Tech Guides

Rolls Royce has been working in the civil aviation domain for quite some time now, to build what they call as ‘intelligent engines’. The IntelligentEngine vision was first announced at the Singapore Airshow in February 2018. The idea was built around how robotics could be used to revolutionise the future of engine maintenance. Rolls Royce aims to build engines which are: Connected, using cloud based nodes and IoT devices with other engines of the fleet, as well as with the customers and operators. Contextually aware, of its operations, constraints, and customers, with modern data analysis and big data mining techniques. Comprehending, of its own experiences and other engines in the fleet using state-of-the-art machine learning and recommendation algorithms. The company has been demonstrating steady progress and showing off their rapidly developing digital capabilities. Using tiny SWARM robots for engine maintenance Their latest inventions are, tiny roach-sized ‘SWARM’ robots, capable of crawling inside airplane engines and fix them. They look like they’ve just crawled straight out of a Transformers movie. This small robot, almost 10mm in size can perform a visual inspection of hard to reach airplane engine parts. The devices will be mounted with tiny cameras providing a live video feed to allow engineers to see what’s going on inside an engine without having to take it apart. These swarm robots will be deposited on the engine via another invention, the ‘snake’ robots. Officially called FLARE, these snake robots are flexible enough to travel through an engine, like an endoscope. Source Another group of robots, the INSPECT robots is a network of periscopes permanently embedded within the engine. These bots can inspect engines using periscope cameras to spot and report any maintenance requirements. Current prototypes of these bots are much larger than the desired size and not quite ready for intricate repairs. They may be production ready in almost two years. Reducing flight delays with data analysis R2 Data Labs (Rolls Royce data science department) offers technical insight capabilities to their Airline Support Teams (ASTs). ASTs generally assess incident reports, submitted after disruption events or maintenance is undertaken. The Technical Insight platform will help ASTs easily capture, categorize and collate report data in a single place. This platform builds a bank of high-quality data (almost 10 times the size of the database ASTs had access to previously), and then analyze it to identify trends and common issues for more insightful analytics. The technical insight platform has so far shown positive results and has been critical to achieving the company’s IntelligentEngine vision. According to their blog, it was able to avoid delays and cancellations in a particular operator’s 757 fleet by 30%, worth £1.5m per year. The social network for engines In May 2018, the company launched an engine network app. This app was designed to bring all of the engine data under a single hood, much like how Facebook brings all your friends on a single platform. In this app, all the crucial information regarding all the engines in a fleet is available in a single place. Much like Facebook, each engine has a ‘profile’, which shows data on how it’s been operated, the aircraft it has been paired with, the parts it contains, and how much service life is left in each component. It also has a ‘Timeline’ which shows the complete story of the engine’s operational history. In fact, you also have a ‘newsfeed’ to display the most important insights from across the fleet. Source The engine also has an in-built recommendation algorithm which suggests future maintenance work for individual engines, based on what it learns from other similar engines in the fleet. As Juan Carlos Cabrejas, Technical Product Manager, R2 Data Labs writes, “This capability is essential to our IntelligentEngine vision, as it underpins our ability to build a frictionless data ecosystem across our fleets.” Transforming Engine Health Management Rolls-Royce is taking Engine Health Management (EHM) to a new level of connectivity. Their latest EHM system can measure thousands of parameters and monitor entirely new parts of the engine. And interestingly, the EHM has a ‘talk back’ feature. An operational center can ask the system to focus on one particular part or parameter of the engine. The system listens and responds back with hundreds of hours of information specifically tailored to that request. Axel Voege, Rolls-Royce, Head of Digital Operations, Germany, says” By getting that greater level of detail, instantly, our engineering teams can work out a solution much more quickly.” This new system will go into service next year making it their most IntelligentEngine yet. As IntelligentEngine makes rapid progress, the company sees itself designing, testing, and managing engines entirely through their digital twin in the near future. You can read more about the IntelligentEngine vision and other stories to discover new products and updates at the Rolls Royce site. Unity announces a new automotive division and two-day Unity AutoTech Summit Apollo 11 source code: A small step for a woman, and a huge leap for ‘software engineering’

As a category manager, I manage the data science portfolio of product ideas for Packt Publishing, a leading tech publisher. In simple terms, I place informed bets on where to invest, what topics to publish on etc.  While I have a decent idea of where the industry is heading and what data professionals are looking forward to learn and why etc, it is high time I walked in their shoes for a couple of reasons. Basically, I want to understand the reason behind Data Science being the ‘Sexiest job of the 21st century’, and if the role is really worth all the fame and fortune. In the process, I also wanted to explore the underlying difficulties, challenges and obstacles that every data scientist has had to endure at some point in his/her journey, or still does, maybe. The cherry on top, is that I get to use the skills I develop, to supercharge my success in my current role that is primarily insight-driven. This is the first of a series of posts on how I got started with Data Science. Today, I’m sharing my experience with devising a learning path and then gathering appropriate learning resources. Devising a learning path To understand the concepts of data science, I had to research a lot. There are tons and tons of resources out there, many of which are very good. Once you seperate the good from the rest, it can be quite intimidating to pick the options that suit you the best. Some of the primary questions that clouded my mind were: What should be my programming language of choice? R or Python? Or something else? What tools and frameworks do I need to learn? What about the statistics and mathematical aspects of machine learning? How essential are they? Two videos really helped me find the answers to the questions above: If you don’t want to spend a lot of your time mastering the art of data science, there’s a beautiful video on how to become a data scientist in six months What are the questions asked in a data science interview? What are the in-demand skills that you need to master in order to get a data science job? This video on 5 Tips For Getting a Data Science Job really is helpful. After a lot of research that included reading countless articles and blogs and discussions with experts, here is my learning plan: Learn Python Per the recently conducted Stack Overflow Developer Survey 2018, Python stood out as the most-wanted programming language, meaning the developers who do not use it yet want to learn it the most. As one of the most widely used general-purpose programming languages, Python finds large applications when it comes to data science. Naturally, you get attracted to the best option available, and Python was the one for me. The major reasons why I chose to learn Python over the other programming languages: Very easy to learn: Python is one of the easiest programming languages to learn. Not only is the syntax clean and easy to understand, even the most complex of data science tasks can be done in a few lines of Python code. Efficient libraries for Data Science: Python has a vast array of libraries suited for various data science tasks, from scraping data to visualizing and manipulating it. NumPy, SciPy, pandas, matplotlib, Seaborn are some of the libraries worth mentioning here. Python has terrific libraries for machine learning: Learning a framework or a library which makes machine learning easier to perform is very important. Python has libraries such as scikit-learn and Tensorflow that makes machine learning easier and a fun-to-do activity. To make the most of these libraries, it is important to understand the fundamentals of Python. My colleague and good friend Aaron has put out a list of top 7 Python programming books which helped as a brilliant starting point to understand the different resources out there to learn Python. The one book that stood out for me was Learn Python Programming - Second Edition - This is a very good book to start Python programming from scratch. There is also a neat skill-map present on Mapt, where you can progressively build up your knowledge of Python - right from the absolute basics to the most complex concepts. Another handy resource to learn the A-Z of Python is Complete Python Masterclass. This is a slightly long course, but it will take you from the absolute fundamentals to the most advanced aspects of Python programming. Task Status: Ongoing Learn the fundamentals of data manipulation After learning the fundamentals of Python programming, the plan is to head straight to the Python-based libraries for data manipulation, analysis and visualization. Some of the major ones are what we already discussed above, and the plan to learn them is in the following order: NumPy - Used primarily for numerical computing pandas - One of the most popular Python packages for data manipulation and analysis matplotlib - The go-to Python library for data visualization, rivaling the likes of R’s ggplot2 Seaborn - A data visualization library that runs on top of matplotlib used for creating visually appealing charts, plots and histograms Some very good resources to learn about all these libraries: Python Data Analysis Python for Data Science and Machine Learning - This is a very good course with a detailed coverage on the machine learning concepts. Something to learn later. The aim is to learn these libraries upto a fairly intermediate level, and be able to manipulate, analyze and visualize any kind of data, including missing, unstructured data and time-series data. Understand the fundamentals of statistics, linear algebra and probability In order to take a step further and enter into the foray of machine learning, the general consensus is to first understand the maths and statistics behind the concepts of machine learning. Implementing them in Python is relatively easier once you get the math right, and that is what I plan to do. I shortlisted some very good resources for this as well: Statistics for Machine Learning Stanford University - Machine Learning Course at Coursera Task Status: Ongoing Learn Machine Learning (Sounds odd I know) After understanding the math behind machine learning, the next step is to learn how to perform predictive modeling using popular machine learning algorithms such as linear regression, logistic regression, clustering, and more. Using real-world datasets, the plan is to learn the art of building state-of-the-art machine learning models using Python’s very own scikit-learn library, as well as the popular Tensorflow package. To learn how to do this, the courses I mentioned above should come in handy: Stanford University - Machine Learning Course at Coursera Python for Data Science and Machine Learning Python Machine Learning, Second Edition Task Status: To be started [box type="shadow" align="" class="" width=""]During the course of this journey, websites like Stack Overflow and Stack Exchange will be my best friends, along with the popular resources such as YouTube.[/box] As I start this journey, I plan to share my experiences and knowledge with you all. Do you think the learning path looks good? Is there anything else that I should include in my learning path? I would really love to hear your comments, suggestions and experiences. Stay tuned for the next post where I seek answers to questions such as ‘How much of Python should I learn in order to be comfortable with Data Science?’, ‘How much time should I devote per day or week to learn the concepts in Data Science?’ and much more.. Read more Why is data science important? 9 Data Science Myths Debunked 30 common data science terms explained

