Tech Guides

Perhaps some of the biggest shifts in software in 2017 have come in software systems and infrastructure. Today we’re living in a world where infrastructure is more than just a ‘thing’ that exists to allow businesses to run - it’s now business critical, something that can change and be a driving force for change. It defines the way all software engineers and other tech professionals work - in terms of both the projects they’re working on, the resources at their disposal, and how they go about building things. That means there’s a wealth of opportunity to make a lasting business impact in 2018. So, if you’re working in systems and infrastructure, what is going to be important in 2018? What should you be paying attention to, discussing, exploring? Here’s our list of 5 things that will matter... 1. Serverless architecture 2018 will be the year that serverless becomes a serious trend. It’s already a hot issue in more adventurous organizations, but we’re likely to see things mature and for serverless to become more visible than it ever has been. The key advantages of serverless tell you a lot about how companies are thinking - it saves money, and offers enhanced scalability. It also speeds things up - as microservices become the dominant architectural mode, with APIs defining the way applications are built, you’re no longer simply interested in what your own server is doing - you’re interested in how the services you need are performing. There’s more value in opening up, not closing yourself off and going it alone. 2. Container orchestration Containers have truly caught the imagination of the software world. They’ve helped to alter how we build and deploy software, and have undoubtedly played an important part in driving forward trends like serverless and DevOps. But in 2018, what’s going to be key is getting containers under control. Tools like Kubernetes will make this possible - but ultimately the issue of orchestration presents another tooling question that will force engineering teams to think carefully about what’s right for them and the way they work. Fundamentally, the importance of container orchestration in 2018 underlines the maturity of containerization over the last half a decade. 3. Taking advantage of system automation Your work is never done. There’s always something else that needs to be built, something that needs to be tested or changed. And as efficiency becomes more and  more important with system administration and architect roles under more pressure to deliver, taking full advantage of system automation is going to be essential in 2018. Ansible will be the key tool here - it’s already hugely popular, and it’s popularity is likely to increase over the next 12 months. Ultimately what system automation offers is a way for engineers to make time - by minimizing the work needed to perform basic tasks, they can instead focus on what’s going to make an impact. 4. Shrinking budgets Shrinking budgets are going to have a big impact on just about every facet of an organization’s software systems and architecture. It’s perhaps the fundamental element driving everything else, from serverless to automation. The pressure will be on, then, to focus resources where it really matters. 5. Making DevOps work We’ve spent years talking about DevOps, but it’s still proving a challenge for many organizations when it comes to implementation. In 2018, however, it’s going to be critical. The time for discussing and arguing what it means is over - instead, it’s going to be down to sharp and ambitious engineers, project leads, Scrum Masters - whoever - to bring people in and show them how DevOps can really improve the way software is managed within businesses.

2017 has been an interesting year in web development. Today the role of a web developer is stretched across the stack - to be a great developer you need to be confident and dexterous with data, and have an eye for design and UX. Yes, all those discrete roles will be important in 2017, but being able to join the pieces of the development workflow together - for maximum efficiency - will be hugely valuable in 2018. What web development tools will matter most in 2018? Find out here. But what will really matter in 2018 in web development? Here's our list of the top 5 things you need to be thinking about… 1. Getting over JavaScript fatigue JavaScript fatigue has been the spectre haunting web development for the last couple of years. But it's effects have been very real - it's exhausting keeping up with the rapidly expanding ecosystem of tools. 'Getting over it', then, won't be easy - and don't think for a minute we're just saying it's time to move on and get real. Instead it's about taking the problem seriously and putting in place strategies to better manage tooling options. This article is a great exploration of JavaScript fatigue and it puts the problem in a rather neat way: JS Fatigue happens when people use tools they don't need to solve problems they don't have. What this means in practical terms, then, is that starting with the problem that you want to solve is going to make life much better in 2018. 2. Web components Web components have been a development that's helping to make the work of web developers that little bit easier. Essentially they're reusable 'bits' that don't require any support from a library (like jQuery for example), which makes front end development much more streamlined. Developments like this hint at a shift in the front end developer skillset - something we'll be watching closely throughout 2018. If components are making development 'easier' there will be an onus on developers to prove themselves in a design and UX sphere. 3. Harnessing artificial intelligence AI has truly taken on wider significance in 2017 and has come to the forefront not only of the tech world's imagination but the wider public one too. It's no longer an academic pursuit. It's not baked into almost everything we do. That means web developers are going to have to get au fait with artificial intelligence. Building more personalized UX is going to be top of the list for many organizations in 2018 - pressure will be on web developers to successfully harness artificial intelligence in innovative ways that drive value for their businesses and clients. 4. Progressive web apps and native-like experiences This builds on the previous two points. But ultimately this is about what user expectations are going to look like in 2018. The demand is going to be for something that is not only personalized (see #3), but something which is secure, fast and intuitive for a user, whatever their specific context. Building successful progressive web apps require a really acute sense of how every moving part is impacting how a user interacts with it - from the way data is utilised to how a UI is built. 2018 is the year where being able to solve and understand problems in a truly holistic way will be vital. 5. Improving the development experience 5. Web development is going to get simultaneously harder and easier - if that makes sense. Web components may speed things up, but you're time will no doubt quickly be filled by something else. This means that in 2018 we need to pay close attention to the development experience. If for example, we're being asked to do new things, deliver products in new ways, we need the tools to be able to do that. If agility and efficiency remain key (which they will of course), unlocking smarter ways of working will be as important as the very things we build. Tools like Docker will undoubtedly help here. In fact, it's worth looking closely at the changing toolchain of DevOps - that's been having an impact throughout 2017 and certainly will in 2018 too.

