Tech Guides

[box type="note" align="" class="" width=""]This is a book excerpt taken from Advanced Analytics with R and Tableau authored by Jen Stirrup & Ruben Oliva Ramos. This book will help you make quick, cogent, and data driven decisions for your business using advanced analytical techniques on Tableau and R.[/box] Today we explore popular data analytics methods such as Microsoft TDSP Process and the CRISP- DM methodology. Introduction There is an increasing amount of data in the world, and in our databases. The data deluge is not going to go away anytime soon! Businesses risk wasting the useful business value of information contained in databases, unless they are able to excise useful knowledge from the data. It can be hard to know how to get started. Fortunately, there are a number of frameworks in data science that help us to work our way through an analytics project. Processes such as Microsoft Team Data Science Process (TDSP) and CRISP-DM position analytics as a repeatable process that is part of a bigger vision. Why are they important? The Microsoft TDSP Process and the CRISP-DM frameworks are frameworks for analytics projects that lead to standardized delivery for organizations, both large and small. In this chapter, we will look at these frameworks in more detail, and see how they can inform our own analytics projects and drive collaboration between teams. How can we have the analysis shaped so that it follows a pattern so that data cleansing is included? Industry standard methodologies for analytics There are a few main methodologies: the Microsoft TDSP Process and the CRISP-DM Methodology. Ultimately, they are all setting out to achieve the same objectives as an analytics framework. There are differences, of course, and these are highlighted here. CRISP-DM and TDSP focus on the business value and the results derived from analytics projects. Both of these methodologies are described in the following sections. CRISP-DM One common methodology is the CRISP-DM methodology (the modeling agency). The Cross Industry Standard Process for Data Mining or (CRISP-DM) model as it is known, is a process model that provides a fluid framework for devising, creating, building, testing, and deploying machine learning solutions. The process is loosely divided into six main phases. The phases can be seen in the following diagram: Initially, the process starts with a business idea and a general consideration of the data. Each stage is briefly discussed in the following sections. Business understanding/data understanding The first phase looks at the machine learning solution from a business standpoint, rather than a technical standpoint. The business idea is defined, and a draft project plan is generated. Once the business idea is defined, the data understanding phase focuses on data collection and familiarity. At this point, missing data may be identified, or initial insights may be revealed. This process feeds back to the business understanding phase. CRISP-DM model — data preparation In this stage, data will be cleansed and transformed, and it will be shaped ready for the modeling phase. CRISP-DM — modeling phase In the modeling phase, various techniques are applied to the data. The models are further tweaked and refined, and this may involve going back to the data preparation phase in order to correct any unexpected issues. CRISP-DM — evaluation The models need to be tested and verified to ensure that they meet the business objectives that were defined initially in the business understanding phase. Otherwise, we may have built models that do not answer the business question. CRISP-DM — deployment The models are published so that the customer can make use of them. This is not the end of the story, however. CRISP-DM — process restarted We live in a world of ever-changing data, business requirements, customer needs, and environments, and the process will be repeated. CRISP-DM summary CRISP-DM is the most commonly used framework for implementing machine learning projects specifically, and it applies to analytics projects as well. It has a good focus on the business understanding piece. However, one major drawback is that the model no longer seems to be actively maintained. The official site, CRISP-DM.org, is no longer being maintained. Furthermore, the framework itself has not been updated on issues on working with new technologies, such as big data. Big data technologies means that there can be additional effort spend in the data understanding phase, for example, as the business grapples with the additional complexities that are involved in the shape of big data sources. Team Data Science Process The TDSP process model provides a dynamic framework to machine learning solutions that have been through a robust process of planning, producing, constructing, testing, and deploying models. Here is an example of the TDSP process: The process is loosely divided into four main phases: Business Understanding Data Acquisition and Understanding Modeling Deployment The phases are described in the following paragraphs. Business understanding The Business understanding process starts with a business idea, which is solved with a machine learning solution. The business idea is defined from the business perspective, and possible scenarios are identified and evaluated. Ultimately, a project plan is generated for delivering the solution. Data acquisition and understanding Following on from the business understanding phase is the data acquisition and understanding phase, which concentrates on familiarity and fact-finding about the data. The process itself is not completely linear; the output of the data acquisition and understanding phase can feed back to the business understanding phase, for example. At this point, some of the essential technical pieces start to appear, such as connecting to data, and the integration of multiple data sources. From the user's perspective, there may be actions arising from this effort. For example, it may be noted that there is missing data from the dataset, which requires further investigation before the project proceeds further. Modeling In the modeling phase of the TDSP process, the R model is created, built, and verified against the original business question. In light of the business question, the model needs to make sense. It should also add business value, for example, by performing better than the existing solution that was in place prior to the new R model. This stage also involves examining key metrics in evaluating our R models, which need to be tested to ensure that the models meet the original business objectives set out in the initial business understanding phase. Deployment R models are published to production, once they are proven to be a fit solution to the original business question. This phase involves the creation of a strategy for ongoing review of the R model's performance as well as a monitoring and maintenance plan. It is recommended to carry out a recurrent evaluation of the deployed models. The models will live in a fluid, dynamic world of data and, over time, this environment will impact their efficacy. The TDSP process is a cycle rather than a linear process, and it does not finish, even if the model is deployed. It is comprised of a clear structure for you to follow throughout the Data Science process, and it facilitates teamwork and collaboration along the way. TDSP Summary The data science unicorn does not exist; that is, the person who is equally skilled in all areas of data science, right across the board. In order to ensure successful projects where each team player contributes according to their skill set, the Team Data Science Summary is a team-oriented solution that emphasizes teamwork and collaboration throughout. It recognizes the importance of working as part of a team to deliver Data Science projects. It also offers useful information on the importance of having standardized source control and backups, which can include open source technology. If you liked our post, please be sure to check out Advanced Analytics with R and Tableau that shows how to apply various data analytics techniques in R and Tableau across the different stages of a data science project highlighted in this article.  

