All the same, it's one thing employers love to hate. Whenever an employer hires a worker, the understanding is that they have offered a fair wage for what you have to offer. Therefore, any time spent outside work should be for recuperating (recreation and sleep) from the busy day. That has been the thinking: "Eight hours labour, eight hours recreation, eight hours rest." It might have worked in the early 20th century, during the industrial revolution, but a rethink for the demands of the 21st century is long due.

All the same, it's one thing employers love to hate. Whenever an employer hires a worker, the understanding is that they have offered a fair wage for what you have to offer. Therefore, any time spent outside work should be for recuperating (recreation and sleep) from the busy day. That has been the thinking: "Eight hours labour, eight hours recreation, eight hours rest." It might have worked in the early 20th century, during the industrial revolution, but a rethink for the demands of the 21st century is long due.

We introduce "extreme summarization," a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question "What is the article about?". We argue that extreme summarization, by nature, is not amenable to extractive strategies and requires an abstractive modeling approach. In the hope of driving research on this task further: (a) we collect a real-world, large scale dataset by harvesting online articles from the British Broadcasting Corporation (BBC); and (b) propose a novel abstractive model which is conditioned on the article's topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans on the extreme summarization dataset.

Reducing traffic accidents is an important public safety challenge, therefore, accident analysis and prediction has been a topic of much research over the past few decades. Using small-scale datasets with limited coverage, being dependent on extensive set of data, and being not applicable for real-time purposes are the important shortcomings of the existing studies. To address these challenges, we propose a new solution for real-time traffic accident prediction using easy-to-obtain, but sparse data. Our solution relies on a deep-neural-network model (which we have named DAP, for Deep Accident Prediction); which utilizes a variety of data attributes such as traffic events, weather data, points-of-interest, and time. DAP incorporates multiple components including a recurrent (for time-sensitive data), a fully connected (for time-insensitive data), and a trainable embedding component (to capture spatial heterogeneity). To fill the data gap, we have - through a comprehensive process of data collection, integration, and augmentation - created a large-scale publicly available database of accident information named US-Accidents. By employing the US-Accidents dataset and through an extensive set of experiments across several large cities, we have evaluated our proposal against several baselines. Our analysis and results show significant improvements to predict rare accident events. Further, we have shown the impact of traffic information, time, and points-of-interest data for real-time accident prediction.

Multimodal datasets contain an enormous amount of relational information, which grows exponentially with the introduction of new modalities. Learning representations in such a scenario is inherently complex due to the presence of multiple heterogeneous information channels. These channels can encode both (a) inter-relations between the items of different modalities and (b) intra-relations between the items of the same modality. Encoding multimedia items into a continuous low-dimensional semantic space such that both types of relations are captured and preserved is extremely challenging, especially if the goal is a unified end-to-end learning framework. The two key challenges that need to be addressed are: 1) the framework must be able to merge complex intra and inter relations without losing any valuable information and 2) the learning model should be invariant to the addition of new and potentially very different modalities. In this paper, we propose a flexible framework which can scale to data streams from many modalities. To that end we introduce a hypergraph-based model for data representation and deploy Graph Convolutional Networks to fuse relational information within and across modalities. Our approach provides an efficient solution for distributing otherwise extremely computationally expensive or even unfeasible training processes across multiple-GPUs, without any sacrifices in accuracy. Moreover, adding new modalities to our model requires only an additional GPU unit keeping the computational time unchanged, which brings representation learning to truly multimodal datasets. We demonstrate the feasibility of our approach in the experiments on multimedia datasets featuring second, third and fourth order relations.

IBM has recently launched its inaugural IBM Women Leaders in A.I. in recognition of women advancing their company's journey to artificial intelligence across diverse industries around the globe--from California's County of Sonoma to South Africa's NedBank. There is an opportunity for women to not only contribute to Artificial Intelligence (A.I.) – one of the modern era's most important technologies – but help lead in its application across various industries around the globe. This position of influence is not solely to appease a diversity mandate or to stand guard against algorithmic biases. Women can stand up as one of the integral factors in bringing transparent, inclusive and trusted A.I. to business. Among those recognized on IBM's list of Women Leaders in A.I., we recognized a common success factor - shared a propensity for bringing stakeholders together for effective work.

Microsoft announced a $1 billion investment in OpenAI, a lab co-founded by Elon Musk to develop "artificial general intelligence." The investment is the start of a long-term partnership between the two organizations. OpenAI will ensure its services work on Microsoft's Azure cloud platform, and the companies will collaborate on new supercomputers. OpenAI's stated mission is to develop "artificial general intelligence," or AGI. In layman's terms, AGI is AI that can think like a human (possibly even better) while carrying out complex tasks autonomously. Whether or not an AGI would immediately decide to incinerate humanity a la Skynet remains to be seen, but OpenAI at least claims its artificial intelligence would be safe and beneficial for the human race.

RIYADH – Saudi Arabia alleged Wednesday an attack by drones and cruise missiles on the heart of the kingdom's oil industry was "unquestionably sponsored by Iran," naming but not directly accusing Tehran of launching the assault. Iran denies being involved in the attack claimed by Yemeni rebels, and has threatened the U.S. that it will retaliate "immediately" if Tehran is targeted in response. The news conference by Saudi military spokesman Col. Turki al-Malki comes after a summer of heightened tensions between Iran and the U.S. over President Donald Trump unilaterally withdrawing America from Tehran's 2015 nuclear deal with world powers. The U.S. alleges Iran launched the attack, which Yemen's Houthi rebels earlier claimed as a response to the yearslong Saudi-led war there that's killed tens of thousands of people. Al-Malki made a point not to directly accuse Iran of firing the weapons or launching them from inside of Iranian territory.

This is the web version of Data Sheet, Fortune's daily newsletter on the top tech news. To get it delivered daily to your in-box, sign up here. There's enthusiasm about the healthcare industry--I badly want to call it infectious or contagious but fear that would be in poor taste--and it is abundantly evident at Fortune's Brainstorm Health conference in San Diego this week. The exceedingly well-funded startup is more than a curio, and its technology vastly bests annoying quad-copters. Zipline's drones are airplanes that land by an innovative tail hook mechanism. Technology is transforming healthcare, mostly for the good.

When some people think of artificial intelligence (AI), they think of humanoid robots turning against their creators in an apocalyptic science fiction movie. In reality, AI is already playing an increasing role in many existing and evolving technologies, from driverless cars to translation software, virtual assistance devices and monitoring agriculture and biodiversity. Satellite images provided by AI can assist policymakers in finding solutions to problems of hunger, drought and climate change, to name only a few. Audrey Azoulay, Director-General of the United Nations Educational, Scientific and Cultural Organization (UNESCO), says that promoting AI in Africa is a top priority for the organization. "Artificial intelligence can help us advance more rapidly towards the achievement of the SDGs by allowing better risk assessment, enabling more accurate forecasting and faster knowledge sharing, by offering innovative solutions in the fields of education, health, ecology, urbanism and the creative industries and by improving standards of living and our daily well-being," Ms. Azoulay explains.