Willis Towers Watson, a leading global advisory, broking and solutions company, and Relativity6, Inc., a machine learning and artificial intelligence (AI) insurance-technology company, today announced that Willis Towers Watson has selected the Relativity6 platform to predict and optimise customer retention and win-back. Brent Lehmann, General Manager Affinity & Commercial Australasia said the partnership with such an innovative technology company will help to ensure Willis Towers Watson remains competitive in the marketplace. "Relativity6's product offerings are a good fit to accomplish our strategic objectives across the organisation, so we are very excited to partner with them to take full advantage of the data that we have accumulated within our core systems in Australia." Alan Ringvald, Chief Executive Officer at Relativity 6, commented: "We are honoured to partner with such a distinguished organization. We believe that our solution will enable Willis Towers Watson to better serve their customers and ultimately drive significant top line revenue growth. We've engaged with top-tier insurers in the U.S. and Latin America, and this is a fantastic opportunity to expand our footprint with a truly global insurance broking brand."

We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head words, which indicate the type of the noun phrases they appear in. We show that these ultra-fine types can be crowd-sourced, and introduce new evaluation sets that are much more diverse and fine-grained than existing benchmarks. We present a model that can predict open types, and is trained using a multitask objective that pools our new head-word supervision with prior supervision from entity linking. Experimental results demonstrate that our model is effective in predicting entity types at varying granularity; it achieves state of the art performance on an existing fine-grained entity typing benchmark, and sets baselines for our newly-introduced datasets. Our data and model can be downloaded from: http://nlp.cs.washington.edu/entity_type

Traffic hell is alive and well in Los Angeles. In 2017, Angelenos were stuck on the road for 102 hours each (more than four full days), costing the city $19.2 billion, according to INRIX's annual global traffic scorecard. Traffic is almost as bad--and costly--in Moscow, Sao Paulo, and London. But this is the 21st century! Can't AI fix these problems by optimizing traffic flow?

Machine learning in finance may work magic, even though there is no magic behind it (well, maybe just a little bit). Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms. Machine learning is making significant inroads in the financial services industry. Let's see why financial companies should care, what solutions they can implement with AI and machine learning, and how exactly they can apply this technology. We can define machine learning (ML) as a subset of data science that uses statistical models to draw insights and make predictions.

The hacker sought buyers for maintenance documents about the MQ-9 Reaper drone, a remotely controlled aerial vehicle used by the Pentagon and other parts of the government to conduct offensive strikes or reconnaissance and surveillance operations. Discovery of the attempted sale of the stolen documents comes amid heightened concern about how U.S. military secrets may be insufficiently protected from hackers. Military officials said last month that the Defense Department's inspector general was investigating a major security breach after Chinese hackers allegedly stole data pertaining to submarine warfare, including plans to build a supersonic antiship missile. There was no evidence that the hacker who acquired the Reaper drone documents was affiliated with a foreign country, or that he was intentionally seeking to obtain military documents, said Andrei Barysevich, a senior threat researcher at Recorded Future, the U.S.-based cybersecurity firm that spotted the attempted sale. Instead, the hacker scanned large parts of the internet for misconfigured Netgear routers and exploited a two-year-old known vulnerability, involving default login credentials, to steal files from compromised machines.

At 6:40 in the morning, a klaxon horn sounds three times. "Gas!" a man in a hard hat and fluorescent vest yells out. There's a hissing noise, and the helium starts flowing. From the tanks stacked like cordwood on a nearby truck, the gas moves through a series of hoses until it's 55 feet up, then through a copper pipe and into the top of a plastic tube that hangs down to the ground, like a shed snake skin held up for inspection. It's a Wednesday in late June in Winnemucca, a solitary mining town in northern Nevada that has avoided oblivion by straddling the I-80 freeway. Along with two Basque restaurants, the Buckaroo Hall of Fame, and a giant W carved into the side of a hill, Winnemucca is the test site for Project Loon, a grandiose scheme launched in 2011 to bring the internet to huge swaths of the planet where sparse population and challenging geography make the usual networks of cell towers a nonstarter.

The API provides pre-trained object detection models that have been trained on the COCO dataset. COCO dataset is a set of 90 commonly found objects. See image below of objects that are part of COCO dataset. In this case we care about classes -- persons and soccer ball which are both part of COCO dataset. The API also has a big set of models it supports. See table below for reference. The models have a trade off between speed and accuracy. Since I was interested in real time analysis, I chose SSDLite mobilenet v2. Once we identify the players using the object detection API, to predict which team they are in we can use OpenCV which is powerful library for image processing.

A recent college grad living in rural Connecticut, they'd been subject to the swipe-and-ghost thing a few too many times. Then, this spring, Juniper submitted an ad to @_personals_, an Instagram for lesbian, queer, transgender, and non-binary people looking for love (and other stuff). The post, titled "TenderQueer Butch4Butch," took Juniper two weeks to craft, but the care paid off: the ad ultimately garnered well over 1,000 likes--and more than 200 messages. Tinder's Days as a Hookup App May Be Over "I was so used to the Tinder culture of nobody wanting to text back," Juniper says. "All of the sudden I had hundreds of queers flooding my inbox trying to hang out."

Moving stuff by air may be quick and convenient, but it's also horridly expensive, accounting for just 1 percent of global shipping by volume--and 35 percent of it by cost. So while autonomous drones dropping a few pounds of snacks or medical supplies are generating plenty of buzz, two Bulgarian brothers see an opening in the long-haul business. And they think they've got the tech to start flying hundreds of pounds of cargo over hundreds of miles, no pilot or 747 required. Svilen and Konstantin Rangelov are the CEO and chief technology officer, respectively, of Dronamics. They've spent the past four years developing an aircraft that can haul nearly 800 pounds of cargo up to 1,550 miles, a far cry from the 10 or 15 miles, or even just a few blocks, that most drone delivery services are targeting.

Better Life Lab is a partnership of Slate and New America. In an age where every day brings more doomsday forecasts of massive technologicallybdriven unemployment, from driverless cars to A.I. robots as caregivers, journalist Annie Lowrey set out to answer a question: Is it possible to live in a world where we get what she calls "wages for breathing"? This week her findings come out in Give People Money: How a Universal Basic Income Would End Poverty, Revolutionize Work, and Remake the World. We spoke about what the idea of giving every American cash--no strings attached--would mean for work, gender inequality, and American identity, and whether it's actually a policy that could pass in the U.S. given the current climate of tying even the most basic benefits to paid work. This interview has been condensed and edited for clarity.