Tags Best Buy $198 Oculus Go How To • Prime Day 2019 best gaming deals: Oculus Go 32GB for $159, Nintendo Switch for $329 and more available now The next big step for Oculus may be into our office buildings. Sarah Tew/CNET When Facebook pitches its Oculus virtual reality headsets to the masses, it talks of their ability to make you feel as though you’re inside a game, visiting another planet or scuba diving next to massive blue whales, without ever leaving your couch.Its next act might be to take that other-worldliness to your work.Via a job posting, Oculus VR is looking for a software expert to work in its Seattle, Washington offices to help build special versions of its $199 Oculus Go and $399 Oculus Quest mobile VR headsets for businesses. This person would help make the headsets work with various types of business software, the job posting said. An Oculus spokeswoman didn’t immediately respond to a request for comment.The Oculus Go is an entry-level headset that works without the need for a computer or mobile phone to power it. The Quest is a mid-level device that’s also self contained, offering higher quality visuals and controls. Both headsets put screens so close to your eyes they trick your brain into thinking you’re in the computer-generated world.The job posting, first noticed by Variety and which as of time of writing is no longer accepting applicants, is the latest sign of Facebook’s willingness to invest in efforts to broaden the appeal of its headsets. Microsoft has taken a similar tack with its $3,500 HoloLens augmented reality headset, which overlays computer images on the real world. In Microsoft’s case, the company explicitly says it does not want to sell the device to you and me — yet.For Oculus, finding success with the business world could help bolster sales as developers continue searching for a “killer app” that will convince consumers to buy in. But it will still have competition.HTC, for example, announced its Vive Focus VR headset for businesses last November, and Microsoft has been helping partners such as Lenovo, Dell and H-P build VR headsets for businesses as well. See It CNET may get a commission from retail offers. News • Flash sale: The Oculus Go VR headset is back down to $159 (Update: Expired) Oculus Dell Facebook HTC Lenovo Microsoft See it $199 Review • Oculus Go review: $199 VR, no strings attached $199 See It Walmart 0 Post a comment Share your voice Wearable Tech Tech Industry Virtual Reality Apps Mentioned Above Oculus Go (32GB)
More information: T. Lin et al. Nitrogen-doped mesoporous carbon of extraordinary capacitance for electrochemical energy storage, Science (2015). DOI: 10.1126/science.aab3798ABSTRACTCarbon-based supercapacitors can provide high electrical power, but they do not have sufficient energy density to directly compete with batteries. We found that a nitrogen-doped ordered mesoporous few-layer carbon has a capacitance of 855 farads per gram in aqueous electrolytes and can be bipolarly charged or discharged at a fast, carbon-like speed. The improvement mostly stems from robust redox reactions at nitrogen-associated defects that transform inert graphene-like layered carbon into an electrochemically active substance without affecting its electric conductivity. These bipolar aqueous-electrolyte electrochemical cells offer power densities and lifetimes similar to those of carbon-based supercapacitors and can store a specific energy of 41 watt-hours per kilogram (19.5 watt-hours per liter). This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only. © 2015 Phys.org (Phys.org)—A team of researchers working in China has found a way to dramatically improve the energy storage capacity of supercapacitors—by doping carbon tubes with nitrogen. In their paper published in the journal Science, the team describes their process and how well the newly developed supercapacitors worked, and their goal of one day helping supercapacitors compete with batteries. Journal information: Science Like a battery, a capacitor is able to hold a charge, unlike a battery, however, it is able to be charged and discharged very quickly—the down side to capacitors is that they cannot hold nearly as much charge per kilogram as batteries. The work by the team in China is a step towards increasing the amount of charge that can be held by supercapacitors (capacitors that have much higher capacitance than standard capacitors—they generally employ carbon-based electrodes)—in this case, they report a threefold increase using their new method—noting also that that their supercapacitor was capable of storing 41 watt-hours per kilogram and could deliver 26 kilowatts per kilogram to a device.The new supercapacitor was made by first forming a template made of tubes of silica. The team then covered the inside of the tubes with carbon using chemical vapor deposition and then etched away the silica, leaving just the carbon tubes, each