Developing the Knowledge of Number Digits in a child like Robot

Number knowledge can be boosted initially by embodied strategies such as the use of fingers. This Article explores the perceptual process of grounding number symbols in artificial agents, particularly the iCub robot—a child-like humanoid with fully functional, five-fingered hands. It studies the application of convolutional neural network models in the context of cognitive developmental robotics, where the training information is likely to be gradually acquired while operating, rather than being abundant and fully available as in many machine learning scenarios. The experimental analyses show increased efficiency of the training and similarities with studies in developmental psychology. Indeed, the proprioceptive information from the robot hands can improve accuracy in the recognition of spoken digits by supporting a quicker creation of a uniform number line. In conclusion, these findings reveal a novel way for the humanization of artificial training strategies, where the embodiment can make the robot’s learning more efficient and understandable for humans

The political philosophy of surveillance : from historical roots to COVID-19

Many theorists have argued that surveillance has become the dominant organizing method of social activities in late modernity. Given the increased prevalence and employment of surveillance systems around the world, this thesis seeks to trace and contextualize the developments in surveillance, both theoretically and practically, that have led to its current extent and nature. We begin by analyzing the philosophical theories that provide the normative frameworks which condone, recommend, limit and make it meaningful. This comprises Jeremy Bentham’s “Panopticon,” Michel Foucault’s “Disciplinary Societies” and “Panopticism,” Fredrick Winslow Taylor’s “Scientific Management,” and Gilles Deleuze’s “Societies of Control.” Next, we describe the difference that digital technologies make to surveillance systems, namely that the former greatly enhance the latter’s ubiquity. As we shall see, COVID-19 is an important subject of analysis regarding surveillance since it has triggered an acceleration of technological development and influence. This second chapter will hence examine surveillance on three levels, describing the contexts in which surveillance has developed in each level, and how it is developing as a response to the COVID-19 pandemic. The first concerns surveillance on a National Level, with a focus on government surveillance. The second involves surveillance on a City Level, including smart city operations and workplace surveillance, and the third assesses surveillance on a Personal Level, covering social media surveillance and smart home technology. In the final chapter, we underscore certain aspects of modern surveillance practices where either Bentham, Foucault, Taylor, or Deleuze’s principles are implicit. For social media surveillance, we also draw from Shoshana Zuboff’s concept of “surveillance capitalism”. Lastly, the inherent differences and impact of surveillance operations for the current geopolitical and social order are highlighted, drawing from accounts that shed light on Autocracy’s empowerment with such technology, on Democracy’s increased potential for misuse, and on the likely repercussions of politically and socially employing surveillance systems. The conclusion then argues that surveillance, as it stands, has major potential to inherently and permanently alter the global political and social landscape

Can Artificial Intelligence (“AI”) Replace Human Arbitrators? Technological Concerns and Legal Implications

Artificial intelligence (“AI”) is no longer a precursor to the future—it is already here in the mainstream. Some countries, for example, have started to implement AI-based technologies into their adjudication processes. It has been reported that Estonia is currently developing an AI judge that can adjudicate small claims disputes of less than º7,000 and that China already has digital courts presided over by an AI judge. Together with the triggering effect of such futuristic news, AI studies that predict the outcome of litigation have stirred heated debate about the possible arrival of AI judges

SHELDON Smart habitat for the elderly.

An insightful document concerning active and assisted living under different perspectives: Furniture and habitat, ICT solutions and Healthcare

Visual Representation Learning with Minimal Supervision

Computer vision intends to provide the human abilities of understanding and interpreting the visual surroundings to computers. An essential element to comprehend the environment is to extract relevant information from complex visual data so that the desired task can be solved. For instance, to distinguish cats from dogs the feature 'body shape' is more relevant than 'eye color' or the 'amount of legs'. In traditional computer vision it is conventional to develop handcrafted functions that extract specific low-level features such as edges from visual data. However, in order to solve a particular task satisfactorily we require a combination of several features. Thus, the approach of traditional computer vision has the disadvantage that whenever a new task is addressed, a developer needs to manually specify all the features the computer should look for. For that reason, recent works have primarily focused on developing new algorithms that teach the computer to autonomously detect relevant and task-specific features. Deep learning has been particularly successful for that matter. In deep learning, artificial neural networks automatically learn to extract informative features directly from visual data. The majority of developed deep learning strategies require a dataset with annotations which indicate the solution of the desired task. The main bottleneck is that creating such a dataset is very tedious and time-intensive considering that every sample needs to be annotated manually. This thesis presents new techniques that attempt to keep the amount of human supervision to a minimum while still reaching satisfactory performances on various visual understanding tasks. In particular, this thesis focuses on self-supervised learning algorithms that train a neural network on a surrogate task where no human supervision is required. We create an artificial supervisory signal by breaking the order of visual patterns and asking the network to recover the original structure. Besides demonstrating the abilities of our model on common computer vision tasks such as action recognition, we additionally apply our model to biomedical scenarios. Many research projects in medicine involve profuse manual processes that extend the duration of developing successful treatments. Taking the example of analyzing the motor function of neurologically impaired patients we show that our self-supervised method can help to automate tedious, visually based processes in medical research. In order to perform a detailed analysis of motor behavior and, thus, provide a suitable treatment, it is important to discover and identify the negatively affected movements. Therefore, we propose a magnification tool that can detect and enhance subtle changes in motor function including motor behavior differences across individuals. In this way, our automatic diagnostic system does not only analyze apparent behavior but also facilitates the perception and discovery of impaired movements. Learning a feature representation without requiring annotations significantly reduces human supervision. However, using annotated dataset leads generally to better performances in contrast to self-supervised learning methods. Hence, we additionally examine semi-supervised approaches which efficiently combine few annotated samples with large unlabeled datasets. Consequently, semi-supervised learning represents a good trade-off between annotation time and accuracy