Much of the literature on suboptimal, polynomial-time algorithms for multi-agent path finding focuses on undirected graphs, where motion is permitted in both directions along a graph edge. Despite this, traveling on directed graphs is relevant in navigation domains, such as path finding in games, and asymmetric communication networks.We consider multi-agent path finding on strongly biconnected directed graphs. We show that all instances with at least two unoccupied positions have a solution, except for a particular, degenerate subclass where the graph has a cyclic shape. We present diBOX, an algorithm for multi-agent path finding on strongly biconnected directed graphs. diBOX runs in polynomial time, computes suboptimal solutions and is complete for instances on strongly biconnected digraphs with at least two unoccupied positions. We theoretically analyze properties of the algorithm and properties of strongly biconnected directed graphs that are relevant to our approach. We perform a detailed empirical analysis of diBOX, showing a good scalability. To our knowledge, our work is the first study of multi-agent path finding focused on directed graphs.

I've been told it helps to take a deep breath. But unfortunately, I cannot do that." The setting is a competitive debate being held at Watson West, IBM's AI outpost in San Francisco's tech-centric SOMA neighborhood. Noa is Noa Ovadia, a champion debater from Israel. The speaker greeting her is her opponent--whose inability to breathe deeply makes perfect sense given that it's a piece of software, generating Siri-like female speech that emanates from a human-sized black column with a screen on its front. This is indeed the first time that the software in question, Project Debater, has shown its stuff outside of secret trial runs. Since 2012, IBM Research has been teaching it to debate humans on a vast array of subjects--making it a successor to Deep Blue (which beat Garry Kasparov in a six-game chess match in 1997) and Watson (which won a Jeopardy tournament against Ken Jennings and Brad Rutter in 2011). That was good enough to make the exhibition a success in the eyes of Noam Slonim, a senior technical staff member at IBM's research center in Haifa, Israel and the person who originally proposed the Project Debater idea in 2011. The effort now includes dozens of researchers at multiple IBM labs and is led by Slonim's Haifa colleague Ranit Aharonov. Merely seeing the software keep the audience engaged over a 20-minute debate "was a very positive feeling," he tells me at a post-debate reception. Witnessing a computer thrash the Kasparovs and Jenningses of competitive debating at their own craft would be an epoch-shifting moment, but "our goal is not to develop yet another system that is better than humans in doing something," stresses Aharonov. Instead, IBM wants to create debating software that can spar with "a reasonably competent human, but not necessarily a world champion, and come across holding its own," says IBM director of research Arvind Krishna. Still, even if the company is keeping its aspirations in check, its latest adventure in AI involves challenges unlike any it's tackled before. Soon after Watson bested Jennings and Rutter in a tournament taped at IBM's Yorktown, N.Y. research center in January 2011, the company began to think about how to top that memorable feat of artificial intelligence. "All of the thousands of researchers … got the same email asking what should be the next AI grand challenge that IBM Research should pursue," remembers Slonim. The goal, he explains, was to come up with a project that was "scientifically interesting and challenging and would have some business value.

With warnings coming fast and furious from tech luminaries as diverse as Bill Gates, Elon Musk, and the late Stephen Hawking, to name a few, most of us are conditioned to think of the potential dangers of artificial intelligence in the hands of bad actors. While armies of AI-powered humanoid bots have not yet materialized, AI is being harnessed by hackers in the creation of "smart malware" that overcomes traditional defenses by using predictive technology. But if this is true, can't the opposite be also? Can AI be harnessed to turn against malware? Experts emphatically say yes, and some, like Barracuda and Cylance, are already doing it.

Last Sunday I was at big retail store in Harare and it was a very busy day because it was month end and people got paid. Grocery shopping was in full swing, I also bought some groceries for my self. When I was in the queue for payment and collection, I saw almost every one making payment either by swiping the magic plastic card or struggling on their mobile handset by punching few numbers etc. The electronic payment queue was moving fast compared to the cash payment queue where I saw only a handful of people with just one/two small item/items. The thought came to my mind out of this whole picture was "Whats happening here besides the payments through mobile and plastic"?