Microsoft buying GitHub is "good news" for open source. - Jim Zemlin, the Executive Director of Linux Foundation Unless you have been living under a rock, you will have heard about the software giant Microsoft’s acquisition of the open source platform giant GitHub for $7.5 Billion. Since the announcement a few weeks ago, the discussions in the open source community have heated up regarding the future of open source. This acquisition has seen a surge in the number of developers migrating to rival version control systems like BitBucket, GitLab, etc., but mostly GitLab. This will affect GitHub’s user base and in turn the contribution to the platform, which is the primary source of funding to keep any open source service alive. This goes to show how difficult it is to create a great product for developers and still make money. Microsoft has created great products for enterprises and has been making money in the process. As such, this acquisition is one worth waiting and watching as it transforms both entities. The common fear among developers is that Microsoft will exploit the limitations inherent to an open source platform and will inject its subscription model into GitHub to make it profitable. The insane price that Microsoft paid to acquire GitHub afterall needs to be recovered. However, it may not be as straightforward. Many believe it’s not the platform’s monetizing potential, but its access to the user base that Microsoft is most interested in. A lot of them also believe, Microsoft has the potential to resurrect GitHub and revolutionize the open source movement. Let us explore some reasons why this acquisition is fruitful for the developer community. GitHub’s losses have been significant GitHub had reportedly been suffering losses and is said to have lost $66 Mn loss in 2016. The software industry is a fierce eat-or-get-eaten jungle. Losing out in the market to giant companies or other emerging startups is a common fear. There is always an alternative tool for every developer need as the software market relentlessly works to make things cheaper while offering variety. Startups are reaching the deflection point sooner in their operation cycle. The GitHub community is the platform’s greatest strengths and the reason why the platform has remained operational through difficult times; but there were regular internal frictions at the management level in GitHub. The strife became apparent when reports came of developers feeling ignored by the GitHub management. The founder, Chris Wanstrath, had to come out and address reports of toxic environment, in a report last year. With Microsoft buying GitHub, there would be a massive cashflow for all the projects in development and the management will be streamlined with Nat Friedman, announced as the head of GitHub operations. Nat’s successful history with leading open source projects such as Xamarin, gives many hope that this time around, Microsoft really does mean well for GitHub with its acquisition. The Azure Cloud advantage for GitHub One of the key challenges that GitHub has faced lately is scaling their infrastructure smoothly without adversely impacting their users. Outages have become a common occurrences that most GitHub users are familiar with. Microsoft has a strong suite of cloud platform and services in the form of Azure. GitHub users can expect receiving the native experience of using the Azure stack as a part of the integration with GitHub. This integration will further enhance the collaboration on the GitHub platform for developers and advance the GitHub ecosystem. Microsoft can integrate GitHub into its enterprise offerings GitHub, in the last few years, has been attempting to extend its reach in the enterprise market with various offerings for business. However, this offering was limited to creating private repositories for some fees. Microsoft, on the other end, has been a leader when it comes to providing enterprise tools and venturing into the subscription market. This acquisition will excite the brand-loyal enterprises, using Microsoft suites. Imagine the new clientele that GitHub now has access to thanks to Microsoft. Just as Microsoft have bundled Skype with their Office 365 suite, it is easy to postulate similar offerings being designed for enterprises with GitHub at the center of such plans. Just like Excel, GitHub could end up as the default version control tool that enterprises use to build new projects, prototype ideas, open source or otherwise. In exchange, Github could be Microsoft’s ace up its sleeve in  strengthening its open source community ties and help put Microsoft in a position to inject innovative strategies in the community. Microsoft’s push to open source projects Microsoft, have plunged head first into open sourcing projects in recent years. The push is not only for their experimental projects, but has also has been for their successful enterprise tools like .NET Core and Visual Studio. Historically, Microsoft has taken a lot of heat from the open source community for opposing the Linux model. But the recent paradigm shift in Microsoft, with a change in its leadership and vision, is focussed on working around the community and doing business from the enterprises. End of last year, Microsoft joined the Linux Foundation and went platinum with the Open Source Initiative. TypeScript is a full open source language and sees regular updates from Microsoft. It is now an established language for web development and is managed better than some of the open source languages. Also, TypeScript is fully hosted on GitHub for developers to improve on it.  This indicates that Microsoft has been able to reach out to the community and has the potential to operate open source projects without necessarily commercializing them. Conclusion Microsoft buying out GitHub is not necessarily bad. The tech giant has been one of the biggest contributors to GitHub with its projects like Visual Studio Code, TypeScript, etc. While the panic is understandable, considering Microsoft’s past strategies to counter the open source model in its early days, the recent activities in Microsoft, especially under the leadership of Satya Nadella are suggesting a paradigm shift in Microsoft’s approach to serving the IT market. You can hate Microsoft for being a profit-driven company, but there is no denying that Microsoft was one of the pioneers of the modern day software industry and more importantly, the bitter pill that GitHub needs to get out of the evergrowing loss making sinkhole. Microsoft understand software better and are capable of doing open source the right way and with more efficiency.  This acquisition was inevitable to sustain the platform and to scale it to serve the increasing demand of developer market. What Microsoft must bear in mind while revamping GitHub policies and the business model is that, it’s greatest challenge and its greatest asset is the paradox of this alliance itself. As GitHub gets more profit conscious, Microsoft must get more community centric to ensure an equilibrium is reached where developers can thrive on a platform that provides a great developing and community experience. The Microsoft-GitHub deal has set into motion an exodus of GitHub projects to GitLab GitHub for Unity 1.0 is here with Git LFS and file locking support Microsoft releases Open Service Broker for Azure (OSBA) version 1.0  

Clearly, there’s lot of competition for web designer jobs, - with the salary rising each year - so it’s crucial you find a way to make your resume stand out. You need to balance creativity with professionalism, all while making sure your experience, personality, and skills shine through. Over the years, people have created numerous resumes for web designers and everybody knows what it takes to get that job. Follow this guide to write a creative and attention-grabbing web designer resume. Note: All images in this article are courtesy of zety.com resume templates page and the guide. Format is a window to your mind Because you’re applying for a design position, the look and format of your resume is very important. It gives prospective employers a sense of your design philosophies. Use white space, and clear, legible fonts to help a hiring manager easily find your information. To stand apart from the crowd, avoid using a word processor to create your resume. Instead use InDesign or Illustrator to design something creative and less cookie-cutter like. Submit your resume in PDF format to avoid formatting errors that will ruin the look of your document. Sometimes a job posting will specifically forbid submitting in PDF though, so watch out for that. Highlight your Experience The key to writing a good experience section for a web designer is keeping it brief and relevant, while highlighting your career achievements. Add no more than three to five bullet points with measurable achievements per past position. Don’t just list your past company and when you worked there. “Discuss what you did and include some tangible accomplishments. If you created a custom graphic set for clients, mention that, and also what percentage were satisfied (hopefully it’s very high.) Prove you have the necessary experience to do the job well,” advises Terrence Wood, resume proofreader at Paper Fellows. Education Your education is not nearly as important as your experience, but you still need to present it right. That means using this section to talk about your strong points. Include coursework and achievements that are relevant to the job description. Maybe you wrote a column about web design in your college’s newspaper, things like this help you stand out to a hiring manager, especially if you are just starting your career in web design. Also, include the GPA here. Showcase your skills Everyone is going to list their skills, if you want that interview you need to do something to make yourself seem exceptional. The first thing you’ll do is take a good look at the job description and note what skills and responsibilities they mention. Now you know what skills to include, but including them is not enough. You need to prove you have them by giving examples of times you used them at past jobs. Don’t just list that you are proficient in Adobe Creative Suite, prove it by describing how you used it to tackle web design for 90% of client projects. Up the ante even more and include samples of your past work. This is a good spot to include your portfolio, any certifications that you have or anything that can help you let them know just how good you are at your job and confirm your skills. If you've had a predominantly freelance career, list the companies or individuals that you've worked for and include examples of work for each of them. You can do the same thing if you had a '9 to 5' career – simply list your previous jobs and show examples of your best work. It's a good idea to let your potential employers know about any future skills you plan to acquire. Use infographics You can give your resume a really unique look by using infographics, while still keeping it professional. Divide your resume layout into a grid with two columns and four or five rows. Now place one category of data into each square of your grid. Next, transform each section into an infographic. Use icons to represent different skills or awards. You can use your design software’s shape tools to create charts and graphs. Programs like Adobe InDesign can be used to create your infographics. You can also use Canva or Visme. Keep it professional It’s a great idea to inject some creativity into you resume. You want to stand out, and after all, it is a design resume. But it’s also important to balance that creativity with professionalism. A hiring manager will make some judgements about your personality based on how your resume looks. Be subtle in your creativity. Use colors that are easy on the eyes, and keep fonts reasonable. There can be a lot of beauty in simplicity. Stick to the basics, place content in an order familiar to recruiters to avoid making them have to work for the information they need. Remember that your primary goal is to communicate your information clearly. Write a cover letter Some people say that it's not really necessary but it's your chance to stand out. Maybe there is something that isn't on your resume or you want to seem more appealing and human – cover letter is a good chance to do all of that. Cover letter is a great place to elaborate how you'll be able to meet their needs. It's a good opportunity to also show them that you have done your research and know their company. Writing resources for your resume ViaWriting and Writing Populist: These are grammar resources you can use to check over your resume for grammatical mistakes. Resume Service: This is a resume service you can use to improve the quality of your web designer resume. Boomessays and EssayRoo: These are online proofreading tools, suggested by Revieweal, you can use to make sure you resume is polished and free of errors. My Writing Way and Academadvisor: Check out these career writing blogs for tips and ideas on how to improve your resume. You’ll find posts here by people who have written web designer resumes before. OXEssays and UKWritings: These are editing tools, recommended by UKWritings review, you can use to go over your resume for typos and other errors. StateofWriting and SimpleGrad: Check out these writing guides for suggestions on how to improve your resume. Even experienced writers can benefit from some extra guidance now and then. ResumeLab: Learn what to include in a cover letter. The job market for web designers is competitive. Make sure you lead with your best qualities and skills, and be sure they fit the job description as closely as possible. Be creative, but ensure you keep your resume professional as well. Now go have fun using this guide to write a creative and attention-grabbing web design resume. Author bio Grace Carter is a resume proofreader at Assignment Writing Service and at Australian Help, where she helps with CV editing and cover letter proofreading. Also, Grace teaches business writing at Academized educational website.   Is your web design responsive? “Be objective, fight for the user, and test with real users on the go!” – Interview with design purist, Will Grant Tips and tricks to optimize your responsive web design  

