Grant money meant to help cities prepare for terror attacks is being spent on "massive purchases of surveillance technology" for US police departments, a new report by the advocacy organizations Action Center on Race and Economy (ACRE), LittleSis, MediaJustice, and the Immigrant Defense Project shows. Shopping for AI-powered spytech: For example, the Los Angeles Police Department used funding intended for counterterrorism to buy automated license plate readers worth at least $1.27 million, radio equipment worth upwards of $24 million, Palantir data fusion platforms (often used for AI-powered predictive policing), and social media surveillance software. Why this matters: For various reasons, a lot of problematic tech ends up in high-stake sectors such as policing with little to no oversight. For example, the facial recognition company Clearview AI offers "free trials" of its tech to police departments, which allows them to use it without a purchasing agreement or budget approval. Federal grants for counterterrorism don't require as much public transparency and oversight.

December 13, 2022 – videantis GmbH, provider of a unified platform for combined processing of algorithms as diverse as AI (Artificial Intelligence), multi-modal sensor data processing and fusion, or video coding on a single architecture, today announced the tape-out of the FlexAISIC AI chip based on its flexible v-MP6000UDX unified processing platform. The tape-out has been achieved in collaboration with the Fraunhofer Institute for Integrated Circuits IIS, and the chip development is funded by the German Federal Ministry of Education and Research (BMBF) within the KI-FLEX project. KI-FLEX is part of a broader initiative driven by the German BMBF to push research of AI-based technologies for autonomous driving. KI-FLEX develops a powerful and highly energy-efficient hardware platform and the associated software framework for AI-based processing and merging of data from different sensors, resulting in fast and reliable perception and localization for autonomous driving. The videantis v-MP6000UDX platform as the core component of the FlexAISIC is a highly scalable multi-core architecture combined with a tailored bus fabric and multi-banked shared on-chip SRAM which delivers extreme efficiency and performance for a variety of algorithm types like deep learning, computer vision, signal processing, and video coding.

Chief technology officers should follow the do no harm mantra of the Hippocratic Oath when incorporating artificial intelligence software into company platforms. While an overarching goal of introducing AI is to increase efficiencies or remove biases, there are often unexpected consequences when good ideas unintentionally cause harm. For example, use of facial recognition technology to identify criminal suspects can sometimes result in the arrest (or worse) of an innocent person. Or the development of a weaponized drone for the military that falls into the wrong hands can stray far from the developer's original intention. Here is how technology companies and those who use the technology might look beyond the intended uses of AI to identify potential unforeseen consequences.

Check out all the on-demand sessions from the Intelligent Security Summit here. But this morning, The Department of Consumer and Worker Protection (DCWP) announced it is postponing enforcement until April 15, 2023. "Due to the high volume of public comments, we are planning a second public hearing," the agency's statement said. Under the AEDT law, it will be unlawful for an employer or employment agency to use artificial intelligence and algorithm-based technologies to evaluate NYC candidates and employees -- unless it conducts an independent bias audit before using the AI employment tools. The bottom line: New York City employers will be the ones taking on compliance obligations around these AI tools, rather than the software vendors who create them. Plenty of unanswered questions remain about the regulations, according to Avi Gesser, partner at Debevoise & Plimpton and co-chair of the firm's Cybersecurity, Privacy and Artificial Intelligence Practice Group.

Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA). To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR. Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines. The transformation matrix is fine-tuned with the annotated event relation types between events that occurred in questions and those in answers, using event-aware question vectors. Experimental results on the Event Semantic Relation Reasoning (ESTER) dataset show significant improvements in both generative and extractive settings compared to the existing strong baselines, achieving over 8.4% gain in the token-level F1 score and 3.0% gain in Exact Match (EM) score under the multi-answer setting. Qualitative analysis reveals the high quality of the generated answers by TranCLR, demonstrating the feasibility of injecting event knowledge into QA model learning. Our code and models can be found at https://github.com/LuJunru/TranCLR.

Our work targets at searching feasible adversarial perturbation to attack a classifier with high-dimensional categorical inputs in a domain-agnostic setting. This is intrinsically an NP-hard knapsack problem where the exploration space becomes explosively larger as the feature dimension increases. Without the help of domain knowledge, solving this problem via heuristic method, such as Branch-and-Bound, suffers from exponential complexity, yet can bring arbitrarily bad attack results. We address the challenge via the lens of multi-armed bandit based combinatorial search. Our proposed method, namely FEAT, treats modifying each categorical feature as pulling an arm in multi-armed bandit programming. Our objective is to achieve highly efficient and effective attack using an Orthogonal Matching Pursuit (OMP)-enhanced Upper Confidence Bound (UCB) exploration strategy. Our theoretical analysis bounding the regret gap of FEAT guarantees its practical attack performance. In empirical analysis, we compare FEAT with other state-of-the-art domain-agnostic attack methods over various real-world categorical data sets of different applications. Substantial experimental observations confirm the expected efficiency and attack effectiveness of FEAT applied in different application scenarios. Our work further hints the applicability of FEAT for assessing the adversarial vulnerability of classification systems with high-dimensional categorical inputs.