approximately 4 to 6 nanometers in length. Then, the carbon tubes were doped with nitrogen atoms. Electrodes were made from the resulting material by pressing it in powder form into a graphene foam. The researchers report that the doping aided in chemical reactions within the supercapacitor without causing any changes to its electrical conductivity, which meant that it was still able to charge and discharge as quickly as conventional supercapcitors. The only difference was the dramatically increased storage capacity.Because of the huge increase in storage capacity, the team believes they are on the path to building a supercapacitor able to compete directly with batteries, perhaps even lithium-ion batteries. They note that would mean being able to charge a phone in mere seconds. But before that can happen, the team is looking to industrialize their current new supercapacitor, to allow for its use in actual devices. Graphene and metal nitrides improve the performance and stability of energy storage devices Explore further Citation: Carbon doped with nitrogen dramatically improves storage capacity of supercapacitors (2015, December 28) retrieved 18 August 2019 from https://phys.org/news/2015-12-carbon-doped-nitrogen-storage-capacity.html Fabrication schematic of ordered mesoporous fewlayer carbon (OMFLC). Credit: Science (2015). DOI: 10.1126/science.aab3798
Close proximity of a staircase to an escalator that seems so much faster and more convenient, discourages people from making the healthy decision of taking the stairs, says a study.To make sure that people take the stairs while shopping in a mall or in the metro station, you just have to make sure that the stairs are far, far away from the escalator, said the study published in the journal Environment and Behaviour.The study by researchers from Concordia University in Canada and Peking University in China looked at how location, height and traffic volume dictate pedestrian choices. Also Read – ‘Playing Jojo was emotionally exhausting’“Environmental factors have been explicitly identified as having an impact on stair-climbing, including the visibility of the stairway and its width,” said study senior author John Zacharias from the University of Peking.“This study shows that staircase location is just as important, and should be factored in when planning new buildings.” The researchers monitored 13 stairways and 12 pairs of escalators in seven connected shopping centres in Montreal, Canada. Also Read – Leslie doing new comedy special with NetflixA total of 33,793 pedestrians were counted ascending or descending over 35 days.When the researchers examined the data, they found that increasing the distance between a stairway and an escalator by 100 per cent, accounted for 71 per cent of variance when shoppers were going up, and 21 per cent of variance when they were going down. Overall, that is a 95 per cent increase in stair use, the study said.
Artificial intelligence might seem intimidating, but it isn’t actually as complex as you might think. Many of the tools that have been developed over the last decade or so have all helped to make artificial intelligence and machine learning more accessible to engineers with varying degrees of experience and knowledge. Today, we’ve got to a stage where it’s now accessible even to people who have barely written a line of code in their life! Pretty exciting, right? But if you’re completely new to the field, it can be challenging to know how to get started – fortunately, we’re about to help you overcome that first hurdle. If you are an AI denier, then be sure to first read ‘why learn Machine Learning as a non-techie’ before you move forward. A strong purpose and belief is the first step to learning anything new. Alright, now here’s how you can get started with artificial intelligence and machine learning techniques quickly. 0. Use a free MLaaS or a no code interactive machine learning tool to experience first hand what is possible with learning machine learning: Some popular examples of no code machine learning as a service option are Microsoft Azure, BigML, Orange, and Amazon ML. Read Q2 under the FAQ section below to know more on this topic. 1. Learn Linear Algebra: Linear Algebra is the elementary unit for ML. It helps you effectively comprehend the theory behind the Machine learning algorithms and how they work. It also improves your math skills such as statistics, programming skills, which are all other skills that helps in ML. Learning Resources: Linear Algebra for Beginners: Open Doors to Great Careers Linear algebra Basics 2. Learn just enough Python or any programming: Now, you can get started with any language of your interest, but we suggest Python as it’s great for people who are new to programming. It’s easy to learn due to its simple syntax. You’ll be able to quickly implement the ML algorithms. Also, It has a rich development ecosystem that offers a ton of