“It’s not who has the best algorithm that wins. It’s who has the most data” ~ Andrew Ng If you would look at the way algorithms were trained in Machine Learning, five or ten years ago, you would notice one huge difference. Training algorithms in Machine Learning are much better and efficient today than it used to be a few years ago. All credit goes to the hefty amount of data that is available to us today. But, how does Machine Learning make use of this data? Let’s have a look at the definition of Machine Learning. “Machine Learning provides computers or machines the ability to automatically learn from experience without being explicitly programmed”. Machines “learn from experience” when they’re trained, this is where data comes into the picture. How’re they trained? Datasets!   This is why it is so crucial that you feed these machines with the right data for whatever problem it is that you want these machines to solve. Why datasets matter in Machine Learning? The simple answer is because Machines too like humans are capable of learning once they see relevant data. But where they vary from humans is the amount of data they need to learn from. You need to feed your machines with enough data in order for them to do anything useful for you. This why Machines are trained using massive datasets. We can think of machine learning data like a survey data, meaning the larger and more complete your sample data size is, the more reliable your conclusions will be. If the data sample isn’t large enough then it won’t be able to capture all the variations making your machine reach inaccurate conclusions, learn patterns that don’t really exist, or not recognize patterns that do. Datasets help bring the data to you. Datasets train the model for performing various actions. They model the algorithms to uncover relationships, detect patterns, understand complex problems as well as make decisions. Apart from using datasets, it is equally important to make sure that you are using the right dataset, which is in a useful format and comprises all the meaningful features, and variations. After all, the system will ultimately do what it learns from the data. Feeding right data into your machines also assures that the machine will work effectively and produce accurate results without any human interference required. For instance, training a speech recognition system with a textbook English dataset will result in your machine struggling to understand anything but textbook English. So, any loose grammar, foreign accents, or speech disorders would get missed out. For such a system, using a dataset comprising all the infinite variations in a spoken language among speakers of different genders, ages, and dialects would be a right option. So keep in mind that it is important that the quality, variety, and quantity of your training data is not compromised as all these factors help determine the success of your machine learning models. Top Machine Learning Datasets for Beginners Now, there are a lot of datasets available today for use in your ML applications. It can be confusing, especially for a beginner to determine which dataset is the right one for your project. It is better to use a dataset which can be downloaded quickly and doesn’t take much to adapt to the models. Further, always use standard datasets that are well understood and widely used. This lets you compare your results with others who have used the same dataset to see if you are making progress. You can pick the dataset you want to use depending on the type of your Machine Learning application. Here’s a rundown of easy and the most commonly used datasets available for training Machine Learning applications across popular problem areas from image processing to video analysis to text recognition to autonomous systems. Image Processing There are many image datasets to choose from depending on what it is that you want your application to do. Image processing in Machine Learning is used to train the Machine to process the images to extract useful information from it. For instance, if you’re working on a basic facial recognition application then you can train it using a dataset that has thousands of images of human faces. This is how Facebook knows people in group pictures. This is also how image search works in Google and in other visual search based product sites. Dataset Name Brief Description 10k US Adult Faces Database This database consists of 10,168 natural face photographs and several measures for 2,222 of the faces, including memorability scores, computer vision, and psychological attributes. The face images are JPEGs with 72 pixels/in resolution and 256-pixel height. Google's Open Images Open Images is a dataset of 9 million URLs to images which have been annotated with labels spanning over 6000 categories. These labels cover more real-life entities and the images are listed as having a Creative Commons Attribution license. Visual Genome This is a dataset of over 100k images densely annotated with numerous region descriptions ( girl feeding elephant), objects (elephants), attributes(large), and relationships (feeding). Labeled Faces in the Wild This database comprises more than 13,000 images of faces collected from the web. Each face is labeled with the name of the person pictured.   Fun and easy ML application ideas for beginners using image datasets: Cat vs Dogs: Using Cat and Stanford Dogs dataset to classify whether an image contains a dog or a cat. Iris Flower classification: You can build an ML project using Iris flower dataset where you classify the flowers in any of the three species. What you learn from this toy project will help you learn to classify physical attributes based content to build some fun real-world projects like fraud detection, criminal identification, pain management ( eg; ePAT which detects facial hints of pain using facial recognition technology), and so on. Hot dog - Not hot dog: Use the Food 101 dataset, to distinguish different food types as a hot dog or not. Who knows, you could end up becoming the next Emmy award nominee! Sentiment Analysis As a beginner, you can create some really fun applications using Sentiment Analysis dataset. Sentiment Analysis in Machine Learning applications is used to train machines to analyze and predict the emotion or sentiment associated with a sentence, word, or a piece of text. This is used in movie or product reviews often. If you are creative enough, you could even identify topics that will generate the most discussions using sentiment analysis as a key tool. Dataset Name Brief Description Sentiment140 A popular dataset, which uses 160,000 tweets with emoticons pre-removed Yelp Reviews An open dataset released by Yelp, contains more than 5 million reviews on Restaurants, Shopping, Nightlife, Food, Entertainment, etc. Twitter US Airline Sentiment Twitter data on US airlines starting from February 2015, labeled as positive, negative, and neutral tweets. Amazon reviews This dataset contains over 35 million reviews from Amazon spanning 18 years. Data include information on products, user ratings, and the plaintext review.   Easy and Fun Application ideas using Sentiment Analysis Dataset: Positive or Negative: Using Sentiment140 dataset in a model to classify whether given tweets are negative or positive. Happy or unhappy: Using Yelp Reviews dataset in your project to help machine figure out whether the person posting the review is happy or unhappy.   Good or Bad: Using Amazon Reviews dataset, you can train a machine to figure out whether a given review is good or bad. Natural Language Processing Natural language processing deals with training machines to process and analyze large amounts of natural language data. This is how search engines like Google know what you are looking for when you type in your search query. Use these datasets to make a basic and fun NLP application in Machine Learning: Dataset Name Brief Description Speech Accent Archive This dataset comprises 2140 speech samples from different talkers reading the same reading passage. These Talkers come from 177 countries and have 214 different native languages. Each talker is speaking in English. Wikipedia Links data This dataset consists of almost 1.9 billion words from more than 4 million articles. Search is possible by word, phrase or part of a paragraph itself. Blogger Corpus A dataset comprising 681,288 blog posts gathered from blogger.com. Each blog consists of minimum 200 occurrences of commonly used English words.   Fun Application ideas using NLP datasets: Spam or not: Using Spambase dataset, you can enable your application to figure out whether a given email is spam or not. Video Processing Video Processing datasets are used to teach machines to analyze and detect different settings, objects, emotions, or actions and interactions in videos. You’ll have to feed your machine with a lot of data on different actions, objects, and activities. Dataset Name Brief Description UCF101 - Action Recognition Data Set This dataset comes with 13,320 videos from 101 action categories. Youtube 8M YouTube-8M is a large-scale labeled video dataset. It contains millions of YouTube video IDs, with high-quality machine-generated annotations from a diverse vocabulary of 3,800+ visual entities.   Fun Application ideas using video processing dataset: Action detection: Using UCF101 - Action Recognition DataSet, or Youtube 8M, you can train your application to detect the actions such as walking, running etc, in a video. Speech Recognition Speech recognition is the ability of a machine to analyze or identify words and phrases in a spoken language. Feed your machine with the right and good amount of data, and it will help it in the process of recognizing speech. Combine speech recognition with natural language processing, and get Alexa who knows what you need. Dataset Name Brief Description Gender Recognition by Voice and speech analysis This database identifies a voice as male or female, depending on the acoustic properties of voice and speech. The dataset contains 3,168 recorded voice samples, collected from male and female speakers. Human Activity Recognition w/Smartphone Human Activity Recognition database consists of recordings of 30 subjects performing activities of daily living (ADL) while carrying a smartphone ( Samsung Galaxy S2 ) on the waist. TIMIT TIMIT provides speech data for acoustic-phonetic studies and for the development of automatic speech recognition systems. It comprises broadband recordings of 630 speakers of eight major dialects of American English, each reading ten phonetically rich sentences, phonetic and word transcriptions. Speech Accent Archive This dataset contains 2140 speech samples, each from a different talker reading the same reading passage. Talkers come from 177 countries and have 214 different native languages. Each talker is speaking in English.   Fun Application ideas using Speech Recognition dataset: Accent detection: Use Speech Accent Archive dataset, to make your application identify different accents from a given sample of accents. Identify the activity: Use Human Activity Recognition w/Smartphone dataset to help your application detect the human activity. Natural Language Generation Natural Language generation refers to the ability of machines to simulate the human speech. It can be used to translate written information into aural information or assist the vision-impaired by reading out aloud the contents of a display screen. This is how Alexa or Siri respond to you. Dataset Name Brief Description Common Voice by Mozilla Common Voice dataset contains speech data read by users on the Common Voice website from a number of public sources like user-submitted blog posts, old books, movies, etc. LibriSpeech This dataset consists of nearly 500 hours of clean speech of various audiobooks read by multiple speakers, organized by chapters of the book with both the text and the speech.   Fun Application ideas using Natural Language Generation dataset: Converting text into Audio: Using Blogger Corpus dataset, you can train your application to read out loud the posts on blogger. Autonomous Driving Build some basic self-driving Machine Learning Applications. These Self-driving datasets will help you train your machine to sense its environment and navigate accordingly without any human interference. Autonomous cars, drones, warehouse robots, and others use these algorithms to navigate correctly and safely in the real world. Datasets are even more important here as the stakes are higher and the cost of a mistake could be a human life. Dataset Name Brief Description Berkeley DeepDrive BDD100k This is one of the largest datasets for self-driving AI currently. It comprises over 100,000 videos of over 1,100-hour driving experiences across different times of the day and weather conditions. Baidu Apolloscapes Large dataset consisting of 26 different semantic items such as cars, bicycles, pedestrians, buildings, street lights, etc. Comma.ai This dataset consists of more than 7 hours of highway driving. It includes details on car’s speed, acceleration, steering angle, and GPS coordinates. Cityscape Dataset This is a large dataset that contains recordings of urban street scenes in 50 different cities. nuScenes This dataset consists of more than 1000 scenes with around 1.4 million image, 400,000 sweeps of lidars (laser-based systems that detect the distance between objects), and 1.1 million 3D bounding boxes ( detects objects with a combination of RGB cameras, radar, and lidar).   Fun Application ideas using Autonomous Driving dataset: A basic self-driving application: Use any of the self-driving datasets mentioned above to train your application with different driving experiences for different times and weather conditions.   IoT Machine Learning in building IoT applications is on the rise these days. Now, as a beginner in Machine Learning, you may not have advanced knowledge on how to build these high-performance IoT applications using Machine Learning, but you certainly can start off with some basic datasets to explore this exciting space. Dataset Name Brief Description Wayfinding, Path Planning, and Navigation Dataset This dataset consists of samples of trajectories in an indoor building (Waldo Library at Western Michigan University) for navigation and wayfinding applications. ARAS Human Activity Dataset This dataset is a Human activity recognition Dataset collected from two real houses. It involves over 26 millions of sensor readings and over 3000 activity occurrences.   Fun Application ideas using IoT dataset: Wearable device to track human activity: Use the ARAS Human Activity Dataset to train a wearable device to identify human activity. Read Also: 25 Datasets for Deep Learning in IoT Once you’re done going through this list, it’s important to not feel restricted. These are not the only datasets which you can use in your Machine Learning Applications. You can find a lot many online which might work best for the type of Machine Learning Project that you’re working on. Some popular sources of a wide range of datasets are Kaggle,  UCI Machine Learning Repository, KDnuggets, Awesome Public Datasets, and Reddit Datasets Subreddit. With all this information, it is now time to use these datasets in your project. In case you’re completely new to Machine Learning, you will find reading, ‘A nonprogrammer’s guide to learning Machine learning’quite helpful. Regardless of whether you’re a beginner or not, always remember to pick a dataset which is widely used, and can be downloaded quickly from a reliable source. How to create and prepare your first dataset in Salesforce Einstein Google launches a Dataset Search Engine for finding Datasets on the Internet Why learn machine learning as a non-techie?

Python is the fastest growing programming language on the planet. This year’s Stack Overflow survey produces clear evidence that it is growing at an impressive rate. And it’s not really that surprising - versatile, dynamic, and actually pretty easy to learn, it’s a language that is accessible and powerful enough to solve problems in a range of fields, from statistics to building APIs. But what does the future hold for Python? How will it evolve to meet the needs of its growing community of engineers and analysts? Read the insights from 3 Python experts on what the future might hold for the programming language, taken from Python Interviews, a book that features 20 conversations with leading figures from the Python community. In the future, Python will spawn other more specialized languages Steve Holden (@HoldenWeb), CTO of Global Stress Index and former chairman and director of The PSF: I'm not really sure where the language is going. You hear loose talk of Python 4. To my mind though, Python is now at the stage where it's complex enough. Python hasn't bloated in the same way that I think the Java environment has. At that maturity level, I think it's rather more likely that Python's ideas will spawn other, perhaps more specialized, languages aimed at particular areas of application. I see this as fundamentally healthy and I have no wish to make all programmers use Python for everything; language choices should be made on pragmatic grounds. I've never been much of a one for pushing for change. Enough smart people are thinking about that already. So mostly I lurk on Python-Dev and occasionally interject a view from the consumer side, when I think that things are becoming a little too esoteric. The needs of the Python community are going to influence where the language goes in future Carol Willing (@WillingCarol), former director of The Python Foundation, core developer of CPython, and Research Software Engineer at Project Jupyter. I think we're going to continue to see growth in the scientific programming part of Python. So things that support the performance of Python as a language and async stability are going to continue to evolve. Beyond that, I think that Python is a pretty powerful and solid language. Even if you stopped development today, Python is a darn good language. I think that the needs of the Python community are going to feed back into Python and influence where the language goes. It's great that we have more representation from different groups within the core development team. Smarter minds than mine could provide a better answer to your question. I'm sure that Guido has some things in mind for where he wants to see Python go. Mobile development has been an Achilles' heel for Python for a long time. I'm hoping that some of the BeeWare stuff is going to help with the cross-compilation. A better story in mobile is definitely needed. But you know, if there's a need then Python will get there. I think that the language is going to continue to move towards the stuff that's in Python 3. Some big code bases, like Instagram, have now transitioned from Python 2 to 3. While there is much Python 2.7 code still in production, great strides have been made by Instagram, as they shared in their PyCon 2017 keynote. There's more tooling around Python 3 and more testing tools, so it's less risky for companies to move some of their legacy code to Python 3, where it makes business sense to. It will vary by company, but at some point, business needs, such as security and maintainability, will start driving greater migration to Python 3. If you're starting a new project, then Python 3 is the best choice. New projects, especially when looking at microservices and AI, will further drive people to Python 3. Organizations that are building very large Python codebases are adopting type annotations to help new developers Barry Warsaw (@pumpichank), member of the Python Foundation team at LinkedIn, former project leader of GNU Mailman: In some ways it's hard to predict where Python is going. I've been involved in Python for 23 years, and there was no way I could have predicted in 1994 what the computing world was going to look like today. I look at phones, IoT (Internet of things) devices, and just the whole landscape of what computing looks like today, with the cloud and containers. It's just amazing to look around and see all of that stuff. So there's no real way to predict what Python is going to look like even five years from now, and certainly not ten or fifteen years from now. I do think Python's future is still very bright, but I think Python, and especially CPython, which is the implementation of Python in C, has challenges. Any language that's been around for that long is going to have some challenges. Python was invented to solve problems in the 90s and the computing world is different now and is going to become different still. I think the challenges for Python include things like performance and multi-core or multi-threading applications. There are definitely people who are working on that stuff and other implementations of Python may spring up like PyPy, Jython, or IronPython. Aside from the challenges that the various implementations have, one thing that Python has as a language, and I think this is its real strength, is that it scales along with the human scale. For example, you can have one person write up some scripts on their laptop to solve a particular problem that they have. Python's great for that. Python also scales to, let's say, a small open source project with maybe 10 or 15 people contributing. Python scales to hundreds of people working on a fairly large project, or thousands of people working on massive software projects. Another amazing strength of Python as a language is that new developers can come in and learn it easily and be productive very quickly. They can pull down a completely new Python source code for a project that they've never seen before and dive in and learn it very easily and quickly. There are some challenges as Python scales on the human scale, but I feel like those are being solved by things like the type annotations, for example. On very large Python projects, where you have a mix of junior and senior developers, it can be a lot of effort for junior developers to understand how to use an existing library or application, because they're coming from a more statically-typed language. So a lot of organizations that are building very large Python codebases are adopting type annotations, maybe not so much to help with the performance of the applications, but to help with the onboarding of new developers. I think that's going a long way in helping Python to continue to scale on a human scale. To me, the language's scaling capacity and the welcoming nature of the Python community are the two things that make Python still compelling even after 23 years, and will continue to make Python compelling in the future. I think if we address some of those technical limitations, which are completely doable, then we're really setting Python up for another 20 years of success and growth.

