In a Fisher market game, a market equilibrium is computed in terms of the utility functions and money endowments that agents reported. As a consequence, an individual buyer may misreport his private information to obtain a utility gain. We investigate the extent to which an agent's utility can be increased by unilateral strategic plays and prove that the percentage of this improvement is at most 2 for markets with weak gross substitute utilities. Equivalently, we show that truthfully reporting is a 0.5-approximate Nash equilibrium in this game. To identify sufficient conditions for truthfully reporting being close to Nash equilibrium, we conduct a parameterized study on strategic behaviors and further show that the ratio of utility gain decreases linearly as buyer's initial endowment increases or his maximum share of an item decreases. Finally, we consider collusive behavior of a coalition and prove that the utility gain is bounded by 1/(1 - maximum share of the collusion). Our findings justify the truthful reporting assumption in Fisher markets by a quantitative study on participants incentive, and imply that under large market assumption, the utility gain of a buyer from manipulations diminishes to 0.

We study efficient algorithms for a natural learning problem in markets. There is one seller with m divisible goods and n buyers with unknown individual utility functions and budgets of money. The seller can repeatedly announce prices and observe aggregate demand bundles requested by the buyers. The goal of the seller is to learn the utility functions and budgets of the buyers. Our scenario falls into the classic domain of ''revealed preference'' analysis. Problems with revealed preference have recently started to attract increased interest in computer science due to their fundamental nature in understanding customer behavior in electronic markets. The goal of revealed preference analysis is to observe rational agent behavior, to explain it using a suitable model for the utility functions, and to predict future agent behavior. Our results are the first polynomial-time algorithms to learn utility and budget parameters via revealed preference queries in classic Fisher markets with multiple buyers. Our analysis concentrates on linear, CES, and Leontief markets, which are the most prominent classes studied in the literature. Some of our results extend to general Arrow-Debreu exchange markets.

An important use of machine learning is to learn what people value. What posts or photos should a user be shown? Which jobs or activities would a person find rewarding? In each case, observations of people's past choices can inform our inferences about their likes and preferences. If we assume that choices are approximately optimal according to some utility function, we can treat preference inference as Bayesian inverse planning. That is, given a prior on utility functions and some observed choices, we invert an optimal decision-making process to infer a posterior distribution on utility functions. However, people often deviate from approximate optimality. They have false beliefs, their planning is sub-optimal, and their choices may be temporally inconsistent due to hyperbolic discounting and other biases. We demonstrate how to incorporate these deviations into algorithms for preference inference by constructing generative models of planning for agents who are subject to false beliefs and time inconsistency. We explore the inferences these models make about preferences, beliefs, and biases. We present a behavioral experiment in which human subjects perform preference inference given the same observations of choices as our model. Results show that human subjects (like our model) explain choices in terms of systematic deviations from optimal behavior and suggest that they take such deviations into account when inferring preferences.

Commonsense knowledge about part-whole relations (e.g., screen partOf notebook) is important for interpreting user input in web search and question answering, or for object detection in images. Prior work on knowledge base construction has compiled part-whole assertions, but with substantial limitations: i) semantically different kinds of part-whole relations are conflated into a single generic relation, ii) the arguments of a part-whole assertion are merely words with ambiguous meaning, iii) the assertions lack additional attributes like visibility (e.g., a nose is visible but a kidney is not) and cardinality information (e.g., a bird has two legs while a spider eight), iv) limited coverage of only tens of thousands of assertions. This paper presents a new method for automatically acquiring part-whole commonsense from Web contents and image tags at an unprecedented scale, yielding many millions of assertions, while specifically addressing the four shortcomings of prior work. Our method combines pattern-based information extraction methods with logical reasoning. We carefully distinguish different relations: physicalPartOf, memberOf, substanceOf. We consistently map the arguments of all assertions onto WordNet senses, eliminating the ambiguity of word-level assertions. We identify whether the parts can be visually perceived, and infer cardinalities for the assertions. The resulting commonsense knowledge base has very high quality and high coverage, with an accuracy of 89% determined by extensive sampling, and is publicly available.

