Abstract--In many contexts, we have access to aggregate data, but individual level data is unavailable. For example, medical studies sometimes report only aggregate statistics about disease prevalence because of privacy concerns. Even so, many a time it is desirable, and in fact could be necessary to infer individual level characteristics from aggregate data. For instance, other researchers who want to perform more detailed analysis of disease characteristics would require individual level data. Similar challenges arise in other fields too including politics, and marketing. In this paper, we present an end-to-end pipeline for processing of aggregate data to derive individual level statistics, and then using the inferred data to train machine learning models to answer questions of interest. We describe a novel algorithm for reconstructing fine-grained data from summary statistics. This step will create multiple candidate datasets which will form the input to the machine learning models. The advantage of the highly parallel architecture we propose is that uncertainty in the generated fine-grained data will be compensated by the use of multiple candidate fine-grained datasets. Consequently, the answers derived from the machine learning models will be more valid and usable. We validate our approach using data from a challenging medical problem called Acute Traumatic Coagulopathy. Reconstructing individual behavior from aggregate data is termed ecological inference [1].

Abstract--Independent Component Analysis (ICA) is a statistical tool that decomposes an observed random vector into components that are as statistically independent as possible. ICA over finite fields is a special case of ICA, in which both the observations and the decomposed components take values over a finite alphabet. This problem is also known as minimal redundancy representation or factorial coding. In this work we focus on linear methods for ICA over finite fields. We introduce a basic lower bound which provides a fundamental limit to the ability of any linear solution to solve this problem. Based on this bound, we present a greedy algorithm that outperforms all currently known methods. Importantly, we show that the overhead of our suggested algorithm (compared with the lower bound) typically decreases, as the scale of the problem grows. In addition, we provide a sub-optimal variant of our suggested method that significantly reduces the computational complexity at a relatively small cost in performance. Finally, we discuss the universal abilities of linear transformations in decomposing random vectors, compared with existing nonlinear solutions. NDEPENDENT Component Analysis (ICA) addresses the recovery of unknown statistically independent source signals from their observed mixtures, without full prior knowledge of the mixing model or the statistics of the source signals. The classical Independent Components Analysis framework usually assumes linear combinations of the independent sources over the field of real valued numbers. A special variant of the ICA problem is when the sources, the mixing model and the observed signals are over a finite field, such as Galois Field of order q, GF(q). Several types of generative mixing models may be assumed when working over GF(q).

Open data refers to data that is freely available for reuse. Although there has been rapid increase in availability of open data to public in the last decade, this has not translated into better decision-support tools for them. We propose intelligent conversation generators as a grand challenge that would automatically create data-driven conversation interfaces (CIs), also known as chatbots or dialog systems, from open data and deliver personalized analytical insights to users based on their contextual needs. Such generators will not only help bring Artificial Intelligence (AI)-based solutions for important societal problems to the masses but also advance AI by providing an integrative testbed for human-centric AI and filling gaps in the state-of-art towards this aim.

Specialization and hierarchical organization are important features of efficient collaboration in economical, artificial, and biological systems. Here, we investigate the hypothesis that both features can be explained by the fact that each entity of such a system is limited in a certain way. We propose an information-theoretic approach based on a Free Energy principle, in order to computationally analyze systems of bounded rational agents that deal with such limitations optimally. We find that specialization allows to focus on fewer tasks, thus leading to a more efficient execution, but in turn requires coordination in hierarchical structures of specialized experts and coordinating units. Our results suggest that hierarchical architectures of specialized units at lower levels that are coordinated by units at higher levels are optimal, given that each unit's information-processing capability is limited and conforms to constraints on complexity costs.

Wagering mechanisms are one-shot betting mechanisms that elicit agents' predictions of an event. For deterministic wagering mechanisms, an existing impossibility result has shown incompatibility of some desirable theoretical properties. In particular, Pareto optimality (no profitable side bet before allocation) can not be achieved together with weak incentive compatibility, weak budget balance and individual rationality. In this paper, we expand the design space of wagering mechanisms to allow randomization and ask whether there are randomized wagering mechanisms that can achieve all previously considered desirable properties, including Pareto optimality. We answer this question positively with two classes of randomized wagering mechanisms: i) one simple randomized lottery-type implementation of existing deterministic wagering mechanisms, and ii) another family of simple and randomized wagering mechanisms which we call surrogate wagering mechanisms, which are robust to noisy ground truth. This family of mechanisms builds on the idea of learning with noisy labels (Natarajan et al. 2013) as well as a recent extension of this idea to the information elicitation without verification setting (Liu and Chen 2018). We show that a broad family of randomized wagering mechanisms satisfy all desirable theoretical properties.

Defense Advanced Research Projects Agency (DARPA) recently announced a $2 billion investment in AI related research while Department of Defense (D0D) created a Joint AI Center to accelerate the AI in defense. On the other hand, China has a big ambition with a plan to lead the AI field by investing $1 trillion by 2030. Similarly, Vladimir Putin has publicly said that "the nation that leads in AI will be the ruler of the world." As the world superpowers are gearing up for AI arms race, how does that impact the whole humanity? Will it create a better future for us or lead to our doom?

As artificial intelligence (AI) becomes more and more a part of our daily lives, we are seeing state governments and state CIOs turning to AI for a broad host of applications. The report also lays out several examples of how states are using AI, along with considerations for its development and implementation. From their role as change managers, to involvement in procurement, the publication also outlines the implications for state CIOs.

As the power of artificial intelligence grows, the likelihood of a future war filled with killer robots grows as well. Proponents suggest that lethal autonomous weapon systems (LAWs) might cause less "collateral damage," while critics warn that giving machines the power of life and death would be a terrible mistake. Last month's UN meeting on'killer robots' in Geneva ended with victory for the machines, as a small number of countries blocked progress towards an international ban. Some opponents of such a ban, like Russia and Israel, were to be expected since both nations already have advanced military AI programs. But surprisingly, the U.S. also agreed with them.

Kent Walker, vice president and general counsel with Google Inc., from right, Colin Stretch, general counsel with Facebook Inc., and Sean Edgett, acting general counsel with Twitter Inc., swear in to a House Intelligence Committee hearing in Washington, D.C., U.S., on Wednesday, Nov. 1, 2017. Technology has advanced rapidly along several related fronts. In just the last few years, there have been dramatic improvements in robotics, sensors, and machine vision, and Artificial Intelligence (AI) can now perform better, per Stanford's AI Index, than humans on multiple dimensions, including image recognition, speech recognition, translation, and strategy games such as Go, Poker and chess. In pursuit of profits from AI-enabled business models, firms are now investing lots of money in these technologies. Worldwide industrial robotics shipments have increased from an annual average of about 100,000 units prior to 2010 to almost 300,000 annual shipments by 2016.

Amazon could use its expertise to disrupt everything from the pharmaceutical supply chain to Medicare management. We break down the healthcare areas best suited for an Amazon entrance. Amazon is looking to dominate more than just online retail. The e-commerce behemoth is serious about entering healthcare, bringing with it a non-traditional business model, infrastructure in logistics & computing, and customer love. Many existing health giants are scrambling to compete, while others are looking for ways to Amazon-proof themselves. Between 1999-2000, the company began investing money into Drugstore.com It eventually ran into the existing web of middlemen, regulators, and more, which brought its ambitions to a halt. Now, Amazon is trying again. Earlier this year, it announced a joint healthcare venture with JPMorgan Chase and Berkshire Hathaway. Before the collaboration, the company acquired online pharmacy PillPack for nearly $1B.