Earlier this year, Google and Mozilla released a version of Chrome and Firefox that has full support for WebGL 2.0. While some of the previous versions of their browsers also have support for WebGL 2.0, those versions by default disable the WebGL 2.0 feature. By enabling WebGL 2.0 in their latest browser version, it seems both Google and Mozilla are confident that this bleeding edge web technology can finally be used by most users without any problems.  So, what is WebGL 2.0? How does it differ from the previous version of WebGL? What, in fact, is WebGL?  To answer those questions, let's go back in time a little bit. In the early 1990s, graphics intensive applications were expensive to create because the software had to be customized for each type of graphic processing hardware. Imagine having to write an app for each smartphone vendor separately; it would cost many man hours. So, to mitigate this problem, a standard for graphics computing was introduced. This standard is called OpenGL (which stands for Open Graphics Library).  When mobile phones with display screens were introduced, people realized that mobile technology also needed a standard for graphics computing. However, OpenGL is a standard primarily for desktop-class hardware, so they realized that they would need a different standard that could work with the limited capability of mobile hardware. And thus OpenGL ES (Embedded System) was branched out from OpenGL, and the initial version was released in the early 2000s.  The same progression happened to web technology. In 2009, web applications became increasingly graphic-intensive, so a graphical standard called WebGL was introduced to help software developers. One thing noted, however, was that users could access web applications from both desktop and mobile devices, so WebGL needed to work on both platforms. To accommodate that, WebGL was created based on the OpenGL ES specification instead of the desktop-focused OpenGL.  Technology keeps advancing. As graphics hardware becomes more capable, additional features get added to the graphical standards. The latest version of OpenGL ES, version 3.0, was released in 2012 to keep up with the advancement in mobile GPU. WebGL 1.0, however, was still based on OpenGL ES 2.0. So in 2017, the specification for WebGL 2.0, which was based on OpenGL ES 3.0, was finally released.  As we can see from the timeline, WebGL 2.0 is really fresh out the oven. In fact, it's so new, that at the time of writing this article, the only browsers that support the standards are Google Chrome, Mozilla Firefox, and the Opera browser. WebGL 2.0 support on Safari is still under development. Also, it's worth noting that no mobile browser supports WebGL 2.0 by default (WebGL 2.0 support on Chrome for Android can be enabled via a hidden menu).  Considering the limited number of compatible platforms, as developers, we really can't rely on the user to have the necessary browser for our apps. So, with that limitation in mind, we have to always check for the browser's capability and prepare a fallback method in the event that the browser does not support WebGL 2.0.  So, how does WebGL version 2.0 differ from version 1.0? Fortunately, nothing major has changed with the way the library is used. This latest version of WebGL simply adds additional features and also makes some optional extensions of the library to be included by default.  One of the WebGL 1.0 extensions that have been made mandatory on WebGL 2.0 is the Instancing extension, which enables developers to render multiple copies of the same mesh efficiently. This feature is very useful for drawing decorative objects, like grass. Another extension that has been included in WebGL 2.0 is Depth Texture, which is used a lot for computing lighting and creating shadow maps.  Another major addition to WebGL 2.0 is the support for GLSL 3.0 ES, the latest programming language for the OpenGL shader. With this version of GLSL, a loop in the shader is no longer restricted to a constant integer. Not just that, GLSL 3.0 ES also brings additional matrix operations (like an inverse function) that will make coding a shader much easier.  WebGL 2.0 also offers much better support for textures. With version 2.0, the non-power of 2D textures are finally supported, which means the size of your texture image is no longer limited to 32, 64, 128, 256, and such. 3D textures are also supported now, which is pretty useful for volumetric effects such as light rays and smoke, as well as for storing medical scans.  WebGL 2.0 also adds support for more texture formats such as RGBA32, RGBA16, R11F_G11F_B10F, SRGB8, and others. More compressed texture formats that are not platform-specific are also supported, including: COMPRESSED_RGB8_ETC2, COMPRESSED_RGBA8_ETC2_EAC, and more.  There are other additions to WebGL 2.0, such as Multiple Draw Buffer, Transform Feedback, Uniform Buffer Object, and more. To learn about these and much more, see the official WebGL 2.0 specifications to check out all those additions in detail.  About this author Raka Mahesa is a game developer at Chocoarts: http://chocoarts.com/, who is interested in digital technology in general. Outside of work hours, he likes to work on his own projects, with Corridoom VR being his latest released game. Raka also regularly tweets as @legacy99. 

During the last couple of years, we’ve seen how DevOps has exploded and has become one of the most competitive differentiators for every organization, regardless of their size. When talking about DevOps, we refer to agility and collaboration, keys that unlock a business’s success. However, to make it work for your business, you have to first understand how DevOps works, and what skills are required for adopting this agile business culture. Let’s look over this in more detail. DevOps culture Leaving the benefits aside, here are the three basic principles of a successful DevOps approach: Well-defined processes Enhanced collaboration across business functions Efficient tools and automation  DevOps skills you need Recently, I came across an infographic showing the top positions that technology companies are struggling to fill, and DevOps was number one on the list. Surprising? Not really. If we're looking at the skills required for a successful DevOps methodology, we will understand why finding a good DevOps engineer akin to finding a needle in a haystack. Besides communication and collaboration, which are the most obvious skills that a DevOps engineer must have, here is what draws the line between success, or failure. Knowledge of Infrastructure – whether we are talking about datacenter-based or cloud infrastructure, a DevOps engineer needs to have a deep understanding of different types of infrastructure and its components (virtualization, networking, load balancing, etc). Experience with infrastructure automation tools – taking into consideration that DevOps is mainly about automation, a DevOps engineer must have the ability to implement automation tools at any level. Coding - when talking about coding skills for DevOps engineers, I am not talking about just writing the code, but rather delivering solutions. In a DevOps organization, you need to have well-experienced engineers that are capable of delivering solutions. Experience with configuration management tools – tools such as Puppet, Chef, or Ansible are mandatory for optimizing software deployment and you need engineers with the know-how. Understanding continuous integration – being an essential part of a DevOps culture, continuous integration is the process that increases the engagement across the entire team and allows source code updates to be run whenever is required. Understanding security incident response – security is the hot button for all organizations, and one of the most pressing challenges to overcome. Having engineers that have a strong understanding of how to address various security incidents and developing a recovery plan, is mandatory for creating a solid DevOps culture.  Beside the above skills that DevOps engineers should have, there are also skills that companies need to adopt: Agile development – agile environment is the foundation on which the DevOps approach has been built. To get the most out of this innovative approach, your team needs to have strong collaboration capabilities to improve their delivery and quality. You can create your dream team by teaching different agile approaches such as Scrum, Kaizen, and Kanban. Process reengineering – forget everything you knew. This is one good piece of advice. The DevOps approach has been developed to polish and improve the traditional Software Development Lifecycle but also to highlight and encourage collaboration among teams, so an element of unlearning is required.  The DevOps approach has changed the way people collaborate with each other, and improving not only the processes, but their products and services as well. Here are the benefits:  Ensure faster delivery times – every business owner wants to see his product or service on the market as soon as possible, and the DevOps approach manages to do that. Moreover, since you decrease the time-to-market, you will increase your ROI; what more could you ask for? Continuous release and deployment – having strong continuous release and deployment practices, the DevOps approach is the perfect way to ensure the team is continuously delivering quality software within shorter timeframes. Improve collaboration between teams – there has always been a gap between the development and operation teams, a gap that has disappeared once DevOps was born. Today, in order to deliver high-quality software, the devs and ops are forced to collaborate, share, and revise strategies together, acting as a single unit.  Bottom line, DevOps is an essential approach that has changed not only results and processes, but also the way in which people interact with each other. Judging by the way it has progressed, it’s safe to assume that it's here to stay.  About the Author Rick Blaisdell is an experienced CTO, offering cloud services and creating technical strategies, which reduce IT operational costs and improve efficiency. He has 20 years of product, business development, and high-tech experience with Fortune 500 companies, developing innovative technology strategies. 

