Modeling 13 Archimedean solids by and object-oriented language

The computational study of structures with chemical relevance is preceded by its modeling in such manner that no calculations can be submitted without the knowledge of their spatial atomic arrangement. In this regard, the use of an object-oriented language can be helpful both to generate the Cartesian coordinates (.xyz file format) and to obtain a ray-traced image. The modeling of chemical structures based on programming has some advantages with respect to other known strategies. The more important advantage is the generation of Cartesian coordinates that can be visualized easily by using free of charge software. Our approach facilitates the spatial vision of complex structures and make tangible the chemistry concepts delivered in the classroom. In this article an undergraduate project is described in which students generate the Cartesian coordinates of 13 Archimedean solids based on a geometrical/programming approach. Students were guided along the project and meetings were held to integrate their ideas in a few lines of programmed codes. They improved their decision-making process and their organization and collecting information capabilities, as much as their reasoning and spatial depth. The final products of this project are the coded algorithms and those made tangible the grade of learning/understanding derived of this activity.

How organisms come to know the world: fundamental limits on artificial general intelligence

Artificial intelligence has made tremendous advances since its inception about seventy years ago. Self-driving cars, programs beating experts at complex games, and smart robots capable of assisting people that need care are just some among the successful examples of machine intelligence. This kind of progress might entice us to envision a society populated by autonomous robots capable of performing the same tasks humans do in the near future. This prospect seems limited only by the power and complexity of current computational devices, which is improving fast. However, there are several significant obstacles on this path. General intelligence involves situational reasoning, taking perspectives, choosing goals, and an ability to deal with ambiguous information. We observe that all of these characteristics are connected to the ability of identifying and exploiting new affordances—opportunities (or impediments) on the path of an agent to achieve its goals. A general example of an affordance is the use of an object in the hands of an agent. We show that it is impossible to predefine a list of such uses. Therefore, they cannot be treated algorithmically. This means that “AI agents” and organisms differ in their ability to leverage new affordances. Only organisms can do this. This implies that true AGI is not achievable in the current algorithmic frame of AI research. It also has important consequences for the theory of evolution. We argue that organismic agency is strictly required for truly open-ended evolution through radical emergence. We discuss the diverse ramifications of this argument, not only in AI research and evolution, but also for the philosophy of science.

D. W. Winnicott, André Green, and Rosemary Dinnage: Some Thoughts on the Interplay of Transitional Objects and Object Destruction

In this paper, the author attempts to arrive at a comprehensive outline of Winnicott's developmental theory. This theory encompasses the infant's emergence from total dependence and subject/object merging to what the author refers to as relative independence and relative subject/object separation (in Winnicott's words, “separation that is a not a separation but a form of union” [1971a, p. 98]). This conceptualization is based mainly on an amalgam of Winnicott's two well-known papers, on transitional objects and phenomena (1953) and on the use of an object (1969). The author also refers to André Green's notions of the importance of the negative and of the “dead mother” in reference to Winnicott's work. To demonstrate the clinical implications of the paper, the author discusses in detail the case of Rosemary Dinnage, as described by both Winnicott and Green and as reported directly by herself.

Sustained Pacifier Use is Associated with Smaller Vocabulary Sizes at 1 and 2 Years of Age.

Pacifier use during childhood has been hypothesised to interfere with language processing.Recent evidence suggests that transient use of an object in the infant’s mouth (a teething toy)impairs speech sound discrimination and that extensive pacifier use translates into slowerprocessing of abstract words at 7-8 years, but to date no studies have revealed detrimentaleffects of prolonged pacifier use on infant vocabulary learning. The present pre-registeredstudy tests the hypothesis that greater accumulated pacifier use is associated with smallervocabulary sizes at 12- (in comprehension and production) and 24-months of age (inproduction).

Impact of population size at a rate of morphological and molecular evolution – the use of an object-oriented model

A generalization of Moran model of evolution is created using object-oriented method of modelling. A population consists of individuals which have a genotype and a phenotype. The genotype is inherited by descendants and it can mutate. The phenotype is dependent on the genotype. Moreover, the phenotype causes changes in the fitness of the individuals (natural selection which four kinds are defined and analysed). Evolution of the population appears spontaneously. This model is used to analyse how population size influence the rate of evolution. Evolution is manifested by two processes: the increase of the phenotype size (morphological evolution) and number of mutations accumulated on genes (molecular evolution). The rate of evolution increases if population size increases. An adaptive natural selection causes nonlinear changes in the phenotype size and number of mutations accumulated on genes. A competitive natural selection causes linear evolution. A surviving natural selection causes the faster evolution than a reproductive natural selection.

Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks

There are many reasons to expect an ability to reason in terms of objects to be a crucial skill for any generally intelligent agent. Indeed, recent machine learning literature is replete with examples of the benefits of object-like representations: generalization, transfer to new tasks, and interpretability, among others. However, in order to reason in terms of objects, agents need a way of discovering and detecting objects in the visual world - a task which we call unsupervised object detection. This task has received significantly less attention in the literature than its supervised counterpart, especially in the case of large images containing many objects. In the current work, we develop a neural network architecture that effectively addresses this large-image, many-object setting. In particular, we combine ideas from Attend, Infer, Repeat (AIR), which performs unsupervised object detection but does not scale well, with recent developments in supervised object detection. We replace AIR’s core recurrent network with a convolutional (and thus spatially invariant) network, and make use of an object-specification scheme that describes the location of objects with respect to local grid cells rather than the image as a whole. Through a series of experiments, we demonstrate a number of features of our architecture: that, unlike AIR, it is able to discover and detect objects in large, many-object scenes; that it has a significant ability to generalize to images that are larger and contain more objects than images encountered during training; and that it is able to discover and detect objects with enough accuracy to facilitate non-trivial downstream processing.