The ability to infer the intentions of others, predict their goals, and deduce their plans are critical features for intelligent agents. For a long time, several approaches investigated the use of symbolic representations and inferences with limited success, principally because it is difficult to capture the cognitive knowledge behind human decisions explicitly. The trend, nowadays, is increasingly focusing on learning to infer intentions directly from data, using deep learning in particular. We are now observing interesting applications of intent classification in natural language processing, visual activity recognition, and emerging approaches in other domains. This paper discusses a novel approach combining few-shot and transfer learning with cross-domain features, to learn to infer the intent of an agent navigating in physical environments, executing arbitrary long sequences of actions to achieve their goals. Experiments in synthetic environments demonstrate improved performance in terms of learning from few samples and generalizing to unseen configurations, compared to a deep-learning baseline approach.

We consider the facility location problem in the one-dimensional setting where each facility can serve a limited number of agents from the algorithmic and mechanism design perspectives. From the algorithmic perspective, we prove t hat the corresponding optimization problem, where the goal is t o locate facilities to minimize either the total cost to all ag ents or the maximum cost of any agent is NPhard. However, we show that the problem is fixed-parameter tractable, and the optimal solution can be computed in polynomial time whenever the number of facilities is bounded, or when all facilit ies have identical capacities. We then consider the problem fro m a mechanism design perspective where the agents are strategic and need not reveal their true locations. We show that sev - eral natural mechanisms studied in the uncapacitated setti ng either lose strategyproofness or a bound on the solution qua l-ity for the total or maximum cost objective. We then propose new mechanisms that are strategyproof and achieve approximation guarantees that almost match the lower bounds.

Home robots may come with many sophisticated built-in abilities, however there will always be a degree of customization needed for each user and environment. Ideally this should be accomplished through one-shot learning, as collecting the large number of examples needed for statistical inference is tedious. A particularly appealing approach is to simply explain to the robot, via speech, what it should be doing. In this paper we describe the ALIA cognitive architecture that is able to effectively incorporate user-supplied advice and prohibitions in this manner. The functioning of the implemented system on a small robot is illustrated by an associated video [11]. 1 INTRODUCTION A typical home robot of the future might have built-in navigation, object recognition, task planning, and dexterous manipulation. Y et, despite these sophisticated capabilities, there are still things it cannot know when it first arrives. For instance, what a particular room in the house is called, even if it can identify the general type.

We survey a burgeoning and promising new research area that considers the online nature of many practical fair division problems. We identify wide variety of such online fair division problems, as well as discuss new mechanisms and normative properties that apply to this online setting. The online nature of such fair division problems provides both opportunities and challenges such as the possibility to develop new online mechanisms as well as the difficulty of dealing with an uncertain future. Introduction Fair division (Brams and Taylor 1996) is an important problem facing society today as increasing economical, environmental, and other pressures require us to try to do more with limited resources. Much previous work in fair division assumes the problem is offline and fixed. That is, we suppose that the agents being allocated resources, and the resources being allocated to these agents are all known and fixed. But practical reality is often quite different (Walsh 2014a; 2015). Fair division problems are often online, with either the agents, or the resources to be allocated, or both not being fixed and potentially changing over time.

While most of humanity is suddenly on the net, the value of this singularity is hampered by the lack of credible digital identities: Social networking, person-to-person transactions, democratic conduct, cooperation and philanthropy are all hampered by the profound presence of fake identities, as illustrated by Facebook's removal of 5.4Bn fake accounts since the beginning of 2019. Here, we introduce the fundamental notion of a \emph{genuine personal identifier}---a globally unique and singular identifier of a person---and present a foundation for a decentralized, grassroots, bottom-up process in which every human being may create, own, and protect the privacy of a genuine personal identifier. The solution employs mutual sureties among owners of personal identifiers, resulting in a mutual-surety graph reminiscent of a web-of-trust. Importantly, this approach is designed for a distributed realization, possibly using distributed ledger technology, and does not depend on the use or storage of biometric properties. For the solution to be complete, additional components are needed, notably a mechanism that encourages honest behavior and a sybil-resilient governance system.

Programmers at OpenAI, an artificial intelligence research company, recently taught a gaggle of intelligent artificial agents -- bots -- to play hide-and-seek. Not because they cared who won: The goal was to observe how competition between hiders and seekers would drive the bots to find and use digital tools. The idea is familiar to anyone who's ever played the game in real life; it's a kind of scaled-down arms race. When your opponent adopts a strategy that works, you have to abandon what you were doing before and find a new, better plan. It's the rule that governs games from chess to StarCraft II; it's also an adaptation that seems likely to confer an evolutionary advantage.

To take a look at what the end goal in terms of end-to-end deep learning for visual SLAM might look like, take a look at gradSLAM from Krishna Murthy, a Ph.D. student in MILA, and collaborators at CMU. Their paper offers a new way of thinking of SLAM as made up of differentiable blocks. From the article, "This amalgamation of dense SLAM with computational graphs enables us to backprop from 3D maps to 2D pixels, opening up new possibilities in gradient-based learning for SLAM." We are seeing more and more practical successes of self-supervised learning for multi-view problems where geometry enables us to get away from strong supervision. Even the ConvNet-based point detector SuperPoint [7], which my team and I developed at Magic Leap, uses self-supervision to train more robust interest point detectors.

At first, PLUMES will choose paths that randomly explore the environment. Each sample, however, provides new information about the targeted values in the surrounding environment -- such as spots with highest concentrations of chemicals or shallowest depths. The Gaussian process model exploits that data to narrow down possible paths the robot can follow from its given position to sample from locations with even higher value. PLUMES uses a novel objective function -- commonly used in machine-learning to maximize a reward -- to make the call of whether the robot should exploit past knowledge or explore the new area.

Sony Corp. has launched Sony AI, a new organization to pursue advanced R&D in artificial intelligence. With this move, the Japanese consumer electronics giant intends to go head-to-head with Google and Facebook, competing for AI talent and projects, and targeting a much bigger role in an ever-accelerating global AI race. The new organization will be worldwide from day one, with research sites in Tokyo, Austin, Texas, and an unnamed city in Europe. Sony AI will formally start operation next month. Hiroaki Kitano, president and CEO, Sony Computer Science Laboratories, Inc., will run Sony AI globally.

This paper studies the theoretical predictive properties of classes of forecast combination methods. The study is motivated by the recently developed Bayesian framework for synthesizing predictive densities: Bayesian predictive synthesis. A novel strategy based on continuous time stochastic processes is proposed and developed, where the combined predictive error processes are expressed as stochastic differential equations, evaluated using Ito's lemma. We show that a subclass of synthesis functions under Bayesian predictive synthesis, which we categorize as non-linear synthesis, entails an extra term that "corrects" the bias from misspecification and dependence in the predictive error process, effectively improving forecasts. Theoretical properties are examined and shown that this subclass improves the expected squared forecast error over any and all linear combination, averaging, and ensemble of forecasts, under mild conditions. We discuss the conditions for which this subclass outperforms others, and its implications for developing forecast combination methods. A finite sample simulation study is presented to illustrate our results.