Today we will explain what a responsive web design is, how it benefits websites, and what the core concepts are. The topics covered are: Responsive design philosophy, principles, grid & columns Smooth user experience & user friendly website Understanding responsive grid systems Adaptive design and methodologies Elegant mobile experience This article is an excerpt taken from the book, Responsive Web Design by Example, written by Frahaan Hussain. Responsive design philosophy There was a time when most web surfing occurred on a computer with a standard-sized/ratio monitor. It was more than adequate to create websites with a non-responsive layout in mind. But over the last 10 years, there has been an exponential boom of new devices in a plethora of form factors, from mobile phones, tablets, watches and a wide range of screen sizes. This growth has created a huge fragmentation problem, so creating websites with a single layout is no longer acceptable. A website with a lot of content that works great on desktops doesn't work very well on a mobile device that has a significantly smaller screen. Such content is unreadable, forcing the user to zoom in and out constantly. One might try making everything bigger so it looks good on mobiles, but then on a desktop, the content doesn't take advantage of the immense real estate offered by bigger screens. Responsive Web Design is a method that allows the design to respond based on the user's input and environment and thus based on the size of the screen, the device, and its orientation. This philosophy blends elements of flexible grids and layouts, images, and media queries in CSS. Enter Responsive Web Design. This alleviates this problem by allowing developers and designers to create websites that adapt to all screen sizes/ratios. There are various approaches that different websites take, but the core concept is illustrated in the following figure: The preceding figure shows how the same website's layout can be adapted to suit different devices. On the desktop, there is a lot more real estate, so the content is bigger and more can fit on a single row. But, as the screen size shrinks and its orientation changes, the content readjusts itself to accommodate this change. This provides a seamless and elegant experience for the user on all form factors. If you look closely at the preceding figure and at modern websites, you will see a grid that the content conforms to. The grid is used to lay out the content of a website, and both of these elements go hand in hand. This grid system is one of the most important aspects of how Responsive Web Design works. Responsive design principles This section will cover the main principles behind designing responsive websites. Though these aren't set in stone and will change over time, they will provide a great foundation. Should you go Responsive or Adaptive? Responsive designs constantly change website layouts depending on their size and orientation. A single pixel resize will tend to have an effect on the layout, usually not by a lot. Adaptive schemes, on the other hand, have preset layouts, which are loaded depending on the size of the screen. This technique doesn't look as fluid and seamless as do responsive designs. Modern-day Responsive Web Design usually incorporates both methods. Set layouts will be provided, as can be seen in the previous figure. But any changes made to a website's size will have an impact in real time through responsive scaling. What are Breakpoints? Breakpoints are points at which a website's layout is no longer fit for the screen size, device, and/or orientation, and we are able to use different and unique layouts to accommodate the various changes that can occur to screens. When these points occur, the current layout is switched to a more suitable layout. For example, a mobile device in portrait mode will not effectively be able to use a layout that is designed for a widescreen desktop display; this just isn't possible. However, by using breakpoints a single website can serve many screen variations whilst making the website feel like it was designed with the user's current screen, device, and/or orientation in mind. This does not occur when reloading the web page, but content moves around dynamically and is scaled accordingly. Without breakpoints, the website would appear with the same layout on all form factors and browser sizes, which using the example we just mentioned, would not be fit for purpose. These breakpoints usually occur when the width of the browser changes and falls into the category of another more appropriate layout. There are a few fundamentals that should be mentioned regarding the philosophy of Responsive Web Design: Why is screen resolution important for responsive design? This is immensely influential in responsive design. The first thought for many designers is to design based on the resolution of the screen. But modern-day phones have resolutions of 1080p and beyond, which for the most part is still the de facto standard for desktops with some exceptions in 4K and ultrawide displays. This would prevent us from fully targeting all devices, as there is so much crossover in resolutions between devices. That is the reason why pixel density is very important when deciding which layout should be used as a 5-inch 1080p mobile display will be cramming the pixels in a lot closer than a 32-inch 1080p display. They both have the same resolution for the mobile device and they have a significantly higher pixel density, which helps distinguish between the device types. The viewport should also be taken into consideration, which is the user's visible area of a web page. This would allow us to rearrange content based on how much content should be displayed. What are media queries? These are amazing facets within CSS that allow us to actually detect changes in a screen such as its size and an event device type. These are the things used to specify code for a specific layout, such as a mobile or desktop display. You can think of media queries as conditional statements, just as an "if" statement would only run a piece of code if the condition was true. A media query is the same, it's far more limited, but as are many things in CSS. I'm positive you will have used a website and noticed that it looks different on a computer compared to a mobile phone, or even a tablet. This is thanks to the use of breakpoints, which are very similar to conditional statements in other languages such as C++. When a certain condition is met, such as screen size range, or, change in form factor, different CSS is applied to provide a better-suited layout. What are Relative units? Relative units take into account the other content and more specifically the content's size, whereas static units do not and have an absolute value regardless of the amount of content. If relative units are not used then static units would be used, which essentially lays the content using fixed units such as pixels. With this method, a box with a width of 400px on an 800px screen would take half the width. But, if the screen size changes to 300px, the box will now be partially off-screen. Again, this would not provide the reader with that seamless experience, which we aim to provide. The units simply display your content relative to everything else, or, more specifically, the screen size or viewport. This allows us, as creators, to display content consistently. Take the previous example, if we would like to display the box at half the screen width, on an 800px screen the box would be 400px wide, and on a 600px screen the box would be 300px wide. Using percentages we can set the width to 50%, which forces the box to always be half the width of its parent container, making its size relative to the rest of the page's content. Why Maximum and minimum values are important? Scaling our content is greatly dependent on the screen size. But with screens such as ultrawide monitors, scaling the content may make it too big, or even too small, on mobile devices. Using maximum and minimum values, we are able to set upper and lower limits providing us with readable and clear results. What are Nested objects? If we displayed every object individually, we would have to make them all adjust accordingly, but nesting allows us to wrap elements using containers. Nested objects are like a paragraph tag, as they contain text, and any changes made to the paragraph tag, such as its position or color, also affect its contents. Objects nested within each other are affected by any change made to their parent containers. An object can be anything from text and images to HTML tags/elements. Take a look at the following example: In this example, there are four elements—a div, paragraph, span, and image tag. The paragraph, span, and image tags are nested within the div tag. If the div tag's maximum width and background color were changed, this would affect all its child objects/tags. But if we were to make a change to the paragraph tag, such as changing its text color, this would not affect any other sibling tags or its parent tag. It would only have an effect on its contents/objects. So, for example, if a container is moved or scaled, the content within the container is also updated. This is where pixels come in use. You may not always want a container to be displayed 10% from the right as, on mobile devices, 10% equates to a lot of real estate potentially being wasted; you could specify 50px instead for example. Should you go Mobile first or desktop first? You can design a website from a small screen such as a phone and scale it up or go the other way round and design it with a large screen in mind. There is actually no right or wrong answer. Depending on the intended target audience and the website's purpose, this will become clear to you. Usually, considering both angles at the same time is the best route to go down. Most responsive frameworks on the market have been designed with a mobile-first philosophy, but that doesn't mean you cannot use it for a desktop-first design; it is on you as the designer to decide how content should be displayed. Should you use Bitmaps or vectors for your images? Bitmaps are great for images with a lot of detail, such as backgrounds and usually logos. Common bitmap formats include .png and .jpg. But these images can be large in file size and require more bandwidth and time to load. On desktop devices, this isn't too much of a problem, but on mobile devices that are heavily reliant on cellular services that don't always provide unlimited data, this can be problematic. Also, when scaling bitmaps, there is a loss in quality, which results in jagged and blurry images. Vectors, on the other hand, are small in size and don't lose quality when scaling. I know you'll be tempted to scream, "Hail vectors!" at this book, but they do have their drawbacks. They are only useful for simple content such as icons. Also, some older browsers do not fully support vectors. Again there is no "right choice"; depending on the content to be displayed, bitmaps or vectors should be used. Understanding Responsive grids and columns The grid system is one of the universal concepts of Responsive Web Design, regardless of the framework a website is built upon. To put it simply, websites are split into rows and columns, and if an object/element occupies half the number of columns, it will always occupy that many regardless of the screen's size. So an element that occupies 3 of the 12 rows will occupy 25% of the width of its parent container, hence providing a responsive design. This is great for small variations in screen sizes, but when a website is viewed on platforms varying from desktops to mobiles, then breakpoints are introduced as covered previously. Though there is no fixed number of columns that a responsive website should have, 12 is a common number used by some of the most popular and widespread frameworks. A framework in this context is anything built on top of the built-in web features. JavaScript is a web feature, but jQuery is a framework built on top of that to allow easier manipulation of the website using prewritten libraries/code. Though a framework isn't absolutely necessary, neither is using an off-the-shelf web browser. You could create your own, but it would be an immense waste of time, and the case for using a responsive framework is essentially the same. The following is an example of a basic responsive grid: Rows allow us as developers to group content together, though there will be a fixed number of columns, not all columns have to be filled to go to the next row. A new row can be used explicitly, as can be seen in the following example: This may be different to how you have developed websites in the past, but if there is anything you are unsure about don’t worry, as things will become clearer when we start working on projects in future chapters. To summarize, we covered responsive design philosophy and principles that are essential to creating an intuitive user experience. If you have enjoyed this excerpt, check out Responsive Web Design by Example to learn how to build engaging responsive websites. What UX designers can teach Machine Learning Engineers? To start with: Model Interpretability 5 things to consider when developing an eCommerce website Responsive Web Design with WordPress  