High dimensional piecewise stationary graphical models represent a versatile class for modelling time varying networks arising in diverse application areas, including biology, economics, and social sciences. There has been recent work in offline detection and estimation of regime changes in the topology of sparse graphical models. However, the online setting remains largely unexplored, despite its high relevance to applications in sensor networks and other engineering monitoring systems, as well as financial markets. To that end, this work introduces a novel scalable online algorithm for detecting an unknown number of abrupt changes in the inverse covariance matrix of sparse Gaussian graphical models with small delay. The proposed algorithm is based upon monitoring the conditional log-likelihood of all nodes in the network and can be extended to a large class of continuous and discrete graphical models. We also investigate asymptotic properties of our procedure under certain mild regularity conditions on the graph size, sparsity level, number of samples, and pre- and post-changes in the topology of the network. Numerical works on both synthetic and real data illustrate the good performance of the proposed methodology both in terms of computational and statistical efficiency across numerous experimental settings.

We address the problem of spatial segmentation of a 2D object in the context of a robotic system for painting, where an optimal segmentation depends on both the appearance of the object and the size of each segment. Since each segment must take into account appearance features at several scales, we take a hierarchical grammar-based parsing approach to decompose the object into 2D segments for painting. Since there are many ways to segment an object the solution space is extremely large and it is very challenging to utilize an exploration based optimization approach like reinforcement learning. Instead, we pose the segmentation problem as an imitation learning problem by using a segmentation algorithm in the place of an expert, that has access to a small dataset with known foreground-background segmentations. During the imitation learning process, we learn to imitate the oracle (segmentation algorithm) using only the image of the object, without the use of the known foreground-background segmentations. We introduce a novel deterministic policy gradient update, DRAG, in the form of a deterministic actor-critic variant of AggreVaTeD, to train our neural network based object parser. We will also show that our approach can be seen as extending DDPG to the Imitation Learning scenario. Training our neural parser to imitate the oracle via DRAG allow our neural parser to outperform several existing imitation learning approaches.

The United Arab Emirates (UAE) is the first country in the world to appoint a State Minister for Artificial Intelligence (AI). The UAE is embracing AI in society at the governmental level, which is leading to a new generations of digital government (which we are labeling Gov. 3.0). This paper argues that the decision to embrace AI will lead to positive impacts on society, including businesses, organizations and individuals, as well as on the AI industry itself. This paper discusses the societal impacts of AI at a macro (country-wide) level. This article is part of the special track on AI and Society.

This year, we have seen an acceleration of Silicon Valley tech giants opening AI research labs around the world as they seek to gain traction among researchers and fulfill their global ambitions. In the past six months or so, Google brought labs to China and France, Facebook opened labs in Pittsburgh and Seattle, and Microsoft announced plans to open labs near universities in Berkeley, California and Melbourne, Australia. This trend shows no signs of slowing down. Last month, Samsung announced labs in Cambridge, Moscow, and Toronto. This week, Nvidia announced plans to open a new lab in Toronto, while Google shared plans to open a lab in Accra, Ghana, Google's first in Africa and perhaps the first of any tech giant in Africa.

England's Mike Brown said he is "not a robot" after he was involved in a verbal altercation with a fan following Saturday's defeat by South Africa. Full-back Brown and prop Joe Marler exchanged words with a supporter after the second Test in Bloemfontein. Brown, 32, told BBC Sport that when an England fan is "screaming obscenities and saying you're not trying, you are going to have a reaction". "I just told him to shut up basically," added the Harlequins player. "You can call me whatever in terms of how I've played, that's fine. But don't turn around and say I'm not trying when I'm coming off with bumps and bruises and aching and I've given everything I can for England."

Google has announced it will open an Artificial Intelligence (A.I.) research center in Accra, Ghana this year, becoming the first A.I research center in Africa. The search engine's giant made this announcement in a blog post on Wednesday. Google Chief executive Sundar Pichai tweeted '' excited that we'll be opening a new Google AI center in Accra, Ghana''. He added '' really looking forward to our research teams solving challenges with AI for Africa and beyond''. The AI center will bring together machine learning experts and engineers on projects focused on technology and its numerous applications.