In a wide range of statistical learning problems such as ranking, clustering or metric learning among others, the risk is accurately estimated by $U$-statistics of degree $d\geq 1$, i.e. functionals of the training data with low variance that take the form of averages over $k$-tuples. From a computational perspective, the calculation of such statistics is highly expensive even for a moderate sample size $n$, as it requires averaging $O(n^d)$ terms. This makes learning procedures relying on the optimization of such data functionals hardly feasible in practice. It is the major goal of this paper to show that, strikingly, such empirical risks can be replaced by drastically computationally simpler Monte-Carlo estimates based on $O(n)$ terms only, usually referred to as incomplete $U$-statistics, without damaging the $O_{\mathbb{P}}(1/\sqrt{n})$ learning rate of Empirical Risk Minimization (ERM) procedures. For this purpose, we establish uniform deviation results describing the error made when approximating a $U$-process by its incomplete version under appropriate complexity assumptions. Extensions to model selection, fast rate situations and various sampling techniques are also considered, as well as an application to stochastic gradient descent for ERM. Finally, numerical examples are displayed in order to provide strong empirical evidence that the approach we promote largely surpasses more naive subsampling techniques.

Artificial intelligence has had its share of ups and downs recently. In what was widely seen as a key milestone for artificial intelligence (AI) researchers, one system beat a former world champion at a mind-bendingly intricate board game. But then, just a week later, a "chatbot" that was designed to learn from its interactions with humans on Twitter had a highly public racist meltdown on the social networking site. How did this happen, and what does it mean for the dynamic field of AI? In early March, a Google-made artificial intelligence system beat former world champ Lee Sedol four matches to one at an ancient Chinese game, called Go, that is considered more complex than chess, which was previously used as a benchmark to assess progress in machine intelligence.

I'll be honest: Even though I'm supposed to be a technology expert, I've long resisted using Siri and my smartphone's voice commands. All the errors were frustrating and often seemed to eat up more time than just typing in commands and opening up apps manually. These days, though, I've found myself using Siri more often. Speech recognition has gotten a lot better, and Siri has gotten a lot smarter and more powerful. You can do virtually anything via your phone's voice commands, from posting to Twitter to finding the best pizza pie to figuring out just how deep 20,000 leagues really is.

Jody Williams received the Nobel Peace Prize in 1997 together with the International Campaign to Ban Landmines for their central role in establishing the 1997 Mine Ban Treaty. The US-based political activist is known across the world for her efforts to enhance understandings of security and related issues in the world today. She is also the chair of the Noble Women's Initiative that she founded in 2006 together with five other women Nobel Peace laureates. She, along with 20 of her fellow Nobel Peace laureates have called for a preemptive ban on Lethal Autonomous Weapons Systems (LAWS)--weapons that could operate without human supervision once activated even in matters of killing human beings. The UN's Convention on Certain Conventional Weapons (CCW) held their third informal government's meet in Geneva from 11-15 April.

As British Airways flight BA727 from Geneva approached the runway at London's Heathrow airport on Sunday, something unexpectedly hit the front of the plane. On board were 132 passengers and five crew. Thankfully, the aircraft was not damaged and landed safely. But a police investigation has been launched and, if the object is confirmed as a drone, it will be the first known collision of its kind in the UK. What can be done to stop this happening again?

The Pentagon's once-secret submarine drones programme is being discussed in the open, with US defence secretary Ashton Carter hinting at their potential use in the disputed South China Sea. Surveillance is the initial function of these unmanned, undersea vehicles, which can operate in shallow water'where manned submarines cannot'. There are also plans to create a'Russian doll' or'mother' sub, which could release a number of far smaller drones to be mines, trackers or missile launchers, reports the Financial Times. The US military hopes the drone development would deter China from dominating the South China Sea. The countries of the South China Sea have long claimed rights to disputed international waters, but as its economic strength has grown, an increasingly confident China has built military bases on artificial islands and militarized one of the disputed Paracel Islands.