Nowadays, it seems like everyone wants to do things faster. We want to pay without taking out a credit card or cash. Social media lets us share images and videos from our lives in a split second. And we get frustrated if Netflix takes more than 3 seconds to start streaming our latest TV show series binge. However, if you want to learn how to code faster, I'm going to present an odd idea: go slower. This has been taken from Skill Up: A Software Developer's Guide to Life and Career by Jordan Hudgens. This may seem like a counterintuitive concept. After all, don't coding bootcamps, even DevCamp where I teach, tell you how you can learn how to code in a few months? Well yes, and research shows that 8 weeks is a powerful number when it comes to learning. The Navy Seal training program specifically chose 8 weeks as its timeframe for conditioning candidates. And if you search the web for the phrase 8 Week Training programs, you'll find courses ranging from running 10ks to speaking Spanish fluently. So yes, I'm huge believer that individuals can learn an incredible amount of information in a short period of time. But what I'm talking about here is becoming more deliberate when it comes to learning new information. Learn how to code faster If you're like me, when you learn a new topic the first thing you'll do is either move onto the next topic or repeat the concept as quickly as humanly possible. For example, when I learn a new Ruby or Scala programming method I'll usually jump right into using it in as many different situations as possible. However, I've discovered that this may not be the best approach because it's very short-sighted. Your default mind is stopping you from coding faster When it comes to learning how to code faster, one of the most challenging requirements is moving knowledge from our short-term memory to our long-term memory. Remember the last time you learned a programming technique. Do you remember how easy it felt when you repeated what the instructor taught? The syntax seemed straightforward and it probably seemed like there was no way you would forget how to implement the feature. But after a few days, if you try to rebuild the component, is it easy or hard? If you're like me, the concept that seemed incredibly easy only a few days ago now causes you to draw a blank. But don't worry. This doesn't mean that we're incompetent. Instead, it means that this piece of knowledge wasn't given the chance to move from our short-term to our long-term memory. Hacking the mind So, if our default mindset is to forget what we've learned after a few days (or a few minutes), how can we learn anything? This is where our brain's default programming comes into play and where we can hack the way that we learn. I'm currently teaching myself the TypeScript programming language. TypeScript is the language that's recommended for Angular 2 development, so I thought it would be a good next language to learn. However, instead of taking my default approach, which is to slam through training guides and tutorials, I'm taking a more methodical approach. Slowing it down To learn TypeScript, I'm going through a number of different books and videos. And as I follow along with the guides, as soon as I learn a new topic I completely stop. I'll stand up. Write the new component on one of my whiteboards. And actually, write the program out by hand. After that, I type the program out on the keyboard… very slowly. So slowly that I know I could go around 4-5x faster. But by taking this approach I'm forcing my mind to think about the new concept instead of rushing through it. When it comes to working with our long-term memory, this approach is more effective than simply flying through a concept because it forces our minds to think through each keystroke. That means when it comes to actually writing code, it will come much more naturally to you. Bend it like Beethoven I didn't learn this technique from another developer. Instead, I heard about how one of the most successful classical music institutions in the world, the Meadowmount School of Music in New York, taught students new music compositions. As a game, the school gives out portions of the sheet music. So, where most schools will give each student the full song, Meadowmount splits the music up into pieces. From there, it hands each student a single piece for them to focus on. From that point, the student will only learn to place that single piece of music. They will start out very slowly. They won't rush through notes because they don't even know how they fit into the song. This approach teaches them how to concentrate on learning a new song one note at a time. From that point, the students trade note cards and then focus on learning another piece of the song. They continue with trading cards until each student has been able to work through the entire set of cards. By forcing the students to break a song into pieces they no longer will have any weak points in a song. Instead, the students will have focused on the notes themselves. From this point, it's trivial for all the students in the class to combine their knowledge and learn how to play the song all the way through. From classical music to coding So, can this approach help you learn how to code faster? I think so. The research shows that by slowing down and breaking concepts into small pieces, it's easier for students to transfer information from the short-term to long-term memory. So, the next time you are learning a coding concept, take a step back. Instead of simply copying what the instructor is teaching, write it down on a piece of paper. Walk through exactly what is happening in a program. If you take this approach, you will discover that you're no longer simply following a teacher's set of steps, but that you'll actually learn how the concepts work. And if you get to the stage of understanding, you will be ready to transfer that knowledge to your long-term memory and remember it for good.

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’

Cybersecurity trends seem to be changing at an incredible rate. That poses new opportunities for criminals and new challenges for the professionals charged with securing our systems. High profile  attacks not only undermine trust in huge organizations, they also highlight a glaring gap in how we manage cybersecurity in a rapidly changing world. It also highlighted that attackers are adaptive and incredibly intelligent, evolving their techniques to adapt to new technologies and new behaviors. The big question is what the future will bring. What cybersecurity trends will impact the way cybersecurity experts work - and the way cybercriminals attack - for the rest of 2018 and beyond. Let’s explore some of the top cyber security trends and predictions of 2018: Artificial Intelligence and machine learning based cyber attacks and defenses AI and ML have started impacting major industries in various ways, but one of the most exciting applications is in cybersecurity. Basically, Artificial Intelligence and Machine Learning algorithms can learn from past events in order to help predict and identify vulnerabilities within a software system. They can also be used to detect anomalies in behavior within a network. A report from Webroot claims that more than 90% of cybersecurity professionals use AI to improve their security skills. However, while AI and machine learning can help security professionals, it is also being used by cybercriminals too. It seems obvious: if cyber security pros can use AI to identify vulnerabilities, so can people that seek to exploit them. Expect this back and forth to continue throughout 2018 and beyond. Ransomware is spreading like fire Storing data on the cloud has many benefits, but it can be an easy target for cyber criminals. Ransomware is one such technique - criminals target a certain area of data and hold it to ransom. It’s already a high profile cyber security concern. Just look at WannaCry, Petya, Meltdown, and Spectre, some of the biggest cyber security attacks in 2017. The bigger players (Google, AWS, and Azure) of the cloud market are trying to make it difficult for attackers, but smaller cloud service providers end up paying customers for data breaches. The only way these attacks can be reduced is by performing regular back-ups, updating security patches, and strengthening real-time defenses. Complying with GDPR GDPR (General Data Protection) is an EU regulation that tightens up data protection and privacy for individuals within the European Union. The ruling includes mandatory rules that all companies will have to follow when processing and storing personal data. From 25 May, 2018, General Data Protection (GDPR) will come into effect where important changes will be implemented to the current data protection directive. To mention a few it will include increased territorial scope,stricter consent laws, elevated rights and more. According to Forrester report 80% companies will fail to comply with GDPR out of which 50% would choose not to, considering the cost of compliance. Penalties for non-compliance would reach upto €20m or 4% of worldwide annual turnover, whichever is greater. The rise of Cyberwar Taking current cybersecurity scenario into consideration, there are high possibilities 2018 will be the year of international conflict in cyberspace. This may include cyber crimes on government and financial systems or their infrastructure and utilities. Chances are cyber-terrorism groups will target sensitive areas like banks, press, government, law-enforcement and more similar areas. The Ashley Madison attack – which involved attackers threatening to release personal information about users if the site was not shut down – shows that ideological motivated attacks are often very targeted and sophisticated with the goal of data theft and extortion. The attack on Ashley Madison is testament to the fact that companies need to be doing more as attackers become more motivated. You should not be surprised to see cyber-attacks going beyond financial benefits. The coming year can witness cyber crimes which are politically motivated that is designed to acquire intelligence to benefit a particular political entity. These methods can also be used to target electronic voting system in order to control public opinion. These kind of sophisticated attacks are usually well-funded and lead to public chaos. Governments will need to take extensive checks to ensure their network and ecosystem is well protected. Such instances might lead to loss of right to remain anonymous on the web. Like everything else, this move will also have two sides of the coin. Attacking cyber currencies and blockchain systems Since Bitcoin and Blockchain were booming in the year 2017, it becomes a crucial target area for hackers. Chances are attackers may target smaller blockchain systems who opt for weaker cryptographic algorithms to increase performance. On the other hand, the possibility of cryptographic attack against Bitcoin can be minimum. The major worry here would about attacking a block with minimum security practices, but eventually that block could lead to larger blockchain system. One of the major advantage for attackers here is they don’t really need to know who the opposite partner is, as only a verified participant is authorised to execute the trade. Here, trust or risk plays an important part and that is blockchain’s sweet spot. For example: Receiving payments in government issued currencies have higher possibilities of getting caught but there is a higher probability of succeeding in cryptocurrency payments. Well, this may be the end of this article but is not an end to the way things might turn out to be in 2018. We still stand midway through another year and the war of cyberthreats rages. Don’t be surprised to hear something different or new as malicious hackers keep trying newer techniques and methodologies to destroy a system. Related links WPA3: Next-generation Wi-Fi security is here The 10 most common types of DoS attacks you need to know 12 common malware types you should know

The Internet of Things is growing all the time. However, as IoT takes over the world, there are more and more aspects of it that needs to be addressed, such as security and standardization. It might not be ideal to live in a wild west where everything is connected but there are no guidelines or rules for how to manage and analyze these networks. A crucial part of all this is metadata – as data grows in size, the way we label, categorize and describe it will become more important than ever. Find our latest and forthcoming IoT eBooks and videos here.  We probably shouldn’t be that surprised – if IoT is all about connecting things that wasn’t previously connected – traffic lights, lamps, car parts – good metadata allows us to make sure those connections remain clear and legible. It helps to ensure that things are working properly. A system without definitions, without words and labels, would, after all, get chaotic pretty quickly. Metadata makes it easier to organize IoT data If metadata is, quite simply, data about data, it’s not hard to see why it might be so important when dealing with the expanse of data that is about to be generated thanks to the internet of things. While IoT will clearly largely run on data – information and messages passing between objects, moving within a given system, metadata is incredibly useful in this new world because it allows us to better understand the systems that we are developing. And what’s more, once we have that level of insight, we can begin to do more to further improve and optimize IoT systems using machine learning and artificial intelligence.  Consider how metadata organizes your media library – it would be a mess without it, practically unusable. When you scale that up, we’ll be able to make much smarter use of IoT. Without it, we might well be lost in a chaotic mess of connections.  Metadata, then, allows us to organize and catalog data.  Metadata solves IoT's interoperability problem Metadata can also help with the biggest problem of IoT: interoperability. Interoperability refers to the ability for one device to communicate and exchange data with another device. And this is really important in the context of the Internet of Things, because having great interoperability means more devices can connect with each other.  How does metadata solve interoperability? Well, by using metadata, a device can quickly identify a new device that tries to connect to it by looking at its model number, device class, and other attributes. Once the new device has been identified, our device can find a suitable communication protocol that's supported by both devices to exchange data. Metadata can also be added on the exchanged data, so both devices can read and process the data correctly, just like adding image format metadata allows any application to display that image. Metadata helps to protect legacy hardware and software There's another aspect that metadata can help with. The Internet of Things is an evolving technology where new products are introduced every day, and bring along with them changes and innovations. But what happens to the old products that have been replaced by new ones? With metadata, we can archive and protect the future accessibility of our devices, making sure that new devices can still communicate with older, legacy devices.  That's why metadata is important for the Internet of Things. There are many benefits that can be gained by having a robust system of metadata in the Internet of Things. And as the Internet of Things grows and is used to manage more crucial aspects of our lives, the need for this system will also grow. Raka Mahesa is a game developer at Chocoarts who is interested in digital technology. Outside of work, he enjoys working on his own projects, with Corridoom VR being his latest relesed gme. Raka also regularly tweets @legacy99.

