Goal recognition is the problem of recognizing the intended goal of autonomous agents or humans by observing their behavior in an environment. Over the past years, most existing approaches to goal and plan recognition have been ignoring the need to deal with imperfections regarding the domain model that formalizes the environment where autonomous agents behave. In this thesis, we introduce the problem of goal recognition over imperfect domain models, and develop solution approaches that explicitly deal with two distinct types of imperfect domains models: (1) incomplete discrete domain models that have possible, rather than known, preconditions and effects in action descriptions; and (2) approximate continuous domain models, where the transition function is approximated from past observations and not well-defined. We develop novel goal recognition approaches over imperfect domains models by leveraging and adapting existing recognition approaches from the literature. Experiments and evaluation over these two types of imperfect domains models show that our novel goal recognition approaches are accurate in comparison to baseline approaches from the literature, at several levels of observability and imperfections.

This article identifies a critical incompatibility between European notions of discrimination and existing statistical measures of fairness. First, we review the evidential requirements to bring a claim under EU non-discrimination law. Due to the disparate nature of algorithmic and human discrimination, the EU's current requirements are too contextual, reliant on intuition, and open to judicial interpretation to be automated. Second, we show how the legal protection offered by non-discrimination law is challenged when AI, not humans, discriminate. Humans discriminate due to negative attitudes (e.g. stereotypes, prejudice) and unintentional biases (e.g. organisational practices or internalised stereotypes) which can act as a signal to victims that discrimination has occurred. Finally, we examine how existing work on fairness in machine learning lines up with procedures for assessing cases under EU non-discrimination law. We propose "conditional demographic disparity" (CDD) as a standard baseline statistical measurement that aligns with the European Court of Justice's "gold standard." Establishing a standard set of statistical evidence for automated discrimination cases can help ensure consistent procedures for assessment, but not judicial interpretation, of cases involving AI and automated systems. Through this proposal for procedural regularity in the identification and assessment of automated discrimination, we clarify how to build considerations of fairness into automated systems as far as possible while still respecting and enabling the contextual approach to judicial interpretation practiced under EU non-discrimination law. N.B. Abridged abstract

The lectern stands before a vast, grassy amphitheater. Above, the sky is a pristine blue, the cloudless quintessence of a spring morning. Perhaps the most arresting feature in this panorama is the stage: a towering neoclassical structure with Doric columns that soar heavenward. Suspended from the columns are banners emblazoned with the letters QU, for Quaranteen University. The name is a wink from the worldbuilders--college and high school students, homebound for weeks now--about the surreal circumstances of their situation.

For many ASEAN nations, manufacturing takes up a sizable chunk of the national GDP. For example, manufacturing in Singapore contributed 20.9 percent of Singapore's GDP in 2019, according to the department of statistics Singapore. Successful companies readily acknowledge one key factor contributing to their achievements – hardworking, committed and skilled employees who are the foundation of the companies. However, manufacturers today face the primary challenge of filling open positions with skilled workers, which in turn affects overall productivity and growth. In addition to the human capital challenge, manufacturers are facing immense costs associated with workplace injuries.

New and Updated Deep Learning Institute Courses Launched, With More Training Delivery Partners Added. Starting this month, NVIDIA's Deep Learning Institute is offering instructor-led workshops that are delivered remotely via a virtual classroom. DLI provides hands-on training in AI, accelerated computing and accelerated data science to help developers, data scientists and other professionals solve their most challenging problems. These in-depth classes are taught by experts in their respective fields, delivering industry-leading technical knowledge to drive breakthrough results for individuals and organizations. DLI has already trained more than 200,000 developers globally and is growing quickly to bridge the digital skills gap worldwide.

Afew years ago, while driving in my father's car, he started to ask me questions about my education, future, life, goals, etc. He asked me: "How big is the job market for your university major?" At the time, I was completing my degree in Artificial Intelligence. I replied: "It's very big." He nodded and continued: "What is this Artificial Intelligence exactly?"

Many tasks aim to measure machine reading comprehension (MRC), often focusing on question types presumed to be difficult. Rarely, however, do task designers start by considering what systems should in fact comprehend. In this paper we make two key contributions. First, we argue that existing approaches do not adequately define comprehension; they are too unsystematic about what content is tested. Second, we present a detailed definition of comprehension -- a "Template of Understanding" -- for a widely useful class of texts, namely short narratives. We then conduct an experiment that strongly suggests existing systems are not up to the task of narrative understanding as we define it.

Federated learning is a rapidly evolving application of distributed optimization for estimation and learning problems in large-scale networks of remote clients [13]. These systems present new challenges, as they are characterized by heterogeneity in computational resources and data across the network, unreliable communication, massive scale, and privacy constraints [16]. A typical application is for developers of cell phones and cellular applications to model the usage of software and devices across millions or even billions of users. Distributed optimization has a rich history and extensive literature (e.g., see the sources [2, 5, 8, 31, 15, 24] and references therein), and federated learning has led to a flurry of interest in the area. A number of different procedures have been proposed for federated learning and related problems, using methods based on stochastic gradient methods or proximal procedures. Notably, McMahan et al. [18] introduced the FedSGD and FedAvg algorithms, which both adapt the classical stochastic gradient method to the federated setting, considering the possibility that clients may fail and may only be subsampled on each round of computation. Another recent proposal has been to use regularized local problems to mitigate possible issues that arise with device heterogeneity and failures [17]. These authors propose the FedProx procedure, an algorithm that applied averaged proximal updates to solve federated minimization problems. Currently, the convergence theory and correctness of these methods is currently lacking, and practitioners have documented failures of convergence in certain settings (e.g., see Figure 3 and related discussion in the work [18]).

In this paper we propose and analyze inexact and stochastic versions of the CGALP algorithm developed in the authors' previous paper, which we denote ICGALP, that allows for errors in the computation of several important quantities. In particular this allows one to compute some gradients, proximal terms, and/or linear minimization oracles in an inexact fashion that facilitates the practical application of the algorithm to computationally intensive settings, e.g. in high (or possibly infinite) dimensional Hilbert spaces commonly found in machine learning problems. The algorithm is able to solve composite minimization problems involving the sum of three convex proper lower-semicontinuous functions subject to an affine constraint of the form $Ax=b$ for some bounded linear operator $A$. Only one of the functions in the objective is assumed to be differentiable, the other two are assumed to have an accessible prox operator and a linear minimization oracle. As main results, we show convergence of the Lagrangian to an optimum and asymptotic feasibility of the affine constraint as well as weak convergence of the dual variable to a solution of the dual problem, all in an almost sure sense. Almost sure convergence rates, both pointwise and ergodic, are given for the Lagrangian values and the feasibility gap. Numerical experiments verifying the predicted rates of convergence are shown as well.

Individual treatment effect (ITE) represents the expected improvement in the outcome of taking a particular action to a particular target, and plays important roles in decision making in various domains. However, its estimation problem is difficult because intervention studies to collect information regarding the applied treatments (i.e., actions) and their outcomes are often quite expensive in terms of time and monetary costs. In this study, we consider a semi-supervised ITE estimation problem that exploits more easily-available unlabeled instances to improve the performance of ITE estimation using small labeled data. We combine two ideas from causal inference and semi-supervised learning, namely, matching and label propagation, respectively, to propose counterfactual propagation, which is the first semi-supervised ITE estimation method. Experiments using semi-real datasets demonstrate that the proposed method can successfully mitigate the data scarcity problem in ITE estimation.