Motivation stems from the problem of estimating a binary relation on the accoun ts of a social network where the notions that one account follows, likes or reacts to another account need neither be symmetric nor asymmetric. In particular, i following j does not need to imply that j follows i, nor does it necessarily imply that j does not follow i . Clustering alone does not capture asymmetric subsets of the relation on the social network because the equivalence relation that characterizes the clustering is symmetric. Similarly, partial ordering alone does not capture symm etric subsets of the relation on the social network because partial orders are antisymmetric. Like in clustering and partial ordering, we work with the assumption t hat the relation on the social network is transitive, i.e., if i follows j and j follows k then i follows k . This assumption simplifies reality and we quantify the deviation empirically. Unlike in clusterin g and partial ordering, we do not assume symmetry or antisymmetry. To model our assump tion we introduce algorithms that output a preorder on a set, i.e., a binary relation on the set that is reflexive and transitiv e. More specifically, we introduce algorithms for the preordering problem: Definition 1. (Wakabayashi, 1998) Given a finite set V and a value c

Google is still firing AI researchers amid criticism of its work. The New York Times has learned Google fired machine learning scientist Satrajit Chatterjee in March, soon after it refused to publish a paper Chatterjee and others wrote challenging earlier findings that computers could design some chip components more effectively than humans. The scientist was reportedly allowed to collaborate on a paper disputing those claims after he and fellow authors expressed reservations, but was dismissed after a resolution committee rejected the paper and the researchers hoped to bring the issue to CEO Sundar Pichai and Alphabet's board of directors. The company hasn't detailed why it fired Chatterjee, but told the Times he'd been "terminated with cause." It also maintained that the original paper had been "thoroughly vetted" and peer-reviewed, and that the study challenging the claims "did not meet our standards." Whether or not the science holds up, the exit underscores the ongoing clash between Google's management and the AI teams that drive many of its projects.

In the not-so-distant future, more robots may be interacting with customers at shopping complexes, serving food at restaurants or cleaning floors at offices in Japan amid a serious labor crunch. A hint of what is to come is visible at the International Robot Exhibition 2019, a major biennial robot trade show that kicked off on Wednesday at Tokyo Big Sight. The event runs until Saturday. Featuring a record 637 firms and organizations, some participants said demand for robotics as helping hands in service sectors is rising to compensate for a shortage of workers. Tokyo-based Omron Social Solutions Co. unveiled a robot capable of performing three tasks: cleaning, security and guiding.

We consider the following general graph clustering problem: given a complete undirected graph G=(V,E,c) with an edge weight function c:E->Q, we are asked to find a partition C of V that maximizes the sum of edge weights within the clusters in C. Owing to its high generality, this problem has a wide variety of real-world applications, including correlation clustering, group technology, and community detection. In this study, we investigate the design of mathematical programming formulations and constraint satisfaction formulations for the problem. First, we present a novel integer linear programming (ILP) formulation that has far fewer constraints than the standard ILP formulation by Groetschel and Wakabayashi (1989). Second, we propose an ILP-based exact algorithm that solves an ILP problem obtained by modifying our above ILP formulation and then performs simple post-processing to produce an optimal solution to the original problem. Third, we present maximum satisfiability (MaxSAT) counterparts of both our ILP formulation and ILP-based exact algorithm. Computational experiments using well-known real-world datasets demonstrate that our ILP-based approaches and their MaxSAT counterparts are highly effective in terms of both memory efficiency and computation time.

Coming off its first quarterly revenue decline in 13 years, Apple Inc. kicks off its annual developers' conference Monday in San Francisco facing questions about whether the company's best days are behind it. The focus of the weeklong Worldwide Developers Conference is expected to be on Siri, Apple's digital assistant. When Apple introduced Siri as an iPhone feature in 2011, it heralded a future of people finding information or completing tasks on their devices by speaking rather than typing or tapping. That vision of Siri hasn't fully materialized, leaving the door open for other technology companies to push into Apple's turf. Google parent Alphabet Inc., Amazon.com Inc. and Microsoft Corp. have all introduced voice-activated digital assistants that rely on artificial intelligence?technology that allows computers to understand inferences and context so they can make decisions like a human brain instead of following programmed instructions.

Coming off its first quarterly revenue decline in 13 years, Apple Inc. kicks off its annual developers' conference Monday in San Francisco facing questions about whether the company's best days are behind it. The focus of the weeklong Worldwide Developers Conference is expected to be on Siri, Apple's digital assistant. When Apple introduced Siri as an iPhone feature in 2011, it heralded a future of people finding information or completing tasks on their devices by speaking rather than typing or tapping. That vision of Siri hasn't fully materialized, leaving the door open for other technology companies to push into Apple's turf. Google parent Alphabet Inc., Amazon.com Inc. and Microsoft Corp. have all introduced voice-activated digital assistants that rely on artificial intelligence -- technology that allows computers to understand inferences and context so they can make decisions like a human brain instead of following programmed instructions.