A new nest in the same old tree. Twitter have finally migrated to Tensorflow as their preferred choice of machine learning framework. While not many are surprised by this move given the popularity of Tensorflow, many have surely asked the question - ‘What took them so long?’ Why Twitter migrated to Tensorflow only now Ever since its inception, Twitter have been using their trademark internal system called as DeepBird. This system was able to utilize the power of machine learning and predictive analytics to understand user data, drive engagement and promote healthier conversations. DeepBird primarily used Lua Torch to power its operations. As the support for the language grew sparse due to Torch’s move to PyTorch, Twitter decided it was high time to migrate DeepBird to support Python as well - and started exploring their options. Given the rising popularity of Tensorflow, it was probably the easiest choice Twitter had to make for some time. Per the recently conducted Stack Overflow Developer Survey 2018, Tensorflow is the most loved framework by the developers, with almost 74% of the respondents showing their loyalty towards it. With Tensorflow 2.0 around the corner, the framework promises to build on its existing capabilities by adding richer machine learning features with cross-platform support - something Twitter will be eager to get the most out of. How does Tensorflow help Twitter? After incorporating Tensorflow into DeepBird, Twitter were quick to share some of the initial results. Some of the features that stand out are: Higher engineer productivity - With the help of Tensorboard and some internal data viz tools such as Model Repo, it has become a lot easier for Twitter engineers to observe the performance of the models and tweak them to obtain better results. Easier access to Machine Learning - Tensorflow simplified machine learning models which can be integrated with other technology stacks due to the general-purpose nature of Python. Better performance - The overall performance of DeepBird v2 was found to be better than its predecessor which was powered by Lua Torch. Production-ready models - Twitter plan to develop models that can be integrated to the workflow with minimal issues and bugs, as compared to other frameworks such as Lua Torch. With Tensorflow in place, Twitter users can expect their timelines to be full of relatable, insightful and high quality interactions which they can easily be a part of. Tweets will be shown to readers based on their relevance, and Tensorflow will be able to predict how a particular user will react to them. A large number of heavyweights have already adopted Tensorflow as their machine learning framework of choice  - eBay, Google, Uber, Dropbox, and Nvidia being some of the major ones. As the list keeps on growing, one can only wonder which major organization will be next on the list. Read more TensorFlow 1.9.0-rc0 release announced Python, Tensorflow, Excel and more – Data professionals reveal their top tools Distributed TensorFlow: Working with multiple GPUs and servers  

The 35th International Conference on Machine Learning (ICML) 2018, took place on July 10, 2018 - July 15, 2018 in Stockholm, Sweden. ICML is one of the most anticipated conferences for every data scientist and ML practitioner and features some of the best ML researchers who come to talk about their research and discuss new ideas. It won’t be wrong to say that Deep learning and its subsets were the showstopper of this conference with a large number of research papers and AI professionals implementing it in their methods. These included sessions and paper presentations on, Gaussian Processes, -Networks and Relational Learning, Time-Series Analysis, Deep Bayesian Non-parametric Tracking, Generative Models, etc. Also, other deep learning subsets such as Representation Learning, Ranking and Preference Learning, Supervised Learning, Transfer and Multi-Task Learning, etc were heavily featured. The conference consisted of one day of tutorials (July 10), followed by three days of main conference sessions (July 11-13), followed by two days of workshops (July 14-15). Best Talks and Seminars of ICML 2018 ICML 2018 featured two informative talks dealing with the applications of Artificial Intelligence in other domains. Day 1 was inaugurated by an invited talk from Prof. Dawn Song on “AI and Security: Lessons, Challenges and Future Directions’’. She talked about the impact of AI in computer security, differential privacy techniques, and the synergy between AI, computer security, and blockchain. She also gave an overview of challenges and new techniques to enable privacy-preserving machine learning. Day 3 featured an inaugural talk by Max Welling on “Intelligence per  Kilowatt hour”, focusing on the connection between physics and AI. According to Max, in the coming future, companies will find it too expensive to run energy absorbing ML tools to power their AI engines, or the heat dissipation in edge devices will be too high to be safe. So the next frontier of AI is going to be finding the most energy efficient combination of hardware and algorithms. There were also two plenary talks. Language to Action: towards Interactive Task Learning with Physical Agents, by Joyce Chai and Building Machines that Learn and Think Like People by Josh Tenenbaum. Best Research Papers of ICML 2018 Among the many interesting research papers that were submitted to the ICML 2018 conference, here are the winners. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples by Anish Athalye, Nicholas Carlini, and David Wagner was lauded and bestowed with the Best Paper award. The paper identifies obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples. They identify the three different types of obfuscated gradients and develop attack techniques to overcome them. Delayed Impact of Fair Machine Learning by Lydia T. Liu, Sarah Dean, Esther Rolf, and Max Simchowitz also got the Best Paper award. This paper examines the circumstances where fairness criteria promotes the long-term well-being of disadvantaged groups, measured in terms of a temporal variable of interest. The paper also introduces a one-step feedback model of decision-making that exposes how decisions change the underlying population over time. Bonus: The Test of Time award Day 4 witnessed Facebook researchers Ronan Collobert and Jason Weston receiving the honorary ‘Test of Time award’ for their 2008 ICML paper, A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning. The paper proposed a single convolutional neural network that takes a sentence and outputs it’s language processing predictions. So the network can identify and distinguish part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically and semantically) using a language model. At the time of the paper publishing there was almost no neural networks research in Natural Language Processing. The paper’s use of word embeddings and how they are trained, the use of auxiliary tasks and multitasking, and the use of convolutional neural nets in NLP, really inspired the neural networks of today. For instance, Facebook’s recent machine translation and summarization tool Fairseq uses CNNs for language. AllenNLP’s Elmo learns improved word embeddings via a neural net language model and applies them to a large number of NLP tasks. Featured Tutorials at ICML 2018 The ICML 2018 featured a total of 9 tutorials in sets of 3 each. All the tutorials took place on Day 1. These included: Imitation Learning by Yisong Yue and Hoang M Le where they gave a broad overview of imitation learning techniques and its recent applications. Learning with Temporal Point Processes by Manuel Gomez Rodriguez and Isabel Valera. They talk about temporal point processes in machine learning from basics to advanced concepts such as marks and dynamical systems with jumps. Machine Learning in Automated Mechanism Design for Pricing and Auctions by Nina Balcan, Tuomas Sandholm, and Ellen Vitercik. This tutorial covered automated mechanism design for revenue maximization. Toward Theoretical Understanding of Deep Learning by Sanjeev Arora where he explained about what kind of theory may ultimately arise for deep learning with examples. Defining and Designing Fair Algorithms by Sam Corbett-Davies and Sharad Goel. They illustrated the problems that lie at the foundation of algorithmic fairness, drawing on ideas from machine learning, economics, and legal theory. Understanding your Neighbors: Practical Perspectives From Modern Analysis by Sanjoy Dasgupta and Samory Kpotufe. This tutorial aimed to cover new perspectives on k-NN, and translate new theoretical insights to a broader audience. Variational Bayes and Beyond: Bayesian Inference for Big Data by Tamara Broderick where she covered modern tools for fast, approximate Bayesian inference at scale. Machine Learning for Personalised Health by Danielle Belgrave and Konstantina Palla. This tutorial evaluated the current drivers of machine learning in healthcare and present machine learning strategies for personalised health. Optimization Perspectives on Learning to Control by Benjamin Recht where he showed how to learn models of dynamical systems, how to use data to achieve objectives in a timely fashion, how to balance model specification etc. Workshops at ICML 2018 Day 5 and 6 of the ICML 2018 conference were dedicated entirely for Workshops based on topics ranging from AI in health to AI in computational psychology to Humanizing AI to AI for Wildlife Conservation. Some other workshops included Bridging the Gap between Human and Automated Reasoning Data Science meets Optimization Domain Adaptation for Visual Understanding Eighth International Workshop on Statistical Relational AI Enabling Reproducibility in Machine Learning MLTrain@RML Engineering Multi-Agent Systems Exploration in Reinforcement Learning Federated AI for Robotics Workshop (F-Rob-2018) This is just a brief overview of the ICML conference, where we have handpicked a select few paper presentations and invited talks. You can see the full schedule along with the list of selected research papers at the ICML website. 7 of the best machine learning conferences for the rest of 2018 Microsoft start AI School to teach Machine Learning and Artificial Intelligence Google introduces Machine Learning courses for AI beginners