Isn’t it spooky how Facebook can identify faces of your friends before you manually tag them? Have you been startled by Cortana, Siri or Google Assistant when they instantly recognize and act on your voice as you speak to these virtual assistants? Deep Learning is the driving force behind these uncanny yet innovative applications. The next thing that is all set to dive into deep learning is the music industry. Neural networks not only ease production and generation of songs, but also assist in music recommendation, transcription and classification. Here are some ways that deep learning will elevate music and the listening experience itself: Generating melodies with Neural Nets At the most basic level, a deep learning algorithm follows 3 simple steps for music generation: First, the neural net is trained with a sample data set of songs which are labelled. The labelling is done based on the emotions you want your song to convey (happy, sad, funny, etc). For training, the program converts the speech of the data set in text format and then creates vector for each word.  The training data can also be in the form of MIDI format which is a standard protocol for encoding musical notes. After completing the training, the program is fed with a set of emotions as input. It identifies the associated input vectors and compares them to training vectors. The output is a melody or chords that represent the desired emotions. Long short-term memory (LSTM) architectures are also used for music generation. They take structured input of a music notation. These inputs are then encoded as vectors and fed into an LSTM at each timestep. LSTM then predicts the encoding of the next timestep. Fully connected convolutional layers are utilized to increase the music quality and to represent rich features in the frequency domain. Magenta, the popular art and music project of Google has launched Performance RNN, which is an LSTM-based recurrent neural network. It is designed to produce multiple sounds with expressive timing and dynamics. In other words, Performance RNN determines which notes to play, when to play them, and how hard to strike each note. IBM’s Watson Beat uses a neural network to produce complete tracks by understanding music theory, structure, and emotional intent. According to Richard Daskas, a music composer working on the Watson Beat project, “Watson only needs about 20 seconds of musical inspiration to create a song.” Transcripting music with deep learning Deep learning methods can also be used for arranging a piece of music for a different instrument. LSTM networks are a popular choice for music transcription and modelling. These networks are trained using a large dataset of pre-labelled music transcriptions (expressed with ABC notation). These transcriptions are then used to generate new music transcriptions. In fact, transformed audio data can be used to predict the group of notes currently being played. This can be achieved by treating the transcription model as an image classification problem. For this, an image of an audio is used, called as Spectrogram. A spectrogram displays how the spectrum or frequency content changes over time. A Short Time Fourier Transform (STFT) or a constant Q transform is used to create this spectrogram. The spectrogram is then feeded to a Convolutional Neural network(CNN). The CNN estimates current notes from audio data and determines what specific notes are present by analysing 88 output nodes for each of the piano keys. This network is generally trained using large number of examples from MIDI files spanning several different genres of music. Magenta has developed The NSynth dataset, which is a high-quality multi-note dataset for music transcription. It is inspired by image recognition datasets and has a huge collection of annotated musical notes. Make better music recommendations Neural Nets are also used to make intelligent music recommendations and are a step ahead of the traditional Collaborative filtering networks. Using neural networks, the system can analyse the songs saved by the users, and then utilize those songs to make new recommendations.  Neural nets can also be used to analyze songs based on musical qualities such as pitch, chord progression, bass, etc. Using the similarities between songs having the same traits as each other, neural networks can detect and predict new songs.  Thus providing recommendation based on similar lyrical and musical styles. Convolutional neural networks (CNNs) are utilized for making music recommendations. A time-frequency representation of the audio signal is fed into the network as the input. 3 second audio clips are randomly chosen from the audio samples to train the neural network. The CNNs are then used to predict latent factors from music audio by taking the average of the predictions for consecutive clips. The feature extraction layers and pooling layers permits operation on several timescales. Spotify is working on a music recommendation system with a CNN. This recommendation system, when trained on short clips of songs, can create playlists based on the audio content only. Classifying music according to genre Classifying music according to a genre is another achievement of neural nets. At the heart of this application lies the LSTM network. At the very first stage, convolutional layers are used for feature extraction from the spectrograms of the audio file. The sequence of features so obtained is given as input to the LSTM layer. LSTM evaluates dependencies of the song across both short time period as well as long term structure. After the LSTM, the input is fed into a fully connected, time-distributed layer which essentially gives us a sequence of vectors. These vectors are then used to output the network's evaluation of the genre of the song at the particular point of time. Deepsound uses GTZAN dataset and an LSTM network to create a model for music genre recognition.  On comparing the mean output distribution with the correct genre, the model gives almost 67% of accuracy. For musical pattern extraction, MFCC feature dataset is used for audio analysis. First, the audio signal is extracted in the MFCC format. Next, the input song is modified into an MFCC map. This Map is then split to feed it as the input of the CNN. Supervised learning is used for automatically obtaining musical pattern extractors, considering the song label is provided. The extractors so acquired, are used for restoring high-order pattern-related features. After high-order classification, the result is combined and undergoes a voting process to produce the song-level label. Scientists from Queen Mary University of London trained a neural net with over 6000 songs in a ballad, hip-hop, and dance to develop a neural network that achieves almost 75% accuracy in song classification. The road ahead Neural networks have advanced the state of music to whole new level where one would no longer require physical instruments or vocals to compose music. The future would see more complex models and data representations to understand the underlying melodic structure. This would help models create compelling artistic content on their own. Combination of music with technology would also foster a collaborative community consisting of artists, coders and deep learning researchers, leading to a tech-driven, yet artistic future.  

Things change quickly in application development. Over the past few years we've seen it merge with other fields. With the web become more app-like, DevOps turning everyone into a part-time sysadmin (well, sort of), and the full-stack trend shifting expectations about the modern programmer skill set, the field has become incredibly fluid and open. That means 2018 will present a wealth of challenges of application developers - but of course there will also be plenty of opportunities for the curious and enterprising… But what's going to be most important in 2018? What's really going to matter? Take a look below at our list of 5 things that will matter in application development in 2018. 1. Versatile languages that can be used on both client and server Versatility is key to be a successful programmer today. That doesn't mean the age of specialists is over, but rather you need to be a specialist in everything. And when versatility is important to your skillset, it also becomes important for the languages we use. It's for that reason that we're starting to see the increasing popularity of languages like Kotlin and Go. It's why Python continues to be popular - it's just so versatile. This is important when you're thinking about how to invest your learning time. Of course everyone is different, but learning languages that can help you do multiple things and solve different problems can be hugely valuable. Investing your energy in the most versatile languages will be well worth your time in 2018. 2. The new six month Java release cycle This will be essential for Java programmers in 2018. Starting with the release of Java 9 early in 2018, the new cycle will kick in. This might mean there's a little more for developers to pay attention to, but it should make life easier, as Oracle will be able to update and add new features to the language with greater effectiveness than ever before. From a more symbolic point of view, this move hints a lot at the deepening of open source culture in 2018, with Oracle aiming to satisfy developers working on smaller systems, keen to constantly innovate, as much as its established enterprise clients. 3. Developing usable and useful conversational UI Conversational UI has been a 'thing' for some time now, but it hasn't quite captured the imagination of users. This is likely because it simply hasn't proved that useful yet - like 3D film it feels like too much of a gimmick, maybe even too much of a hassle. It's crucial - if only to satisfy the hype - that developers finally find a way to make conversational UI work. To really make it work we're ultimately going to need to join the dots between exceptionally good artificial intelligence and a brilliant user experience - making algorithms that 'understand' user needs, and can adapt to what people want. 4. Microservices Microservices certainly won't be new in 2018, but they are going to play a huge part in how software is built in 2018. Put simply, if they're not important to you yet, they will be. We're going to start to see more organizations moving away from monolithic architectures, looking to engineering teams to produce software in ways that is much more dynamic and much more agile. Yes, these conversations have been happening for a number of years; but like everything when it comes to tech, change happens at different speeds. It's only now as technologies mature, developer skillsets change, and management focus shifts that broader changes take place. 5. Taking advantage of virtual and augmented reality Augmented Reality (AR) and Virtual Reality (VR) have been huge innovations within fields like game development. But in 2018, we're going to see both expand beyond gaming and into other fields. It's already happening in many areas, such as healthcare, and for engineers and product developers/managers, it's going to be an interesting 12 months to see how the market changes.

