This paper explores the state of the art on to methods to verify and validate navigation algorithms for autonomous surface ships. We perform a systematic mapping study to find research works published in the last 10 years proposing new algorithms for autonomous navigation and collision avoidance and we have extracted what verification and validation approaches have been applied on these algorithms. We observe that most research works use simulations to validate their algorithms. However, these simulations often involve just a few scenarios designed manually. This raises the question if the algorithms have been validated properly. To remedy this, we propose the use of a systematic scenario-based testing approach to validate navigation algorithms extensively.

In the present work we study classifiers' decision boundaries via Brownian motion processes in ambient data space and associated probabilistic techniques. Intuitively, our ideas correspond to placing a heat source at the decision boundary and observing how effectively the sample points warm up. We are largely motivated by the search for a soft measure that sheds further light on the decision boundary's geometry. En route, we bridge aspects of potential theory and geometric analysis (Mazya, 2011, Grigoryan-Saloff-Coste, 2002) with active fields of ML research such as adversarial examples and generalization bounds. First, we focus on the geometric behavior of decision boundaries in the light of adversarial attack/defense mechanisms. Experimentally, we observe a certain capacitory trend over different adversarial defense strategies: decision boundaries locally become flatter as measured by isoperimetric inequalities (Ford et al, 2019); however, our more sensitive heat-diffusion metrics extend this analysis and further reveal that some non-trivial geometry invisible to plain distance-based methods is still preserved. Intuitively, we provide evidence that the decision boundaries nevertheless retain many persistent "wiggly and fuzzy" regions on a finer scale. Second, we show how Brownian hitting probabilities translate to soft generalization bounds which are in turn connected to compression and noise stability (Arora et al, 2018), and these bounds are significantly stronger if the decision boundary has controlled geometric features.

The first wave of artificial intelligence (AI) has already replaced humans for repetitive physical tasks like inspecting equipment, manufacturing goods, repairing things, and crunching numbers. That shift started way back with the Industrial Revolution. This gave rise to our current Thinking Economy, where employment and wages are more tied to workers' abilities to process, analyze and interpret information to make decisions and solve problems … Just like the industrial revolution automated physical tasks by decreasing the value of human strength and increasing the value of human cognition, AI is now reshaping the landscape and ushering in a Feeling Economy. What characterizes this emerging economy? Consider, for example, the role of a financial analyst, which seems pretty quantitative and thinking-oriented.

We propose a deep-learning based method for obtaining standardized data coordinates from scientific measurements.Data observations are modeled as samples from an unknown, non-linear deformation of an underlying Riemannian manifold, which is parametrized by a few normalized latent variables. By leveraging a repeated measurement sampling strategy, we present a method for learning an embedding in $\mathbb{R}^d$ that is isometric to the latent variables of the manifold. These data coordinates, being invariant under smooth changes of variables, enable matching between different instrumental observations of the same phenomenon. Our embedding is obtained using a LOcal Conformal Autoencoder (LOCA), an algorithm that constructs an embedding to rectify deformations by using a local z-scoring procedure while preserving relevant geometric information. We demonstrate the isometric embedding properties of LOCA on various model settings and observe that it exhibits promising interpolation and extrapolation capabilities. Finally, we apply LOCA to single-site Wi-Fi localization data, and to $3$-dimensional curved surface estimation based on a $2$-dimensional projection.

As we start to encounter AI systems in various morally and legally salient environments, some have begun to explore how the current responsibility ascription practices might be adapted to meet such new technologies [19, 33]. A critical viewpoint today is that autonomous and self-learning AI systems pose a so-called responsibility gap [27]. These systems' autonomy challenges human control over them [13], while their adaptability leads to unpredictability. Hence, it might infeasible to trace back responsibility to a specific entity if these systems cause any harm. Considering responsibility practices as the adoption of certain attitudes towards an agent [40], scholarly work has also posed the question of whether AI systems are appropriate subjects of such practices [15, 29, 37] -- e.g., they might "have a body to kick," yet they "have no soul to damn" [4].