Before I begin, one thing that needs to be established, is we can’t for a second ignore the fact that we are going to compare two different JavaScript frameworks - React and Vue. Both frameworks are clearly different in terms of popularity and usage. While ReactJS is a JavaScript library, great for building huge web applications where data can be updated on a regular basis. Vue.js is a JavaScript framework, fit for creating highly adaptable user interfaces and sophisticated Single-page applications. On the other hand, we all have mostly come across is how React and Vue are similar in their fundamental approach. They both have a virtual DOM based approach; they both have component based structure and are Reactive in their architecture. They both tend to work around a root library with all the other tasks transferred to other libraries. Having said that, as per npm trends report, React stands well ahead in terms of monthly downloads at 2.4 million whereas Vue stands joint second with Angular at around 239k downloads. Now that we have established the popularity of front end web development frameworks, it’s time to talk about what works and what doesn’t in React and Vue.js. Comparing React.js and Vue.js Template vs JSX While everything in React is a JavaScript code written in JSX syntax, Vue depends majorly on its templates which are HTML5 and CSS3 based. Now if you are a front end developer how can this possibly affect you? Well it depends on your choice of working methodology. If you want to write code on your own and control every aspect of your application, then the React way will be much more suitable for you. But if you want to start working on a readymade template and then add features as you go then Vue should be your best choice. React is a better framework if you want to scale up Make no mistake about this. The size and scalability of your application plays a determining role on your choice of framework. The fact that React gives you more control over your application architecture is the single most reason why it is easier in React to scale up your application. But since Vue is so much dependent inherently on the templating structure, it becomes tough when to build an industrial grade application made with Vue as changing the template can be difficult. It's easier to update data with Vue than React Updating data on Vue is much simpler. The middle stage of transpiling is not needed in Vue as it directly renders into the browser and hence the process is faster. Whereas in React, the data is analyzed, then stored, then the Document Object Model (DOM) is invoked and thereafter the change takes place, which is a time consuming process. React has a bigger community than Vue React is backed by Facebook. There are many similar libraries like React such as Preact which render support to React making it a  larger community than Vue. And with larger communities developers can expect faster resolution of issues and regular community support with timely updates. Building for mobile with React and Vue The capabilities of all modern day frameworks are often judged in terms of how they allow developers build for mobile. React has a world class companion in this domain: React Native. React Native is very similar to React in terms of its component structure, and is a fairly short learning curve for anyone already using React. The introduction of Vue Native, which offers a way of writing mobile applications with NativeScript using Vue, has made it easier for mobile developers to use Vue mobile development. Chinese tech company Alibaba has also created a cross platform framework called Weex. Weex has support for Vue, and while it doesn't yet have the capabilities of React Native, it could be a mobile framework to watch. Which is better? React or Vue? To summarize, there are different aspects of Vue and React which are useful and developer friendly. However if you intend to judge them on your own, you are better off assessing what your development needs are first? How big an issue scalability is going to be? Would you be needing something for the mobile platform too? Once you have figured out these questions, the choice should be easy. Read Next: What is React.js and how does it work Is React Native really a Native framework Using React Router for Client Side Redirecting

You must have been hearing the recent cambridge analytica scandal involving facebook and user data theft. As an aftermath of the recent Facebook Cambridge Analytica scandal, many have become cautious about using Facebook, and wondering how safe their personal data’s going to be. Now, Facebook has filed for a patent for a technology that will allow an ambient audio signal to activate your mobile phone’s microphone remotely, and record without you even knowing. This news definitely comes as a shock, especially after Facebook’s senate hearing early this year and their apologetic messages regarding the cambridge analytica scandal. If you weren’t taking your data privacy seriously, then it’s high time you do. According to Facebook, this is how the patent pending tech would work: Smartphones can detect signals outside of the human perception range - meaning we can neither hear or see those signals. Advertisements on TV or or any devices will be preloaded with such signals. When your smartphone detects such hidden signals from the adverts or any other commercials, it would automatically activate the phone microphone and start recording ambient noise and sounds. The sound recorded would include everything in the background - from your normal conversations to the ambient noise of the program or any other kind of noise. This would be stored online and sent back to Facebook for analysis. Facebook claim they will only look at the user reaction to the advert. For example, if the ambient advert is heard in the background, it means the users moved away from it after seeing it. If they change channels that means they are not interested either in the advert or in the product. If the ambient sound is direct then that means the users were bound to the couch as the ad was playing. This will give Facebook a rich set of data on which ads people are more interested to watch and also get a count of the people watching a particular ad. This data in turn will help Facebook place the right kind of ads for their users with prior knowledge of their interest in it. All these are explained from the point of view of Facebook which at the moment sounds very very idealistic. Do we really believe that Facebook is applying for this patent with such naive intentions to save our time from unwanted ads and show the ads that matter to us? Or is there something more devious involved? The capability to listen to our private conversations, recording them unknowingly and then saving them online with our identities attached to it sounds more like a plot from a Hollywood espionage movie. The patent was filed back in 2016 but has resurfaced in discussions now. The only factor that is a bit comforting is that Facebook is not actively pursuing this patent. Does it mean a change of heart? Or is it a temporary pause which will resume after the current tensions are doused. The Cambridge Analytica scandal and ethics in data science Alarming ways governments are using surveillance tech to watch you F8 AR Announcements

The rise in the adoption of the internet is directly proportional to the rise in cybersecurity attacks. We feel that just by having layers of firewall or browsing over ‘https’, where ‘s’ stands for secure will indeed secure us from all those malware from attacking our systems. We also feel safe by having Google secure all our credentials, just because it is Google! All this is a myth. In fact, the biggest loophole in security breakouts is us, humans! It is innate human nature to help out those in need or get curious over a sale or a competition that can fetch a huge sum of money. These and many other factors act as a bait using which hackers or attackers find out ways to fish account credentials. These ways lead to social engineering attacks, which if unnoticed can highly affect one’s security online. Common Social Engineering Attacks Phishing This method is analogous to fishing where the bait is laid to attract fishes. Similarly, here the bait are emails sent out to customers with a malicious attachment or a clickable link. These emails are sent across to millions of users who are tricked to log into fake versions of popular websites, for instance, IBM, Microsoft, and so on. The main aim of a phishing attack is to gain the login information for instance passwords, bank account information, and so on. However, some attacks might be targeted at specific people or organizations. Such a targeted phishing is known as spear phishing. Spear phishing is a targeted phishing attack where the attackers craft a message for a specific individual. Once the target is identified, for instance, a manager of a renowned firm, via browsing his/her profile on social media sites such as Twitter or LinkedIn. The attacker then creates a spoof email address, which makes the manager believe that it’s from his/her higher authority. The mail may comprise of questions on important credentials, which should be confidential among managers and the higher authorities. Ads Often while browsing the web, users encounter flash advertisements asking them permissions to allow a blocked cookie. However, these pop-ups can be, at times, malicious. Sometimes, these malicious ads attack the user’s browser and get them redirected to another new domain. While being in the new domain the browser window can’t be closed. In another case, instead of redirection to a new site, the malicious site appears on the current page, using an iframe in HTML. After any one of the two scenarios is successful, the attacker tries to trick the user to download a fake Flash update, prompting them to fill up information on a phishing form, or claiming that their system is affected with a malware. Lost USB Drive What would you do if you find a USB drive stranded next to a photocopy machine or near the water cooler? You would insert it into your system to find out who really the owner is. Most of us fall prey to such social help, while this is what could result into USB baiting. A social engineering attack where hackers load malicious file within the USB drive and drop it near a crowded place or library. The USB baiting also appeared in the famous American show Mr. Robot in 2016. Here, the USB key simply needed a fraction of seconds to start off using HID spoofing to gather FBI passwords. A similar flash drive attack actually took place in 2008 when an infected flash drive was plugged into a US military laptop situated in the middle east. The drive caused a digital breach within the foreign intelligence agency. How can you protect yourself from these attacks? For organizations to avoid making such huge mistakes, which can lead to huge financial loss, the employees should be given a good training program. In this training program employees can be made aware of the different kinds of social engineering attacks and the channels via which attackers can approach. One way could be giving them a hands-on experience by putting them into the attacker's shoes and letting them perform an attack. Tools such as Kali Linux could be used in order to find out ways and techniques in which hackers think and how to safeguard individual or organizational information. The following video will help you in learning how a social engineering attack works. The author has made use of Kali Linux to better explain the attack practically. YouTube has a $25 million plan to counter fake news and misinformation 10 great tools to stay completely anonymous online Twitter allegedly deleted 70 million fake accounts in an attempt to curb fake news      

