We are not only living in an age where women are being under-represented in many spheres of economic life, but technology could make this even worse. Women hold just 19% of board directorships in the US and Europe. This gender gap in the boardroom persists, despite the fact that, on average, women have obtained higher educational qualifications than their male counterparts for more than two decades in many OECD countries. And the main reason is social bias. This is on the verge of being further reinforced by artificial intelligence, as current data being used to train machines to learn are often biased.

With the Apple HomePod, the cotton that has been in our ears since the arrival of the first smart speaker has been removed. The HomePod sounds far better than the popular smart speakers from Amazon, Google--and even Sonos. That's what I've been asking myself during my week testing the HomePod, which goes on sale Friday for $350. In the last three years, Amazon Echo and Google Home have set tens of millions of us at ease with speakers that listen for our commands. Of course, Apple has a long history of crushing incumbents--see MP3 players and smartphones.

While it's not clear that Porsche is ready to confirm the details, it's clearly open to the idea. Company sales lead Detlev von Platen noted that it takes him "at least half an hour" just to drive from Porsche's plant in Zuffenhausen to the airport in Stuttgart, but just "three and a half minutes" with an aircraft. It may seem odd for Porsche to not only venture into flying vehicles, but hands-off vehicles. Isn't that anathema to enthusiasts used to taking the wheel? However, it's likely feeling pressure to do something in the passenger drone space.

Japanese scientists know what you're looking at -- but don't worry, there's no need to close your other browser tabs yet. Using an artificial intelligence (AI) system alongside fMRI scans, researchers were able to create an apparently mind-reading AI -- "or perhaps at this point just mind skimming," said Umut Güçlü, a researcher at Radboud University in the Netherlands who was not involved in the research, to New Scientist. The system is actually similar to AI technologies that have been used successfully to caption images. To do this for someone's brain, the AI first needs an image of their brain taken with a fMRI scanner while the person is looking at an image. These scans show activity in the brain through blood flow.

Emergency dispatchers have a tough job, trying to handle as many calls as they can as quickly as they can, while still making sure they're asking the right kinds of questions that could help end up saving someone's life, and in the UK, in order to tackle the challenge, the National Health Service (NHS) recently announced they were rolling out a bot to help handle calls. Now though many more dispatchers could start getting additional support from another source, an Artificial Intelligence (AI) called Corti, an AI agent that dispatchers in Copenhagen first started trialling in 2016. Unlike other AI's what makes Corti unique is the fact that it can listen in on calls, understand words and sounds, and even recognise, just from verbal cues, the "sound" of heart attacks and other medical conditions, a technique that researchers in the USA have also been pursing with some success. Corti then prompts the emergency professional with the right questions to get a more accurate diagnosis. Corti helps out in other more obvious ways too, such as reminding to ask whoever's on the phone for the address of the incident and ensuring the ambulance en route is headed to the right place, but that said much of its value lies in helping dispatchers refine their diagnosis by detecting and analysing background clues.

As Futurism previously reported, in the real world, Estonia seems to be pioneering discussion in this area. In a mix between high-tech and mythology, Estonia proposes that any discussion of robot rights should begin with a test inspired by Kratts, an inanimate object brought to life with magic to perform tasks for the owner. The proposed Kratt Law will allow the law to determine the level of sophistication of an AI, which, in turn, will help determine what legal protections or obligations should be placed on the AI.

An important metric of users' satisfaction and engagement within on-line streaming services is the user session length, i.e. the amount of time they spend on a service continuously without interruption. Being able to predict this value directly benefits the recommendation and ad pacing contexts in music and video streaming services. Recent research has shown that predicting the exact amount of time spent is highly nontrivial due to many external factors for which a user can end a session, and the lack of predictive covariates. Most of the other related literature on duration based user engagement has focused on dwell time for websites, for search and display ads, mainly for post-click satisfaction prediction or ad ranking. In this work we present a novel framework inspired by hierarchical Bayesian modeling to predict, at the moment of login, the amount of time a user will spend in the streaming service. The time spent by a user on a platform depends upon user-specific latent variables which are learned via hierarchical shrinkage. Our framework enjoys theoretical guarantees, naturally incorporates flexible parametric/nonparametric models on the covariates and is found to outperform state-of- the-art estimators in terms of efficiency and predictive performance on real world datasets.

We propose SEARNN, a novel training algorithm for recurrent neural networks (RNNs) inspired by the "learning to search" (L2S) approach to structured prediction. RNNs have been widely successful in structured prediction applications such as machine translation or parsing, and are commonly trained using maximum likelihood estimation (MLE). Unfortunately, this training loss is not always an appropriate surrogate for the test error: by only maximizing the ground truth probability, it fails to exploit the wealth of information offered by structured losses. Further, it introduces discrepancies between training and predicting (such as exposure bias) that may hurt test performance. Instead, SEARNN leverages test-alike search space exploration to introduce global-local losses that are closer to the test error. We first demonstrate improved performance over MLE on two different tasks: OCR and spelling correction. Then, we propose a subsampling strategy to enable SEARNN to scale to large vocabulary sizes. This allows us to validate the benefits of our approach on a machine translation task.

Predictive Analytics World for Industry 4.0 is coming to Munich, 12-13 Jun 2018. Find the latest trends and technologies in machine & deep learning for the era of Internet of Things and artificial intelligence. Super Early Bird Rates end Mar 2.

All countries will feel the impact of automation, but at different speeds and in different ways. In this podcast, McKinsey Global Institute looks at its likely impact in China, Europe, and India. New technologies such as artificial intelligence and automation are reshaping the workplace globally. All countries will feel the impact in some way, shape, or form. In this episode for the McKinsey Global Institute's New World of Work podcast, MGI directors Jonathan Woetzel and Jacques Bughin and MGI partner Anu Madgavkar examine automation's likely impact in China, Europe, and India. I'm Peter Gumbel from the McKinsey Global Institute, and today we'll be taking a look at the quite different ways that new technologies like automation and artificial intelligence will affect work in different parts of the world. Specifically, we'll be looking at China, Europe, and India. These differences come about for a number of reasons that we explain in our new MGI report on the future of work, which is called Jobs lost, jobs gained: Workforce transitions in a time of automation. Among the reasons for these differences are different levels of economic development, different wage rates, and different potential for automation adoption in different economies. First, let's talk about China. Here to do so is Jonathan Woetzel, director of the McKinsey Global Institute, based in Shanghai. Jonathan, perhaps you can start by telling us where the Chinese workforce is at the moment.