It’s easy to get drawn into cliches around technology that things move quickly. In actual fact, in the tech world what passes for ‘progress’ takes place at different rates in different places. Yes, Silicon Valley companies may be trying to push boundaries, and teenagers are able to hack into even the most secure systems on the planet, but there are still plenty of companies for where data science is a folder of excel spreadsheets, where a cloud infrastructure is a Google Drive system. That makes it hard to say exactly what will matter  in 2018 in tech. But we’re going to try anyway. So, whatever you do, and whatever you’re going to be working on in 2018, here’s 5 things that will definitely matter in 2018… 1. More Empathy It sounds obvious but technology isn’t just a human invention; it’s also a human activity. It’s something people build - together - and something people use. This is a fact that is all too often forgotten in the chaotic reality of modern work. However, if we can all have more empathy for both those around us - other developers, our colleagues - as well as for users, we’ll not only build better software, we’ll also be much happier while doing so. 2. Developing standards and best practices Open source has been a true revolution, impacting not only the software that’s used today, but also the way we think about it. But with open source, the ground beneath us has opened up. And, if we’re going to deliver on point #1, we’re going to need to start thinking about setting standards for how we build software alongside each other. Living in the wild-west might be fun for a while but it will quickly grow old and difficult to produce anything of lasting value. 3. Ignoring the hype and focusing on what matters to you Open source has made things chaotic - there’s simply a lot of software out there. Even within a very specific area, there’s a range of potential solutions to a single problem. It’s important to remember that the most hyped or popular tool isn’t necessarily going to be the one for you. Focus on the problem you’re trying to solve and what solution is going to be most effective. 4. Dedicating time to personal development While we shouldn’t be distracted by the fluctuations of the tech landscape, it’s important to nevertheless take steps to try and tame it. If it’s going to work for you, you still need to do some work. Identify what tools are going to be key in your job next year, or what skills you need to move your career forward and then create a plan for when you’re going to actively invest time in learning those tools and skills. 5. Security This may be obvious, but 2018 needs to be the year that everyone gets serious about security. High-profile hacks are only leading to more and more confusion around digital media. And on a slightly different but related note, with governments spending more time interested in the internet habits of its citizens and ensuring the internet is ‘safe’ it’s going to take a lot of commitment on the part of the tech world to challenge lazy established thinking and to make sure individuals are secure - whether that’s from criminals or otherwise...

While you were away attending NIPS 2017 this week, a lot has been happening around you in the data science and machine learning space. No worries! Here is a brief roundup of the best of what we published on the Datahub this week for your weekend reading. [box type="shadow" align="" class="" width=""]If you would like to share your insights and takeaways from NIPS with our readers on the DataHub, write to us at contributors@packtpub.com.[/box] NIPS 2017 Highlights - Part 1 3 great ways to leverage Structures for Machine Learning problems by Lise Getoor at NIPS 2017 Top Research papers showcased at NIPS 2017 – Part 2 Top Research papers showcased at NIPS 2017 – Part 1 Watch out for more in this area in the coming weeks. Expert in Focus Kate Crawford, Principal Researcher at Microsoft Research and a Distinguished Research Professor at New York University, on 20 lessons on bias in machine learning systems, Keynote at NIPS 2017 3 Things that happened this week in Data Science News PyTorch 0.3.0 releases, ending stochastic functions DeepVariant: Using Artificial Intelligence in Human Genome Sequencing Amazon unveils Sagemaker: An end-to-end machine learning service For a more comprehensive roundup of top news stories this week, check out our weekly news roundup post.   Get hands-on with these Tutorials Understanding Streaming Applications in Spark SQL Implementing Linear Regression Analysis with R What are Slowly Changing Dimensions (SCD) and why you need them in your Data Warehouse? Do you agree with these Insights & Opinions? 4 popular algorithms for Distance-based outlier detection Stitch Fix: Full Stack Data Science and other winning strategies Admiring the many faces of Facial Recognition with Deep Learning One Shot Learning: Solution to your low data problem

The data science tech market is buzzing with new and interesting Machine Learning libraries and tools almost everyday. In an increasingly growing market, it becomes difficult to choose the right tool or set of tools. More importantly, Artificial Intelligence and Deep Learning based projects require a different approach than traditional programming which makes things tricky to zero-in on one library or a framework. The choice of a framework is largely based upon the type of problem, one is expecting to solve. But there are other considerations too. Speed is one such factor that more or less would always play an important role in decision making. Other reasons could be how open-ended it is, architecture, functions, complexity of use, support for algorithms, and so on. Here, we present to you six Java libraries for your next Deep Learning and Artificial Intelligence project you shouldn’t miss if you are a Java loyalist or simply a web developer who wants to enter the world of deep learning. DeepLearning4j (DL4J) One of the first, commercial grade, and most popular deep learning frameworks developed in Java. It also supports other JVM languages (Java, Clojure, Scala). What’s interesting about the DL4J, is that it comes with an in-built GPU support for the training process. It also supports Hadoop YARN for distributed application management. It is popular for solving problems related to image recognition, fraud detection and NLP. MALLET Mallet (Machine Learning for Language Toolkit) is an open source Java Machine Learning toolkit. It supports NLP, clustering, modelling, and classification. The most important capability of Mallet is its support for a wide variety of algorithms such as Naive Bayes and Decision Trees. Another useful feature it has is topic modelling toolkit. Topic models are useful when analyzing large collections of unlabelled texts.   Massive Online Analysis (MOA) MOA is an open source data streaming and mining framework for real time analytics. It has a strong and growing community and is similar and related to Weka. It also has the ability to deal with massive data streams. Encog This framework supports a wide array of algorithms and neural networks such as Artificial Neural Network, Bayesian Network, Genetic Programming and algorithms. Neuroph Neuroph as the name suggests offers great simplicity when working on neural networks. The main USP of Neuroph is its incredibly useful GUI (Graphical User Interface) tool that helps in creating and training neural networks. Neuroph is a good choice of framework when you have a quick project on hand and you don’t want to spend hours learning the theory. Neuroph helps you quickly set up and running in putting neural networks to work for your project. Java Machine Learning Library The Java Machine Learning Library offers a great set of reference implementation of algorithms that you can’t miss for your next Machine Learning project. Some of the key highlights are support vector machines and clustering algorithms. These are a few key frameworks and tools you might want to consider when working on your next research work. The Java ML library ecosystem is vast with many tools and libraries to support, and we just touched the tip of that iceberg in this article. One particular tool that deserve an honourable mention is Environment for Developing KDD-Applications Supported by Index-Structure (ELKI). It is designed particularly with researchers and research students kept in mind. The main focus of ELKI is its broad coverage of data algorithms which makes it a natural fit for research work. What’s really important while choosing any of the above or tools outside of the list is a good understanding of the requirements and the problems you intend to solve. To reiterate, some of the key considerations to bear in mind before zeroing in on a tool would be - support for algorithms, implementation of neural networks, dataset size (small, medium, large), and speed.