What is the difference between DevOps and continuous delivery? The tech world is full of buzzwords; DevOps is undoubtly one of the best-known of the last few years. Essentially DevOps is a concept that attempts to solve two key problems for modern IT departments and development teams - the complexity of a given infrastructure or service topology and market agility. Or, to put it simply, there are lots of moving parts in modern software infrastructures, which make changing and fixing things hard.  Project managers who know their agile inside out - not to mention customers too - need developers to: Quickly release new features based on client feedback Keep the service available, even during large deployments Have no lasting crashes, regressions, or interruptions How do you do that ? You cultivate a DevOps philosophy and build a continous integration pipeline. The key thing to notice there are the italicised verbs - DevOps is cultural, continuous delivery is a process you construct as a team. But there's also more to it than that. Why do people confuse DevOps and continuous delivery? So, we've established that there's a lot of confusion around DevOps and continuous delivery (CD). Let's take a look at what the experts say.  DevOps is defined on AWS as: "The combination of cultural philosophies, practices, and tools that increases an organization’s ability to deliver applications and services at high velocity." Continuous Delivery, as stated by Carl Caum from Puppet, "…is a series of practices designed to ensure that code can be rapidly and safely be deployed to production by delivering every change to a production-like environment and ensuring that business applications and services function as expected through rigorous automated testing." So yes, both are about delivering code. Both try to enforce practices and tools to improve velocity and reliability of software in production. Both want the IT release pipeline to be as cost effective and agile as possible. But if we're getting into the details, DevOps is focused on managing challenging time to market expectations, while continuous delivery was a process to manage greater service complexity - making sure the code you ship is solid, basically. Human problems and coding problems In its definition of DevOps, Atlassian puts forward a neat formulation: "DevOps doesn’t solve tooling problems. It solves human problems." DevOps, according to this understanding promotes the idea that development and operational teams should work seamlessly together. It argues that they should design tools and processes to ensure rapid and efficient development-to-production cycles. Continuous Delivery, on the other hand, narrows this scope to a single mentra: your code should always be able to be safely released. It means that any change goes through an automated pipeline of tests (units, integrations, end-to-end) before being promoted to production. Martin Fowler nicely sums up the immediate benefits of this sophisticated deployment routine: "reduced deployment risk, believable progress, and user feedback." You can't have continuous delivery without DevOps Applying CD is difficult and requires advanced operational knowledge and enough resources to set up a pipeline that works for the team. Without a DevOps culture, you're team won't communicate properly, and technical resources won't be properly designed. It will certainly hurt the most critical IT pipeline: longer release cycles, more unexpected behaviors in production, and a slow feedback loop. Developers and management might fear the deployment step and become less agile. You can have DevOps without continuous delivery... but it's a waste of time The reverse this situation, DevOps without CD, is slightly less dangerous. But it is, unfortunately, pretty inefficient. While DevOps is a culture, or a philosophy, it is by no means supposed to remain theoretical. It's supposed to be put into practice. After all, the main aim isn't chin stroking intellectualism, it's to help teams build better tools and develop processes to deliver code. The time (ie. money) spent to bootstrap such a culture shouldn't be zeroed by a lack of concrete actions. CD delivery is a powerful asset for projects trying to conquer a market in a lean fashion. It overcomes the investments with teams of developers focused on business problems anddelevering to clients tested solutions as fast as they are ready. Take DevOps and continuous delivery seriously What we have, then, are two different, but related, concepts in how modern development teams understand operations. Ignoring one of them induces waste of resources and poor engineering efficiency. However, it is important to remember that the scope of DevOps - involving an entire organizational culture, potentially - and the complexity of continuous delivery mean that adoption shouldn't be rushed or taken lightly. You need to make an effort to do it properly. It might need a mid or long term roadmap, and will undoubtedly require buy-in from a range of stakeholders. So, keep communication channels open, consider using built cloud services if required, understand the value of automated tests and feedback-loops, and, most importantly, hire awesome people to take responsibility. A final word of caution. As sophisticated as they are, DevOps and continuous delivery aren't magical methodologies. A service as critical as AWS S3 claims 99.999999999% durability, thanks to rigorous engineering methods and yet, on February 28, it suffered a large service disruption. Code delivery is hard so keep your processes sharp! About the author Xavier Bruhiere is a Senior Data Engineer at Kpler. He is a curious, sharp, entrepreneur, and engineer who has built many projects, broke most of them, and launched and scaled what was left, learning from them all.

The cloud computing revolution has well and truly begun. But the market is fiercely competitive - there are a handful of cloud vendors leading the pack and it’s not easy to say which one is best. AWS, Google Cloud Platform, Microsoft Azure, and Oracle are leading the way when it comes to modern cloud-based infrastructure and it’s hard to separate them. Big data is in high demand as businesses can flesh out useful insights. Organizations carry out advanced analytics in order to leverage deep and exploratory perspective on the data. After a deep analysis is performed, BI tools such as Tableau, Microsoft Power BI, Qlik Sense, and so on, are used in drafting out dashboard visualizations, reports, performance management metrics etc. that makes the data analytics actionable. Thus, we see how analytics and BI tools are important in getting the best out of big data. In this year’s Skill Up survey, there emerged a frontrunner for developers: AWS Source : Packt Skill Up Survey Let’s talk AWS Amazon is said to outplay any other cloud platform players in the market. AWS provides its customers with a highly robust infrastructure with commendable security options. In its inception year, 2006, AWS already had more than 150,000 developers who signed up to use the AWS services. Amazon announced this at a press release that year. In a recent survey conducted by the Synergy Research, AWS is among the top cloud platform providers with a 35% market share. The top customers of AWS include NASA, Netflix, Adobe Systems, Airbnb, and many more. Cloud technology is not a new and emerging trend anymore and has truly become mainstream. What sets AWS on a different plateau is, it has caught developers’ attention by its impressive suite of developer tools. It’s a cloud platform that is designed with continuous delivery and DevOps in mind. AWS: Every developer’s den Once you’re an AWS’ member, you can experience hundreds of different platforms that it offers. Starting form Core Computation and Content Delivery Networks, one can even take advantage of the IoT and game development platforms. If you’re worried how to payback for all that you have used, don’t worry, AWS offers its complete package of solutions across six modes of payments. It also offers hundreds of templates in every programming language to glide along one’s choice of project. Pay-as-you-go feature in AWS enables customers to use the features that are required. This avoids unnecessary buying of resources that would add no value to businesses. Security on AWS is something users appreciate. AWS’ configuration options, management policies, and their reliable security are the reasons why one can easily trust their cloud services. AWS has layers of security encryptions that enable high-end user data protection. It also decides on user privileges using the IAM (Identity and Access Management) roles. This helps to keep restrictions on the number of resources used by the user. It also helps in greatly reducing malpractices. AWS provides developers with autoscaling, as it is one of the most important features that every developer needs. With AutoScaling, developers can suspend their unimportant management issues on autopilot; AWS takes care of it. Developers can instead focus more on processes, development, and programming. The free tier within AWS runs an Amazon EC2, which includes an S3 storage, EC2 compute hours, Elastic Load balancer time, and so on. This enables developers to try AWS’ API within their software to enhance it further. AWS cuts down deployment time required to provision a web server. By using Amazon Machine Images, one can have a machine deployed and ready to accept connections in a short time. Amazon’s logo says it all. It provides A to Z services under one hood for developers, businesses,and for general users. Each service is tailored to serve different purposes and also has a dedicated and specialized hardware. Developers can easily choose Amazon for their development needs with their pay-as-you-go and make the most of it without even buying stuff. Though, there are other service providers such as Microsoft Azure, Google Cloud Platform and so on, Amazon offers functionalities which others are yet to match. Verizon chooses Amazon Web Services(AWS) as its preferred cloud provider How to secure ElasticCache in AWS How to create your own AWS CloudTrail

