Deep learning has been widely used for supervised learning and classification/regression problems. Recently, a novel area of research has applied this paradigm to unsupervised tasks; indeed, a gradient-based approach extracts, efficiently and autonomously, the relevant features for handling input data. However, state-of-the-art techniques focus mostly on algorithmic efficiency and accuracy rather than mimic the input manifold. On the contrary, competitive learning is a powerful tool for replicating the input distribution topology. This paper introduces a novel perspective in this area by combining these two techniques: unsupervised gradient-based and competitive learning. The theory is based on the intuition that neural networks are able to learn topological structures by working directly on the transpose of the input matrix. At this purpose, the vanilla competitive layer and its dual are presented. The former is just an adaptation of a standard competitive layer for deep clustering, while the latter is trained on the transposed matrix. Their equivalence is extensively proven both theoretically and experimentally. However, the dual layer is better suited for handling very high-dimensional datasets. The proposed approach has a great potential as it can be generalized to a vast selection of topological learning tasks, such as non-stationary and hierarchical clustering; furthermore, it can also be integrated within more complex architectures such as autoencoders and generative adversarial networks.

In mid-July, a UPS subsidiary called Flight Forward and the drone company Matternet started a project with the Wake Forest Baptist Health system in North Carolina. The companies' aims are decidedly futuristic: to ferry specialty medicines and protective equipment between two of the system's facilities, less than a half-mile apart. Think of it: little flying machines, zipping about at speeds up to 43 mph, bearing the goods to heal. At this point, though, the drone operations are a little, well, human. The quadcopters must be operated by specialized drone pilots, who must pass a challenging aeronautical knowledge test to get their licenses.

In a new report called "Regulating Biometrics: Global Approaches and Urgent Questions," the AI Now Institute says that there's a growing sense among regulation advocates that a biometric surveillance state is not inevitable. The release of AI Now's report couldn't be more timely. As the pandemic drags on into the fall, businesses, government agencies, and schools are desperate for solutions that ensure safety. From tracking body temperatures at points of entry to issuing health wearables to employing surveillance drones and facial recognition systems, there's never been a greater impetus for balancing the collection of biometric data with rights and freedoms. Meanwhile, there's a growing number of companies selling what seem to be rather benign products and services that involve biometrics, but that could nonetheless become problematic or even abusive.

There is an interesting link between critical theory and certain genres of literature that may be of interest to the current debate on AI ethics. While critical theory generally points out certain deficiencies in the present to criticize it, futurology and literary genres such as Cyberpunk, extrapolate our present deficits in possible dystopian futures to criticize the status quo. Given the great advance of the AI industry in recent years, an increasing number of ethical matters have been raised and debated, usually in the form of ethical guidelines and unpublished manuscripts by governments, the private sector, and academic sources. However, recent meta-analyses in the field of AI ethics have raised important questions such as: what is being omitted from published ethical guidelines? Does AI governance occur inclusively and diversely? Is this form of "ethics", based on soft rules and principles, efficient? In this study, I would like to present aspects omitted or barely mentioned in the current debate on AI ethics and defend the point that applied ethics should not be based on creating only soft versions of real legislation, but rather on criticizing the status quo for everything of value that is disregarded.

This thesis develops a conceptual framework considering social data as representing the surface layer of a hierarchy of human social behaviours, needs and cognition which is employed to transform social data into representations that preserve social behaviours and their causalities. Based on this framework two platforms were built to capture insights from fast-paced and slow-paced social data. For fast-paced, a self-structuring and incremental learning technique was developed to automatically capture salient topics and corresponding dynamics over time. An event detection technique was developed to automatically monitor those identified topic pathways for significant fluctuations in social behaviours using multiple indicators such as volume and sentiment. This platform is demonstrated using two large datasets with over 1 million tweets. The separated topic pathways were representative of the key topics of each entity and coherent against topic coherence measures. Identified events were validated against contemporary events reported in news. Secondly for the slow-paced social data, a suite of new machine learning and natural language processing techniques were developed to automatically capture self-disclosed information of the individuals such as demographics, emotions and timeline of personal events. This platform was trialled on a large text corpus of over 4 million posts collected from online support groups. This was further extended to transform prostate cancer related online support group discussions into a multidimensional representation and investigated the self-disclosed quality of life of patients (and partners) against time, demographics and clinical factors. The capabilities of this extended platform have been demonstrated using a text corpus collected from 10 prostate cancer online support groups comprising of 609,960 prostate cancer discussions and 22,233 patients.