Facial recognition technology is not new. In fact, it has been around for more than a decade. However, with the recent rise in artificial intelligence and deep learning, facial technology has achieved new heights. In addition to facial detection, modern day facial recognition technology also recognizes faces with high accuracy and in unfavorable conditions. It can also recognize expressions and analyze faces to generate insights about an individual. Deep learning has enabled a power-packed face recognition system, all geared up to achieve widespread adoption. How has deep learning modernised facial recognition Traditional facial recognition algorithms would recognize images and people using distinct facial features (placement of eye, eye color, nose shape etc.) However, they failed in correct identification in cases of different lighting or slight change in the appearance ( beard growth, aging, or pose). In order to develop facial recognition techniques for a dynamic and ever-changing face, deep learning is proving to be a game changer. Deep Neural nets go beyond the approach of manual extraction. These AI based Neural Networks rely on image pixels to analyze features of a particular face. So they scan faces irrespective of the lighting, ageing, pose, or emotions. Deep learning algorithms remember each time they recognize or fail to recognize a problem. Thus, avoiding repeat mistakes and getting better at each attempt. Deep learning algorithms can also be helpful in converting 2D images to 3D. Facial recognition in practice: Facial Recognition Technology in Multimedia Deep learning enabled facial recognition technologies can be used to track audience reaction and measure different levels of emotions. Essentially it can predict how a member of the audience will react to the remaining film. Not only this, it also helps determine what percentage of users will be interested in a particular movie genre. For example, Microsoft’s Azure Emotion,  an emotion API detects emotions by analysing the facial expressions on an image or video content over time. Caltech and Disney have collaborated to develop a neural network which can track facial expressions. Their deep learning based Factorised Variational Autoencoders (FVAEs) analyze facial expressions of audience for about 10 minutes and then predict how their reaction will be for the rest of the film. These techniques help in estimating whether the viewers are giving the expected reactions at the right place. For example, the viewer is not expected to yawn on a comical scene. With this, Disney can also predict the earning potential of a particular movie. It can generate insights that may help producers create compelling movie trailers to maximize the number of footfalls. Smart TVs are also equipped with sophisticated cameras and deep learning algos for facial recognition ability. They can recognize the face of the person watching and automatically show channels and web applications programmed as their favorites. The British broadcasting corporation uses the facial recognition technology, built by CrowdEmotion. By tracking faces of almost 4,500 audience members watching show trailers, they gauge exact customer emotions about a particular programme. This in turn helps them generate insights to showcase successful commercials. Biometrics in Smartphones A large number of smartphones nowadays are instilled with biometric capabilities. Facial recognition in smartphones are not only used as a means of unlocking and authorizing, but also for making secure transactions and payments. In present times, there has been a rise in chips with built-in deep learning ability. These chips are embedded into smartphones. By having a neural net embedded inside the device, crucial face biometric data never leaves the device or sent to the cloud. This in turn improves privacy and reduces latency. Some of the real-world examples include Intel’s Nervana Neural Network Processor, Google’s TPU, Microsoft’s FPGA, and Nvidia’s Tesla V100. Deep learning models, embedded in a smartphone, can construct a mathematical model of the face which is then stored in the database. Using this mathematical face model, smartphones can easily recognize users even as their face ages or when it is obstructed by wearable accessories. Apple has recently launched the iPhone X facial recognition system termed as FaceID. It maps thousands of points on a user’s face using a projector and an infrared camera (which can operate under varied lighting conditions). This map is then passed to a bionic chip embedded in the smart phone. The chip has a neural network which constructs a mathematical model of the user’s face, used for biometric face verification and recognition. Windows Hello is also a facial recognition technology to unlock Windows smart devices equipped with infrared cameras. Qualcomm, a mobile technology organization, is working on a new depth-perception technology. It will include an image signal processor and high-resolution 3D depth-sensing cameras for facial recognition. Face recognition for Travel Facial recognition technologies can smoothen the departure process for a customer by eliminating the need for a boarding pass. A traveller is scanned by cameras installed at various check points, so they don’t have to produce a boarding pass at every step. Emirates is collaborating with Dubai Customs, Police and Airports to use a facial recognition technology solution integrated with the UAE Wallet app. The project is known as Together Initiative, it allows travellers to register and store their biometric facial data at several kiosks placed at the check-in area. This facility helps passengers to avoid presenting their physical documents at every touchpoint. Face recognition can also be used for determining illegal immigration. The technology compares the photos of passengers taken immediately before boarding, with the photos provided in their visa application. Biometric Exit, is an initiative by US government, which uses facial recognition to identify individuals leaving the country. Facial recognition technology can also be used at train stations to reduce the waiting time for  buying a train ticket or going through other security barriers. Bristol Robotics Laboratory has developed a software which uses infrared cameras to identify passengers as they walk onto the train platform. They do not need to carry tickets. Retail and shopping In the area of retail, smart facial recognition technologies can be helpful in fast checkout by keeping a track of each customer as they shop across a store. This smart