Virtual Reality is evolving at a staggering rate. Some of the humankind’s most exciting tools and technologies are coming to the Virtual reality Space. One such technology which is taking over the VR world and making it more powerful is the VR haptics technology. VR Haptics technology offers an extra dimension to the VR world by letting users feel the virtual environment via the sense of touch, in addition to visual and aural perception. It makes you feel truly immersive in the artificial world. Imagine yourself in a desert seeing the sand and feeling it glide under your feet as you walk. It uses external devices like Gloves, Shoes, Joysticks, etc, via which users can receive feedback in the form of vibrations from these computer applications. This feedback provides physical sensations in the hand or other parts of the body. It also provides a realistic simulation of the movements and behaviors, similar to those realized in the real world. VR Haptics: a growing domain The VR haptics technology is growing beyond creating vibrations in game controllers. Now, in the near future, you might able to cuddle a dog and feel it licking your face in the VR world. This speaks volumes about the pace at which the haptic technology is growing. One famous example which discusses modern VR is the popular sci-fi novel “Ready Player One”. It illustrates the possibilities of haptic technology in the future. The novel explores the journey of a guy as he sets foot into a virtual reality simulator (OASIS). He uses a headset and a pair of gloves to maneuver around the virtual world. Apart from the gloves, a lot of future concept products are also covered in the novel which makes the illusion of immersion easier to picture, such as towers emitting smells in the VR world and Wind/Temperature generators that mimic real-life. Haptics came about just as head mounted displays (HMD) came to light in the 2010s. HMDs allowed people to see the virtual reality while haptic feedback gave people the opportunity to experience the virtual world and to act within it. Texture, temperature, pressure, taste, smell and other non-visual sensory inputs became real in VR. Apart from virtual reality games and apps, Haptics feedback is used widely in personal computers, mobile devices, robots, and more. But, in this article, we’ll stick to the use of haptic technology or haptic feedback in the VR space. Usually, most VR users use Touch Controllers for haptic feedback. But, recently, a lot of third-party companies are coming out with products such as gloves for systems like the Oculus Rift & HTC Vive. Here is a list of recent developments in the haptic technology for the VR world. Super affordable VR Haptic gloves by Plexus Most of the currently available options in the VR haptics field are somewhat pricey but earlier this month, Plexus announced their new product, a VR haptic and sensor glove. https://vimeo.com/276517370 Source: Plexus Key features Plexus VR haptics gloves offer a fully modular tracking solution which is capable of tracking up to 0.01 degrees of precision. These gloves are capable of individual finger tracking as well as tracking each joint on the finger, thereby, offering higher precision in the VR world. It is compatible with the HTC Vive, Oculus Rift as well as Windows Mixed Reality devices. The VR haptic gloves also come with additional adapter plates. The development kit version of the Plexus haptic gloves, priced at $249 per glove pair, can be pre-ordered on the official Plexus Website. The company will begin shipping in August 2018 but at the moment, shipping is only available to USA, Europe, Canada and Australia. Kaaya Tech’s full body tracking HoloSuit Kaaya came out with a motion capture (MoCap) suit called HoloSuit, last month, which offers motion capture as well as haptic feedback. HoloSuit is the world’s first affordable, wireless, easy to use, bi-directional, full body motion capture suit. User’s entire body movement data is captured by Holosuit and it uses haptic feedback to send information back to the user. https://www.youtube.com/watch?v=SEQsDR32gII&t=122s  Source: HoloSuit It can be used in various areas such as sports, healthcare, education, entertainment or industrial operations. Key Features The HoloSuit consists of 36 embedded sensors in the pro version and 26 embedded sensors in the less complex version. Embedded sensors carry out all the work of capturing body motion which is necessary for world-scale tracking. It also consists of 9 haptic feedback devices, and 6 embedded firing buttons ( buttons that govern specific tasks such as saving the game, pausing, etc ) which are dispersed across both arms, legs, and all the ten fingers. It delivers data wirelessly either through Wifi or Bluetooth LE to a VR setup by using Unity or a Wi-Fi SDK. The HoloSuit doesn’t come with an external camera tracking option. It supports all the major platforms such as Windows, macOS, iOS, and Android devices. A complete HoloSuit is quite expensive and starts at a regular price of $999. Jacket and Jersey are priced at $499, jersey or track pants for $399, and a pair of gloves are available for $799. HoloSuit Pro is priced at $1,599. Shipping for the full body VR haptic HoloSuit will start this November. Disney’s VR Haptic “Force Jacket” Disney came out with their VR haptic jacket, namely, “Force Jacket” back in April. It provides users with precisely directed force along with a high-frequency vibration which is felt against the user’s upper body in sync with the visual medium. The prototype is made out of a converted life jacket and is provided with 26 airbags. https://www.youtube.com/watch?v=5BOFHEow608   Source: DisneyResearchHub The Force Jacket is created by engineers at Disney Research, MIT and Carnegie Mellon University. Key Features The Haptic Jacket uses an air compressor and a vacuum pump. These air compartments in the jacket can be inflated to exert a force on the user’s body relative to force sensitive resistors. 26 air compartments are activated using microcontrollers for either pressure or vibrotactile feedback or both. Controllers are used to activating the solenoid valves which are connected to the vacuum. There are certain Jacket inflation parameters like speed, force, and duration which are specified using the haptic effects editor. The jacket makes use of the motion interface to sequentially inflate the compartments for simulating motion across the body. Each airbag within the haptic jacket can be influenced to mimic sensations such as being hit in the chest by a snowball, getting tapped on the shoulder, lime dripping on their back, getting punched in the side, and a snake coiling its body around the user. The jacket is mainly to be used in the entertainment and gaming industry and is not available for the consumer market. But, it seems to have great potential in the future for other applications as well. VR gloves by Haptx Haptx announced a pair of VR gloves back in November of last year. The gloves use micro-pneumatics technology for detailed haptics and force feedback (the ability to restrict your fingers’ movement to simulate holding objects) in the fingers. https://www.youtube.com/watch?v=2C2_kbjtjRU Source: HaptX Key Features It features technology that enables it to provide 100 points of tactile displacement feedback. It offers up to five pounds of resistance per finger. It also comes with sub-millimeter precision motion tracking The glove uses SDK of HaptX’s design, which is created by using Unreal Engine’s physics system. This tells the glove when and where it needs to apply haptic effects as well as when and how to engage the force feedback. No information on pricing or worldwide availability has been released by the company yet. But, it is rumored to launch the VR gloves for the consumer market sometime later this year. Apart from these products, there are other minor advancements that keep happening in the VR haptics space. For example, Heather Culbertson, Assistant Professor of USC's computer department, recently created a haptic armband which is capable of mimicking the sensation of a human touch. VR aims to provide you with an environment where you feel truly immersive and where you can feel the objects as in the real world. These products are bringing the VR world a step closer to achieve richer levels of immersive experiences. Gone are the days when haptic feedback was limited to just vibrating controllers and joysticks. As the technology advances, the whole new world of VR haptic devices is here to make your VR experience as seamlessly immersive as possible. In fact, some people even believe that without Haptics, VR is nothing but a picture and a sound. Game developers say Virtual Reality is here to stay CTA announces its first AR/VR Standard terminology Top 7 modern Virtual Reality hardware systems  

