That's why the technology team at the well-known nonprofit Crisis Text Line in New York City analyzed some 65 million text messages to determine what words were most statistically associated with a high risk of suicide. This scale of analysis would clearly be infeasible without some form of automated analysis, and its results surprised the team. Use of the term "EMS" in a text, for example, is five times more predictive of a high risk of suicide than the actual word "suicide." As a result, the organization is now able to respond to 94 percent of high-risk texters in fewer than five minutes. This is just one example of "mission-driven artificial intelligence"--the responsible application of artificial intelligence (AI) to solve societal and ecological challenges. Sometimes dubbed "AI for good," mission-driven AI is the use of machine-learning techniques to streamline operations and enhance programs at nonprofits, nongovernmental organizations (NGOs), and social enterprises.

Researchers have developed a deep learning algorithm that can automatically identify, count and describe animals in their natural habitats. A new paper, published in Proceedings of the National Academy of Sciences (PNAS), decribes how the cutting-edge artificial intelligence technique can automatically describe photographs that have been collected by motion-sensor cameras on deep neural networks. The result is a system that can automate animal identification for up to 99.3 per cent of images while still performing at the same 96.6 per cent accuracy rate of crowd-sourced teams of human volunteers. "This technology lets us accurately, unobtrusively and inexpensively collect wildlife data, which could help catalyse the transformation of many fields of ecology, wildlife biology, zoology, conservation biology and animal behaviour into'big data' sciences," explained Jeff Clune, the senior author of the paper and Harris Associate Professor at the University of Wyoming. "This will dramatically improve our ability to both study and conserve wildlife and precious ecosystems."

Remark Holdings (MARK) is what one would call a contested company. It has long supporters with strong conviction, but there has also been some kind of a short drive, and the present short count is almost 16% of the nearly 25M float. That short count is far from the highest we've seen, a company like Applied Optoelectronics (AAOI) still has 78% of the float shorted (at least according to the latest figures) but it seems to have done major damage already. Who to believe, the conviction longs or the shorts who put out a troubling report. Questions like these are very difficult to solve, especially if you're not a forensic accountant. Since we're no forensic accountants ourselves, we'll try to gather some stylized facts and see what these add up to, and whether there is some chance for the longs to recoup some of their losses. This company was one of two highest conviction longs for SA contributor Yale Bock, who is the President of Y H & C Investments (see here).

South African construction sites may experience a drone hovering above them. Gauteng province's department of infrastructure development has launched a fleet of drones that'll be used to monitor building projects including those in and around Johannesburg. "It is possible for the public sector to be efficient and to be productive in what we do," says Jacob Mamabolo, Head of Provincial Department. The drones would monitor the quality of work and also ensure that the projects stick to their schedules. The unmanned aerial vehicles (UAVs) are just the latest effort by Mamabolo to make his department more tech-savvy and to'leap frog' with opportunities from the'fourth industrial revolution'.

Banks That Have Implemented Social Media Payments Through Facebook and Twitter, Let's Talk Payments

This paper presents the recently published Cerema AWP (Adverse Weather Pedestrian) dataset for various machine learning tasks and its exports in machine learning friendly format. We explain why this dataset can be interesting (mainly because it is a greatly controlled and fully annotated image dataset) and present baseline results for various tasks. Moreover, we decided to follow the very recent suggestions of datasheets for dataset, trying to standardize all the available information of the dataset, with a transparency objective.

Recent work in graph models has found that probabilistic hyperedge replacement grammars (HRGs) can be extracted from graphs and used to generate new random graphs with graph properties and substructures close to the original. In this paper, we show how to add latent variables to the model, trained using Expectation-Maximization, to generate still better graphs, that is, ones that generalize better to the test data. We evaluate the new method by separating training and test graphs, building the model on the former and measuring the likelihood of the latter, as a more stringent test of how well the model can generalize to new graphs. On this metric, we find that our latent-variable HRGs consistently outperform several existing graph models and provide interesting insights into the building blocks of real world networks.

Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document. Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently there exist no resources to train and test this capability. We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods. In our task, a model learns to seek and combine evidence - effectively performing multi-hop (alias multi-step) inference. We devise a methodology to produce datasets for this task, given a collection of query-answer pairs and thematically linked documents. Two datasets from different domains are induced, and we identify potential pitfalls and devise circumvention strategies. We evaluate two previously proposed competitive models and find that one can integrate information across documents. However, both models struggle to select relevant information, as providing documents guaranteed to be relevant greatly improves their performance. While the models outperform several strong baselines, their best accuracy reaches 42.9% compared to human performance at 74.0% - leaving ample room for improvement.

A paraphrase is a restatement of the meaning of a text in other words. Paraphrases have been studied to enhance the performance of many natural language processing tasks. In this paper, we propose a novel task iParaphrasing to extract visually grounded paraphrases (VGPs), which are different phrasal expressions describing the same visual concept in an image. These extracted VGPs have the potential to improve language and image multimodal tasks such as visual question answering and image captioning. How to model the similarity between VGPs is the key of iParaphrasing. We apply various existing methods as well as propose a novel neural network-based method with image attention, and report the results of the first attempt toward iParaphrasing.