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.

As algorithmic decision-making takes a more and more important role in myriad application domains, incentives emerge to change the inputs presented to these algorithms--people may either invest in truly relevant attributes or strategically lie about their data. Recently, a collection of very interesting papers has explored various models of strategic behavior on the part of the classified individuals in learning settings, and ways to mitigate the harms to accuracy that can arise from falsified features [Dalvi et al., 2004, Brückner et al., 2012, Hardt et al., 2016, Dong et al., 2018]. Additionally, some recent work has focused on the design of learning algorithms that incentivize the classified individuals to make"good" investments in true changes to their variables[Kleinberg and Raghavan, 2019]. The present paper takes a different tack, and explores another potential effect of strategic investment in true changes to variables, in an online learning setting: we claim that interaction between the online learning and the strategic individuals may actually aid the learning algorithm in identifying causal variables. By causal, we mean, informally, variables such that changes in their true value cause changes in the true label and lead agents to improve.

Stochastic processes provide a mathematically elegant way model complex data. In theory, they provide flexible priors over function classes that can encode a wide range of interesting assumptions. In practice, however, efficient inference by optimisation or marginalisation is difficult, a problem further exacerbated with big data and high dimensional input spaces. We propose a novel variational autoencoder (VAE) called the prior encoding variational autoencoder ($\pi$VAE). The $\pi$VAE is finitely exchangeable and Kolmogorov consistent, and thus is a continuous stochastic process. We use $\pi$VAE to learn low dimensional embeddings of function classes. We show that our framework can accurately learn expressive function classes such as Gaussian processes, but also properties of functions to enable statistical inference (such as the integral of a log Gaussian process). For popular tasks, such as spatial interpolation, $\pi$VAE achieves state-of-the-art performance both in terms of accuracy and computational efficiency. Perhaps most usefully, we demonstrate that the low dimensional independently distributed latent space representation learnt provides an elegant and scalable means of performing Bayesian inference for stochastic processes within probabilistic programming languages such as Stan.

Self-attention mechanisms have achieved great success on a variety of NLP tasks due to its flexibility of capturing dependency between arbitrary positions in a sequence. For problems such as query-based summarization (Qsumm) and knowledge graph reasoning where each input sequence is associated with an extra query, explicitly modeling such conditional contextual dependencies can lead to a more accurate solution, which however cannot be captured by existing self-attention mechanisms. In this paper, we propose \textit{conditional self-attention} (CSA), a neural network module designed for conditional dependency modeling. CSA works by adjusting the pairwise attention between input tokens in a self-attention module with the matching score of the inputs to the given query. Thereby, the contextual dependencies modeled by CSA will be highly relevant to the query. We further studied variants of CSA defined by different types of attention. Experiments on Debatepedia and HotpotQA benchmark datasets show CSA consistently outperforms vanilla Transformer and previous models for the Qsumm problem.

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.

Deloitte today launched its Middle East inaugural Experience Analytics event in Dubai at Dubai Studio City. Experience Analytics is a globally recognised Deloitte event and has previously taken place in London, Amsterdam and Berlin. The theme is'Me, Myself, and AI' and brings together a combination of technology showcases and practical sessions that explore a number of topics across Analytics and Artificial Intelligence. It is not about people vs. machines but about how human collaboration and decision-making can be enhanced through the use of machines – this has been coined by Deloitte as the "Age of With". "We believe that we are entering an important phase for society in the Age of With, and in order to make a true impact that matters, we understand how important it is to collaborate and leverage our relationships with our alliances and eco-system partners to build the best solutions for our clients. Some of our global alliance partners for the event are Google, SAP, Informatica and Cloudera," said Rajeev Lalwani, Deloitte's Leader for Strategy, Analytics and M&A in the Middle East.

Gen. Qassem Soleimani was killed in a U.S. drone strike in Iraq, the Tehran-backed Lebanese organization Hezbollah urgently met with Iraqi militia leaders, seeking to unite them in the face of a huge void left by their powerful mentor's death, two sources with knowledge of the meetings said. The meetings were meant to coordinate the political efforts of Iraq's often-fractious militias, which lost not only Soleimani but also Abu Mahdi al-Muhandis, a unifying Iraqi paramilitary commander, in the Jan. 3 attack at Baghdad airport, the sources said. While offering few details, two additional sources in a pro-Iran regional alliance confirmed that Hezbollah, which is sanctioned as a terrorist group by the United States, has stepped in to help fill the void left by Soleimani in guiding the militias. All sources in this article spoke on condition of anonymity to address sensitive political activities rarely addressed in public. Officials with the governments of Iraq and Iran did not respond to requests for comment, nor did a spokesperson for the militia groups.

They are trying to get a head start, and unlike most of the 11,000 athletes who will be in Tokyo for the games, and thousands more for the Paralympics, they will be able to speak Japanese. "Just the language itself, I love it," said Abraham Majok, a runner who arrived in Japan in November with three other South Sudanese athletes and a coach. "And it's nice and since we started learning it. But, you know, we are moving well with it and we just love it." They are training northwest of Tokyo in Maebashi, Gunma Prefecture, supported mainly by donations from the public.

In images taken from a satellite floating 400 kilometers above the Earth, Europe's humanitarian crisis shows up as white pixels against the blue-green vastness of the Mediterranean. Captured by the sensors in space, small overcrowded boats with migrants leaving Africa headed north look like tiny white comets bursting through the ocean, leaving a tail where they stir waves. "It's not that with every image I look at, I think about how someone could be dying right now," said Elisabeth Wittmann as she clicked through satellite footage on her laptop showing the coast west of the Libyan port of Sabratha. "That's also to protect myself," she added. The 26-year-old computer scientist from southern Germany is one of a dozen researchers who have teamed up with a new NGO called Space-Eye to develop artificial intelligence technology that allows computers to detect migrant boats in satellite images.

Mediation Perspectives is a periodic blog entry that's provided by the CSS' Mediation Support Team and occasional guest authors. How is artificial intelligence (AI) affecting conflict and its resolution? Peace practitioners and scholars cannot afford to disregard ongoing developments related to AI-based technologies – both from an ethical and a pragmatic perspective. In this blog, I explore AI as an evolving field of information management technologies that is changing both the nature of armed conflict and the way we can respond to it. AI encompasses the use of computer programmes to analyse big amounts of data (such as online communication and transactions) in order to learn from patterns and predict human behaviour on a massive scale.