Imagine a watch without a watch face to indicate the time. An interface provides important information to us, such as time, so that we can make informed decisions. For example, whether we have enough time to get ice cream before the movie starts. When it comes to games, the User Interface (UI) plays a vital role in how information is conveyed to a player during gameplay. The implementation of a UI is one of the main ways to exchange information with the player about moment-to-moment interactions and their consequences (for example, taking damage). However, UIs are not just about the exchange of information, they are also about how information is provided to a player and when. This can range from the subtle glow of a health bar as it depletes, to dirt covering the screen as you drive a high-speed vehicle through the desert. There are four main ways that UIs are provided to players within a game, which we will discuss shortly. The purpose of this article is to prime you with the fundamentals of UIs so that you not only know how to implement them within Unity but also how they relate to a player's experience. Toward the end, we will see how Unity handles UIs, and we will implement a UI for our first game. In fact, we will insert a scoring system as well as a Game Over Screen. There will be some additional considerations that you can experiment with in terms of adding additional UI elements that you can try implementing on your own. This article is a part of the book titled "Unity 2017 2D Game Development Projects" written by Lauren S. Ferro & Francesco Sapio. Designing the user interface Think about reading a book, is the text or images in the center of the page, where is the page number located, and are the pages numbered consecutively? Typically, such things are pretty straightforward and follow conventions. Therefore, to some extent, we begin to expect things to be the same, especially if they are located in the same place, such as page numbers or even the next button. In the context of games, players also expect the same kinds of interactions, not only with gameplay but also with other on-screen elements, such as the UI. For example, if most games show health in a rectangular bar or with hearts, then that's something that players will be looking for when they want to know whether or not they are in danger. The design of a UI needs to consider a number of things. For example, the limitations of the platform that you are designing for, such as screen size, and the types of interaction that it can afford (does it use touch input or a mouse pointer?). Physiological reactions that the interface might give to the player need to be considered since they will be the final consumer. In fact, another thing to keep in mind is that some people read from right to left in their native languages, and the UI should reflect this as well. Players or users of applications are used to certain conventions and formats. For example, a house icon usually indicates home or the main screen, an email icon usually indicates contact, and an arrow pointing to the right usually indicates that it will continue to the next item in the list or the next question, and so on. Therefore, to improve ease of use and navigation, it is ideal to stick to these or to at least to keep these in mind during the design process. In addition to this, how the user navigates through the application is important. If there is only one way to get from the home screen to an option, and it's via a lot of screens, the whole experience is going to be tiresome. Therefore, make sure that you create navigation maps early on to determine the route for each part of the experience. If a user has to navigate through six screens before they can reach a certain page, then they won't be doing it for very long! In saying all of this, don't let the design overtake the practicality of the user's experience. For example, you may have a beautiful UI but if it makes it really hard to play the game or it causes too much confusion, then it is pretty much useless. Particularly during fast-paced gameplay, you don't want the player to have to sift through 20 different on-screen elements to find what they are looking for. You want the level mastery to be focused on the gameplay rather than understanding the UI. Another way to limit the number of UI elements presented to the player (at any one time) is to have sliding windows or pop-up windows that have other UI elements present. For example, if your player has the option to unlock many different types of ability but can only use one or two of them at any single moment during gameplay, there is no point in displaying them all. Therefore, having a UI element for them to click that then displays all of the other abilities, which they can swap for the existing ones, is one way to minimize the UI design. Of course, you don't want to have multiple pop-up windows, otherwise, it becomes a quest in itself to change in-game settings. Programming the user interface As we have seen in the previous section, designing the UI can be tough and requires experience to get into, especially if you take into consideration all the elements you should, such as the psychology of your audience. However, this is just halfway through. In fact, designing is one thing; making it work is another. Usually, in large teams, there are artists who design the UI and programmers who implement it, based on the artists' graphics. Is UI programming that different? Well, the answer is no, programming is still programming; however, it's quite an interesting branch of the field of programming. If you are building your game engine from scratch, implementing an entire system that handles input is not something you can create with just a couple of hours of work. Catching all the events that the player performs in the game and in the UI is not easy to implement, and requires a lot of practice. Luckily, in the context of Unity, most of this backend for UIs is already done. Unity also provides a solid framework on the frontend for UIs. This framework includes different components that can be easily used without knowing anything about programming. But if we are really interested in unlocking the potential of the Unity framework for UIs, we need to both understand and program within it. Even with a solid framework, such as the one in Unity, UI programming still needs to take into consideration many factors, enough to have a specific role for this in large teams. Achieving exactly what designers have in mind, and is possible without impacting the performance of the game too much, is most of the job of a UI programmer (at least using Unity). Four types of UI Before, moving on, I just want to point out a technical term about UIs, since it also appears in the official documentation of Unity. Some UIs are not fixed on the screen, but actually, have a physical space within the game environment. In saying this, the four types of interfaces are diegetic, non-diegetic, meta, and spatial. Each of these has its own specific use and effect when it comes to the player's experience and some are implicit (for example, static graphics) while others are explicit (blood and dirt on the screen). However, these types can be intermixed to create some interesting interfaces and player experiences. For Angel Cakes, we will implement a simple non-diegetic UI, which will show all of the information the player needs to play the game. Diegetic Diegetic UIs differ from to non-diegetic UIs because they exist in the game world instead of being on top of it and/or completely removed from the game's fiction. Diegetic UIs within the game world can be seen and heard by other players. Some examples of diegetic UI include the screens on computers, ticking clocks, remaining ammunition, and countdowns. To illustrate this, if you have a look at the following image from the Tribes Ascend game, you can see the amount of ammunition remaining: Non-diegetic Non-diegetic interfaces are ones that are rendered outside of the game world and are only visible to the player. They are your typical game UIs that overlay on top of the game. They are completely removed from the game's fiction. Some common uses of non-diegetic UIs can represent health and mana via a colored bar. Non-diegetic UIs are normally represented in 2D, like in the following screenshot of Star Trek Online: Spatial Spatial UI elements are physically presented in the game's space. These types of UIs may or not may be visible to the other players within the game space. This is something that is particularly featured in Virtual Reality (VR) experiences. Spatial UIs are effective when you want to guide players through a level or to indicate points of interest. The following example is from Army of Two. On the ground, you can see arrows directing the player where to go next. You can find out more about implementing Spatial UIs, like the one in the following screenshot, in Unity by visiting the link to the official documentation at: Meta Lastly, Meta UIs can exist in the game world but aren't necessarily visualized like they would be as Spatial UIs. This means that they may not be represented within the 3D Space. In most cases, Meta UIs represent an effect of various actions such as getting shot or requiring more oxygen. As you can see in the following image of Metro 2033, when the player is in an area that is not suitable for them, the view through the mask begins to get hazy. When they get shot or engage in combat, their mask also receives damage. You can see this through the cracks that appear on the edges of the mask: To summarize, we saw the importance of UI in game development and what are the different types of UI available. To know more, check out this book Unity 2017 2D Game Development Projects written by Lauren S. Ferro & Francesco Sapio. Google Cloud collaborates with Unity 3D; a connected gaming experience is here! Working with Unity Variables to script powerful Unity 2017 games How to use arrays, lists, and dictionaries in Unity for 3D game development

Microservices are great! They’ve solved several problems created by large monoliths, like scalability, fault tolerance, and testability, among others. However, let me assure you that everything’s not rosy yet, and there are tonnes of ways you can blow your microservices to smithereens! Here are 6 sure shot ways to meet failure with microservices, and to spice it up, I’ve included the Batman sound effects too! Disclaimer: Unless you’re Sheldon Cooper, what is and what isn’t sarcasm should be pretty evident in this one! #1 The Polyglot Conspiracy One of the most spoken about benefits of using the microservices pattern, is that you can use a variety of tools and languages to build your application. Great! Let’s say you’re building an e-commerce website with a chat option, maybe VR/AR thrown in too, and then the necessities like a payment page, etc. Obviously you’ll want to build it with microservices. Now, you also thought you might have different teams work on the app using different languages and tools. Maybe Java for the main app, Golang for some services and JavaScript for something else. Moreover, you also decided to use Angular as well as React on various components of your UI. Then one day the React team needs to fix bugs in production on Angular, because the Angular team called in sick. Your Ops team is probably pulling out their hair right now! You need to understand that different tech stacks behave differently in production! Going the Microservices route, doesn’t give you a free ticket to go to town on polyglot services. #2 Sharing isn’t always Caring Let’s assume you’ve built an app where various microservices connect to a single, shared database. It’s quite a good design decision, right? Simple, effective and what not. Now a business requirement calls for a change in the character length on one of the microservices. The team goes ahead and changes the length on one of the tables, and... That’s not all, what if you decide to use connection pools so you can reuse request to the database when required. Awesome choice! Imagine your microservices decided to run amok, submitting query after query to the database. It would knock out every other service for weeks! #3 WET is in; DRY is out? Well, everybody’s been saying Don't Repeat Yourself, these days - architects, developers, my mom. Okay, so you’ve built this application that’s based on event sourcing. There’s a list or store of events and a microservice in your application, that publishes a new event to the store when something happens. For the sake of an example, let’s say it’s a customer microservice that publishes an event “in-cart” whenever the customer selects a product. Another microservice, say “account”, subscribes to that aggregate type and gets informed about the event. Now here comes the best part! Suppose your business asks for a field type to be changed, the easiest way out is to go WET (We Enjoy Typing), making the change in one microservice and copying the code to all the others. Imagine you’ve copied to a scale of hundreds of microservices! Better still, you decided to avoid using Git and just use your event history to identify what’s wrong! You’ll be fixing bugs till you find a new job! #4 Version Vendetta We usually get carried away sometimes, when we’re building microservices. You tend to toss Kafka out of the window and rather build your own framework for your microservices. Not a bad idea at all! Okay, so you’ve designed a framework for the app that runs on event sourcing. So naturally, every microservice that’s connected will use event sourcing to communicate with the others. One fine day, your business asked for a major change in a part of the application, which you did, and the new version of one of the microservices sends the new event to the other microservices and… When you make a change in one microservice, you can’t be sure that all others will work fine, unless versions are changed in them too. You can make things worse by following a monolithic release plan for your microservices. You could keep your customers waiting for months to make their systems compatible, while you have your services ready but are waiting to release a new framework on a monolithic schedule. An awesome recipe for customer retention! #5 SPA Treatment! Oh yeah, Single Page Apps are a great way to build front end applications! So your application is built on the REST architecture and your microservices are connected to a single, massive UI. One day, your business requests for a new field to be added to the UI. Now, each microservice has it’s individual domain model and the UI has its own domain model. You’re probably clueless about where to add the new field. So you identify some free space on the front end and slap it on! Side effects add to the fun! Imagine you’ve changed a field on one service, side effects work like a ripple - passing it on to the next microservice, and then to next and they all will blow up in series like dominoes. This could keep your testers busy for weeks and no one will know where to look for the fault! #6 Bye Bye Bye, N Sync Let’s consider you’ve used synchronous communication for your e-commerce application. What you didn’t consider was that not all your services are going to be online at the same time. An offline service or a slow one can potentially lock or slow thread communication, ultimately blowing up your entire system, one service at a time! The best part is that it’s not always possible to build an asynchronous communication channel within your services. So you’ll have to use workarounds like local caches, circuit breakers, etc. So there you have it, six sure shot ways to blow up your microservices and make your Testing and Ops teams go crazy! For those of you who think that microservices have killed the monolith, think again! For the brave, who still wish to go ahead and build microservices, the above are examples of what you should beware of, when you’re building away those microservices! How to publish Microservice as a service onto a Docker How to build Microservices using REST framework Why microservices and DevOps are a match made in heaven    

