We all agree that artificial intelligence (AI) has the power to drive development and even out global inequalities. Because it can process vast amounts of data rapidly, AI is ensuring more and more people in developing countries have access to microfinance, healthcare and remote-learning opportunities. AI helps make climate change mitigation more efficient, and can help deliver housing at a quarter of the usual costs when combined with 3D printing technology. It is easy to see how it could be a game-changer in the rapidly urbanising developing world. But AI's potential to help us achieve the Sustainable Development Goals, and to reduce global poverty is far from being realised.

Hyperspectral feature spaces are useful for many remote sensing applications ranging from spectral mixture modeling to discrete thematic classification. In such cases, characterization of the feature space dimensionality, geometry and topology can provide guidance for effective model design. The objective of this study is to compare and contrast two approaches for identifying feature space basis vectors via dimensionality reduction. These approaches can be combined to render a joint characterization that reveals spectral properties not apparent using either approach alone. We use a diverse collection of AVIRIS-NG reflectance spectra of the snow-firn-ice continuum to illustrate the utility of joint characterization and identify physical properties inferred from the spectra. Spectral feature spaces combining principal components (PCs) and t-distributed Stochastic Neighbor Embeddings (t-SNEs) provide physically interpretable dimensions representing the global (PC) structure of cryospheric reflectance properties and local (t-SNE) manifold structures revealing clustering not resolved in the global continuum. Joint characterization reveals distinct continua for snow-firn gradients on different parts of the Greenland Ice Sheet and multiple clusters of ice reflectance properties common to both glacier and sea ice in different locations. Clustering revealed in t-SNE feature spaces, and extended to the joint characterization, distinguishes differences in spectral curvature specific to location within the snow accumulation zone, and BRDF effects related to view geometry. The ability of PC+t-SNE joint characterization to produce a physically interpretable spectral feature spaces revealing global topology while preserving local manifold structures suggests that this characterization might be extended to the much higher dimensional hyperspectral feature space of all terrestrial land cover.

Advancements in Intelligent Traffic Systems (ITS) have made huge amounts of traffic data available through automatic data collection. A big part of this data is stored as trajectories of moving vehicles and road users. Automatic analysis of this data with minimal human supervision would both lower the costs and eliminate subjectivity of the analysis. Trajectory clustering is an unsupervised task. In this paper, we perform a comprehensive comparison of similarity measures, clustering algorithms and evaluation measures using trajectory data from seven intersections. We also propose a method to automatically generate trajectory reference clusters based on their origin and destination points to be used for label-based evaluation measures. Therefore, the entire procedure remains unsupervised both in clustering and evaluation levels. Finally, we use a combination of evaluation measures to find the top performing similarity measures and clustering algorithms for each intersection. The results show that there is no single combination of distance and clustering algorithm that is always among the top ten clustering setups.

The generation of feasible adversarial examples is necessary for properly assessing models that work on constrained feature space. However, it remains a challenging task to enforce constraints into attacks that were designed for computer vision. We propose a unified framework to generate feasible adversarial examples that satisfy given domain constraints. Our framework supports the use cases reported in the literature and can handle both linear and non-linear constraints. We instantiate our framework into two algorithms: a gradient-based attack that introduces constraints in the loss function to maximize, and a multi-objective search algorithm that aims for misclassification, perturbation minimization, and constraint satisfaction. We show that our approach is effective on two datasets from different domains, with a success rate of up to 100%, where state-of-the-art attacks fail to generate a single feasible example. In addition to adversarial retraining, we propose to introduce engineered non-convex constraints to improve model adversarial robustness. We demonstrate that this new defense is as effective as adversarial retraining. Our framework forms the starting point for research on constrained adversarial attacks and provides relevant baselines and datasets that future research can exploit.

