice. There are currently no clear methods for the analysis of recorded creative practice, nor are there any suitable theories of the cognition underlying creative practice that can serve as the basis for the development of such methods. This thesis develops a formal descriptive theory of the cognition underlying software-based creative practice, with the aim of informing the development of analysis of recorded creative practice. The theory, called the Software-based Creative Practice Framework (SbCPF), fits with extended and predictive views of cognition. It characterises creative practice as a process of iteratively working from an abstract idea to a concrete artefact, whereby the required lowlevel detail to decide on action is imagined in flight, during practice. Furthermore, it argues that this iterative just-in-time imagination is necessary, because of the predictive nature of the mind. The SbCPF was developed through the use of a novel method for the analysis of creative practice displayed in video tutorials. This method is based on Grounded Theory, Rhetorical Structure Theory, Gesture Theory, Category Theory, and a novel taxonomy describing the relation of action to speech. The method is applied to produce a grounded theory of the creative practice of 3D modelling and animation with the Blender software. The grounded theory forms the basis of the aforementioned formal theory. Finally, the formal theory is further illustrated, evaluated, and explored by way of implementing a computational model.Queen Mary University of London, and the EPSRC Centre for Doctoral Training in Media and Arts Technology EP/G03723X/