Quadrotors have a reputation for being both fun and expensive, but it's not usually obvious how dangerous they can be. While it's pretty clear from the get-go that it's in everyone's best interest to avoid the spinny bits whenever possible, quadrotor safety primarily involves doing little more than trying your level best not to run into people. Not running into people with your drone is generally good advice, but the problems tend to happen when for whatever reason the drone escapes from your control. Maybe it's your fault, maybe it's the drone's fault, but either way, those spinny bits can cause serious damage. Safety-conscious quadrotor pilots have few options for making their drones safer, and none of them are all that great, due either to mediocre effectiveness or significant cost and performance tradeoffs.

Whether you're excited about it or not, robots and artificial intelligence are an inevitable part of a future that we're fast approaching. Thanks to books and films littered with rogue A.I.s and malevolent robots, some people are understandably a bit frightened by the prospect of a world overrun by such technologies. They're not alone, as many experts across fields as diverse as technology and economics are also expressing their fears over the rise of the robots. While these fears are certainly valid, it's important to note that these concerns are being voiced in the hopes that technology can be improved, not prohibited. Here are just some of the pressing concerns regarding robots and A.I.s that experts want to see addressed before the new era of technology commences.

Tinder is testing a new location-sharing feature to connect lonely hearts. Dubbed Places, the tool aims to match users who are in the same specific location, like the cinema, or local coffee shop. Places is completely separate from the usual swipe-based feed Tinder users are accustomed to. Tinder users need to tap the newly-added pin icon, which lives at the top of the main app window, to access a map which shows all the locations you have visited within the last month. Tinder Places lets users view potential matches who have visited the same locations.

At least half of the world's population cannot obtain essential health services. But low-cost, easy-to-use technologies powered by Artificial Intelligence (AI) promise to deliver quality and affordable health care to the people who need it most, no matter how hard to reach. At the AI for Good Global Summit last week, entrepreneurs, AI experts, academics and UN representatives described many AI technologies for health, allowing for the early detection of various pathologies such as osteoarthritis, diabetic retinopathy, child malnutrition, snakebites and others. These technologies don't place a heavy burden on doctors, and can lead to prompt diagnosis and effective treatment. They agreed that AI can add tremendous value in developing countries where there is a low density of physicians.

One of the hardest parts of dating has always been getting up the nerve to hit on someone you see often, but don't know--like the guy on the other side of the bar. Tinder says it might soon have a fix for that specific issue: The dating app is testing a new feature aimed at connecting people who like to hang out in the same bars, restaurants, and other public places. The new product, aptly named "Places," will begin testing in cities in Australia and Chile today. The announcement comes a month after Facebook announced it too was testing a new dating product, which will allow users to connect with people who attend the same events. The move feels like a marked change for Tinder, which has been best-known for facilitating hookups since it launched in 2012.

A robotic system, which draws from both nautical and biological designs, has been developed by engineers from the Massachusetts Institute of Technology (MIT). Their innovative robotic glider can skim along the water's surface. The research team say their robotic device rides the wind like an albatross while also surfing the waves like a sailboat. In high wind conditions the robot is designed to stay aloft, much like its avian counterpart, whereas in calmer winds, the robot has a keel it can dip into the water allowing it to ride in the manner of a highly efficient sailboat. The robotic system is relatively lightweight, weighing about 6 pounds, and can cover a given distance using one-third as much wind as an albatross and traveling 10 times faster than a typical sailboat.

Nvidia has demonstrated a robot with a groundbreaking AI which learns to complete tasks by observing the actions of a human. The researchers, led by Stan Birchfield and Jonathan Tremblay, claim their development is a'first of its kind' deep learning-based system. "For robots to perform useful tasks in real-world settings, it must be easy to communicate the task to the robot; this includes both the desired result and any hints as to the best means to achieve that result. With demonstrations, a user can communicate a task to the robot and provide clues as to how to best perform the task." Nvidia's robot is powered by the firm's TITAN X graphics cards which features 3584 NVIDIA CUDA cores running at 1.5GHz for a total performance of around 11 TFLOPS.

Betterplace Safety Solutions Pvt Ltd, a platform that manages the semi-formal workforce, has raised $3 million (Rs 20.4 crore) in a Series A round of funding from a new investor 3one4 Capital, a top executive told VCCircle. Existing investors Venture Highway and Unitus Ventures also participated in the round, said Pravin Agarwala, co-founder and chief executive, BetterPlace. The fresh funding will help the company expand its solutions into skilling, compliance and fintech services as it aims to consolidate its position as a single platform for the semi-formal workforce in the country. "A significant part of the funds will be utilised to strengthen the existing ecosystem using frictionless solutions powered by artificial intelligence and machine learning," added Agarwala. The Bangalore-based company had initially raised $970,000 (Rs 6.6 crore) in seed funding from India-focused early-stage investor Unitus Ventures in February 2016.

Partially observable Markov decision processes (POMDPs) are a powerful abstraction for tasks that require decision making under uncertainty, and capture a wide range of real world tasks. Today, effective planning approaches exist that generate effective strategies given black-box models of a POMDP task. Yet, an open question is how to acquire accurate models for complex domains. In this paper we propose DELIP, an approach to model learning for POMDPs that utilizes amortized structured variational inference. We empirically show that our model leads to effective control strategies when coupled with state-of-the-art planners. Intuitively, model-based approaches should be particularly beneficial in environments with changing reward structures, or where rewards are initially unknown. Our experiments confirm that DELIP is particularly effective in this setting.

Marketers with blind faith in adtech and martech are at risk of putting advertising in front of people who were going to buy anyway and waste time and money on useless personalisation activities. This was the message from IAG's director of media and technology, Dr Willem Paling, and one-to-one marketing director, Jason Ridge, who took to the stage at CeBIT 2018 to discuss where and when to use AI-enabled martech, and why it's vital to set the right goals around it. Ridge said all consumers expect personalised experiences, and marketers can throw data into AI platforms to ensure the content coming out of their tech engines is personalised. But IAG is starting to ask if it really needs the technology to achieve relevance. "No one will say delivering customers' personalisation isn't a good thing. However, Amazon has hundreds of millions of products, and Facebook has millions of advertisers, and given the opportunity to put a product or content in front of customers, they are left with a dilemma of which product or content to put in front of which customer. So of course it makes sense to use AI or ML to do this," Ridge said.