technology, can also use machine learning and analytics to find trends in the shopper’s purchasing behavior over time and devise personalized recommendations. Facial video analytics and deep learning algorithms can also identify loyal and VIP shoppers from the moving crowd, giving them a privileged VIP experience. Thus, enabling them with more reasons to come back and make repeat purchases. Facial biometrics can also accumulate rich statistics about demographics(age, gender, shopping history) of an individual. Analyzing these statistics can generate insights, which helps organizations develop their products and marketing strategies. FindFace is one such platform that uses sophisticated deep learning technologies to generate meaningful data about the shopper. Its e-facial recognition system can verify faces with almost 99% accuracy. It can also help route the shopper data to a salesperson’s notice for personalized assistance. Facial recognition technology can also be used to make secure payment transactions simply by analysing a person’s face. AliBaba has set up a Smile to Pay face recognition system in KFC's. This system allows customers to make secure payments by merely scanning their face. Facial recognition has emerged as a hot topic of interest and is poised to grow. On the flip side, organizations deploying such technology should incorporate privacy policies as a standard measure. Data collected from such facial recognition software can also be used wrongly for targeting customers with ads, or for other illegal purposes. They should implement a methodical and systematic approach for using facial recognition for the benefit of their customers. This will not only help businesses generate a new source of revenue, but will also usher in a new era of judicial automation.  

Last week, a company in San Francisco was popping bottles of champagne for their achievements. And trust me, they’re not at all small. Not even a couple of weeks gone by, since it was listed on the stock market and it has soared to over 50%. Stitch Fix is an apparel company run by co-founder and CEO, Katrina Lake. In just a span of 6 years, she’s been able to build the company with an annual revenue of a whopping $977 odd million. The company has been disrupting traditional retail and aims to bridge the gap of personalised shopping, that the former can’t accomplish. Stitch Fix is more of a personalized stylist, rather than a traditional apparel company. It works in 3 basic steps: Filling a Style Profile: Clients are prompted to fill out a style profile, where they share their style, price and size preferences. Setting a Delivery Date: The clients set a delivery date as per their availability. Stitch Fix mixes and matches various clothes from their warehouses and comes up with the top 5 clothes that they feel would best suit the clients, based on the initial style profile, as well as years of experience in styling. Keep or Send Back: The clothes reach the customer on the selected date and the customer can try on the clothes, keep whatever they like or send back what they don’t. The aim of Stitch Fix is to bring a personal touch to clothes shopping. According to Lake, “There are millions and millions of products out there. You can look at eBay and Amazon. You can look at every product on the planet, but trying to figure out which one is best for you is really the challenge” and that’s the tear Stitch Fix aims to sew up. In an interview with eMarketer, Julie Bornstein, COO of Stitch Fix said “Over a third of our customers now spend more than half of their apparel wallet share with Stitch Fix. They are replacing their former shopping habits with our service.” So what makes Stitch Fix stand out among its competitors? How do they do it? You see, Stitch Fix is not just any apparel company. It has created the perfect formula by blending human expertise with just the right amount of Data Science to enable it to serve its customers. When we’re talking about the kind of Data Science that Stitch Fix does, we’re talking about a relatively new and exciting term that’s on the rise - Full Stack Data Science. Hello Full Stack Data Science! For those of you who’ve heard of this before, cheers! I hope you’ve had the opportunity to experience its benefits. For those of you who haven’t heard of the term, Full Stack Data Science basically means a single data scientist does their own work, which is mining data, cleans it, writes an algorithm to model it and then visualizes the results, while also stepping into the shoes of an engineer, implementing the model, as well as a Project Manager, tracking the entire process and ensuring it’s on track. Now while this might sound like a lot for one person to do, it’s quite possible and practical. It’s practical because of the fact that when these roles are performed by different individuals, they induce a lot of latency into the project. Moreover, a synchronization of priorities of each individual is close to impossible, thus creating differences within the team. The Data (Science) team at Stitch Fix is broadly categorized based on what area they work on: Because most of the team focuses on full stack, there are over 80 Data Scientists on board. That’s a lot of smart people in one company! On a serious note, although unique, this kind of team structure has been doing well for them, mainly because it gives each one the freedom to work independently. Tech Treasure Trove When you open up Stitch Fix’s tech toolbox, you won’t find Aladdin’s lamp glowing before you. Their magic lies in having a simple tech stack that works wonders when implemented the right way. They work with Ruby on Rails and Bootstrap for their web applications that are hosted on Heroku. Their data platform relies on a robust Postgres implementation. Among programming languages, we found Python, Go, Java and JavaScript also being used. For an ML Framework, we’re pretty sure they’re playing with TensorFlow. But just working with these tools isn’t enough to get to the level they’re at. There’s something more under the hood. And believe it or not, it’s not some gigantic artificial intelligent system running on a zillion cores! Rather, it’s all about the smaller, simpler things in life. For example, if you have 3 different kinds of data and you need to find a relationship between them, instead of bringing in the big guns (read deep learning frameworks), a simple tensor decomposition using word vectors would do the deed quite well. Advantages galore: Food for the algorithms One of the main advantages Stitch Fix has, is that they have almost 5 years’ worth client data. This data is obtained from clients in several ways like through a Client Profile, After-Delivery Feedback, Pinterest photos, etc. All this data is put through algorithms that learn more about the likes and dislikes of clients. Some interesting algorithms that feed on this sumptuous data are on the likes of collaborative filtering recommenders to group clients based on their likes, mixed-effects modeling to learn about a client’s interests over time, neural networks to derive vector descriptions of the Pinterest images and to compare them with in-house designs, NLP to process customer feedback, Markov chain models to predict demand, among several others. A human Touch: When science meets art While the machines do all the calculations and come up with recommendations on what designs customers would appreciate, they still lack the human touch involved. Stitch Fix employs over 3000 stylists. Each client is assigned a stylist who knows the entire preference of the client at the glance of a custom-built interface. The stylist finalizes the selections from the inventory list also adding in a personal note that describes