libraries and frameworks in Machine Learning such as Scikit Learn, Lasagne, Numpy, Scipy, Theano, Tensorflow, etc. Learning Resources: Python Machine Learning Learn Python in 7 Days Python for Beginners 2017 [Video] Learn Python with codecademy Python editor for beginner programmers 3. Learn basic Probability Theory and statistics: A lot of fundamental Statistical and Probability Theories form the basis for ML. You’ve probably already learned Probability and statistics in school, it easy to dive into advanced statistics for ML. Machine learning in its currently widely used form is a way to predict odds and see patterns. Knowing statistics and probability is important as it will help you with better understanding of why any machine learning algorithm works. For example, your grounding in this area, will help to ask the right questions, choose the right set of algorithms and know what to expect as answers from your ML model on questions such as: What are the odds of this person also liking this movie given their current movie watching choices ( Collaborative filtering and content-based filtering) How similar is this user to that group of users who brought a bunch of stuff on my site (clustering, collaborative filtering, and classification) Could this person be at risk of cancer given a certain set of traits and health indicator observations (logistic regression) Should you buy that stock (decision tree) Also, check out our interview with James D. Miller to know more about why learning stats is important in this field. Learning resources: Statistics for Data Science [Video] 4. Learn machine learning algorithms: Do not get intimidated! You don’t have to be an expert to learn ML algorithms. Knowing basic ML algorithms that are majorly used in the real world applications like linear regression, naive Bayes, and decision trees, are enough to get you started. Learn what they do and how they are used in Machine Learning. 5. Learn numpy sci-kit learn,Keras or any other popular machine learning framework: It can be confusing initially to decide which framework to learn. Each one has its own advantages and disadvantages. Numpy is a linear algebra library which is useful for performing mathematical and logical operations. You can easily work with large multidimensional arrays using Numpy. Sci-kit learn helps with quick implementation of popular algorithms on datasets as just one line of code makes different algorithms available for you. Keras is minimalistic and straightforward with high-levels of extensibility, so it is easier to approach. Learning Resources: Hands-on Machine Learning with TensorFlow [Video] Hands-on Scikit-learn for Machine Learning [Video] If you have reached till here, it is time to put your learning into practice. Go ahead and create a simple linear regression model using some publicly available dataset in your area of interest. Kaggle, ourworldindata.org, UC Irvine Machine Learning repository, elitedatascience, all have a rich set of clean datasets in varied fields. Now, it is necessary to commit and put in daily efforts to practise these skills. Quora, Reddit, Medium, and stackoverflow will be your best friends when it comes to solving doubts regarding any of these skills. Data Helpers is another great resource that provides newcomers with help on queries regarding entering the ML field and related topics. Additionally, once you start getting hang of these skills, identify your strengths and interests, to realign your career goals. Research on the kind of work you want to put your newly gained Machine Learning skill to use. It needn’t be professional or serious, it just needs to be something that you deeply care about or are passionate about. This will pull you through your learning milestones, should you feel low at some point. Also, don’t forget to collaborate with other people and learn from them. You can work with web developers, software programmers, data analysts, data administrators, game developers etc. Finally, keep yourself updated with all the latest happenings in the ML world. Follow top experts and influencers on social media, top blogs on Machine Learning, and conferences. Once you are done checking off these steps off your list, you’ll be ready to start off with your ML project. Now, we’ll be looking at the most frequently asked questions by beginners in the field of Machine learning. Frequently asked questions by Beginners in ML As a beginner, it’s natural to have a lot of questions regarding ML. We’ll be addressing the top three frequently asked questions by beginners or non-programmers when it comes to Machine learning: Q.1 I am looking to make a career in Machine learning but I have no prior programming experience. Do I need to know programming for Machine learning? In a nutshell, Yes. If you want a career in Machine learning then having some form of programming knowledge really helps. As mentioned earlier in this