The dreaded El Niño strikes the globe every 2 to 7 years. As warm waters in the tropical Pacific Ocean shift eastward and trade winds weaken, the weather pattern ripples through the atmosphere, causing drought in southern Africa, wildfires in South America, and flooding on North America's Pacific coast. Climate scientists have struggled to predict El Niño events more than 1 year in advance, but artificial intelligence (AI) can now extend forecasts to 18 months, according to a new study. The work could help people in threatened regions better prepare for droughts and floods, for example by choosing which crops to plant, says William Hsieh, a retired climate scientist in Victoria, Canada, who worked on early El Niño forecasts but who was not involved in the current study. Longer forecasts could have "large economic benefits," he says.

A former Google software engineer is sounding the alarm on killer robots. Laura Nolan resigned from Google last year when the tech giant started working with the U.S. military on drone technology, and since then, she has joined the Campaign to Stop Killer Robots, warning that autonomous robots with lethal capabilities could become a threat to humanity. Discussions concerning possibly banning autonomous weapons fell apart on August 21 during a United Nations meeting in Geneva, when Russian diplomats allegedly made a fuss over the language that was used in a document meant to begin the process of establishing a ban. "If you're a despot, how much easier is it to have a small cadre of engineers control a fleet of autonomous weapons for you than to have to keep your troops in line?" Nolan tells Inverse. "Autonomous weapons are potential weapons of mass destruction. They need to be made taboo in the same way that chemical and biological weapons are."

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.

Whenever a social media user decides to share a story, she is typically pleased to receive likes, comments, shares, or, more generally, feedback from her followers. As a result, she may feel compelled to use the feedback she receives to (re-)estimate her followers' preferences and decides which stories to share next to receive more (positive) feedback. Under which conditions can she succeed? In this work, we first look into this problem from a theoretical perspective and then provide a set of practical algorithms to identify and characterize such behavior in social media. More specifically, we address the above problem from the viewpoint of sequential decision making and utility maximization. For a wide variety of utility functions, we first show that, to succeed, a user needs to actively trade off exploitation-- sharing stories which lead to more (positive) feedback--and exploration-- sharing stories to learn about her followers' preferences. However, exploration is not necessary if a user utilizes the feedback her followers provide to other users in addition to the feedback she receives. Then, we develop a utility estimation framework for observation data, which relies on statistical hypothesis testing to determine whether a user utilizes the feedback she receives from each of her followers to decide what to post next. Experiments on synthetic data illustrate our theoretical findings and show that our estimation framework is able to accurately recover users' underlying utility functions. Experiments on several real datasets gathered from Twitter and Reddit reveal that up to 82% (43%) of the Twitter (Reddit) users in our datasets do use the feedback they receive to decide what to post next.

The biggest impact DataRobot has had on Lenovo is that decisions are now made in a more proactive and precise way. As we get better and forecast accuracy keeps improving, people are becoming more confident and trusting of the process, the data, the models, and DataRobot.

With the rise of artificial intelligence and voice recognition technology, there has been a plenty discussion about how industries, the labour force, and business models will change, but how will these technologies change the way consumers and brands interact? The explosion of smartphones and social media opened a new world of opportunities to communicate. Mobile messaging apps like Facebook Messenger, WhatsApp, and WeChat became the norm. Chatbots followed, which allowed brands to communicate on a one-to-one, personal level. Voice recognition erupted in the same way as social media and is completely changing the way we interact.

"We lived and breathed data science," DataRobot CEO Jeremy Achin says of himself and his cofounder Tom de Godoy. "And we asked ourselves, 'How would we automate our jobs?'" DataRobot wants to make machine learning so simple that a business analyst with basic training can run predictive models without breaking a sweat. The Boston-based startup just raised a $206 million Series E funding round led by Sapphire Ventures to expand the business, which sells software that helps companies across industries develop and deploy in-house AI models. The billion-dollar valuation makes it the highest-ranking of the "picks-and-shovels" startups featured on Forbes' inaugural AI 50 list (meaning the companies that provide tools to help their customers develop their own AI).

Skin cancer is one of the most common types of cancer around the world. For this reason, over the past years, different approaches have been proposed to assist detect it. Nonetheless, most of them are based only on dermoscopy images and do not take into account the patient clinical information. In this work, first, we present a new dataset that contains clinical images, acquired from smartphones, and patient clinical information of the skin lesions. Next, we introduce a straightforward approach to combine the clinical data and the images using different well-known deep learning models. These models are applied to the presented dataset using only the images and combining them with the patient clinical information. We present a comprehensive study to show the impact of the clinical data on the final predictions. The results obtained by combining both sets of information show a general improvement of around 7% in the balanced accuracy for all models. In addition, the statistical test indicates significant differences between the models with and without considering both data. The improvement achieved shows the potential of using patient clinical information in skin cancer detection and indicates that this piece of information is important to leverage skin cancer detection systems.

Service 6.1.2.1 Managed 6.1.2.2 Professional 6.2 By Deployment 6.2.1 Cloud 6.2.2 On-Premise 6.3 By Application 6.3.1 Supply Chain & Logistics 6.3.2

H3 Dynamics has partnered with Curitiba-based EPH Engineering in Brazil, a firm that specializes in hydropower design, dam inspections and safety plans, to launch a turnkey dam inspection solution that combines AI-enabled damage assessment and HYCOPTER fuel cell drones capable of flying 3.5 hours at a time. With over 5,000 dams submitted to the Brazilian Dam Safety Plan, and two recent collapse incidents causing more than 300 deaths and major environmental damage, Brazilian authorities have tightened inspection and upkeep requirements in the country. "Many accident reports show that problems were not detected by instrumentation but by visual observation. Drones can help, but due to the large dimensions of these structures we need much longer flight times." Some of the dams are so large that they would require months of battery-powered drone flights to fully scan their surfaces.