Billionaire and Virgin Group founder Richard Branson is the latest businessman to say that universal basic income (UBI) can help people as jobs are lost to automation. In a post, Branson detailed his experience is Finland, where the country is already experimenting with UBI. Since January, Finland has been paying those who are unemployed €560, part of its two-year experiment on 2,000 Finns aged 25-58. The money, which replaces previous benefits, is paid even if the individual finds a job, in an effort to reduce unemployment and loss of income from taking low-paid jobs to get by. "The hope is that policies like these can help people struggling just to survive and allow them to get on their feet, be entrepreneurial and be more creative," said Branson is the post.

About this course: This one-week accelerated on-demand course provides participants a a hands-on introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn machine learning (ML) and TensorFlow concepts, and develop hands-on skills in developing, evaluating, and productionizing ML models. OBJECTIVES This course teaches participants the following skills: Identify use cases for machine learning Build an ML model using TensorFlow Build scalable, deployable ML models using Cloud ML Know the importance of preprocessing and combining features Incorporate advanced ML concepts into their models Productionize trained ML models PREREQUISITES To get the most of out of this course, participants should have: Completed Google Cloud Fundamentals- Big Data and Machine Learning course OR have equivalent experience Basic proficiency with common query language such as SQL Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such Python Familiarity with Machine Learning and/or statistics Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google is currently blocked in China).

At the moment, only about 12–15% of the engineers who are building the internet and its software are women. We don't want a repeat of these kinds of situations. And we've been working to address this at Women 2.0 for over a decade. We think a lot about how diversity -- or lack thereof. We think about it has affected -- and is going to affect -- the technology outputs that enter our lives.

Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. The paper distinguishes four phases by discussing different levels of NLP and components of Natural Language Generation (NLG) followed by presenting the history and evolution of NLP, state of the art presenting the various applications of NLP and current trends and challenges.

Under a big-data setting, the storage and analysis of data can no longer be performed on a single machine, and in this case dividing data into many sub-samples becomes a critical 1 procedure for any numerical algorithm to be implemented. Distributed statistical estimation and distributed optimization have received increasing attention in recent years, and a flurry of research towards solving very large scale problems have emerged recently, such as Mcdonald et al. (2009); Zhang et al. (2013, 2015); Rosenblatt et al. (2016) and the references therein. In general, distributed algorithm can be classified into two families: data parallelism and task parallelism. Data parallelism aims at distributing the data across different parallel computing nodes or machines; and task parallelism distributes different tasks across parallel computing nodes. We are only concerned with data parallelism in this paper. In particular, we primarily consider the distributed estimation for partially linear models via using the standard divide and conquer strategy. Divide-and-conquer technology is a simple and communication-efficient way for handling big data, which is commonly used in the literature of statistical learning. To be precise, the whole data is randomly allocated among m machines, a local estimator is computed independently on each machine, and then the central node averages the local solutions into a global estimate. Partially linear models (PLM) (Hardle and Liang, 2007; Heckman, 1986), as the leading example of semiparametric models, are a class of important tools for modeling complex data, which retain model interpretation and flexibility simultaneously.

Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain. In this paper, we present joint adaptation networks (JAN), which learn a transfer network by aligning the joint distributions of multiple domain-specific layers across domains based on a joint maximum mean discrepancy (JMMD) criterion. Adversarial training strategy is adopted to maximize JMMD such that the distributions of the source and target domains are made more distinguishable. Learning can be performed by stochastic gradient descent with the gradients computed by back-propagation in linear-time. Experiments testify that our model yields state of the art results on standard datasets.

Digital media entrepreneur Jessica Naziri recently made waves when she shared photos of her technology-themed wedding, complete with bouquets made of USB cables and portable charging packs for guests instead of sugar coated almonds. She wasn't the first bride to garner media attention for eccentric nuptials fueled by mobile apps and gadgets. Guests watched through headsets as a community manager from the San Francisco startup AltspaceVR officiated the ceremony. Time reported the couple spent $2,531 a piece on their headsets and computer. New technologies and social networks are completely revamping the wedding industry. Today, there are dozens of popular apps taking the place of wedding planners, while couples are registering for bitcoin or Airbnb bookings instead of fine china.

Here is a very real and true thing: 2017 is making it really hard to be a science fiction writer. To be sure, these times -- by which I mean the Trump era to date, let's go ahead and avoid cutesy winking allusions -- are making it hard for lots of writers, not just the ones who write science fiction. It's difficult to focus on writing, particularly fiction, when the world feels like it's on fire and everyone you know is trying to decide between hiding in a hole or taking up recreational alcoholism to get by. The rapid-fire pace of events is such that you (or at least I) end up sitting at the computer sort of paralyzed. In the last few weeks we've had (in no particular order) the healthcare vote, hurricane Scaramucci, North Korea and racists marching with Tiki torches like the domestic terrorists they are.

It was a news story like no other. The recent Musk-Zuckerberg AI debate symbolises the two camps regarding artificial intelligence (AI) in many years to come. Both of the business giants argued about the hypothetical but still disputed with conviction and sincerity. For decades people have wondered what role AI technology will play within society. For now, it seems likely there will be a diverse range of investment opportunities for investors as companies are quickly developing their own robotics technologies or dynamic learning systems. Recently, CommonSense Robotics raised $6m in seed funding from Aleph VC and Innovation Endeavors.

You probably don't have a landline phone, because it's not 1995. But you miss it sometimes, don't you? Knowing where the phone was all the time, having something anyone could pick up and use, avoiding the rock-paper-scissors over who has to waste their cell phone battery calling Dominos. Earlier this year at its developer conference, Google promised to turn its Home smart speaker into a sort of futuristic landline. You can now call any business or person in your contacts, as long as they live in the US or Canada, just by asking Google to do so.