article, learning a programming language can really help you with implementing ML algorithms. It also lets you know the internal mechanism behind Machine learning. So, having programming as a prior skill is great. Again, as mentioned before, you can get started with Python which is the easiest and the most common languages for ML. However, programming is just a part of Machine learning. For instance, “machine learning engineers” typically write more code than develop models, while “research scientists” work more on modelling and analyzing different models. Now, ML is based on the principles of statistical inference and for talking statistically to the computer, we need a language, there comes Coding. So, even though the nature of your job in ML might not require you to code as much, there’s still some amount of coding required. Read Also: Why is Python so good for AI and ML? 5 Python Experts Explain Top languages for Artificial Intelligence development Q.2 Are there any tools that can help me with Machine learning without touching a single line of code? Yes. With the rise of MLaaS (Machine learning as a service), there are certain tools that help you get started with machine learning right-away. These are especially useful for business applications of ML, such as predictive modelling and clustering. Read Also: How MLaaS is transforming cloud Some of the most popular ones are: BigML: This cloud based web-service lets you upload your data, prepare it and run algorithms on it. It’s great for people with not so extensive data science backgrounds. It offers a clean and easy to use interfaces for configuring algorithms (decision trees) and reviewing the results. Being focused “only” on Machine Learning, it comes with a wide set of features, all well integrated within a usable Web UI. Other than that, it also offers an API so that if you like it you can build an application around it. Microsoft Azure: The Microsoft Azure ML studio is a “GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure”. So, via an integrated development environment called ML Studio, people without data science background or non-programmers can also build data models with the help of drag-and-drop gestures and simple data flow diagrams. This also saves a lot of time through ML Studio’s library of sample experiments. Learning resources: Microsoft Azure Machine Learning Machine Learning In The Cloud With Azure ML[Video] Orange: This is an open source machine learning and data visualization studio for novice and experts alike. It provides a toolbox comprising of text mining (topic modelling) and image recognition. It also offers a design tool for visual programming which allows you to connect together data preparation, algorithms, and result evaluation, thereby, creating machine learning “programs”. Apart from that, it provides over 100 widgets for the environment and there’s also a Python API and library available which you can integrate into your application. Amazon ML: Amazon ML is a part of Amazon Web Services ( AWS ) that combines powerful machine learning algorithms with interactive visual tools to guide you towards easily creating, evaluating, and deploying machine learning models. So, whether you are a data scientist or a newbie, it offers ML services and tools tailored to meet your needs and level of expertise. Building ML models using Amazon ML consists of three operations: data analysis, model training, and evaluation. Learning Resources: Effective Amazon Machine Learning Q.3 Do I need to know advanced mathematics ( college graduate level ) to learn Machine learning? It depends. As mentioned earlier, understanding of the following mathematical topics: Probability, Statistics and Linear Algebra can really make your machine learning journey easier and also help simplify your code. These help you understand the “why” behind the working of the machine learning algorithms, which is quite fundamental to understanding ML. However, not knowing advanced mathematics is not an excuse to not learning Machine Learning. There a lot of libraries which makes the task of applying an ML algorithm to solve a task easier. One such example is the widely used Python’s scikit-learn library. With scikit-learn, you just need one line of code and you’ll have the most common algorithms there for you, ready to be used. But, if you want to go deeper into machine learning then knowing advanced mathematics is a prerequisite as it will help you understand the algorithms, the formulas, how the learning is done and many other Machine Learning concepts. Also, with so many courses and tutorials online, you can always learn advanced mathematics on the side while exploring Machine learning. So, we looked at the three most asked questions by beginners in the field of Machine Learning. In the past, machine learning has provided us with self-driving cars, effective web search, speech recognition, etc. Machine learning is extremely pervasive, in fact, many researchers believe that ML is the best way to make progress towards human-level AI. Learning ML is not an easy task but its not next to impossible either. In the end, it all depends on the amount of dedication and efforts that you’re willing to put in to get a grasp of it. We just touched the tip of the iceberg in this article, there’s a lot more to know in Machine Learning which you will get a hang of as you get your feet dirty in it. That being said, all the best for the road ahead! Read Next Facebook launches a 6-part ML video series 7 of the best ML conferences for the rest of 2018 Google introduces Machine Learning courses for AI beginners