Explainable AI (XAI) research has been booming, but the question "$\textbf{To whom}$ are we making AI explainable?" is yet to gain sufficient attention. Not much of XAI is comprehensible to non-AI experts, who nonetheless, are the primary audience and major stakeholders of deployed AI systems in practice. The gap is glaring: what is considered "explained" to AI-experts versus non-experts are very different in practical scenarios. Hence, this gap produced two distinct cultures of expectations, goals, and forms of XAI in real-life AI deployments. We advocate that it is critical to develop XAI methods for non-technical audiences. We then present a real-life case study, where AI experts provided non-technical explanations of AI decisions to non-technical stakeholders, and completed a successful deployment in a highly regulated industry. We then synthesize lessons learned from the case, and share a list of suggestions for AI experts to consider when explaining AI decisions to non-technical stakeholders.

In this paper, we explore how we can build upon the data and models of Internet images and use them to adapt to robot vision without requiring any extra labels. We present a framework called Self-supervised Embodied Active Learning (SEAL). It utilizes perception models trained on internet images to learn an active exploration policy. The observations gathered by this exploration policy are labelled using 3D consistency and used to improve the perception model. We build and utilize 3D semantic maps to learn both action and perception in a completely self-supervised manner. The semantic map is used to compute an intrinsic motivation reward for training the exploration policy and for labelling the agent observations using spatio-temporal 3D consistency and label propagation. We demonstrate that the SEAL framework can be used to close the action-perception loop: it improves object detection and instance segmentation performance of a pretrained perception model by just moving around in training environments and the improved perception model can be used to improve Object Goal Navigation.

Since 2018, Health Canada has undertaken an initiative to adapt its regulatory approach to better support digital health technologies, specifically medical devices. Key focus areas include artificial intelligence, software as a medical device, cybersecurity, medical device interoperability, wireless medical devices, mobile medical apps and telemedicine. To meet this goal, Health Canada established the Digital Health Division under the Medical Devices Bureau and has been increasing its efforts to build in-house expertise. On October 27, 2021, Health Canada, the US Food and Drug Administration (FDA), and the United Kingdom's Medicines and Healthcare Products Regulatory Agency (MHRA) jointly published the Good Machine Learning Practice for Medical Device Development: Guiding Principles. The document consists of 10 guiding principles to help promote safe, effective, and high-quality use of artificial intelligence and machine learning (AI/ML) in medical devices.

Editors and writers curious about AI's ability to generate long-form writing will want to check-out this piece by SEPGRA, an economic think tank. The group decided to give GPT-3 -- one of the world's most powerful AI text generators -- a run for its money by inputting one, simple phrase and asking GPT-3 to respond. The phrase: "Write an essay about text written by AI." The resulting 900-word essay published in this article is emblematic of the tech's current prowess. Essentially: The piece begins with an excellent focus on the specific topic, but becomes ever-more generalized as the article unfolds. In fact, by the close of the essay, GPT-3 completely veers-off into a discussion of AI's oft-reported ability to beat the world's greatest chess masters.

So far, artificial intelligence (AI) is a new enough technology in the business world that it's mostly evaded the long arm of regulatory agencies and standards. But with mounting concerns over privacy and other sensitive areas, that grace period is about to end, according to predictions released on Wednesday by consulting firm Deloitte. Looking at the overall AI landscape, including machine learning, deep learning and neural networks, Deloitte said it believes that next year will pave the way for greater discussions about regulating these popular but sometimes problematic technologies. These discussions will trigger enforced regulations in 2023 and beyond, the firm said. Fears have arisen over AI in a few areas.

Video surveillance has long been a necessary tool for law enforcement to keep communities safe and reduce crime. Yet sifting through hundreds of hours of footage can be time-consuming and slow down investigations. Video content analytics make this footage significantly more valuable by extracting, identifying and classifying video metadata, making the footage searchable, actionable and quantifiable. The ability to efficiently review, analyze, and respond to events captured by video surveillance has revolutionized law enforcement operations. Video analytics help these agencies accelerate investigations, attain situational awareness, and derive operational intelligence.