Approval ballot based committee formation is concerned with aggregating individual approvals of voters. Voters submit their approvals of candidates and these approvals are aggregated to arrive at the optimal committee of specified size. There are several aggregation techniques proposed in the literature and these techniques differ among themselves on the criterion function they optimize. Voters preferences for a candidate is based on his/her opinion on candidate suitability. We note that candidates have attributes that make him/her suitable or otherwise. Hence, it is relevant to approve attributes and select candidates who have the approved attributes. This paper addresses the committee selection problem when voters submit their approvals on attributes. Though attribute based preference is addressed in several contexts, committee selection problem with attribute approval has not been attempted earlier. We note that extending the theory of candidate approval to attribute approval in committee selection problem is not trivial. In this paper, we study different aspects of this problem and show that none of the existing aggregation rules satisfies Unanimity and Justified Representation when attribute based approvals are considered. We propose a new aggregation rule that satisfies both the above properties. We also present other analysis of committee selection problem with attribute approval.

Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relatively simple but powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples. TuckER outperforms all previous state-of-the-art models across standard link prediction datasets. We prove that TuckER is a fully expressive model, deriving the bound on its entity and relation embedding dimensionality for full expressiveness which is several orders of magnitude smaller than the bound of previous state-of-the-art models ComplEx and SimplE. We further show that several previously introduced linear models can be viewed as special cases of TuckER.

As machine learning models become more accurate, they typically become more complex and uninterpretable by humans. The black-box character of these models holds back its acceptance in practice, especially in high-risk domains where the consequences of failure could be catastrophic such as health-care or defense. Providing understandable and useful explanations behind ML models or predictions can increase the trust of the user. Example-based reasoning, which entails leveraging previous experience with analogous tasks to make a decision, is a well known strategy for problem solving and justification. This work presents a new explanation extraction method called LEAFAGE, for a prediction made by any black-box ML model. The explanation consists of the visualization of similar examples from the training set and the importance of each feature. Moreover, these explanations are contrastive which aims to take the expectations of the user into account. LEAFAGE is evaluated in terms of fidelity to the underlying black-box model and usefulness to the user. The results showed that LEAFAGE performs overall better than the current state-of-the-art method LIME in terms of fidelity, on ML models with non-linear decision boundary. A user-study was conducted which focused on revealing the differences between example-based and feature importance-based explanations. It showed that example-based explanations performed significantly better than feature importance-based explanation, in terms of perceived transparency, information sufficiency, competence and confidence. Counter-intuitively, when the gained knowledge of the participants was tested, it showed that they learned less about the black-box model after seeing a feature importance-based explanation than seeing no explanation at all. The participants found feature importance-based explanation vague and hard to generalize it to other instances.

The sequencing of ancient Neanderthal and Denisovan fossils supported introgression events into anatomically modern humans (AMH) (out of Africa). However, recent studies also support the presence of gene flow from AMH into Neanderthals, thus suggesting a complex hominin evolution. All modern humans are genetically related to each other at a time depth of up to 300 thousand years ago and share a common African root. The migratory routes used by AMH after the African diaspora and aspects of the interbreeding between AMH and presently extinct hominins living at the time in Eurasia (here referred as Eurasian Extinct Hominins, EEH) are still under debate. Recently, for the first time, deep learning has been successfully used to explain human history, paving the way for this technology to be applied in other questions in medicine, genomics, and evolution.

Geneva: Are robots taking away human jobs? Where will the jobs of the future come from? Are our skills becoming increasingly outdated? Is the gig economy creating precarious jobs? The rapidly transforming world of work has made headlines of these social anxieties.

This blog post was authored by Peter Cooper, Senior Product Manager, Microsoft IoT. From smart factories and smart cities to virtual personal assistants and self-driving cars, artificial intelligence (AI) and the Internet of Things (IoT) are transforming how people around the world live, work, and play. But fundamentally changing the ways people, devices, and data interact is not simple or easy work. Microsoft's AI & IoT Insider Labs was created to help all types of organizations accelerate their digital transformation. Member organizations around the world get access to support both technology development and product commercialization, for everything from hardware design to manufacturing to building applications and turning data into insights using machine learning.