At a time when software systems are growing in complexity, and when the expectations and demands from users have never been more critical, it’s easy to forget that just making things work can be a huge challenge. That’s where site reliability engineering (SRE) comes in; it’s one of the reasons we’re starting to see it grow as a discipline and job role. The central philosophy behind site reliability engineering can be seen in trends like chaos engineering. As Gremlin CTO Matt Fornaciari said, speaking to us in June, “chaos engineering is simply part of the SRE toolkit.” For site reliability engineers, software resilience isn’t an optional extra – it’s critical. In crude terms, downtime for a retail site means real monetary losses, but the example extends beyond that. Because people and software systems are so interdependent, SRE is a useful way for thinking about how we build software more broadly. To get to the heart of what site reliability engineering is, I spoke to Nat Welch, an SRE currently working at First Look Media, whose experience includes time at Google and Hillary Clinton’s 2016 presidential campaign. Nat has just published a book with Packt called Real-World SRE. You can find it here. Follow Nat on Twitter: @icco What is site reliability engineering? Nat Welch: The idea [of site reliability engineering] is to write and modify software to improve the reliability of a website or system. As a term and field, it was founded by Google in the early 2000s, and has slowly spread across the rest of the industry. Having engineers dedicated to global system health and reliability, working with every layer of the business to improving reliability for systems. “By building teams of engineers focused exclusively on reliability, there can be someone arguing for and focusing on reliability in a way to improve the speed and efficiency of product teams.” Why do we need site reliability engineering? Nat Welch: Customers get mad if your website is down. Engineers often were having trouble weighing system reliability work versus new feature work. Because of this, product feature work often takes priority, and reliability decisions are made by guess work. By building teams of engineers focused exclusively on reliability, there can be someone arguing for and focusing on reliability in a way to improve the speed and efficiency of product teams. Why do we need SRE now, in 2018? Nat Welch: Part of it is that people are finally starting to build systems more like how Google has been building for years (heavy use of containers, lots of services, heavily distributed). The other part is a marketing effort by Google so that they can make it easier to hire. What are the core responsibilities of an SRE? How do they sit within a team? Nat Welch: SRE is just a specialization of a developer. They sit on equal footing with the rest of the developers on the team, because the system is everyone’s responsbility. But while some engineers will focus primarily on new features, SRE will primarily focus on system reliability. This does not mean either side does not work on the other (SRE often write features, product devs often write code to make the system more reliable, etc), it just means their primary focus when defining priorities is different. What are the biggest challenges for site reliability engineers? Nat Welch: Communication with everyone (product, finance, executive team, etc.), and focus – it’s very easy to get lost in fire fighting. What are the 3 key skills you need to be a good SRE? Nat Welch: Communication skills, software development skills, system design skills. You need to be able to write code, review code, work with others, break large projects into small pieces and distribute the work among people, but you also need to be able to take a system (working or broken) and figure out how it is designed and how it works. Thanks Nat! Site reliability engineering, then, is a response to a broader change in the types of software infrastructure we are building and using today. It’s certainly a role that offers a lot of scope for ambitious and curious developers interested in a range of problems in software development, from UX to security. If you want to learn more, take a look at Nat’s book.
