Artificial Intelligence (AI) systems have been increasingly used to make decision-making processes faster, more accurate, and more efficient. However, such systems are also at constant risk of being attacked. While the majority of attacks targeting AI-based applications aim to manipulate classifiers or training data and alter the output of an AI model, recently proposed Sponge Attacks against AI models aim to impede the classifier's execution by consuming substantial resources. In this work, we propose \textit{Dual Denial of Decision (DDoD) attacks against collaborative Human-AI teams}. We discuss how such attacks aim to deplete \textit{both computational and human} resources, and significantly impair decision-making capabilities. We describe DDoD on human and computational resources and present potential risk scenarios in a series of exemplary domains.

Knowledge graph (KG) is used to represent data in terms of entities and structural relations between the entities. This representation can be used to solve complex problems such as recommendation systems and question answering. In this study, a set of candidate drugs for COVID-19 are proposed by using Drug repurposing knowledge graph (DRKG). DRKG is a biological knowledge graph constructed using a vast amount of open source biomedical knowledge to understand the mechanism of compounds and the related biological functions. Node and relation embeddings are learned using knowledge graph embedding models and neural network and attention related models. Different models are used to get the node embedding by changing the objective of the model. These embeddings are later used to predict if a candidate drug is effective to treat a disease or how likely it is for a drug to bind to a protein associated to a disease which can be modelled as a link prediction task between two nodes. RESCAL performed the best on the test dataset in terms of MR, MRR and Hits@3.

Photo-identification (photo-id) is one of the main non-invasive capture-recapture methods utilised by marine researchers for monitoring cetacean (dolphin, whale, and porpoise) populations. This method has historically been performed manually resulting in high workload and cost due to the vast number of images collected. Recently automated aids have been developed to help speed-up photo-id, although they are often disjoint in their processing and do not utilise all available identifying information. Work presented in this paper aims to create a fully automatic photo-id aid capable of providing most likely matches based on all available information without the need for data pre-processing such as cropping. This is achieved through a pipeline of computer vision models and post-processing techniques aimed at detecting cetaceans in unedited field imagery before passing them downstream for individual level catalogue matching. The system is capable of handling previously uncatalogued individuals and flagging these for investigation thanks to catalogue similarity comparison. We evaluate the system against multiple real-life photo-id catalogues, achieving mAP@IOU[0.5] = 0.91, 0.96 for the task of dorsal fin detection on catalogues from Tanzania and the UK respectively and 83.1, 97.5% top-10 accuracy for the task of individual classification on catalogues from the UK and USA.

Adversarial attacks on Graph Neural Networks (GNNs) reveal their security vulnerabilities, limiting their adoption in safety-critical applications. However, existing attack strategies rely on the knowledge of either the GNN model being used or the predictive task being attacked. Is this knowledge necessary? For example, a graph may be used for multiple downstream tasks unknown to a practical attacker. It is thus important to test the vulnerability of GNNs to adversarial perturbations in a model and task agnostic setting. In this work, we study this problem and show that GNNs remain vulnerable even when the downstream task and model are unknown. The proposed algorithm, TANDIS (Targeted Attack via Neighborhood DIStortion) shows that distortion of node neighborhoods is effective in drastically compromising prediction performance. Although neighborhood distortion is an NP-hard problem, TANDIS designs an effective heuristic through a novel combination of Graph Isomorphism Network with deep Q-learning. Extensive experiments on real datasets and state-of-the-art models show that, on average, TANDIS is up to 50% more effective than state-of-the-art techniques, while being more than 1000 times faster.

Individual-level data (microdata) that characterizes a population, is essential for studying many real-world problems. However, acquiring such data is not straightforward due to cost and privacy constraints, and access is often limited to aggregated data (macro data) sources. In this study, we examine synthetic data generation as a tool to extrapolate difficult-to-obtain high-resolution data by combining information from multiple easier-to-obtain lower-resolution data sources. In particular, we introduce a framework that uses a combination of univariate and multivariate frequency tables from a given target geographical location in combination with frequency tables from other auxiliary locations to generate synthetic microdata for individuals in the target location. Our method combines the estimation of a dependency graph and conditional probabilities from the target location with the use of a Gaussian copula to leverage the available information from the auxiliary locations. We perform extensive testing on two real-world datasets and demonstrate that our approach outperforms prior approaches in preserving the overall dependency structure of the data while also satisfying the constraints defined on the different variables.

The workhorse of machine learning is stochastic gradient descent. To access stochastic gradients, it is common to consider iteratively input/output pairs of a training dataset. Interestingly, it appears that one does not need full supervision to access stochastic gradients, which is the main motivation of this paper. After formalizing the "active labeling" problem, which focuses on active learning with partial supervision, we provide a streaming technique that provably minimizes the ratio of generalization error over the number of samples. We illustrate our technique in depth for robust regression.

The regulation's scope encompasses all sectors (except for military) and aims to introduce a common regulatory and legal framework for AI, ensuring that all AI systems are safe and respect existing law on fundamental rights and values. Personally, I think AI regulation and governance is very important. We've all seen the sci-fi movies where artificially intelligent robots (sorry, beings) take over the world and attempt to bring about the end of humanity as we know it, until some bruised and battered hero saves the day. While that's the worst-case scenario meant only for our screens, there are some real use-cases for AI that are actually quite scary. Think about deepfakes, for example, where AI is used to forge an image, video, or audio recording with such precision that the average human is unlikely to detect any manipulation.

'Botched experiments' by Elon Musk's Neuralink allegedly'kept suffering animals alive for no reason and malpractice caused monkey's brains to hemorrhage' during rushed brain chip testing, a former Neuralink employee and internal lab notes reveal. The billionaire's startup is accused of violating the Animal Welfare Act with its experiments at the University of California, Davis, from 2017 through 2020, which'sacrificed all the animals involved,' a former Neuralink employee, who asked to remain anonymous, told DailyMail.com. One case stood out to them- a monkey sacrificed ahead of schedule due to errors allegedly made during surgery. 'There was no reason to use it,' the former employee, who worked as a necropsy technician, told DailyMail.com. 'BioGlue was not FDA-approved for brain surgery and would never be able to be carried over to human trials.

Musk is already embroiled in controversies over his recent acquisition of Twitter. Shortly after taking the platform private for $44 billion, he laid off half its staff, then saw defections of hundreds more workers after vowing to impose a "hardcore" work environment. Days later, he admitted former president Donald Trump and leaders of hate groups back on to the platform after previous suspensions.

Ukraine's drone strikes on two air bases deep inside Russia mark a new chapter in this war, but their significance--whether they escalate the conflict or alter the war's course in some other way--is unclear. Much depends on Moscow's reaction, and Kyiv's response to that, in the next several days. For now, it's worth probing some possibilities, though first let's lay out the implications of these strikes, regardless of their consequences. The strikes followed several days of massive Russian air and missile attacks on Ukrainian civilian targets, mainly power plants, shutting off heat and electricity as Ukraine's winter is getting brutal. The Russians launched those attacks from the airfields that the Ukrainians subsequently hit.