Advancements in artificial intelligence (AI) and machine learning has enabled the evolution of mobile applications that we see today. With AI, apps are now capable of recognizing speech, images, and gestures, and translate voices with extraordinary success rates. With a number of apps hitting the app stores, it is crucial that they stand apart from competitors by meeting the rising standards of consumers. To stay relevant it is important that mobile developers keep up with these advancements in artificial intelligence. As AI and machine learning become increasingly popular, there is a growing selection of tools and software available for developers to build their apps with. These cloud-based and device-based artificial intelligence tools provide developers a way to power their apps with unique features. In this article, we will look at some of these tools and how app developers are using them in their apps. Caffe2 - A flexible deep learning framework Source: Qualcomm Caffe2 is a lightweight, modular, scalable deep learning framework developed by Facebook. It is a successor of Caffe, a project started at the University of California, Berkeley. It is primarily built for production use cases and mobile development and offers developers greater flexibility for building high-performance products. Caffe2 aims to provide an easy way to experiment with deep learning and leverage community contributions of new models and algorithms. It is cross-platform and integrates with Visual Studio, Android Studio, and Xcode for mobile development. Its core C++ libraries provide speed and portability, while its Python and C++ APIs make it easy for you to prototype, train, and deploy your models. It utilizes GPUs when they are available. It is fine-tuned to take full advantage of the NVIDIA GPU deep learning platform. To deliver high performance, Caffe2 uses some of the deep learning SDK libraries by NVIDIA such as cuDNN, cuBLAS, and NCCL. Functionalities Enable automation Image processing Perform object detection Statistical and mathematical operations Supports distributed training enabling quick scaling up or down Applications Facebook is using Caffe2 to help their developers and researchers train large machine learning models and deliver AI on mobile devices. Using Caffe2, they significantly improved the efficiency and quality of machine translation systems. As a result, all machine translation models at Facebook have been transitioned from phrase-based systems to neural models for all languages. OpenCV - Give the power of vision to your apps Source: AndroidPub OpenCV short for Open Source Computer Vision Library is a collection of programming functions for real-time computer vision and machine learning. It has C++, Python, and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android. It also supports the deep learning frameworks TensorFlow and PyTorch. Written natively in C/C++, the library can take advantage of multi-core processing. OpenCV aims to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. The library consists of more than 2500 optimized algorithms including both classic and state-of-the-art computer vision algorithms. Functionalities These algorithms can be used for the following: To detect and recognize faces Identify objects Classify human actions in videos Track camera movements and moving objects Extract 3D models of objects Produce 3D point clouds from stereo cameras Stitch images together to produce a high-resolution image of an entire scene Find similar images from an image database Applications Plickers is an assessment tool, that lets you poll your class for free, without the need for student devices. It uses OpenCV as its graphics and video SDK. You just have to give each student a card called a paper clicker, and use your iPhone/iPad to scan them to do instant checks-for-understanding, exit tickets, and impromptu polls. Also check out FastCV BoofCV TensorFlow Lite and Mobile - An Open Source Machine Learning Framework for Everyone Source: YouTube TensorFlow is an open source software library for building machine learning models. Its flexible architecture allows easy model deployment across a variety of platforms ranging from desktops to mobile and edge devices. Currently, TensorFlow provides two solutions for deploying machine learning models on mobile devices: TensorFlow Mobile and TensorFlow Lite. TensorFlow Lite is an improved version of TensorFlow Mobile, offering better performance and smaller app size. Additionally, it has very few dependencies as compared to TensorFlow Mobile, so it can be built and hosted on simpler, more constrained device scenarios. TensorFlow Lite also supports hardware acceleration with the Android Neural Networks API. But the catch here is that TensorFlow Lite is currently in developer preview and only has coverage to a limited set of operators. So, to develop production-ready mobile TensorFlow apps, it is recommended to use TensorFlow Mobile. Also, TensorFlow Mobile supports customization to add new operators not supported by TensorFlow Mobile by default, which is a requirement for most of the models of different AI apps. Although TensorFlow Lite is in developer preview, its future releases “will greatly simplify the developer experience of targeting a model for small devices”. It is also likely to replace TensorFlow Mobile, or at least overcome its current limitations. Functionalities Speech recognition Image recognition Object localization Gesture recognition Optical character recognition Translation Text classification Voice synthesis Applications The Alibaba tech team is using TensorFlow Lite to implement and optimize speaker recognition on the client side. This addresses many of the common issues of the server-side model, such as poor network connectivity, extended latency, and poor user experience. Google uses TensorFlow for advanced machine learning models including Google Translate and RankBrain. Core ML - Integrate machine learning in your iOS apps Source: AppleToolBox Core ML is a machine learning framework which can be used to integrate machine learning model in your iOS apps. It supports Vision for image analysis, Natural Language for natural language processing, and GameplayKit for evaluating learned decision trees. Core ML is built on top of the following low-level APIs, providing a simple higher level abstraction to these: Accelerate optimizes large-scale mathematical computations and image calculations for high performance. Basic neural network subroutines (BNNS) provides a collection of functions using which you can implement and run neural networks trained with previously obtained data. Metal Performance Shaders is a collection of highly optimized compute and graphic shaders that are designed to integrate easily and efficiently into your Metal app. To train and deploy custom models you can also use the Create ML framework. It is a machine learning framework in Swift, which can be used to train models using native Apple technologies like Swift, Xcode, and Other Apple frameworks. Functionalities Face and face landmark detection Text detection Barcode recognition Image registration Language and script identification Design games with functional and reusable architecture Applications Lumina is a camera designed in Swift for easily integrating Core ML models - as well as image streaming, QR/Barcode detection, and many other features. ML Kit by Google - Seamlessly build machine learning into your apps Source: Google ML Kit is a cross-platform suite of machine learning tools for its Firebase mobile development platform. It comprises of Google's ML technologies, such as the Google Cloud Vision API, TensorFlow Lite, and the Android Neural Networks API together in a single SDK enabling you to apply ML techniques to your apps easily. You can leverage its ready-to-use APIs for common mobile use cases such as recognizing text, detecting faces, identifying landmarks, scanning barcodes, and labeling images. If these APIs don't cover your machine learning problem, you can use your own existing TensorFlow Lite models. You just have to upload your model on Firebase and ML Kit will take care of the hosting and serving. These APIs can run on-device or in the cloud. Its on-device APIs process your data quickly and work even when there’s no network connection. Its cloud-based APIs leverage the power of Google Cloud Platform's machine learning technology to give you an even higher level of accuracy. Functionalities Automate tedious data entry for credit cards, receipts, and business cards, or help organize photos. Extract text from documents, which you can use to increase accessibility or translate documents. Real-time face detection can be used in applications like video chat or games that respond to the player's expressions. Using image labeling you can add capabilities such as content moderation and automatic metadata generation. Applications A popular calorie counter app, Lose It! uses Google ML Kit Text Recognition API to quickly capture nutrition information to ensure it’s easy to record and extremely accurate. PicsArt uses ML Kit custom model APIs to provide TensorFlow–powered 1000+ effects to enable millions of users to create amazing images with their mobile phones. Dialogflow - Give users new ways to interact with your product Source: Medium Dialogflow is a Natural Language Understanding (NLU) platform that makes it easy for developers to design and integrate conversational user interfaces into mobile apps, web applications, devices, and bots. You can integrate it on Alexa, Cortana, Facebook Messenger, and other platforms your users are on. With Dialogflow you can build interfaces, such as chatbots and conversational IVR that enable natural and rich interactions between your users and your business. It provides this human-like interaction with the help of agents. Agents can understand the vast and varied nuances of human language and translate that to standard and structured meaning that your apps and services can understand. It comes in two types: Dialogflow Standard Edition and Dialogflow Enterprise Edition. Dialogflow Enterprise Edition users have access to Google Cloud Support and a service level agreement (SLA) for production deployments. Functionalities Provide customer support One-click integration on 14+ platforms Supports multilingual responses Improve NLU quality by training with negative examples Debug using more insights and diagnostics Applications Domino’s simplified the process of ordering pizza using Dialogflow’s conversational technology. Domino's leveraged large customer service knowledge and Dialogflow's NLU capabilities to build both simple customer interactions and increasingly complex ordering scenarios. Also check out Wit.ai Rasa NLU Microsoft Cognitive Services - Make your apps see, hear, speak, understand and interpret your user needs Source: Neel Bhatt Cognitive Services is a collection of APIs, SDKs, and services to enable developers easily add cognitive features to their applications such as emotion and video detection, facial, speech, and vision recognition, among others. You need not be an expert in data science to make your systems more intelligent and engaging. The pre-built services come with high-quality RESTful intelligent APIs for the following: Vision: Make your apps identify and analyze content within images and videos. Provides capabilities such as image classification, optical character recognition in images, face detection, person identification, and emotion identification. Speech: Integrate speech processing capabilities into your app or services such as text-to-speech, speech-to-text, speaker recognition, and speech translation. Language: Your application or service will understand the meaning of the unstructured text or the intent behind a speaker's utterances. It comes with capabilities such as text sentiment analysis, key phrase extraction, automated and customizable text translation. Knowledge: Create knowledge-rich resources that can be integrated into apps and services. It provides features such as QnA extraction from unstructured text, knowledge base creation from collections of Q&As, and semantic matching for knowledge bases. Search: Using Search API you can find exactly what you are looking for across billions of web pages. It provides features like ad-free, safe, location-aware web search, Bing visual search, custom search engine creation, and many more. Applications To safeguard against fraud, Uber uses the Face API, part of Microsoft Cognitive Services, to help ensure the driver using the app matches the account on file. Cardinal Blue developed an app called PicCollage, a popular mobile app that allows users to combine photos, videos, captions, stickers, and special effects to create unique collages. Also check out AWS machine learning services IBM Watson These were some of the tools that will help you integrate intelligence into your apps. These libraries make it easier to add capabilities like speech recognition, natural language processing, computer vision, and many others, giving users the wow moment of accomplishing something that wasn’t quite possible before. Along with choosing the right AI tool, you must also consider other factors that can affect your app performance. These factors include the accuracy of your machine learning model, which can be affected by bias and variance, using correct datasets for training, seamless user interaction, and resource-optimization, among others. While building any intelligent app it is also important to keep in mind that the AI in your app is solving a problem and it doesn’t exist because it is cool. Thinking from the user’s perspective will allow you to assess the importance of a particular problem. A great AI app will not just help users do something faster, but enable them to do something they couldn’t do before. With the growing popularity and the need to speed up the development of intelligent apps, many companies ranging from huge tech giants to startups are providing AI solutions. In the future we will definitely see more developer tools coming into the market, making AI in apps a norm. 6 most commonly used Java Machine learning libraries 5 ways artificial intelligence is upgrading software engineering Machine Learning as a Service (MLaaS): How Google Cloud Platform, Microsoft Azure, and AWS are democratizing Artificial Intelligence