Wednesday, November 7, 2018 Tags: IBEROSTAR Hotels & Resorts TORONTO — Iberostar says the opening of its first hotel in Rome, set for 2019, is the latest step in its strategy to boost its city hotels segment.Situated in the heart of the Italian capital in a historic building next to Rome’s most famous fountain, and just off Via del Corso, the Iberostar Fontana di Trevi will open next year with five-star status following renovation work.The hotel is housed in a six storey 19th century building that combines Renaissance and Neo-Renaissance style elements, according to reports. In addition to the 67 rooms and a wide range of exclusive services, the hotel will also feature a “spectacular” rooftop terrace – the perfect venue for admiring Rome’s skyline, says Iberostar.The inclusion of the Iberostar Fontana di Trevi in Rome brings the number of properties in the company’s city hotel portfolio to 10, joining those located in New York, Miami, Lisbon, Havana, Madrid, Barcelona and Budapest.More news: Flight Centre Travel Group takes full ownership of Quebec-based agencyIt also represents a further move forward in its city hotels expansion strategy, designed to boost the company’s presence in the world’s leading tourist capitals with hotels offering prime central locations that reflect the authenticity and charm of the cities they are located in, providing guests with unique experiences.Says Aurelio Vázquez, the Iberostar Group’s Chief Operations Officer: “We are really excited about this new project. Rome is one of the top international destinations attracting both business and leisure travellers, and Iberostar Fontana di Trevi is designed as a boutique hotel tailored to cater for both types of guest. The building is truly unique and boasts a matchless location, and we are working hard to offer first class services and facilities that will guarantee guests a memorable stay with us in Rome.” Iberostar Fontana di Trevi set to open in 2019 Posted by Travelweek Group Share << Previous PostNext Post >>
Share MEXICO CITY — AMResorts and Grupo Hotelero Santa Fe are bringing Breathless Resorts & Spas to Tulum, Mexico.AMResorts will oversee brand management, sales and marketing of the resort.“Our strong relationship with Grupo Hotelero Santa Fe demonstrates ALG is the right resort brand management partner for owners seeking to maximize their investments through our powerful distribution channels,” says Javier Coll, Executive Vice President and Chief Strategy Officer of ALG.“As Tulum welcomed more than 300,000 visitors this summer alone, we are excited to expand our presence in one of Mexico’s top travel destinations.”Breathless Tulum Resort & Spa is expected to open in 2021 with 300 suites and gourmet dining, 24-hour room and concierge services, an unlimited premium beverage program, daily refreshed mini bar, daytime activities, live nighttime entertainment and included taxes and gratuities. A “stunning” beach club will raise the bar to a new level in the entire region, says the company.More news: Le Boat has EBBs along with its new 2020 brochure“Breathless is the perfect match for Tulum, as it is the first to offer all the services and amenities of a Grand Tourism category property in the area, which will allow more sophisticated travellers to reach this destination,” said Francisco Zinser Cieslik, Executive Vice President of Grupo Hotelero Santa Fe.The companies first announced their partnership earlier this year, co-branding three resorts in Punta Cancun, Los Cabos and Nuevo Vallarta under AMResorts’ new brand, Reflect Resorts & Spas and Krystal Grand.AMResorts has 54 resorts in its current portfolio, plus 10,000 rooms under development. Meanwhile with this deal Group Hotelero Santa Fe’s portfolio will reach 28 hotels and 6,932 rooms in 18 cities across Mexico, including hotels currently in construction or expansion. Breathless Tulum Resort & Spa slated to open in 2021 Posted by Tags: Breathless Resorts & Spas, Openings & Renovations, Tulum Wednesday, December 12, 2018 Travelweek Group << Previous PostNext Post >>
Netflix is aiming for 104 million international subscribers by 2020 in the foreign markets where it is already launched, according to Digital TV Research estimates.The figure is extrapolated from comments made by Netflix CEO Reed Hastings last week at the CTAM EuroSummit in Copenhagen, where he said that in general Netflix aims to establish itself in a third of homes seven years after launching in a new market.Based on Digital TV Research’s projected numbers, in 2020 Netflix can expect to have 11.3 million subscribers in Germany, 9.5 million in the UK and 8.3 million in France – the biggest European TV markets where it is currently live.Add to this the Baltic countries, Latin America, and the remainder of its European footprint – namely Luxembourg, Belgium, Austria and Swtizerland, where it rolled out last week – and Netflix’s total 2020 international figures could total 103.9 million.However, in actuality, the figure could well be higher still. At CTAM last week, Hastings said that Netflix will make decisions next year about expanding more in Southern, Central and Eastern Europe.He also hinted at a possible Asian push, saying “over the next three or four years, think of us as at least trying to expand around the world.”