Many commodity crops have growth stages during which they are particularly vulnerable to stress-induced yield loss. In-season crop progress information is useful for quantifying crop risk, and satellite remote sensing (RS) can be used to track progress at regional scales. At present, all existing RS-based crop progress estimation (CPE) methods which target crop-specific stages rely on ground truth data for training/calibration. This reliance on ground survey data confines CPE methods to surveyed regions, limiting their utility. In this study, a new method is developed for conducting RS-based in-season CPE in unsurveyed regions by combining data from surveyed regions with synthetic crop progress data generated for an unsurveyed region. Corn-growing zones in Argentina were used as surrogate 'unsurveyed' regions. Existing weather generation, crop growth, and optical radiative transfer models were linked to produce synthetic weather, crop progress, and canopy reflectance data. A neural network (NN) method based upon bi-directional Long Short-Term Memory was trained separately on surveyed data, synthetic data, and two different combinations of surveyed and synthetic data. A stopping criterion was developed which uses the weighted divergence of surveyed and synthetic data validation loss. Net F1 scores across all crop progress stages increased by 8.7% when trained on a combination of surveyed region and synthetic data, and overall performance was only 21% lower than when the NN was trained on surveyed data and applied in the US Midwest. Performance gain from synthetic data was greatest in zones with dual planting windows, while the inclusion of surveyed region data from the US Midwest helped mitigate NN sensitivity to noise in NDVI data. Overall results suggest in-season CPE in other unsurveyed regions may be possible with increased quantity and variety of synthetic crop progress data.

Machine Learning-as-a-Service systems (MLaaS) have been largely developed for cybersecurity-critical applications, such as detecting network intrusions and fake news campaigns. Despite effectiveness, their robustness against adversarial attacks is one of the key trust concerns for MLaaS deployment. We are thus motivated to assess the adversarial robustness of the Machine Learning models residing at the core of these security-critical applications with categorical inputs. Previous research efforts on accessing model robustness against manipulation of categorical inputs are specific to use cases and heavily depend on domain knowledge, or require white-box access to the target ML model. Such limitations prevent the robustness assessment from being as a domain-agnostic service provided to various real-world applications. We propose a provably optimal yet computationally highly efficient adversarial robustness assessment protocol for a wide band of ML-driven cybersecurity-critical applications. We demonstrate the use of the domain-agnostic robustness assessment method with substantial experimental study on fake news detection and intrusion detection problems.

Experimental sciences have come to depend heavily on our ability to organize and interpret high-dimensional datasets. Natural laws, conservation principles, and inter-dependencies among observed variables yield geometric structure, with fewer degrees of freedom, on the dataset. We introduce the frameworks of semiclassical and microlocal analysis to data analysis and develop a novel, yet natural uncertainty principle for extracting fine-scale features of this geometric structure in data, crucially dependent on data-driven approximations to quantum mechanical processes underlying geometric optics. This leads to the first tractable algorithm for approximation of wave dynamics and geodesics on data manifolds with rigorous probabilistic convergence rates under the manifold hypothesis. We demonstrate our algorithm on real-world datasets, including an analysis of population mobility information during the COVID-19 pandemic to achieve four-fold improvement in dimensionality reduction over existing state-of-the-art and reveal anomalous behavior exhibited by less than 1.2% of the entire dataset. Our work initiates the study of data-driven quantum dynamics for analyzing datasets, and we outline several future directions for research.

Text summarization is a user-preference based task, i.e., for one document, users often have different priorities for summary. As a key aspect of customization in summarization, granularity is used to measure the semantic coverage between the summary and source document. However, developing systems that can generate summaries with customizable semantic coverage is still an under-explored topic. In this paper, we propose the first unsupervised multi-granularity summarization framework, GranuSum. We take events as the basic semantic units of the source documents and propose to rank these events by their salience. We also develop a model to summarize input documents with given events as anchors and hints. By inputting different numbers of events, GranuSum is capable of producing multi-granular summaries in an unsupervised manner. Meanwhile, we annotate a new benchmark GranuDUC that contains multiple summaries at different granularities for each document cluster. Experimental results confirm the substantial superiority of GranuSum on multi-granularity summarization over strong baselines. Further, by exploiting the event information, GranuSum also exhibits state-of-the-art performance under the conventional unsupervised abstractive setting. Dataset for this paper can be found at: https://github.com/maszhongming/GranuDUC