In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Input: Consider Input: Since its appearance, Generative Adversarial Networks (GANs) [2] have received a lot of interest in the AI community. In image generation several projects showed how GANs are able to generate photorealistic images but the results so far didn’t look adequate for the quality standard of visual media production industry. We present an optimized image generation process based on a Deep Convolutional Generative Adversarial Networks (DCGANs), in order to create photorealistic high-resolution images (up to 1024x1024 pixels). Furthermore, the system was fed with a limited dataset of images, less than two thousand images. All these results give more clue about future exploitation of GANs in Computer Graphics and Visual Effects.

Output: Megapixel Size Image Creation using Generative Adversarial Networks


Input: Consider Input: Recent years have witnessed increasing interest in the potential benefits of ‘intelligent’ autonomous machines such as robots. Honda’s Asimo humanoid robot, iRobot’s Roomba robot vacuum cleaner and Google’s driverless cars have fired the imagination of the general public, and social media buzz with speculation about a utopian world of helpful robot assistants or the coming robot apocalypse! However, there is a long way to go before autonomous systems reach the level of capabilities required for even the simplest of tasks involving human-robot interaction especially if it involves communicative behaviour such as speech and language. Of course the field of Artificial Intelligence (AI) has made great strides in these areas, and has moved on from abstract high-level rule-based paradigms to embodied architectures whose operations are grounded in real physical environments. What is still missing, however, is an overarching theory of intelligent communicative behaviour that informs system-level design decisions in order to provide a more coherent approach to system integration. This chapter introduces the beginnings of such a framework inspired by the principles of Perceptual Control Theory (PCT). In particular, it is observed that PCT has hitherto tended to view perceptual processes as a relatively straightforward series of transformations from sensation to perception, and has overlooked the potential of powerful generative model-based solutions that have emerged in practical fields such as visual or auditory scene analysis. Starting from first principles, a sequence of arguments is presented which not only shows how these ideas might be integrated into PCT, but which also extend PCT towards a remarkably symmetric architecture for a needs-driven communicative agent. It is concluded that, if behaviour is the control of perception (the central tenet of PCT), then perception (at least for communicative agents) is the simulation of behaviour.

Output: PCT and Beyond: Towards a Computational Framework for ‘Intelligent’ Communicative Systems


Input: Consider Input: A perfectly rational decision-maker chooses the best action with the highest utility gain from a set of possible actions. The optimality principles that describe such decision processes do not take into account the computational costs of finding the optimal action. Bounded rational decision-making addresses this problem by specifically trading off information-processing costs and expected utility. Interestingly, a similar trade-off between energy and entropy arises when describing changes in thermodynamic systems. This similarity has been recently used to describe bounded rational agents. Crucially, this framework assumes that the environment does not change while the decision-maker is computing the optimal policy. When this requirement is not fulfilled, the decision-maker will suffer inefficiencies in utility, that arise because the current policy is optimal for an environment in the past. Here we borrow concepts from non-equilibrium thermodynamics to quantify these inefficiencies and illustrate with simulations its relationship with computational resources.


Output: Bounded Rational Decision-Making in Changing Environments