Keeping up with threat intelligence is a must for a security analyst today. There is a volume of information present in `the wild' that affects an organization. We need to develop an artificial intelligence system that scours the intelligence sources, to keep the analyst updated about various threats that pose a risk to her organization. A security analyst who is better `tapped in' can be more effective. In this paper we present, Cyber-All-Intel an artificial intelligence system to aid a security analyst. It is a system for knowledge extraction, representation and analytics in an end-to-end pipeline grounded in the cybersecurity informatics domain. It uses multiple knowledge representations like, vector spaces and knowledge graphs in a 'VKG structure' to store incoming intelligence. The system also uses neural network models to pro-actively improve its knowledge. We have also created a query engine and an alert system that can be used by an analyst to find actionable cybersecurity insights.

We aim to better understand attention over nodes in graph neural networks and identify factors influencing its effectiveness. Motivated by insights from the work on Graph Isomorphism Networks (Xu et al., 2019), we design simple graph reasoning tasks that allow us to study attention in a controlled environment. We find that under typical conditions the effect of attention is negligible or even harmful, but under certain conditions it provides an exceptional gain in performance of more than 40% in some of our classification tasks. However, we have yet to satisfy these conditions in practice.

Efficient explorative data analysis systems must take into account both what a user knows and wants to know. This paper proposes a principled framework for interactive visual exploration of relations in data, through views most informative given the user's current knowledge and objectives. The user can input pre-existing knowledge of relations in the data and also formulate specific exploration interests, then taken into account in the exploration. The idea is to steer the exploration process towards the interests of the user, instead of showing uninteresting or already known relations. The user's knowledge is modelled by a distribution over data sets parametrised by subsets of rows and columns of data, called tile constraints. We provide a computationally efficient implementation of this concept based on constrained randomisation. Furthermore, we describe a novel dimensionality reduction method for finding the views most informative to the user, which at the limit of no background knowledge and with generic objectives reduces to PCA. We show that the method is suitable for interactive use and robust to noise, outperforms standard projection pursuit visualisation methods, and gives understandable and useful results in analysis of real-world data. We have released an open-source implementation of the framework.

Apple will be the subject of an anti-competition investigation by the European Union according to a report by the Financial Times. The investigation will focus on allegations by music-streaming platform, Spotify, who filed a complaint with the EU in March. According to the company, Apple -- which offers its own music streaming service called Apple Music -- has unfairly used the popularity of its platform to put Spotify and other companies like it at a disadvantage. This includes it's operating system, iOS, and the App Store. Spotify says Apple puts companies at a disadvantage by leveraging its App Store and iOS to lock companies out and charge them lofty fees.

CAPE CANAVERAL, FLORIDA - A SpaceX shipment arrived at the International Space Station on Monday with a "cosmic catch" by a pair of Canadians. The Dragon capsule delivered 5,500 pounds (2,500 kg) of equipment and experiments. Canadian astronaut David Saint-Jacques used the station's big robot arm -- also made in Canada -- to capture the Dragon approximately 250 miles (400 kilometers) above the North Atlantic Ocean. An external cable that normally comes off during launch dangled from the capsule, but it did not interfere with the grappling. "Welcome on board, Dragon," Saint-Jacques radioed. He congratulated ground teams for their help, in both English and French.

When I first became focused on the military and existential concerns of AI in 2012, there was only a small handful of publications and organizations focused on the ethical concerns of AI. MIRI, the Future of Humanity Institute, the Institute for Ethics and Emerging Technologies, and the personal blogs of Ben Goertzel and Nick Bostrom was most of my reading at the time. These limited sources focused mostly on the consequences of artificial general intelligence (i.e. By 2014, artificial intelligence made its way firmly onto the radar of almost everyone in the tech world. New startups began (by 2015) ubiquitously including "machine learning" in their pitch decks, and 3-4-year-old startups were re-branding themselves around the value proposition of "AI." Not until later 2016 did the AI ethics wave make it into the mainstream beyond the level of Elon Musk's tweets. By 2017, some business conferences began having breakout sessions around AI ethics – mostly the practical day-to-day concerns (privacy, security, transparency).

Today, cyber threats have become one of the major concerns for several industry leaders, as they cause electrical blackouts, breaches of national security secrets as well as the military equipment. They can also result in the theft of valuable and sensitive data like medical records. They are capable of disrupting phone and computer networks or paralyze systems, making the data unavailable. However, such situations can be avoided with the modern technologies like Artificial Intelligence and Machine Learning. AI has been portraying limitless potential in various applications across different industries.

While some people might think Cinco de Mayo is about Mexican independence, it's actually a holiday that celebrates the day three Americans fought and defeated El Guapo at the Battle of Santa Poco. And it's just one of the many things Mexico is famous for. Her rich cultural heritage has resulted in some of the world's best cuisine that has been exported to every corner of the planet. Then there are the other exports, like those depicted in the recent third season of Narcos, a gripping thriller about the country's cartels in the 1980s. Mexico also plays a key role in regional international trade as the US' neighbor and second largest export market.

At the end of March, Google launched a group to advise on ethical issues around artificial intelligence and other emerging technologies. It called the group the advanced technology external advisory council. A week later, Google announced it was shutting the council down. A group of Google employees had criticised the inclusion of the leader of what they considered to be a rightwing thinktank and had called for her removal because of previous remarks she had made that were thought to be anti-LGBT and anti-immigrant. Google said: "It's become clear that in the current environment, ATEAC can't function as we wanted.

There is an abundance of complex dynamic systems that are critical to our daily lives and our society but that are hardly understood, and even with today's possibilities to sense and collect large amounts of experimental data, they are so complex and continuously evolving that it is unlikely that their dynamics will ever be understood in full detail. Nevertheless, through computational tools we can try to make the best possible use of the current technologies and available data. We believe that the most useful models will have to take into account the imbalance between system complexity and available data in the context of limited knowledge or multiple hypotheses. The complex system of biological cells is a prime example of such a system that is studied in systems biology and has motivated the methods presented in this paper. They were developed as part of the DARPA Rapid Threat Assessment (RTA) program, which is concerned with understanding of the mechanism of action (MoA) of toxins or drugs affecting human cells. Using a combination of Gaussian processes and abstract network modeling, we present three fundamentally different machine-learning-based approaches to learn causal relations and synthesize causal networks from high-dimensional time series data. While other types of data are available and have been analyzed and integrated in our RTA work, we focus on transcriptomics (that is gene expression) data obtained from high-throughput microarray experiments in this paper to illustrate capabilities and limitations of our algorithms. Our algorithms make different but overall relatively few biological assumptions, so that they are applicable to other types of biological data and potentially even to other complex systems that exhibit high dimensionality but are not of biological nature.