Endpoint specialist Cybereason Inc., which incorporates AI into its platform, also raised $200 million in Series E funding in August, and has discussed plans for an initial public offering in the near future. A smaller, but still significant, deal this year includes $6.4 million in seed funding for Senseon Tech Ltd. The vast amounts of data that laptops, smartphones and other endpoint devices create, and the need for fast identification of any anomalies in network traffic, mean that machine learning and deep learning are particularly well suited for threat detection, similar to how AI has been deployed on stock markets to detect aberrant trading patterns. Studies conducted in 2019 by the Sans Institute, a computer-security training organization, found that laptops and desktop computers were by far the most commonly breached endpoints. Endpoint security is one of the largest and most mature segments of the cybersecurity market.

Artificial Intelligence (AI) is a new exciting opportunity to better protect European consumers. As the European Commission calls for a human-centric AI, we see exciting developments which put the welfare of users and citizens at the heart of new AI tools. During this CERRE Breakfast Conversation, a new promising application to screen the compliance of consumer contracts with EU law will be presented. Policy-makers and stakeholders will discuss how to reinforce EU consumer protection policy with AI. This event will bring together key actors from the digital sector, regulatory authorities, policy-makers and academic experts for a morning of stimulating discussions.

Through collecting a massively large number of tweets and building machine learning models, we investigate the dynamics of the Twitter social network formed by the interactions among millions of Twitter supporters. Our predictions are updated daily at 2AM.

When Arnold Schwarzenegger's "Terminator" character said "I'll be back," this probably wasn't what he had in mind. The actor and former governor of California is suing a robotics company for $10 million, after the business decided to use his name and likeness. Schwarzenegger's lawsuit against the tech startup, called Promobot, alleges that the robot lookalike... "diminishes his hard-earned and well-deserved reputation as a major motion picture star," according to TMZ. The robot isn't just meant to look like Schwarzenegger, it also has his name. Promobot advertises the creation on its site as a "companion robot," one of several that emulates the appearance of world-famous celebrities.

Artificial intelligence (AI) is no longer the stuff of science fiction. While robot maids may not yet be a reality, researchers are working hard to create reasoning, problem-solving machines whose "brains" might rival our own. Seán Ó hÉigeartaigh (anglicized as Sean O'Hegarty), while enthusiastic about the benefits that AI can bring, is also wary of the technology's dark side. He holds a doctorate in genomics from Trinity College Dublin and is now executive director of the Center for the Study of Existential Risk at the University of Cambridge. He has played a central role in international research on the long-term impacts and risks of AI.

Outlier detection is a key data mining task for identifying abnormal objects from massive data. Due to the high expense of acquiring ground truth, practitioners lean towards building a large number of unsupervised models for further combination and analysis, rather than relying on a single model out of reliability consideration. However, this poses scalability challenge to high-dimensional, large datasets. In this study, we propose a three-module framework called SUOD to address the challenge. It can accelerate outlier model building and scoring when a large number of base models are used. It focuses on three complementary levels to speed up the process while controlling prediction performance degradation at the same time. At the data level, its Random Projection module projects high-dimensional data onto diversified low-dimensional subspaces while preserving the pairwise distance relationship. At the model level, SUOD's Pseudo-supervised Approximation module can approximate and replace fitted unsupervised models by low-cost supervised regressors, leading to fast offline scoring on new-coming samples with better interpretability. At the system level, Balanced Parallel Scheduling module mitigates the workload imbalance within distributed systems, which is helpful for heterogeneous outlier ensembles. As the three modules are independent with different focuses, they have great flexibility to "mix and match". The framework is also designed with extensibility in mind. One may customize each module based on specific use cases, and the framework may be generalized to other learning tasks as well. Extensive experiments on more than 20 benchmark datasets demonstrate SUOD's effectiveness. In addition, a real-world deployment system on fraudulent claim analysis by IQVIA is also discussed. The full framework, documentation, and examples are openly shared at https://github.com/yzhao062/SUOD.