how the client can accessorize the purchased items for a particular occasion and how they can pair them with any other piece of clothing in their closet. This truly advocates “Humans are much better with the machines, and the machines are much better with the humans”. Cool, ain't it? Data Platform Apart from the Heroku platform, Stitch Fix seems to have internal SaaS platforms where the data scientists effectively carry out analysis, write algorithms and put them into production. The platforms exhibit properties like data distribution, parallelization, auto-scaling, failover, etc. This lets the data scientists focus on the science aspect while still enjoying the benefits of a scalable system. The good, the bad and the ugly: Microservices, Monoliths and Scalability Scalability is one of the most important aspects a new company needs to take into account before taking the plunge. Using a microservice architecture helps with this, by allowing small independent services/mini applications to run on their own. Stitch Fix uses this architecture to improve scalability although, their database is a monolith. They now are breaking the monolith database into microservices. This is a takeaway for all entrepreneurs just starting out with their app. Data Driven Applications Data-driven applications ensure that the right solutions are built for customers. If you’re a customer-centric organisation, there’s something you can learn from Stitch Fix. Data-Driven Apps seamlessly combine the operational and analytic capabilities of the organisation, thus breaking down the traditional silos. TDD + CD = DevOps Simplified Both Test Driven Development and Continuous Delivery go hand in hand and it’s always better to imbibe this culture right from the very start. In the end, it’s really great to see such creative and technologically driven start-ups succeed and sail to the top. If you’re on the journey to building that dream startup of yours and you need resources for your team, here’s a few books you’ll want to pick up to get started with: Hands-On Data Science and Python Machine Learning by Frank Kane Data Science Algorithms in a Week by Dávid Natingga Continuous Delivery and DevOps : A Quickstart Guide - Second Edition by Paul Swartout Practical DevOps by Joakim Verona    

In 2003, when The New York Times announced that the human genome project was successfully complete two years ahead of its schedule (leave aside the conspiracy theory that the genome was never ‘completely’ sequenced), it heralded a new dawn in the history of modern science. The challenge thereafter was to make sense out of the staggering data that became available. The High Throughput Sequencing technology came to revolutionize the processing of genomic data in a way, but had its own limitations (such as the high rate of erroneous base calls produced). Google has now launched an artificial intelligence tool, DeepVariant, to analyze the huge data resulting from the sequencing of the genome. It took two years of research for Google to build DeepVariant. It's a combined effort from Google’s Brain team, a group that focuses on developing and applying AI techniques, and Verily Life Sciences, another Alphabet subsidiary that is focused on the life sciences. How the DeepVariant makes sense of your genome? DeepVariant uses the latest deep learning techniques to turn high-throughput sequencing readouts into a picture of a full genome. It automatically identifies small insertion and deletion mutations and single-base-pair mutations in sequencing data. Ever since the high-throughput sequencing made genome sequencing more accessible, the data produced has at best offered error-prone snapshot of a full genome. Researchers have found it challenging to distinguish small mutations from random errors generated during the sequencing process, especially in repetitive portions of a genome. A number of tools and methods have come out to interpret these readouts (both public and private funded), but all of them have used simpler statistical and machine-learning approaches to identify mutations. Google claims DeepVariant offers significantly greater accuracy than all previous classical methods. DeepVariant transforms the task of variant calling (the process to identify variants from sequence data) into an image classification problem well-suited to Google's existing technology and expertise. Google's team collected millions of high-throughput reads and fully sequenced genomes from the Genome in a Bottle (GIAB) project, and fed the data to a deep-learning system that interpreted sequenced data with a high level of accuracy. “Using multiple replicates of GIAB reference genomes, we produced tens of millions of training examples in the form of multi-channel tensors encoding the HTS instrument data, and then trained a TensorFlow-based image classification model to identify the true genome sequence from the experimental data produced by the instruments.” Google said. The result has been remarkable. Within a year, DeepVariant went on to win first place in the PrecisionFDA Truth Challenge, outperforming all state-of-the-art methods in accurate genetic sequencing. “Since then, we've further reduced the error rate by more than 50%,” the team claims. Image Source: research.googleblog.com “The success of DeepVariant is important because it demonstrates that in genomics, deep learning can be used to automatically train systems that perform better than complicated hand-engineered systems,” says Brendan Frey, CEO of Deep Genomics, one of the several companies using AI on genomics for potential drugs. DeepVariant is ‘open’ for all The best thing about DeepVariant is that it has been launched as an open source software. This will encourage enthusiastic researchers for collaboration and possibly accelerate its adoption to solve real world problems. “To further this goal, we partnered with Google Cloud Platform (GCP) to deploy DeepVariant workflows on GCP, available today, in configurations optimized for low-cost and fast turnarounds using scalable GCP technologies like the Pipelines API,” Google said. This paired set of releases could facilitate a scalable, cloud-based solution to handle even the largest genomics datasets. The road ahead: What DeepVariant means for future According to Google, DeepVariant is the first of “what we hope will be many contributions that leverage Google's computing infrastructure and Machine learning expertise” to better understand the genome and provide deep learning-based genomics tools to the community. This is, in fact, all part of a “broader goal” to apply Google technologies to healthcare and other scientific applications. As AI starts to propel different branches of medicine take big leaps forward in coming years, there is a whole lot of medical data to mine and drive insights from. But with genomic medicine, the scale is huge. We are talking about an unprecedented set of data that is equally complex. “For the first time in history, our ability to measure our biology, and even to act on it, has far surpassed our ability to understand it,” says Frey. “The only technology we have for interpreting and acting on these vast amounts of data is AI. That’s going to completely change the future of medicine.” These are exciting times for medical research. In 1990, when the human genome project was initiated, it met with a lot of skepticism from many people, including scientists and non-scientists alike. But today, we have completely worked out each A, T, C, and G that makes up the DNA of all 23 pairs of human chromosomes. After high-throughput sequencing made the genomic data accessible, Google’s DeepVariant could just be the next big thing to take genetic sequencing to a whole new level.

