On the independence between phenomenal consciousness and computational intelligence

Consciousness and intelligence are properties commonly understood as dependent by folk psychology and society in general. The term artificial intelligence and the kind of problems that it managed to solve in the recent years has been shown as an argument to establish that machines experience some sort of consciousness. Following the analogy of Russell, if a machine is able to do what a conscious human being does, the likelihood that the machine is conscious increases. However, the social implications of this analogy are catastrophic. Concretely, if rights are given to entities that can solve the kind of problems that a neurotypical person can, does the machine have potentially more rights that a person that has a disability? For example, the autistic syndrome disorder spectrum can make a person unable to solve the kind of problems that a machine solves. We believe that the obvious answer is no, as problem solving does not imply consciousness. Consequently, we will argue in this paper how phenomenal consciousness and, at least, computational intelligence are independent and why machines do not possess phenomenal consciousness, although they can potentially develop a higher computational intelligence that human beings. In order to do so, we try to formulate an objective measure of computational intelligence and study how it presents in human beings, animals and machines. Analogously, we study phenomenal consciousness as a dichotomous variable and how it is distributed in humans, animals and machines. As phenomenal consciousness and computational intelligence are independent, this fact has critical implications for society that we also analyze in this work.

Towards Automatic Bayesian Optimization: A first step involving acquisition functions

Bayesian Optimization is the state of the art technique for the optimization of black boxes, i.e., functions where we do not have access to their analytical expression nor its gradients, they are expensive to evaluate and its evaluation is noisy. The most popular application of bayesian optimization is the automatic hyperparameter tuning of machine learning algorithms, where we obtain the best configuration of machine learning algorithms by optimizing the estimation of the generalization error of these algorithms. Despite being applied with success, bayesian optimization methodologies also have hyperparameters that need to be configured such as the probabilistic surrogate model or the acquisition function used. A bad decision over the configuration of these hyperparameters implies obtaining bad quality results. Typically, these hyperparameters are tuned by making assumptions of the objective function that we want to evaluate but there are scenarios where we do not have any prior information about the objective function. In this paper, we propose a first attempt over automatic bayesian optimization by exploring several heuristics that automatically tune the acquisition function of bayesian optimization. We illustrate the effectiveness of these heurisitcs in a set of benchmark problems and a hyperparameter tuning problem of a machine learning algorithm.

A Machine Consciousness architecture based on Deep Learning and Gaussian Processes

Recent developments in machine learning have pushed the tasks that machines can do outside the boundaries of what was thought to be possible years ago. Methodologies such as deep learning or generative models have achieved complex tasks such as generating art pictures or literature automatically. On the other hand, symbolic resources have also been developed further and behave well in problems such as the ones proposed by common sense reasoning. Machine Consciousness is a field that has been deeply studied and several theories based in the functionalism philosophical theory like the global workspace theory or information integration have been proposed that try to explain the ariseness of consciousness in machines. In this work, we propose an architecture that may arise consciousness in a machine based in the global workspace theory and in the assumption that consciousness appear in machines that has cognitive processes and exhibit conscious behaviour. This architecture is based in processes that use the recent developments in artificial intelligence models which output are these correlated activities. For every one of the modules of this architecture, we provide detailed explanations of the models involved and how they communicate with each other to create the cognitive architecture.