When a neural network is partitioned and distributed across physical nodes, failure of physical nodes causes the failure of the neural units that are placed on those nodes, which results in a significant performance drop. Current approaches focus on resiliency of training in distributed neural networks. However, resiliency of inference in distributed neural networks is less explored. We introduce ResiliNet, a scheme for making inference in distributed neural networks resilient to physical node failures. ResiliNet combines two concepts to provide resiliency: skip connection in residual neural networks, and a novel technique called failout, which is introduced in this paper. Failout simulates physical node failure conditions during training using dropout, and is specifically designed to improve the resiliency of distributed neural networks. The results of the experiments and ablation studies using three datasets confirm the ability of ResiliNet to provide inference resiliency for distributed neural networks.

The notions of distance and similarity play a key role in many machine learning approaches, and artificial intelligence (AI) in general, since they can serve as an organizing principle by which individuals classify objects, form concepts and make generalizations. While distance functions for propositional representations have been thoroughly studied, work on distance functions for structured representations, such as graphs, frames or logical clauses, has been carried out in different communities and is much less understood. Specifically, a significant amount of work that requires the use of a distance or similarity function for structured representations of data usually employs ad-hoc functions for specific applications. Therefore, the goal of this paper is to provide an overview of this work to identify connections between the work carried out in different areas and point out directions for future work.

A Relational Markov Decision Process (RMDP) is a first-order representation to express all instances of a single probabilistic planning domain with possibly unbounded number of objects. Early work in RMDPs outputs generalized (instance-independent) first-order policies or value functions as a means to solve all instances of a domain at once. Unfortunately, this line of work met with limited success due to inherent limitations of the representation space used in such policies or value functions. Can neural models provide the missing link by easily representing more complex generalized policies, thus making them effective on all instances of a given domain? We present the first neural approach for solving RMDPs, expressed in the probabilistic planning language of RDDL. Our solution first converts an RDDL instance into a ground DBN. We then extract a graph structure from the DBN. We train a relational neural model that computes an embedding for each node in the graph and also scores each ground action as a function over the first-order action variable and object embeddings on which the action is applied. In essence, this represents a neural generalized policy for the whole domain. Given a new test problem of the same domain, we can compute all node embeddings using trained parameters and score each ground action to choose the best action using a single forward pass without any retraining. Our experiments on nine RDDL domains from IPPC demonstrate that neural generalized policies are significantly better than random and sometimes even more effective than training a state-of-the-art deep reactive policy from scratch.

Data annotation is an essential stage in supervised learning. However, the annotation process is exhaustive and time consuming, specially for large datasets. Activities of Daily Living (ADL) recognition is an example of systems that exploit very large raw sensor data readings. In such systems, sensor readings are collected from activity-monitoring sensors in a 24/7 manner. The size of the generated dataset is so huge that it is almost impossible for a human annotator to give a certain label to every single instance in the dataset. This results in annotation gaps in the input data to the adopting supervised learning system. The performance of the recognition system is negatively affected by these gaps. In this work, we propose and investigate three different paradigms to handle these gaps. In the first paradigm, the gaps are taken out by dropping all unlabeled readings. A single "Unknown" or "Do-Nothing" label is given to the unlabeled readings within the operation of the second paradigm. The last paradigm handles these gaps by giving every one of them a unique label identifying the encapsulating deterministic labels. Also, we propose a semantic preprocessing method of annotation gaps by constructing a hybrid combination of some of these paradigms for further performance improvement. The performance of the proposed three paradigms and their hybrid combination is evaluated using an ADL benchmark dataset containing more than $2.5\times 10^6$ sensor readings that had been collected over more than nine months. The evaluation results emphasize the performance contrast under the operation of each paradigm and support a specific gap handling approach for better performance.