Recently, hetero-functional graph theory (HFGT) has developed as a means to mathematically model the structure of large flexible engineering systems. In that regard, it intellectually resembles a fusion of network science and model-based systems engineering. With respect to the former, it relies on multiple graphs as data structures so as to support matrix-based quantitative analysis. In the meantime, HFGT explicitly embodies the heterogeneity of conceptual and ontological constructs found in model-based systems engineering including system form, system function, and system concept. At their foundation, these disparate conceptual constructs suggest multi-dimensional rather than two-dimensional relationships. This paper provides the first tensor-based treatment of some of the most important parts of hetero-functional graph theory. In particular, it addresses the "system concept", the hetero-functional adjacency matrix, and the hetero-functional incidence tensor. The tensor-based formulation described in this work makes a stronger tie between HFGT and its ontological foundations in MBSE. Finally, the tensor-based formulation facilitates an understanding of the relationships between HFGT and multi-layer networks.

The movie'The Social Dilemma' by Jeff Orlowski (2020) explores the rise of social media and the damage it has caused to society. With a rather negative connotation, the directors address the topic of digital platforms and how their users are influenced and persuaded in surveillance capitalism (Economist 2019). Persuasion is an activity that involves one party, the persuader, trying to induce another party, the persuadee, to believe or disbelieve something or to do something (Iyer & Sycara 2019). The Economist (2019) claims that as a central tenet of surveillance capitalism, and persuasion is, furthermore, important in many aspects of daily life. Consider, for example, an employee demanding an increase in compensation, a physician trying to get a patient to enter a slimming programme, a charity volunteer trying to raise funds for a school project (Hunter et al. 2019), or a government advisor trying to get people to take a vaccination in the midst of a pandemic for the greater good. A persuasive Natural Language Generation (NLG) artificial intelligence (AI) is a system that can create communications aimed at a user (the persuadee) in order to persuade her to accept a specific argument through persuasive messages.

We propose an instrumental variable (IV) selection procedure which combines the agglomerative hierarchical clustering method and the Hansen-Sargan overidentification test for selecting valid instruments for IV estimation from a large set of candidate instruments. Some of the instruments may be invalid in the sense that they may fail the exclusion restriction. We show that under the plurality rule, our method can achieve oracle selection and estimation results. Compared to the previous IV selection methods, our method has the advantages that it can deal with the weak instruments problem effectively, and can be easily extended to settings where there are multiple endogenous regressors and heterogenous treatment effects. We conduct Monte Carlo simulations to examine the performance of our method, and compare it with two existing methods, the Hard Thresholding method (HT) and the Confidence Interval method (CIM). The simulation results show that our method achieves oracle selection and estimation results in both single and multiple endogenous regressors settings in large samples when all the instruments are strong. Also, our method works well when some of the candidate instruments are weak, outperforming HT and CIM. We apply our method to the estimation of the effect of immigration on wages in the US.

There are some wild stats about Microsoft Flight Simulator. In the sim, all 117 million lakes in the world are rendered in their appropriate places. Each plane has more than 1,000 points that respond to a variety of conditions at any given time, including wind, atmosphere and player input. Developers pushed 2.5 petabytes of Bing Maps satellite photo data through Azure's machine learning systems to construct the sim's world. In a chat with Engadget at CES 2021, Flight Simulator head Jorg Neumann said developers basically build the planet every 72 hours, procedurally planting somewhere in the realm of 2 trillion trees and creating 2 billion buildings in that timeframe alone.

The hls4ml library [1, 2] is an open source software designed to facilitate the deployment of machine learning (ML) models on field-programmable gate arrays (FPGAs), targeting low-latency and low-power edge applications. Taking as input a neural network model, hls4ml generates C/C code designed to be transpiled into FPGA firmware by processing it with a high-level synthesis (HLS) library. The development of hls4ml was historically driven by the need to integrate ML algorithms in the first stage of the real-time data processing of particle physics experiments operating at the CERN Large Hadron Collider (LHC). The LHC produces high-energy proton collisions (or events) every 25 ns, each consisting of about 1 MB of raw data. Since this throughput is overwhelming for the currently available processing and storage resources, the LHC experiments run a real-time event selection system, the so-called Level-1 trigger (L1T), to reduce the event rate from 40 MHz to 100 kHz [3-6]. Due to the size of the buffering system, the L1T system operates with a fixed latency of O(1 µs). While hls4ml excels as a tool to automatically generate low-latency ML firmware for L1T applications, it also offers interesting opportunities for edge-computing applications beyond particle physics whenever efficient, e.g.