DevOps is a much more realistic and efficient way to organize the creation and delivery of technology solutions to customers. But like practically everything else in the world of technology, DevOps has become a buzzword and is often thrown around willy-nilly. Let's cut through the fog and highlight concrete steps that will help an organization implement DevOps. DevOps is about bringing your development and operations teams together This might seem like a no-brainer, but DevOps is often explained in terms of tools rather than techniques or philosophical paradigms. At its core, DevOps is about uniting developers and operators, getting these groups to effectively communicate with each other, and then using this new communication to streamline various processes. This could include a physical change to the layout of an organization's workspace. It's incredible the changes that can happen just by changing the seating arrangements in an office. If you have a very large organization, development and operations might be in separate buildings, separate campuses, or even separate cities. While the efficacy of web-based communication has increased dramatically over the last few years, there is still no replacement for face-to-face daily human interactions. Putting developers and operators in the same physical space is going to increase the rate of adoption and efficacy of various DevOps tools and techniques. DevOps is all about updates Updates can be aimed at expanding functionality or simply fixing or streamlining existing processes. Updates present a couple of problems to developers and operators. First, we need to keep everybody working on the same codebase. This can be achieved by using a variety of continuous integration tools. The goal of continuous integration is to make sure that changes and updates to the codebase are implemented as close to continuously as possible. This helps avoid merging problems that can result from multiple developers working on the same codebase at the same time. Second, these updates need to be integrated into the final product. For this task, DevOps applies the concept of continuous deployment. This is essentially the same thing as continuous integration, but has to do with deploying changes to the codebase as opposed to integrating changes to the codebase. In terms of importance to the DevOps process, continues integration and deployment are equally important. Moving updates from a developer's workspace to the codebase to production should be seamless, smooth, and continuous. Implementing a microservices structure is imperative for an effective DevOps approach Microservices are an extension of the service-based structure. Basically a service structure calls for modulation of a solution’s codebase into units based on functionality. Microservices takes this a step further by implementing what consists of a service-based structure in which each service performs a single task. While a service-based or microservice structure is not required for implementation of DevOps, I have no idea why you wouldn’t because microservices lend themselves so well with DevOps. One way to think of a microservice structure is by imagining an ant hill in which all of the worker ants are microservices. Each ant has a specific set of abilities and is given a task from the queen. The ant then autonomously performs this task, usually gathering food, along with all of its ant friends. Remove a single ant from the pile, nothing really happens. Replace an old ant with a new ant, nothing really happens. The metaphor isn’t perfect, but it strikes at the heart of why microservices are valuable in a DevOps framework. If we need to be continuously integrating and deploying, shouldn’t we try to impact the codebase as directly as we can? When microservices are in use, changes can be made at an extremely granular level. This allows for continuous integration and deployment to really shine. Monitor your DevOps solutions In order to continuously deploy, applications need to also be continuously monitored. This allows for problems to be identified quickly. When problems are quickly identified, it tends to reduce the total effort required to fix the problems. Your application should obviously be monitored from the perspective of whether or not it is working as it currently should, but users need to be able to give feedback on the application’s functionality. When reasonable, this feedback can then be integrated into the application somehow. Monitoring user feedback tends to fall by the wayside when discussing DevOps. It shouldn’t. The whole point of the DevOps process is to improve the user experience. If you’re not getting feedback from users in a timely manner, it's kind of impossible to improve their experience. Keep it loose and experiment Part of the beauty of DevOps is that it can allow for more experimentation than other development frameworks. When microservices and continuous integration and deployment are being fully utilized, it's fairly easy to incorporate experimental changes to applications. If an experiment fails, or doesn’t do exactly what was expected, it can be removed just as easily. Basically, remember why DevOps is being used and really try to get the most out of it. DevOps can be complicated. Boiling anything down to five steps can be difficult but if you act on these five fundamental principles you will be well on your way to putting DevOps into practice. And while its fun to talk about what DevOps is and isn't, ultimately that's the whole point - to actually uncover a better way to work with others.

The fact that machines are successful in replicating human intelligence is mind-boggling. However, this is only possible if machines are fed with correct mix of algorithms, huge collection of data, and most importantly the training given to it, which in turn leads to faster prediction or recognition of objects within the images. On the other hand, when you train humans to recognize a car for example, you simply have to show them a live car or an image. The next time they see any vehicle, it would be easy for them to distinguish a car among-st other vehicles. In a similarly way, can machines learn with single training example like humans do? Computers or machines lack a key part that distinguishes them from humans, and that is, ‘Memory’. Machines cannot remember; hence it requires millions of data to be fed in order to understand the object detection, be it from any angle. In order to reduce this supplement of training data and enabling machines to learn with less data at hand, One shot learning is brought to its assistance. What is one shot learning and how is it different from other learning? Deep Neural network models outperform various tasks such as image recognition, speech recognition and so on. However, such tasks are possible only due to extensive, incremental training on large data sets. In cases when there is a smaller dataset or fewer training examples, a traditional model is trained on the data that is available. During this process, it relearns new parameters and incorporates new information, and completely forgets the one previously learned. This leads to poor training or catastrophic inference. One shot learning proves to be a solution here, as it is capable of learning with one, or a minimal number of training samples, without forgetting. The reason for this is, they posses meta-learning; a capability often seen in neural network that has memory. How One shot learning works? One shot learning strengthens the ability of the deep learning models without the need of a huge dataset to train on. Implementation of One shot learning can be seen in a Memory Augmented Neural Network (MANN) model. A MANN has two parts, a controller and an external memory model. The controller is either a feed forward neural network or an LSTM (Long Short Term Memory) network, which interacts with the external memory module using number of read/write heads. These heads fetch or place representations to and fro the memory. LSTMs are proficient in long term storage through slow updates of weights and short term storage via the external memory module. They are trained to meta-learn; i.e. it can rapidly learn unseen functions with fewer data samples.Thus, MANNs are said to be capable of metalearning. The MANN model is later trained on datasets that include different classes with very few samples. For instance, the Omniglot dataset, a collection of handwritten samples of different languages, with very few samples of each language. After continuously training the model with thousands of iterations by using few samples, the model was able to recognize never-seen-before image samples, taken from a disjoint sample of the Omniglot dataset. This proves that MANN models are able to outperform various object categorization tasks with minimal data samples.   