China's Communist Government has extracted over 6 billion peoples biometrics, including facial, voice and personal health data to empower their Quantum Artificial Intelligence program meant for military purposes. This includes almost every American, Canadian, and European persons living today, every person in China, and Less so from groups in Africa, the Middle East, and South America. I initially made the finding public by publishing the discovery in the book AI, Trump, China and the Weaponization of Robotics without providing company names. Later, I included the findings with company names in the updated book Artificial Intelligence Dangers to Humanity. More than 1,000 AI, Robotics and Bio-Metric companies were researched to obtain the results of over 6 billion human beings who have had their bio-metrics stolen or transferred to China.

Artificial intelligence has been growing in popularity in many industries, and with more and more advances in technology, it is becoming increasingly commonplace every single day. Artificial Intelligence, or AI, is broadly the concept of machines having the ability to carry out tasks in a smart manner. This has led to further applications such as machine learning, which is the concept that machines can take the relevant data and learn from it. As more and more businesses are beginning to adopt various forms of artificial intelligence, it is essential to be aware of the laws and regulations surrounding these technologies. This article will cover the things you need to know surrounding AI and the law.

All these tasks were performed in fundus imaging from PM patients and they are requirements to participate in the Pathologic Myopia Challenge (PALM). The challenge was organized as a half day Challenge, a Satellite Event of The IEEE International Symposium on Biomedical Imaging in Venice Italy. Our method applies different Deep Learning techniques for each task. Transfer learning is applied in all tasks using Xception as the baseline model. Also, some key ideas of YOLO architecture are used in the Optic Disc segmentation algorithm pipeline. We have evaluated our model's performance according the challenge rules in terms of AUC-ROC, F1-Score, Mean Dice Score and Mean Euclidean Distance. For initial activities our method has shown satisfactory results.

Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports, and climate science. Large-scale spatial data often contains complex higher-order correlations across features and locations. While tensor latent factor models can describe higher-order correlations, they are inherently computationally expensive to train. Furthermore, for spatial analysis, these models should not only be predictive but also be spatially coherent. However, latent factor models are sensitive to initialization and can yield inexplicable results. We develop a novel Multi-resolution Tensor Learning (MRTL) algorithm for efficiently learning interpretable spatial patterns. MRTL initializes the latent factors from an approximate full-rank tensor model for improved interpretability and progressively learns from a coarse resolution to the fine resolution for an enormous computation speedup. We also prove the theoretical convergence and computational complexity of MRTL. When applied to two real-world datasets, MRTL demonstrates 4 ~ 5 times speedup compared to a fixed resolution while yielding accurate and interpretable models.

SafetyPay's platform allows non-card holders and fraud-wary consumers to participate in the online marketplace via bank transfer or cash without sharing their information online. The platform opens the door for e-commerce merchants to tap into a larger consumer base by accepting alternative forms of payment. Meanwhile, Feedzai monitors patterns in payment transaction activity and compares against a customer's historical data to authenticate transactions. With a shared goal of making banking and commerce safe, the partnership with Feedzai enhances SafetyPay's security, harnessing AI to protect customers across borders from fraudulent risks in real-time. "Secure payments have been a core focus for us since SafetyPay was founded more than a decade ago," said Gustavo Ruiz Moya, CEO, SafetyPay.

Argument Mining is the research area which aims at extracting argument components and predicting argumentative relations (i.e., support and attack) from text. In particular, numerous approaches have been proposed in the literature to predict the relations holding between the arguments, and application-specific annotated resources were built for this purpose. Despite the fact that these resources have been created to experiment on the same task, the definition of a single relation prediction method to be successfully applied to a significant portion of these datasets is an open research problem in Argument Mining. This means that none of the methods proposed in the literature can be easily ported from one resource to another. In this paper, we address this problem by proposing a set of dataset independent strong neural baselines which obtain homogeneous results on all the datasets proposed in the literature for the argumentative relation prediction task. Thus, our baselines can be employed by the Argument Mining community to compare more effectively how well a method performs on the argumentative relation prediction task.