We look at a dataset of tropical storm data in the Atlantic Ocean from 1982 to 2017 and perform deep learning predictions with uncertainty bounds on trajectories of the storms. The result of these storms, particularly the strongest ones called hurricanes--defined as having wind speeds exceeding 74 mph--can be devastating because of their strong winds and heavy precipitation that can cause dangerous tides. Tropical storms can cause major environmental disasters when they reach land, such as the 2005 Hurricane Katrina that resulted in over 850 deaths and caused major economic damage and the 2012 Hurricane Sandy that caused almost $70 billion in damage across much of the eastern United States, with peak winds of 115 mph (Hurricane). According to the National Oceanic and Atmospheric Administration, it is likely that global warming will cause hurricanes in the upcoming century to be more intense by 1 to 10% globally (with higher peak winds and lower central pressures), which will result in a higher proportion of more severe storms (NOAA). Historically, hurricane trajectory predictions have used statistical methods that can be limiting because of the nonlinearity and complexity of atmospheric systems. Deep learning techniques and specifically recurrent neural networks have grown in popularity in recent years as a strong method for approaching prediction problems because of the ability to extract important features and relationships from complex high-dimensional data, especially for forecasting and classification (McDermott and Wikle, 2019). We implemented a number of improvements over previous deep learning prediction work (Alemany et al., 2019), including predicting exact storm locations in latitude/longitude instead of a grid value and using a prediction window that uses all previous hurricane data rather than a fixed-size sliding window. While hurricane trajectory predictions have seen improvements recently (SHIPS), we build on previous work to include a fundamental uncertainty measure in the prediction for the first time as part of a neural network framework. The uncertainty measure is especially valuable for understanding a defined location range rather than only a point estimate, which is important for evacuation and safety/preparation purposes.

In order to compute fast approximations to the singular value decompositions (SVD) of very large matrices, randomized sketching algorithms have become a leading approach. However, a key practical difficulty of sketching an SVD is that the user does not know how far the sketched singular vectors/values are from the exact ones. Indeed, the user may be forced to rely on analytical worst-case error bounds, which do not account for the unique structure of a given problem. As a result, the lack of tools for error estimation often leads to much more computation than is really necessary. To overcome these challenges, this paper develops a fully data-driven bootstrap method that numerically estimates the actual error of sketched singular vectors/values. In particular, this allows the user to inspect the quality of a rough initial sketched SVD, and then adaptively predict how much extra work is needed to reach a given error tolerance. Furthermore, the method is computationally inexpensive, because it operates only on sketched objects, and it requires no passes over the full matrix being factored. Lastly, the method is supported by theoretical guarantees and a very encouraging set of experimental results.

The Fairness, Accountability, and Transparency in Machine Learning (FAT-ML) literature proposes a varied set of group fairness metrics to measure discrimination against socio-demographic groups that are characterized by a protected feature, such as gender or race. Such a system can be deemed as either fair or unfair depending on the choice of the metric. Several metrics have been proposed, some of them incompatible with each other. We present here a framework to navigate the tensions between various group-wise metrics and to study fairness in data-driven decision making without the constraint of choosing a single metric. We do so empirically, by observing that several of these metrics cluster together in two or three main clusters for the same groups and machine learning methods. In addition, we propose a robust way to visualize multidimensional fairness in two dimensions through a Principal Component Analysis (PCA) of the group fairness metrics. Experimental results on multiple datasets show that the PCA decomposition explains the variance between the metrics with one to three components.

Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classification, link prediction and graph clustering. However, they expose uncertainty and unreliability against the well-designed inputs, i.e., adversarial examples. Accordingly, various studies have emerged for both attack and defense addressed in different graph analysis tasks, leading to the arms race in graph adversarial learning. For instance, the attacker has poisoning and evasion attack, and the defense group correspondingly has preprocessing- and adversarial- based methods. Despite the booming works, there still lacks a unified problem definition and a comprehensive review. To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically. Specifically, we survey and unify the existing works w.r.t. attack and defense in graph analysis tasks, and give proper definitions and taxonomies at the same time. Besides, we emphasize the importance of related evaluation metrics, and investigate and summarize them comprehensively. Hopefully, our works can serve as a reference for the relevant researchers, thus providing assistance for their studies. More details of our works are available at https://github.com/gitgiter/Graph-Adversarial-Learning.