This paper proposes a fast and scalable method for uncertainty quantification of machine learning models' predictions. First, we show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. Importantly, the approach allows to disentangle explicitly aleatoric and epistemic uncertainties. The resulting method works directly in the feature space. However, one can apply it to any neural network by considering an embedding of the data induced by the network. We demonstrate the strong performance of the method in uncertainty estimation tasks on a variety of real-world image datasets, such as MNIST, SVHN, CIFAR-100 and several versions of ImageNet.

The convenient access to copious multi-faceted data has encouraged machine learning researchers to reconsider correlation-based learning and embrace the opportunity of causality-based learning, i.e., causal machine learning (causal learning). Recent years have therefore witnessed great effort in developing causal learning algorithms aiming to help AI achieve human-level intelligence. Due to the lack-of ground-truth data, one of the biggest challenges in current causal learning research is algorithm evaluations. This largely impedes the cross-pollination of AI and causal inference, and hinders the two fields to benefit from the advances of the other. To bridge from conventional causal inference (i.e., based on statistical methods) to causal learning with big data (i.e., the intersection of causal inference and machine learning), in this survey, we review commonly-used datasets, evaluation methods, and measures for causal learning using an evaluation pipeline similar to conventional machine learning. We focus on the two fundamental causal-inference tasks and causality-aware machine learning tasks. Limitations of current evaluation procedures are also discussed. We then examine popular causal inference tools/packages and conclude with primary challenges and opportunities for benchmarking causal learning algorithms in the era of big data. The survey seeks to bring to the forefront the urgency of developing publicly available benchmarks and consensus-building standards for causal learning evaluation with observational data. In doing so, we hope to broaden the discussions and facilitate collaboration to advance the innovation and application of causal learning.

Artificial Intelligence (AI) presents opportunities to develop tools and techniques for addressing some of the major global challenges and deliver solutions with significant social and economic impacts. The application of AI has far-reaching implications for the 17 Sustainable Development Goals (SDGs) in general and sustainable urban development in particular. However, existing attempts to understand and use the opportunities offered by AI for SDG 11 have been explored sparsely, and the shortage of empirical evidence about the practical application of AI remains. In this chapter, we analyze the contribution of AI to support the progress of SDG 11 (Sustainable Cities and Communities). We address the knowledge gap by empirically analyzing the AI systems (N 29) from the AI SDG database and the Community Research and Development Information Service (CORDIS) database. Our analysis revealed that AI systems have indeed contributed to advancing sustainable cities in several ways (e.g., waste management, air quality monitoring, disaster response management, transportation management), but many projects are still working for citizens and not with them. This snapshot of AI's impact on SDG11 is inherently partial yet useful to advance our understanding as we move towards more mature systems and research on the impact of AI systems for the social good. Introduction Artificial intelligence (AI) has the potential to mitigate several issues facing cities, such as road safety, waste management, air pollution, and disaster risk reduction (Gupta et al., 2021). Examples of recent AI systems for improved well-being in cities include a tool for semi-automatic digitization of sketch maps to support the inclusion of indigenous communities through the documentation of their land rights (Degbelo et al., 2021; Chipofya et al., 2020), a system for traffic monitoring based on Wireless Signals (Gupta et al., 2018), approaches for efficient waste management (Barns, 2019), air quality modelling (Gupta et al., 2018) and urban health monitoring systems (Allam and Jones, 2020).

This story was contributed to TechCabal by Conrad Onyango/bird. African governments are ramping up national strategies on the adoption of Artificial Intelligence (AI) in a fresh hunt for crucial data that would help improve public service delivery and governance. AI is no longer a preserve of the private sector as Africa's public sector hops on a global trend where governments join the hunt for robust data to transform how they deliver services to an increasingly tech-savvy population. Oxford Insights in its'Government AI Readiness Index 2021,' shows governments across the continent are turning to AI to improve their public services and gain strategic economic advantages. More governments, the report says, are building up AI ecosystems-backed by national strategies to capitalize on a 10-year global boom that has seen private sector firms commercialize AI research and development.

Artificial intelligence has probably already made decisions about your life. It might have decided whether your insurance claim was accepted or rejected as fraudulent. It may have assessed your credit score, predicting if you were worthy of a loan or deemed too high risk. It could even have watched you drive, detecting if you are flouting the road rules and should be fined. And if AI hasn't already made a decision that affects your life, it almost certainly will, whether that be shaping what you see on social media or keeping tabs on you while you work from home with tracking software or some other application.