In JSconf 2018, the former Redux head, Dan Abramov, announced a small tool that he built to simplify data fetching and state management. It was called the future-fetcher/React Context API. Redux, so far, is one of the most popular state management tools that is used widely with React. Even Angular users are quite fond of it. Vue also has a provision for using Redux in its ecosystem. However tools like Mobx are also getting enough popularity because of their simplicity and ease of use. What’s the problem with Redux? The honest answer is that it’s simply too complicated. If we have to understand the real probability of it being replaced by any other tool, then we will have to understand how it works. The workflow is illustrated in the below figure. Source: Freecodecamp.org The above image shows how basic Flux architecture functions and Redux is based quite heavily on this architecture model. This can be very complicated for a novice web developer. A beginner level developer might just get overwhelmed with the use of functional programming concepts like ‘creation’, ‘dispatcher’ and ‘Action’ functions and using them in appropriate situations. Redux follows the same application logic and those who are not comfortable with functional programming, might find using Redux quite cumbersome. That’s where the Future-Fetcher/ Context API comes in. Context API is a production-grade, efficient API that supports things like static type checking and deep updates. In React, different application levels and layers consist of React components and these components have nested relations with each other. In other words, they are connected to each other like a tree and if one component needs to change its state, and it has to pass on the information to the next component, then it transfers an entity called ‘prop’. The state management is important because you would want your application layers to be consistent with your data, so that when one component changes state, the relevant data has to passed on to the component which will allow it to respond accordingly. In Redux, you will have to write functions as mentioned above to implement this. But in context API, the architecture looks a bit different than the Redux-Flux architecture and there lies the difference. Source: Freecodecamp.org In case of Context API, the need to write functions like Action, Dispatch etc. vanishes, that makes the job of a developer quite easy. Here, we only have ‘view’ and the ‘store’ component, where “Store” contains the dynamic state of the application layers. This simplifies a lot of processes. Although the problem of scaling might be an issue in this particular form of architecture. Still, for normal web applications, where dynamic and real time behavior are important, Context API provides a much easier way of implementation. Since this feature has been developed by the primary architect of Redux, the developer community is of the opinion that it might face a tough challenge in the days to come. Still it’s early days to say - Game Over Redux. Creating Reusable Generic Modals in React and Redux Connecting React to Redux & Firebase – Part 1 Connecting React to Redux and Firebase – Part 2

Reinforcement learning is a branch of artificial intelligence that deals with an agent that perceives the information of the environment in the form of state spaces and action spaces and acts on the environment thereby resulting in a new state and receiving a reward as feedback for that action. This received reward is assigned to the new state. Just like when we had to minimize the cost function in order to train our neural network, here the reinforcement learning agent has to maximize the overall reward to find the optimal policy to solve a particular task. This article is an extract from the book Reinforcement Learning with TensorFlow.  How is reinforcement learning different from supervised and unsupervised learning? In supervised learning, the training dataset has input features, X, and their corresponding output labels, Y. A model is trained on this training dataset, to which test cases having input features, X', are given as the input and the model predicts Y'. In unsupervised learning, input features, X, of the training set are given for the training purpose. There are no associated Y values. The goal is to create a model that learns to segregate the data into different clusters by understanding the underlying pattern and thereby, classifying them to find some utility. This model is then further used for the input features X' to predict their similarity to one of the clusters. Reinforcement learning is different from both supervised and unsupervised. Reinforcement learning can guide an agent on how to act in the real world. The interface is broader than the training vectors, like in supervised or unsupervised learning. Here is the entire environment, which can be real or a simulated world. Agents are trained in a different way, where the objective is to reach a goal state, unlike the case of supervised learning where the objective is to maximize the likelihood or minimize cost. Reinforcement learning agents automatically receive the feedback, that is, rewards from the environment, unlike in supervised learning where labeling requires time-consuming human effort. One of the bigger advantages of reinforcement learning is that phrasing any task's objective in the form of a goal helps in solving a wide variety of problems. For example, the goal of a video game agent would be to win the game by achieving the highest score. This also helps in discovering new approaches to achieving the goal. For example, when AlphaGo became the world champion in Go, it found new, unique ways of winning. A reinforcement learning agent is like a human. Humans evolved very slowly; an agent reinforces, but it can do that very fast. As far as sensing the environment is concerned, neither humans nor and artificial intelligence agents can sense the entire world at once. The perceived environment creates a state in which agents perform actions and land in a new state, that is, a newly-perceived environment different from the earlier one. This creates a state space that can be finite as well as infinite. The largest sector interested in this technology is defense. Can reinforcement learning agents replace soldiers that not only walk, but fight, and make important decisions? Basic terminologies and conventions The following are the basic terminologies associated with reinforcement learning: Agent: This we create by programming such that it is able to sense the environment, perform actions, receive feedback, and try to maximize rewards. Environment: The world where the agent resides. It can be real or simulated. State: The perception or configuration of the environment that the agent senses. State spaces can be finite or infinite. Rewards: Feedback the agent receives after any action it has taken. The goal of the agent is to maximize the overall reward, that is, the immediate and the future reward. Rewards are defined in advance. Therefore, they must be created properly to achieve the goal efficiently. Actions: Anything that the agent is capable of doing in the given environment. Action space can be finite or infinite. SAR triple: (state, action, reward) is referred as the SAR triple, represented as (s, a, r). Episode: Represents one complete run of the whole task. Let's deduce the convention shown in the following diagram: Every task is a sequence of SAR triples. We start from state S(t), perform action A(t) and thereby, receive a reward R(t+1), and land on a new state S(t+1). The current state and action pair gives rewards for the next step. Since, S(t) and A(t) results in S(t+1), we have a new triple of (current state, action, new state), that is, [S(t),A(t),S(t+1)] or (s,a,s'). Pioneers and breakthroughs in reinforcement learning Here are the pioneers, industrial leaders, and research breakthroughs in the field of deep reinforcement learning. David Silver Dr. David Silver, with an h-index of 30, heads the research team of reinforcement learning at Google DeepMind and is the lead researcher on AlphaGo. David co-founded Elixir Studios and then completed his PhD in reinforcement learning from the University of Alberta, where he co-introduced the algorithms used in the first master-level 9x9 Go programs. After this, he became a lecturer at University College London. He used to consult for DeepMind before joining full-time in 2013. David lead the AlphaGo project, which became the first program to defeat a top professional player in the game of Go. Pieter Abbeel Pieter Abbeel is a professor at UC Berkeley and was a Research Scientist at OpenAI. Pieter completed his PhD in Computer Science under Andrew Ng. His current research focuses on robotics and machine learning, with a particular focus on deep reinforcement learning, deep imitation learning, deep unsupervised learning, meta-learning, learning-to-learn, and AI safety. Pieter also won the NIPS 2016 Best Paper Award. Google DeepMind Google DeepMind is a British artificial intelligence company founded in September 2010 and acquired by Google in 2014. They are an industrial leader in the domains of deep reinforcement learning and a neural turing machine. They made news in 2016 when the AlphaGo program defeated Lee Sedol, 9th dan Go player. Google DeepMind has channelized its focus on two big sectors: energy and healthcare. Here are some of its projects: In July 2016, Google DeepMind and Moorfields Eye Hospital announced their collaboration to use eye scans to research early signs of diseases leading to blindness In August 2016, Google DeepMind announced its collaboration with University College London Hospital to research and develop an algorithm to automatically differentiate between healthy and cancerous tissues in head and neck areas Google DeepMind AI reduced the Google's data center cooling bill by 40% The AlphaGo program As mentioned previously in Google DeepMind, AlphaGo is a computer program that first defeated Lee Sedol and then Ke Jie, who at the time was the world No. 1 in Go. In 2017 an improved version, AlphaGo zero was launched that defeated AlphaGo 100 games to 0. Libratus Libratus is an artificial intelligence computer program designed by the team led by Professor Tuomas Sandholm at Carnegie Mellon University to play Poker. Libratus and its predecessor, Claudico, share the same meaning, balanced. In January 2017, it made history by defeating four of the world's best professional poker players in a marathon 20-day poker competition. Though Libratus focuses on playing poker, its designers mentioned its ability to learn any game that has incomplete information and where opponents are engaging in deception. As a result, they have proposed that the system can be applied to problems in cybersecurity, business negotiations, or medical planning domains. You enjoyed an excerpt on Reinforcement learning and got to know about breakthrough research in this field. If you want to leverage the power of reinforcement learning techniques, grab our latest edition Reinforcement Learning with TensorFlow. Top 5 tools for reinforcement learning How to implement Reinforcement Learning with TensorFlow How to develop a stock price predictive model using Reinforcement Learning and TensorFlow