Amba Kak was in law school in India when the country rolled out the Aadhaar project in 2009. The national biometric ID system, conceived as a comprehensive identity program, sought to collect the fingerprints, iris scans, and photographs of all residents. It wasn't long, Kak remembers, before stories about its devastating consequences began to spread. "We were suddenly hearing reports of how manual laborers who work with their hands--how their fingerprints were failing the system, and they were then being denied access to basic necessities," she says. "We actually had starvation deaths in India that were being linked to the barriers that these biometric ID systems were creating. So it was a really crucial issue."

We define a modular multi-concept extension of the lexicographic closure semantics for defeasible description logics with typicality. The idea is that of distributing the defeasible properties of concepts into different modules, according to their subject, and of defining a notion of preference for each module based on the lexicographic closure semantics. The preferential semantics of the knowledge base can then be defined as a combination of the preferences of the single modules. The range of possibilities, from fine grained to coarse grained modules, provides a spectrum of alternative semantics.

Distributed computing is a standard way to scale up machine learning and data science algorithms to process large amounts of data. In such settings, avoiding communication amongst machines is paramount for achieving high performance. Rather than distribute the computation of existing algorithms, a common practice for avoiding communication is to compute local solutions or parameter estimates on each machine and then combine the results; in many convex optimization problems, even simple averaging of local solutions can work well. However, these schemes do not work when the local solutions are not unique. Spectral methods are a collection of such problems, where solutions are orthonormal bases of the leading invariant subspace of an associated data matrix, which are only unique up to rotation and reflections. Here, we develop a communication-efficient distributed algorithm for computing the leading invariant subspace of a data matrix. Our algorithm uses a novel alignment scheme that minimizes the Procrustean distance between local solutions and a reference solution, and only requires a single round of communication. For the important case of principal component analysis (PCA), we show that our algorithm achieves a similar error rate to that of a centralized estimator. We present numerical experiments demonstrating the efficacy of our proposed algorithm for distributed PCA, as well as other problems where solutions exhibit rotational symmetry, such as node embeddings for graph data and spectral initialization for quadratic sensing.

The Area Under the the Receiver Operating Characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain. Due to its compelling features, it has been employed in a number of studies to evaluate and compare the performance of different classifiers. In this work, we explore AUC as a performance measure in the unsupervised learning domain, more specifically, in the context of cluster analysis. In particular, we elaborate on the use of AUC as an internal/relative measure of clustering quality, which we refer to as Area Under the Curve for Clustering (AUCC). We show that the AUCC of a given candidate clustering solution has an expected value under a null model of random clustering solutions, regardless of the size of the dataset and, more importantly, regardless of the number or the (im)balance of clusters under evaluation. In addition, we demonstrate that, in the context of internal/relative clustering validation, AUCC is actually a linear transformation of the Gamma criterion from Baker and Hubert (1975), for which we also formally derive a theoretical expected value for chance clusterings. We also discuss the computational complexity of these criteria and show that, while an ordinary implementation of Gamma can be computationally prohibitive and impractical for most real applications of cluster analysis, its equivalence with AUCC actually unveils a computationally much more efficient and practical algorithmic procedure. Our theoretical findings are supported by experimental results.

Scientists have demonstrated tiny boats that float upside down underneath a levitating layer of liquid in an amazing quirk of physics. Researchers in Paris were investigating the effect of vertical shaking, which can be used to suspend a layer of liquid in mid-air. Not only was the layer of liquid able to float on a suspended cushion of air, but small model boats floated on the bottom surface, thanks to intense air pressure. This counter-intuitive behaviour is a result of the constant vibrations, which change the forces acting on the floating object. This case of'reverse-buoyancy' might have a practical uses in transporting materials through fluids and separating pollutants from water.