Computational Creativity

In: Encyclopedia of Systems Biology, W. Dubitzky, O. Wolkenhauer, K-H Cho, H. Yokota (Eds.), Springer 2011Understanding brain processes behind creativity and modeling them using computational means is one of the grand challenges for systems biology. Computational creativity is a new field, inspired by cognitive psychology and neuroscience. In many respects human-level intelligence is far beyond what artificial intelligence can provide now, especially in regard to the high-level functions, involving thinking, reasoning, planning and the use of language. Intuition, insight, imagery and creativity are important aspects of all these functions

Similarity-based methods: a general framework for classification, approximation and association

Similarity-based methods (SBM) are a generalization of the minimal distance (MD) methods which form a basis of several machine learning and pattern recognition methods. Investigation of similarity leads to a fruitful framework in which many classification, approximation and association methods are accommodated. Probability p(C|X;M) of assigning class C to a vector X, given a classification modelM, depends on adaptive parameters and procedures used in construction of the model. Systematic overview of choices available for model building is described and numerous improvements suggested. Similarity-Based Methods have natural neural-network type realizations. Such neural network models as the Radial Basis Functions (RBF) and the Multilayer Perceptrons (MLPs) are included in this framework as special cases. SBM may also include several different submodels and a procedure to combine their results. Many new versions of similarity-based methods are derived from this framework. A search in the space of all methods belonging to the SBM framework finds a particular combination of parameterizations and procedures that is most appropriate for a given data. No single classification method can beat this approach. Preliminary implementation of SBM elements tested on a realworld datasets gave very good results

What can we know about ourselves and how do we know it?

Recent developments in cognitive neuroscience radically changed the perspective on understanding human nature. For the first time in history many philosophical questions can be placed on scientific, rather than on philosophical grounds. These questions include understanding of the mind, self, free will, religious and cultural beliefs, morality, politics and social organization. Scientific consensus based on these discoveries is slowly being developed and will have far reaching consequences. Evolutionary perspective explains how homo sapiens has evolved, why do we have specific structures of the body, brain, sensory abilities, and how the mind emerges from embodiment and social in teractions. Social neuroscience shows that there is emergent causality: biology determines affective and cognitive abilities, preferences and beliefs, personality, but it is itself influenced by the environment that changes our brains and bodies. All these mechanisms are deeply hidden from ordinary introspection, creating a wrong perception of human nature. Traditional views on human nature are briefly summa rized and radical reductionist inerpretations of neurobiologists presented, comparing humans to a bag of chemicals. Scientific discoveries cannot be ignored, but their interpretation is not so obvious. Problems with describing our mental states and knowing ourselves are analyzed. Treating brains as a substrate that enables partially autonomic mental processes, and identifying oneself with the whole organims rather than some abstract model of self, allows for more optimistic interpretation o

Brains and Education: Towards Neurocognitive Phenomics

Phenomics is concerned with detailed description of all aspects of organisms, from their physical foundations at genetic, molecular and cellular level, to behavioural and psychological traits. Neuropsychiatric phenomics tries to understand mental disease from such broad perspective. It is clear that learning sciences also need similar approach that should integrate efforts to understand cognitive processes from the perspective of the brain development, in temporal, spatial, psychological and social aspects. A new branch of science called neurocognitive phenomics is proposed, treating the brain as a substrate shaped by the genetic, epigenetic, cellular and environmental factors, in which learning processes due to the individual experiences, social contacts, education and culture take place. A brief review of selected aspects, from genes to learning styles, is presented, and a link between central, peripheral and motor processes in the brain linked to learning styles

Meta-learning

In: Encyclopedia of Systems Biology, W. Dubitzky, O. Wolkenhauer, K-H Cho, H. Yokota (Eds.), Springer 2011Meta-learning methods are aimed at automatic discovery of interesting models of data. They belong to a branch of Machine Learning that tries to replace human experts involved in the Data Mining process of creating various computational models learning from data

Artificial intelligence and the limits of the humanities

The complexity of cultures in the modern world is now beyond human comprehension. Cognitive sciences cast doubts on the traditional explanations based on mental models. The core subjects in humanities may lose their importance. Humanities have to adapt to the digital age. New, interdisciplinary branches of humanities emerge. Instant access to information will be replaced by instant access to knowledge. Understanding the cognitive limitations of humans and the opportunities opened by the development of artificial intelligence and interdisciplinary research necessary to address global challenges is the key to the revitalization of humanities. Artificial intelligence will radically change humanities, from art to political sciences and philosophy, making these disciplines attractive to students and enabling them to go beyond current limitations.Comment: 39 pages, 1 figur