The problem of determining if a military unit has correctly understood an order and is properly executing on it is one that has bedeviled military planners throughout history. The advent of advanced language models such as OpenAI's GPT-series offers new possibilities for addressing this problem. This paper presents a mechanism to harness the narrative output of large language models and produce diagrams or "maps" of the relationships that are latent in the weights of such models as the GPT-3. The resulting "Neural Narrative Maps" (NNMs), are intended to provide insight into the organization of information, opinion, and belief in the model, which in turn provide means to understand intent and response in the context of physical distance. This paper discusses the problem of mapping information spaces in general, and then presents a concrete implementation of this concept in the context of OpenAI's GPT-3 language model for determining if a subordinate is following a commander's intent in a high-risk situation. The subordinate's locations within the NNM allow a novel capability to evaluate the intent of the subordinate with respect to the commander. We show that is is possible not only to determine if they are nearby in narrative space, but also how they are oriented, and what "trajectory" they are on. Our results show that our method is able to produce high-quality maps, and demonstrate new ways of evaluating intent more generally. N the 1979 motion picture Apocalypse Now, Captain Willard (played by Martin Sheen) is sent on a mission to assassinate Colonel Kurtz (played by Marlon Brando), a highly decorated officer who, in the words of the general authorizing the mission, has gone from "one of the most outstanding officers this country has ever produced" to someone "out there operating without any decent restraint, totally beyond the pale of any acceptable human conduct." The movie explores the paradoxes in war, where some illegal acts are embraced by the command structure, some tolerated, and some are to be terminated, "with extreme prejudice." Willard has to navigate these conflicts as he moves towards Kurtz' compound deep in Cambodia. Apocalypse Now provides an example of the difficulty that any intent-aware system must face in a military context [1]. Not only does the system need to determine if an order is being followed, it should also determine if the order itself is valid, so that the warriors implementing the order are not placed in ethical dilemmas. This is the goal that we attempt to address in this paper, with the concept of Neural Narrative Mapping (NNM). By placing narrative elements at coordinates in a virtual space, we can determine sophisticated relationships between concepts that go well beyond textual comparison.

Data poisoning attacks aim at manipulating model behaviors through distorting training data. Previously, an aggregation-based certified defense, Deep Partition Aggregation (DPA), was proposed to mitigate this threat. DPA predicts through an aggregation of base classifiers trained on disjoint subsets of data, thus restricting its sensitivity to dataset distortions. In this work, we propose an improved certified defense against general poisoning attacks, namely Finite Aggregation. In contrast to DPA, which directly splits the training set into disjoint subsets, our method first splits the training set into smaller disjoint subsets and then combines duplicates of them to build larger (but not disjoint) subsets for training base classifiers. This reduces the worst-case impacts of poison samples and thus improves certified robustness bounds. In addition, we offer an alternative view of our method, bridging the designs of deterministic and stochastic aggregation-based certified defenses. Empirically, our proposed Finite Aggregation consistently improves certificates on MNIST, CIFAR-10, and GTSRB, boosting certified fractions by up to 3.05%, 3.87% and 4.77%, respectively, while keeping the same clean accuracies as DPA's, effectively establishing a new state of the art in (pointwise) certified robustness against data poisoning.

The graph represents a network of 1,514 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 04 February 2022 at 13:10 UTC. The requested start date was Friday, 04 February 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 21-hour, 31-minute period from Tuesday, 01 February 2022 at 02:59 UTC to Friday, 04 February 2022 at 00:30 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Neom Tech & Digital Company, a subsidiary of Neom, is building a 3D cognitive digital twin metaverse platform, which aims to enable a "ground-breaking, mixed-reality" model for urban living. Called XVRS, the platform was announced at the Leap22 technology event which has been taking place in Riyadh, Saudi Arabia from 1-3 February. Combining digital and physical architectures with hyper-connected technologies and artificial intelligence (AI), XVRS seeks to seamlessly integrate the virtual and real worlds. Neom is a region in northwest Saudi Arabia on the Red Sea being built from the ground up as a living laboratory. Neom Tech & Digital Company was founded in 2021 as its first subsidiary, charged with helping to create an ecosystem of cognitive technologies to co-invent the future of living.

People often ask me about the real-life potential for inhumane, merciless systems like Hal 9000 or the Terminator to destroy our society. Growing up in Belgium and away from Hollywood, my initial impressions of robots were not so violent. In retrospect, my early positive affiliations with robots likely fueled my drive to build machines to make our everyday lives more enjoyable. Robots working alongside humans to manage day-to-day mundane tasks was a world I wanted to help create. Now, many years later, after emigrating to the United States, finishing my PhD under Andrew Ng, starting the Berkeley Robot Learning Lab, and co-founding Covariant, I'm convinced that robots are becoming sophisticated enough to be the allies and helpful teammates that I hoped for as a child.