Anyone who's ever flown a drone of any sort will tell you that sooner or later, you're going to crash it. The question is how exactly you will go about doing this, and how much of the drone will be functional after it's happened. Most flying animals somewhat frustratingly don't have this problem: Birds and insects run into things occasionally (or all the time, for small bugs), and just shrug it off and keep on going, thanks to their biological design, which includes both stiffness and flexibility. Now roboticists at the EPFL, in Lausanne, Switzerland, are relying on these same qualities to design a highly resilient quadrotor that's impressively difficult to destroy. There are three primary strategies for designing drones with impact resistance.

This post originally appeared on the Yhat blog. Yhat is a Brooklyn based company whose goal is to make data science applicable for developers, data scientists, and businesses alike. Yhat provides a software platform for deploying and managing predictive algorithms as REST APIs, while eliminating the painful engineering obstacles associated with production environments like testing, versioning, scaling and security. In this post, I'll detail how you can use K-Means clustering to help with some of the exploratory aspects of customer segmentation. I'll be walking through the example using Yhat's own Python IDE, Rodeo, which you can download for Windows, Mac or Linux here.

The era where brands connect with consumers through direct selling and mass-market advertising is long gone. These days, no marketing strategy can adequately address the customer journey without addressing aspects such as social, mobility and multi-screen use. As we start this brand-new year, we look at two upcoming trends that savvy marketers need to watch out for. The role of data has been rising over the last few years, and no CMOs will oppose or question its relevance when it comes to marketing today. For one, wearables are set to become ubiquitous this year, if they are not already so. Elsewhere, the number of Internet of Things (IoT) devices is also expected to surge with an influx of new connected cameras, sensors and other networked devices.

Sometimes, in their eagerness to deliver excellent customer experiences, organizations design "special" moments for individual customer touch points. First, that delightful moment you created for a single touch point has nothing to do with what the customer wants from you. Second, the level of service quality that moment promises can't be backed up by any of your other processes and systems, which injects inconsistency in the customer journey. Customers witness some random flash of genius, which leaves them more confused than delighted. Delivering excellent customer service means providing what your customers want and delivering it consistently.

In the world of speech recognition software, 5.1% is kind of a magic number. Companies that can create software with error rates falling in that ballpark are essentially matching the capabilities of humans, who miss roughly 5% of the words in a given conversation. On March 7, IBM announced it had become the first to home in on that benchmark, having achieved a rate of 5.5%. The breakthrough signals a big win for artificial intelligence that could eventually live in smartphones and voice assistants like Siri, Alexa, and Google Assistant. "The ability to recognize speech as well as humans do is a continuing challenge, since human speech, especially during spontaneous conversation, is extremely complex," Julia Hirschberg, a professor of computer science at Columbia University, told IBM in a statement.

Investors kind of have crush on NVIDIA Corporation (NASDAQ:NVDA). The company has posted quarter after quarter of strong sales, grown gaming GPU market share, introduced new driverless car technologies, and expanded its artificial intelligence (AI) opportunities -- all of which have led investors to swoon to the stock, pushing it up over 200% over the past 12 months. If you're bullish on NVIDIA's prospects in gaming, AI, and driverless cars, then perhaps you should give Amazon (NASDAQ:AMZN), Tesla (NASDAQ:TSLA), and Sony (NYSE:SNE) a good look as well. These companies aren't making the exact same moves as NVIDIA, but each is poised to dominate one of these segments in their own way. NVIDIA is already taking big steps to make AI a priority through its investments in deep learning technologies like Drive PX 2 (for cars) and servers (DGX-1).

In recent years, almost every big name in tech (Microsoft, Google, Facebook, Salesforce, Uber, etc.) has jumped onboard the Artificial Intelligence (AI) bandwagon. Investing in research labs and acquiring companies focused in these areas, each is looking for a way to incorporate AI into their products and services to more intelligently and proactively serve their clients. Although some have called for more regulatory oversight when it comes to AI, tech companies don't seem to be slowing down their endeavours to create human-like machines that can think independently and make decisions. In September 2016, Microsoft formed the Microsoft AI and Research Group, which joined their research organisation with thousands of computer scientists and engineers, with the goal of democratising AI for all. Microsoft CEO, Satya Nadella, described this commitment saying, 'At Microsoft, we are focused on empowering both people and organisations, by democratising access to intelligence to help solve our most pressing challenges.

A Volkswagen'Cedric' self-driving automobile is presented during the Volkswagen Group Shaping The Future / Create Innovation event ahead of the 87th Geneva International Motor Show on March 6, 2017 in Geneva, Switzerland. As many of the most innovative companies in the world race to bring autonomous vehicle solutions to market, a fierce debate has emerged in the industry about the best way to build those solutions. An AV must make countless tactical choices moment to moment to navigate through its environment, choices that are second nature to experienced human drivers: how fast to go, whether to stop at a traffic signal, whether to slow to let another vehicle merge, whether to change lanes to avoid a parked car. These are highly safety-critical decisions. They can mean the difference between life and death on the road, millions of times over, every day. Given the stakes, it is no surprise that the question as to which technological approach to apply here has taken on huge importance and inspired vigorous debate.

While artificial intelligence, machine learning and other futuristic-seeming technologies have been resigned to the likes of Apple, Google, Microsoft, Amazon and Facebook, traditional companies are also getting in the game, including Equifax and SAS. According to a report, Equifax is using deep-learning tools to enhance its credit scoring system, and SAS is using deep learning to improve its data mining tools and provide deep learning APIs. In an interview, Peter Maynard, senior vice president of global analytics at Equifax, said the company realized a few years ago that it wasn't getting enough "statistical lift" from its traditional credit scoring methods and thus started to embrace advanced deep-learning technology. The report noted that modern machine-learning technologies, such as deep neural networks, which boast much more accurate results, were perceived to not be interpretable, posing a challenge for any company wanting to use them. The complexity also added another layer of challenge for Equifax.

On most days, we do not wake up anticipating that we may be suddenly thrust into the sky while popcorn shrimp rains down like confetti, as some guy roars from above: "Hey, there, I'm Jack. And you are in a computer simulation." Instead, we wake up thinking that an atom is an atom, that our physics is inherent to this universe and not prone to arbitrary change by coders, and that our reality is, well, real. Yet there may be another possibility. Game developers have opened up massive, explorable universes and populated them with computer-generated characters based on advanced A.I.