Language is a tool, and just like any tool, it has equal capacity to inflict both good and bad on the world. Language is a beautiful, human thing; the connective tissue that transfers culture, knowledge and critical information across borders and generations. It's that second function -- the divisive one -- that inspired developer Rami Ismail and voice actor Sarah Elmaleh to produce a conference for game creators that removes language as a barrier to entry. Gamedev.world is billed as the first truly global online games conference, with plans to host 48 hours of expert panels and live Q&A sessions on Twitch, YouTube and Mixer, translated in real-time into English, Japanese, Spanish, Portuguese, Russian, Arabic and Simplified Chinese. It's all scheduled to take place later this year. "If games can be played by anyone, and made by anyone, we want to make sure everyone feels like they truly belong here," Ismail told Engadget.

We characterize the communication complexity of the following distributed estimation problem. Alice and Bob observe infinitely many iid copies of $\rho$-correlated unit-variance (Gaussian or $\pm1$ binary) random variables, with unknown $\rho\in[-1,1]$. By interactively exchanging $k$ bits, Bob wants to produce an estimate $\hat\rho$ of $\rho$. We show that the best possible performance (optimized over interaction protocol $\Pi$ and estimator $\hat \rho$) satisfies $\inf_{\Pi,\hat\rho}\sup_\rho \mathbb{E} [|\rho-\hat\rho|^2] = \Theta(\tfrac{1}{k})$. Furthermore, we show that the best possible unbiased estimator achieves performance of $1+o(1)\over {2k\ln 2}$. Curiously, thus, restricting communication to $k$ bits results in (order-wise) similar minimax estimation error as restricting to $k$ samples. Our results also imply an $\Omega(n)$ lower bound on the information complexity of the Gap-Hamming problem, for which we show a direct information-theoretic proof. Notably, the protocol achieving (almost) optimal performance is one-way (non-interactive). For one-way protocols we also prove the $\Omega(\tfrac{1}{k})$ bound even when $\rho$ is restricted to any small open sub-interval of $[-1,1]$ (i.e. a local minimax lower bound). %We do not know if this local behavior remains true in the interactive setting. Our proof techniques rely on symmetric strong data-processing inequalities, various tensorization techniques from information-theoretic interactive common-randomness extraction, and (for the local lower bound) on the Otto-Villani estimate for the Wasserstein-continuity of trajectories of the Ornstein-Uhlenbeck semigroup.

Artificial intelligence (AI) presents limitless opportunity, but not without potential pitfalls and risks. This paradox has become increasingly evident for government leaders. They want to give domestic companies an edge over the competition, but are also expected to protect their citizens and use AI for social good. They want to support innovation, while still maintaining some level of control over how new technologies impact society at large. With a huge payoff on the line -- by our own estimates, AI has the potential to increase worldwide GDP by 14 percent by 2030, an infusion of US$15.7 trillion into the global economy -- it should come as no surprise that governments are eager to claim their share.

Google is pouring an additional $3.1 million into Wikipedia, bringing its total contribution to the free encyclopedia over the past decade to more than $7.5 million, the company announced at the World Economic Forum Tuesday. A little over a third of those funds will go toward sustaining current efforts at the Wikimedia Foundation, the nonprofit that runs Wikipedia, and the remaining $2 million will focus on long-term viability through the organization's endowment. Google will also begin allowing Wikipedia editors to use several of its machine learning tools for free, the tech giant said. What's more, Wikimedia and Google will soon broaden Project Tiger, a joint initiative they launched in 2017 to increase the number of Wikipedia articles written in underrepresented languages in India, and to include 10 new languages in a handful of countries and regions. It will now be called GLOW, Growing Local Language Content on Wikipedia.