Probability of immortality and God’s existence. A mathematical perspective

What are the probabilities that this universe is repeated exactly the same with you in it again? Is God invented by human imagination or is the result of human intuition? The intuition that the same laws/mechanisms (evolution, stability winning probability) that have created something like the human being capable of self-awareness and controlling its surroundings, could create a being capable of controlling all what it exists? Will be the characteristics of the next universes random or tend to something? All these ques-tions that with different shapes (but the same essence) have been asked by human be-ings from the beginning of times will be developed in this paper

Translationese and post-editese : how comparable is comparable quality?

Whereas post-edited texts have been shown to be either of comparable quality to human translations or better, one study shows that people still seem to prefer human-translated texts. The idea of texts being inherently different despite being of high quality is not new. Translated texts, for example,are also different from original texts, a phenomenon referred to as ‘Translationese’. Research into Translationese has shown that, whereas humans cannot distinguish between translated and original text,computers have been trained to detect Translationesesuccessfully. It remains to be seen whether the same can be done for what we call Post-editese. We first establish whether humans are capable of distinguishing post-edited texts from human translations, and then establish whether it is possible to build a supervised machine-learning model that can distinguish between translated and post-edited text

Turing Test, Chinese Room Argument, Symbol Grounding Problem. Meanings in Artificial Agents

The Turing Test (TT), the Chinese Room Argument (CRA), and the Symbol Grounding Problem (SGP) are about the question “can machines think?”. We propose to look at that question through the capability for Artificial Agents (AAs) to generate meaningful information like humans. We present TT, CRA and SGP as being about generation of human-like meanings and analyse the possibility for AAs to generate such meanings. We use for that the existing Meaning Generator System (MGS) where a system submitted to a constraint generates a meaning in order to satisfy its constraint. Such system approach allows comparing meaning generation in animals, humans and AAs. The comparison shows that in order to design AAs capable of generating human-like meanings, we need the possibility to transfer human constraints to AAs. That requirement raises concerns coming from the unknown natures of life and human consciousness which are at the root of human constraints. Corresponding implications for the TT, the CRA and the SGP are highlighted. The usage of the MGS shows that designing AAs capable of thinking and feeling like humans needs an understanding about the natures of life and human mind that we do not have today. Following an evolutionary approach, we propose as a first entry point an investigation about extending life to AAs in order to design AAs carrying a “stay alive” constraint.\ud Ethical concerns are raised from the relations between human constraints and human values.\ud Continuations are proposed

How to be Helpful? Implementing Supportive Behaviors for Human-Robot Collaboration

The field of Human-Robot Collaboration (HRC) has seen a considerable amount of progress in the recent years. Although genuinely collaborative platforms are far from being deployed in real-world scenarios, advances in control and perception algorithms have progressively popularized robots in manufacturing settings, where they work side by side with human peers to achieve shared tasks. Unfortunately, little progress has been made toward the development of systems that are proactive in their collaboration, and autonomously take care of some of the chores that compose most of the collaboration tasks. In this work, we present a collaborative system capable of assisting the human partner with a variety of supportive behaviors in spite of its limited perceptual and manipulation capabilities and incomplete model of the task. Our framework leverages information from a high-level, hierarchical model of the task. The model, that is shared between the human and robot, enables transparent synchronization between the peers and understanding of each other's plan. More precisely, we derive a partially observable Markov model from the high-level task representation. We then use an online solver to compute a robot policy, that is robust to unexpected observations such as inaccuracies of perception, failures in object manipulations, as well as discovers hidden user preferences. We demonstrate that the system is capable of robustly providing support to the human in a furniture construction task