There is a rift in the tech landscape that has been shifting quietly for some time. But 2018 is the year it has finally properly opened. This is the rift between the tech’s entrepreneurial ‘superstars’ and a nascent solidarity movement, both of which demonstrate the two faces of the modern tech industry. But within this ‘culture war’ there’s a broader debate about what technology is for and who has the power to make decisions about it. And that can only be a good thing - this is a conversation we’ve needed for some time. With the Cambridge Analytica scandal, and the shock election results to which it was tied, much contemporary political conversation is centered on technology’s impact on the social sphere. But little attention has been paid to the way these social changes or crises are actually enforcing changes within the tech industry itself. If it feels like we’re all having to pick sides when it comes to politics, the same is true when it comes to tech. The rise of the tech ego If you go back to the early years of software, in the early part of the twentieth century, there was little place for ego. It’s no accident that during this time computing was feminized - it was widely viewed as administrative. It was only later that software became more male dominated, thanks to a sexist cultural drive to establish male power in the field. This was arguably the start of egos tech takeover- after all, men wanted their work to carry a certain status. Women had to be pushed out to give them it. It’s no accident that the biggest names in technology - Bill Gates, Steve Wozniak, Steve Jobs - are all men. Their rise was, in part, a consequence of a cultural shift in the sixties. But it’s recognise the fact that in the eighties, these were still largely faceless organizations. Yes, they were powerful men, but the organizations they led were really just the next step out from the military industrial complex that helped develop software as we know it today. It was only when ‘tech’ properly entered the consumer domain that ego took on a new value. As PCs became part of every day life, attaching these products to interesting and intelligent figures was a way of marketing these products. It’s worth remarking that it isn’t really important whether these men had huge egos at all. All that matters is that they were presented in that way, and granted an incredible amount of status and authority. This meant that complexity of software and the literal labor of engineering could be reduced to a relatable figure like Gates or Jobs. We can still feel the effects of that today: just think of the different ways Apple and Microsoft products are perceived. Tech leaders personify technology. They make it marketable. Perhaps tech ‘egos’ were weirdly necessary. Because technology was starting to enter into everyone’s lives, these figures - as much entrepreneurs as engineers - were able to make it accessible and relatable. If that sounds a little far fetched, consider what the tech ‘ninja’ or the ‘guru’ really means for modern businesses. It often isn’t so much about doing something specific, but instead about making the value and application of those technologies clear, simple, and understandable. When companies advertise for these roles using this sort of language they’re often trying to solve an organizational problem as much as a technical one. That’s not to say that being a DevOps guru at some middling eCommerce company is the same as being Bill Gates. But it is important to note how we started talking in this way. Similarly, not everyone who gets called a ‘guru’ is going to have a massive ego (some of my best friends are cloud gurus!), but this type of language does encourage a selfish and egotistical type of thinking. And as anyone who’s worked in a development team knows, that can be incredibly dangerous. From Zuckerberg to your sprint meeting - egos don’t care about you Today, we are in a position where the discourse of gurus and ninjas is getting dangerous. This is true on a number of levels. On the one hand we have a whole new wave of tech entrepreneurs. Zuckerberg, Musk, Kalanick, Chesky, these people are Gates and Jobs for a new generation. For all their innovative thinking, it’s not hard to discern a certain entitlement from all of these men. Just look at Zuckerberg and his role in the Cambridge Analytica Scandal. Look at Musk and his bizarre intervention in Thailand. Kalanick’s sexual harassment might be personal, but it reflects a selfish entitlement that has real professional consequences for his workforce. Okay, so that’s just one extreme - but these people become the images of how technology should work. They tell business leaders and politicians that tech is run by smart people who ostensibly should be trusted. This not only has an impact on our civic lives but also on our professional lives too. Ever wonder why your CEO decides to spend big money on a CTO? It’s because this is the model of modern tech. That then filters down to you and the projects you don’t have faith in. If you feel frustrated at work, think of how these ideas and ways of describing things cascade down to what you do every day. It might seem small, but it does exist. The emergence of tech worker solidarity While all that has been happening, we’ve also seen a positive political awakening across the tech industry. As the egos come to dictate the way we work, what we work on, and who feels the benefits, a large group of engineers are starting to realize that maybe this isn’t the way things should be. Disaffection in Silicon Valley This year in Silicon Valley, worker protests against Amazon, Microsoft and Google have all had an impact on the way their companies are run. We don’t necessarily hear about these people - but they’re there. They’re not willing to let their code be used in ways that don’t represent them. The Cambridge Analytica scandal was the first instance of a political crisis emerging in tech. It wasn’t widely reported, but some Facebook employees asked to move across to different departments like Instagram or WhatsApp. One product designer, Westin Lohne, posted on Twitter that he had left his position saying “morally, it was extremely difficult to continue working there as a product designer.” https://twitter.com/westinlohne/status/981731786337251328 But while the story at Facebook was largely disorganized disaffection, at Google there was real organization against Project Maven. 300 Google employees signed a petition against the company’s AI initiative with the Pentagon. In May, a number of employees resigned over the issue. One is reported as saying “over the last couple of months, I’ve been less and less impressed with Google’s response and the way our concerns are being listened to.” Read next: Google employees quit over company’s continued Artificial Intelligence ties with the Pentagon A similar protest happened at Amazon, with an internal letter to Jeff Bezos protesting the use of Rekognition - Amazon’s facial recognition technology - by law enforcement agencies, including ICE. “Along with much of the world we watched in horror recently as U.S. authorities tore children away from their parents,” the letter stated, according to Gizmodo. “In the face of this immoral U.S. policy, and the U.S.’s increasingly inhumane treatment of refugees and immigrants beyond this specific policy, we are deeply concerned that Amazon is implicated, providing infrastructure and services that enable ICE and DHS.” Microsoft saw a similar protest, sparked, in part, by the shocking images of families being separated at the U.S./Mexico border. Despite the company distancing itself over ICE’s activities, many employees were vocal in their opposition. “This is the sort of thing that would make me question staying,” said one employee, speaking to Gizmodo. A shift in attitudes as tensions emerge True, when taken individually, these instances of disaffection may not look like full-blown solidarity. But together, it amounts to a changing consciousness across Silicon Valley. Of course, it wouldn’t be wrong to say that a relationship between tech, the military, and government has always existed. But the reason things are different is precisely because these tensions have become more visible, attitudes more prominent in public discourse. It’s worth thinking about these attitudes and actions in the context of hyper-competitive Silicon Valley where ego is the norm, and talent and flair is everything. Signing petitions carries with it some risk - leaving a well-paid job you may have spent years working towards is no simple decision. It requires a decisive break with the somewhat egotistical strand that runs through tech to make these sorts of decisions. While it might seem strange, it also shouldn’t be that surprising. If working in software demands a high level of collaboration, then collaboration socially and politically is really just the logical development from our professional lives. All this talk about ‘ninjas’, ‘gurus’ and geniuses only creates more inequality within the tech job market - whether you’re in Silicon Valley, Stoke, or Barcelona, or Bangalore, this language actually hides the skills and knowledge that are actually most valuable in tech. Read next: Don’t call us ninjas or rockstars, say developers Where do we go next? The future doesn’t look good. But if the last six months or so are anything to go by there are a number of things we can do. On the one hand more organization could be the way forward. The publishing and media industries have been setting a great example of how unionization can work in a modern setting and help workers achieve protection and collaborative power at work. If the tech workforce is going to grow significantly over the next decade, we’re going to see more unionization. We’ve already seen technology lead to more unionization and worker organization in the context of the gig economy - Deliveroo and Uber drivers, for example. Gradually it’s going to return to tech itself. The tech industry is transforming the global economy. It’s not immune from the changes it’s causing. But we can also do more to challenge the ideology of the modern tech ego. Key to this is more confidence and technological literacy. If tech figureheads emerge to make technology marketable and accessible, the way to remove that power is to demystify it. It’s to make it clear that technology isn’t a gift, the genius invention of an unfathomable mind, but instead that it’s a collaborative and communal activity, and a skill that anyone can master given the right attitude and resources. At its best, tech culture has been teaching the world that for decades. Think about this the next time someone tells you that technology is magic. It’s not magic, it’s built by people like you. People who want to call it magic want you to think they’re a magician - and like any other magician, they’re probably trying to trick you.