This post will show you how to choose a robot chassis kit with wheels and motors, a motor controller, and some power for the robot, talking through the trade-offs and things to avoid. This article is an excerpt from a book written by Danny Staple titled Learn Robotics Programming. In this book, you will learn you'll gain experience of building a next-generation collaboration robot Choosing a robot chassis kit The chassis, like the controller, is a fundamental decision when making a robot. Although these can be self-made using 3D printing or toy hacking, the most simple place to start is with a robot chassis kit. These kits contain sets of parts to start off your robot build. A chassis can be changed, but it would mean rebuilding the robot. The internet has plenty of robot chassis kits around. Too many, so how do you choose one? Size Getting the size for a robot right matters too. Take a look at the following photos: Chassis 1 is 11 cm in and just about fits a controller in it, but is too tiny. This will make it hard to build your robot. Squeezing the controller, power, and all the sensors into this small space would need skill and experience beyond the scope of a first robot build. Chassis 2 is Armbot. This large robot is 33 cm by 30 cm, with an arm reach of another 300 mm. It needs eight AA batteries, big motors, and a big controller. These add to the expense and may cause issues around power handling for a new builder. It has lots of space, but issues around weight and rigidity. Armbot is one of my most expensive robots, excluding the cost of the arm! Chassis 3 in the preceding image will fit the Pi, batteries, and sensor, but without being large and bulky. It is around the right dimensions, being between 15-20 cm long and 10-15 cm wide. Those that have split levels might be great for this, but only one or two levels, as three or four will make a robot top heavy and may cause it to topple. This has enough space and is relatively easy to build. Wheel count Some robot chassis kits have elaborate movement methods, legs, tank tracks, and tri-star wheels, to name a few. While these are fun and I encourage experimenting with them, this is not the place to start at. So, I recommend a thoroughly sensible, if basic, wheels on motors version. There are kits with four-wheel drive and six-wheel drive. These can be quite powerful and will require larger motor controllers. They may also chew through batteries, and you are increasing the likelihood of overloading something. This also makes for trickier wiring, as seen in the following: Two-wheel drive is the simplest to wire in. It usually requires a third wheel for balance. This can be a castor wheel, roller ball, or just a Teflon sled for tiny robots. Two wheels are also the easiest to steer, avoiding some friction issues seen with robots using four or more wheels. Two wheels won't have the pulling power of four or six-wheel drive, but they are simple and will work. They are also less expensive: Wheels and motors A kit for a beginner should come with the wheels and the motors. The wheels should have simple non-pneumatic rubber tires. The most obvious style for inexpensive robots is shown in the following photo. There are many kits with these in them: The kit should also come with two motors, one for each wheel, and include the screws or parts to mount them onto the chassis. I recommend DC Gear motors, as the gearing will keep the speed usable while increasing the mechanical pushing power the robot has. Importantly, the motors should have the wires connected, like the first motor in the following photo: It is tricky to solder or attach these wires to the small tags on motors, and poorly attached ones do have a frustrating habit of coming off. The kits you will want to start with have these wires attached, as can be seen in the following: Another point to note is that where the motors are mounted, the kits should have some encoder wheels, and a slot to read them through. The encoder wheels are also known as odometry, tacho, or tachometer wheels. Simplicity You don't want to use a complex or hard-to-assemble kit for your first robot build. I've repeated this throughout with two-wheel drive, two motors with the wires soldered on and steering clear of large robots, or unusual and interesting locomotion systems, not because they are flawed, but because it's better to start simple. There is a limit to this, a robot kit that is a fully built and enclosed robot leaves little room for learning or experimentation and would actually require toy hacking skills to customize. Cost Related to simplicity is cost. Robot chassis kits can be brought from around $15, up to thousands of dollars. Larger and more complex robots tend to be far more costly. Here, I am aiming to keep to the less costly options or at least show where they are possible. Conclusion So, now you can choose a chassis kit, with two wheels and a castor, two motors with wires soldered on them, slots, and encoder wheels. These are not expensive, and widely available on popular internet shopping sites as "Smart Car Chassis," with terms like "2WD": The kit I'm working with looks like the preceding photo when assembled without the Raspberry Pi. Choosing a motor controller The next important part you'll need is a motor controller. Much like the motors, there are a number of trade-offs and considerations before buying one. Integration level Motor controllers can be as simple as motor power control driven from GPIO pins directly, such as the L298. This is the cheapest solution: a generic L298N motor controller can be connected to some of the IO pins on the Raspberry Pi. These are reasonably robust and have been easily available for a long time. They are flexible, but using parts like this will take up more space and need to be wired point to point, adding complexity to the build: Others are as complex as whole IO controller boards, many of which hide their own controller similar to an Arduino, along with motor control chips. Although the cheapest and most flexible ways are the most basic controllers, those with higher integration will reduce size, keep the pin usage count low (handy when you are connecting a lot to the robot), and may simplify your robot build. They often come integrated with a power supply too. Motor controllers can be bought as fully integrated Raspberry Pi hats, boards designed to fit exactly on top of a Raspberry Pi. These tend to have a high level of integration, as discussed before, but may come at the cost of flexibility, especially if you plan to use other accessories. Pin usage When buying a motor controller in Raspberry Pi hat form, pin usage is important. If we intend to use microphones (PCM/I2S), servo motors, and I2c and SPI devices with this robot, having boards that make use of these pins is less than ideal. Simply being plugged into pins doesn't mean they are all used, so only a subset of the pins is usually actually connected on a hat. To get an idea of how pins in different boards interact on the Raspberry Pi, take a look at https://pinout.xyz , which lets you select Raspberry Pi boards and see the pin configuration for them. Controllers that use the I2C or serial bus are great because they make efficient use of pins and that bus can be shared. At the time of writing, PiConZero, the Stepper Motor Hat, and ZeroBorg all use I2C pins. The Full Function Stepper Motor Hat is able to control DC motors and servo motors, is cheap, and is widely available. It also has the pins available straight through on the top and an I2C connector on the side. It's designed to work with other hats and allow more expansion. Size The choice of this depends on the chassis, specifically the size of the motors you have. In simple terms, the larger your chassis, the larger a controller you will need. The power handling capacity of a motor controller is specified in amps. For a robot like the The Robot Kit I'm Using image, around 1 to 1.5 amps per channel is good. The consequence of too low a rating can be disaster, resulting in a robot that barely moves, while the components cook themselves or violently go bang. Too large a controller has consequences for space, weight, and cost: The level of integration can also contribute to size. A tiny board that stacks on a Pi would take up less space than separate boards. Related to size is if the board keeps the camera port on the Raspberry Pi accessible. Soldering As you choose boards for a robot, you will note that some come as kits themselves, requiring parts to be soldered on. If you are already experienced with this, it may be an option. For experienced builders, this becomes a small cost in time depending on the complexity of the soldering. A small header is going to be a very quick and easy job, and a board that comes as a bag of components with a bare board will be a chunk of an evening. Here, I will recommend components that require the least soldering. Connectors Closely related to soldering are the connectors for the motors and batteries. I tend to prefer the screw type connectors. Other types may require matching motors or crimping skills: Conclusion Our robot is space constrained; for this reason, we will be looking at the Raspberry Pi hat type form factor. We are also looking to keep the number of pins it binds to really low. An I2C-based hat will let us do this. The Full Function Stepper Motor Hat (also known as the Full Function Robot Expansion Board) gets us access to all the Pi pins while being a powerful motor controller: It's available in most countries, has space for the ribbon for the camera, and controls servo motors. I recommend the 4tronix PiConZero hat, or assembling a stack of PiBorg hats. These may be harder to source outside of the UK. The reader will need to adapt the code, and consider a tiny shim to retain access to the GPIO pins if using a different board. In this article, we learned about selecting the parts needed to build a basic robot. We looked at the size, wheel, cost, and connectors for the robot chassis and a controller. To learn more about robotics and build your own robot check out this book Learn Robotics Programming. Real-time motion planning for robots made faster and efficient with RapidPlan processor Boston Dynamics adds military-grade mortor (parkour) skills to its popular humanoid Atlas Robot Sex robots, artificial intelligence, and ethics: How desire shapes and is shaped by algorithms

According to Gartner analyst Dave Cappuccio, 80% of enterprises will have shut down their traditional data centers by 2025, compared to just 10% today. The figures are fitting considering the host of problems faced by traditional data centers. The solution to all these problems lies right in front of us- Incorporating Intelligence in traditional data centers. To support this claim, Gartner also predicts that by 2020, more than 30 percent of data centers that fail to implement AI and Machine Learning will cease to be operationally and economically viable. Across the globe, Data science and AI are influencing the design and development of the modern data centers. With the surge in the amount of data everyday, traditional data centers will eventually get slow and result in an inefficient output. Utilizing AI in ingenious ways, data center operators can drive efficiencies up and costs down. A fitting example of this is the tier-two automated control system implemented at Google to cool its data centers autonomously. The system makes all the cooling-plant tweaks on its own, continuously, in real-time- thus saving up to 30% of the plant’s energy annually. Source: DataCenter Knowledge AI has enabled data center operators to add more workloads on the same physical silicon architecture. They can aggregate and analyze data quickly and generate productive outputs, which is specifically beneficial to companies that deal with immense amounts of data like hospitals, genomic systems, airports, and media companies. How is AI facilitating data centers Let's look at some of the ways that Intelligent data centers will serve as a solution to issues faced by traditionally operated data centers. #1 Energy Efficiency The Delta Airlines data center outage in 2016, that was attributed to electrical-system failure over a three day period, cost the airlines around $150 million, grounding about 2,000 flights. This situation could have been easily averted had the data centers used Machine Learning for their workings. As data centers get ever bigger, more complex and increasingly connected to the cloud, artificial intelligence is becoming an essential tool for keeping things from overheating and saving power at the same time. According to the Energy Department’s U.S. Data Center Energy Usage Report, the power usage of data centers in the United States has grown at about a 4 percent rate annually since 2010 and is expected to hit 73 billion kilowatt-hours by 2020, more than 1.8 percent of the country’s total electricity use. Data centers also contribute about 2 percent of the world’s greenhouse gas emissions, AI techniques can do a lot to make processes more efficient, more secure and less expensive. One of the keys to better efficiency is keeping things cool, a necessity in any area of computing. Google and DeepMind (Alphabet Inc.’s AI division)use of AI to directly control its data center has reduced energy use for cooling by about 30 percent. #2 Server optimization Data centers have to maintain physical servers and storage equipment. AI-based predictive analysis can help data centers distribute workloads across the many servers in the firm. Data center loads can become more predictable and more easily manageable. Latest load balancing tools with built-in AI capabilities are able to learn from past data and run load distribution more efficiently. Companies will be able to better track server performance, disk utilization, and network congestions. Optimizing server storage systems, finding possible fault points in the system, improve processing times and reducing risk factors will become faster. These will in turn facilitate maximum possible server optimization. #3 Failure prediction / troubleshooting Unplanned downtime in a datacenter can lead to money loss. Datacenter operators need to quickly identify the root case of the failure, so they can prioritize troubleshooting and get the datacenter up and running before any data loss or business impact take place. Self managing datacenters make use of AI based deep learning (DL) applications to predict failures ahead of time. Using ML based recommendation systems, appropriate fixes can be inferred upon the system in time. Take for instance the HPE artificial intelligence predictive engine that identifies and solves trouble in the data center. Signatures are built to identify other users that might be affected. Rules are then developed to instigate a solution, which can be automated. The AI-machine learning solution, can quickly interject through the entire system and stop others from inheriting the same issue. #4 Intelligent Monitoring and storing of Data Incorporating machine learning, AI can take over the mundane job of monitoring huge amounts of data and make IT professionals more efficient in terms of the quality of tasks they handle. Litbit has developed the first AI-powered, data center operator, Dac. It uses a human-to-machine learning interface that combines existing human employee knowledge with real-time data. Incorporating over 11,000 pieces of innate knowledge, Dac has the potential to hear when a machine is close to failing, feel vibration patterns that are bad for HDD I/O, and spot intruders. Dac is proof of how AI can help monitor networks efficiently. Along with monitoring of data, it is also necessary to be able to store vast amounts of data securely. AI holds the potential to make more intelligent decisions on - storage optimization or tiering. This will help transform storage management by learning IO patterns and data lifecycles, helping storage solutions etc. Mixed views on the future of AI in data centers? Let’s face the truth, the complexity that comes with huge masses of data is often difficult to handle. Humans ar not as scalable as an automated solution to handle data with precision and efficiency. Take Cisco’s M5 Unified Computing or HPE’s InfoSight as examples. They are trying to alleviate the fact that humans are increasingly unable to deal with the complexity of a modern data center. One of the consequences of using automated systems is that there is always a possibility of humans losing their jobs and being replaced by machines at varying degrees depending on the nature of job roles. AI is predicted to open its doors to robots and automated machines that will soon perform repetitive tasks in the datacenters. On the bright side, organizations could allow employees, freed from repetitive and mundane tasks, to invest their time in more productive, and creative aspects of running a data center. In addition to new jobs, the capital involved in setting up and maintaining a data center is huge. Now add AI to the Datacenter and you have to invest double or maybe triple the amount of money to keep everything running smoothly. Managing and storing all of the operational log data for analysis also comes with its own set of issues. The log data that acts as the input to these ML systems becomes a larger data set than the application data itself. Hence firms need a proper plan in place to manage all of this data. Embracing AI in data centers would mean greater financial benefits from the outset while attracting more customers. It would be interesting to see the turnout of tech companies following Google’s footsteps and implementing AI in their data centers. Tech companies should definitely watch this space to take their data center operations up a notch. 5 ways artificial intelligence is upgrading software engineering Intelligent Edge Analytics: 7 ways machine learning is driving edge computing adoption in 2018 15 millions jobs in Britain at stake with Artificial Intelligence robots set to replace humans at workforce