Relational Identities and Other-Than-Human Agency in Archaeology

Relational Identities and Other-than-Human Agency in Archaeology explores the benefits and consequences of archaeological theorizing on and interpretation of the social agency of nonhumans as relational beings capable of producing change in the world. The volume cross-examines traditional understanding of agency and personhood, presenting a globally diverse set of case studies that cover a range of cultural, geographical, and historical contexts. Agency (the ability to act) and personhood (the reciprocal qualities of relational beings) have traditionally been strictly assigned to humans. In case studies from Ghana to Australia to the British Isles and Mesoamerica, contributors to this volume demonstrate that objects, animals, locations, and other nonhuman actors also potentially share this ontological status and are capable of instigating events and enacting change. This kind of other-than-human agency is not a one-way transaction of cause to effect but requires an appropriate form of reciprocal engagement indicative of relational personhood, which in these cases, left material traces detectable in the archaeological record. Modern dualist ontologies separating objects from subjects and the animate from the inanimate obscure our understanding of the roles that other-than-human agents played in past societies. Relational Identities and Other-than-Human Agency in Archaeology challenges this essentialist binary perspective. Contributors in this volume show that intersubjective (inherently social) ways of being are a fundamental and indispensable condition of all personhood and move the debate in posthumanist scholarship beyond the polarizing dichotomies of relational versus bounded types of persons. In this way, the book makes a significant contribution to theory and interpretation of personhood and other-than-human agency in archaeology.https://cupola.gettysburg.edu/books/1141/thumbnail.jp

Image-based Recommendations on Styles and Substitutes

Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.Comment: 11 pages, 10 figures, SIGIR 201

Complex networks: new trends for the analysis of brain connectivity

Today, the human brain can be studied as a whole. Electroencephalography, magnetoencephalography, or functional magnetic resonance imaging techniques provide functional connectivity patterns between different brain areas, and during different pathological and cognitive neuro-dynamical states. In this Tutorial we review novel complex networks approaches to unveil how brain networks can efficiently manage local processing and global integration for the transfer of information, while being at the same time capable of adapting to satisfy changing neural demands.Comment: Tutorial paper to appear in the Int. J. Bif. Chao

Deadly Predators and Virtuous Buddhists: Dog Population Control and the Politics of Ethics in Ladakh

The region of Ladakh in the Indian Himalayas has recently seen a rise in attacks by stray dogs, some of which have been fatal. The dogs’ claims on territory have not gone uncontested in an emotional landscape fraught with anxieties over religious identities as tensions prevail between a Buddhist and a Muslim population. Consideration for the political effects of ethical discourses about dogs in Ladakh reveals how dog population control, and the intricately linked question of dog care have implications for the shaping of an animal ethics as a contentious political question. In the public sphere, some interpret matters related to dogs as a problem of human territoriality, while others foreground animal care as a virtue of Tibetan Buddhists. While these ideas about dogs and their treatment are shaped by a network of local and translocal ideas and practices about animal welfare and about religious identity, the politics of dog ethics in Ladakh is not an exclusively human product. Dogs are also agents of this politics, both in their physical capacity, to define dog-human interactions, as they are capable of being both affectionate and extremely violent, and because they have the potential to act on human’s production of meaning and exceed human expectations

Learning cloth manipulation with demonstrations

Recent advances in Deep Reinforcement learning and computational capabilities of GPUs have led to variety of research being conducted in the learning side of robotics. The main aim being that of making autonomous robots that are capable of learning how to solve a task on their own with minimal requirement for engineering on the planning, vision, or control side. Efforts have been made to learn the manipulation of rigid objects through the help of human demonstrations, specifically in the tasks such as stacking of multiple blocks on top of each other, inserting a pin into a hole, etc. These Deep RL algorithms successfully learn how to complete a task involving the manipulation of rigid objects, but autonomous manipulation of textile objects such as clothes through Deep RL algorithms is still not being studied in the community. The main objectives of this work involve, 1) implementing the state of the art Deep RL algorithms for rigid object manipulation and getting a deep understanding of the working of these various algorithms, 2) Creating an open-source simulation environment for simulating textile objects such as clothes, 3) Designing Deep RL algorithms for learning autonomous manipulation of textile objects through demonstrations.Peer ReviewedPreprin