Similarly, One shot learning can also be achieved using Neural Turing Machine and Active One shot learning. Therefore, learning with a single attempt/one shot actually involves meta-learning. This means, the model gradually learns useful representations from the raw data using certain algorithms, for instance, the gradient descent algorithm. Using these learnings as a base knowledge, the model can rapidly cohere never seen before information with a single or one-shot appearance via an external memory module. Use cases of One shot learning Image Recognition: Image representations are learnt using supervised metric based approach. For instance,  siamese neural network, an identical sister network, discriminates between the class-identity of an image pair. Features of this network are reused for one-shot learning without the need for retraining. Object Recognition within images: One shot learning allows neural network models to recognize known objects and its category within an image. For this, the model learns to recognize the object with a few set of training samples. Later it compares the probability of the object to be present within the image provided. Such a model trained on one shot can recognize objects in an image despite the clutter, viewpoint, and lighting changes.   Predicting accurate drugs: The availability of datasets for a drug discovery are either limited or expensive. The molecule found during a biological study often does not end up being a drug due to ethical reasons such as toxicity, low-solubility and so on. Hence, a less amount of data is available about the candidate molecule. Using one shot learning, an iterative LSTM combined with Graph convolutional neural network is used to optimize the candidate molecule. This is done by finding similar molecules with increased pharmaceutical activity and lesser risks to patients. A detailed explanation of how using low data, accurate drugs can be predicted is discussed in a research paper published by the American Chemical Society(ACS). One shot learning is in its infancy and therefore use cases can be seen in familiar applications such as image and object recognition. As the technique will advance with time and the rate of adoption, other applications of one shot learning will come into picture. Conclusion One shot learning is being applied in instances of machine learning or deep learning models that have less data available for their training. A plus point in future is, that organizations will not have to collect huge amount of data for their ML models to be trained, only a few training samples would do the job! Large number of organizations are looking forward to adopt one shot learning within their deep learning models. It would be exciting to see how one shot learning will glide through being the base of every neural network implementation.  

Imagine yourself playing a strategy game, like Age of Empires perhaps. You are in a world that looks real and you are pitted against the computer, and your mission is to protect your empire and defeat the computer, at the same time. What if you could create an army of soldiers who could explore the map and attack the enemies on their own, based on just a simple command you give them? And what if your soldiers could have real, unscripted conversations with you as their commander-in-chief to seek instructions? And what if the game’s scenes change spontaneously based on your decisions and interactions with the game elements, like a movie? Sounds too good to be true? It’s not far-fetched at all - thanks to the rise of Artificial Intelligence! The gaming industry today is a market worth over a hundred billion dollars. The Global Games Market Report says that about 2.2 billion gamers across the world are expected to generate an incredible $108.9 billion in game revenue by the end of 2017. As such, gaming industry giants are seeking newer and more innovative ways to attract more customers and expand their brands. While terms like Virtual Reality, Augmented Reality and Mixed Reality come to mind immediately as the future of games, the rise of Artificial Intelligence is an equally important stepping stone in making games smarter and more interactive, and as close to reality as possible. In this article, we look at the 5 ways AI is revolutionizing the gaming industry, in a big way! Making games smarter While scripting is still commonly used for control of NPCs (Non-playable character) in many games today, many heuristic algorithms and game AIs are also being incorporated for controlling these NPCs. Not just that, the characters also learn from the actions taken by the player and modify their behaviour accordingly. This concept can be seen implemented in Nintendogs, a real-time pet simulation video game by Nintendo. The ultimate aim of the game creators in the future will be to design robust systems within games that understand speech, noise and other sounds within the game and tweak the game scenario accordingly. This will also require modern AI techniques such as pattern recognition and reinforcement learning, where the characters within the games will self-learn from their own actions and evolve accordingly. The game industry has identified this and some have started implementing these ideas - games like F.E.A.R and The Sims are a testament to this. Although the adoption of popular AI techniques in gaming is still quite limited, their possible applications in the near-future has the entire gaming industry buzzing. Making games more realistic This is one area where the game industry has grown leaps and bounds over the last 10 years. There have been incredible advancements in 3D visualization techniques, physics-based simulations and more recently, inclusion of Virtual Reality and Augmented Reality in games. These tools have empowered game developers to create interactive, visually appealing games which one could never imagine a decade ago. Meanwhile, gamers have evolved too. They don’t just want good graphics anymore; they want games to resemble reality. This is a massive challenge for game developers, and AI is playing a huge role in addressing this need. Imagine a game which can interpret and respond to your in-game actions, anticipate your next move and act accordingly. Not the usual scripts where an action X will give a response Y, but an AI program that chooses the best possible alternative to your action in real-time, making the game more realistic and enjoyable for you. Improving the overall gaming experience Let’s take a real-world example here. If you’ve played EA Sports’ FIFA 17, you may be well-versed with their Ultimate Team mode. For the uninitiated, it’s more of a fantasy draft, where you can pick one of the five player choices given to you for each position in your team, and the AI automatically determines the team chemistry based on your choices. The team chemistry here is important, because the higher the team chemistry, the better the chances of your team playing well. The in-game AI also makes the playing experience better by making it more interactive. Suppose you’re losing a match against an opponent - the AI reacts by boosting your team’s morale through increased fan chants, which in turn affects player performances positively. Gamers these days pay a lot of attention to detail - this not only includes the visual appearance and the high-end graphics, but also how immersive and interactive the game is, in all possible ways. Through real-time customization of scenarios, AI has the capability to play a crucial role in taking the gaming experience to the next level. Transforming developer skills The game developer community have always been innovators in adopting cutting edge technology to hone their technical skills and creativity. Reinforcement Learning, a sub-set of Machine Learning, and the algorithm behind the popular AI computer program AlphaGo, that beat the world’s best human Go player is a case in point. Even for the traditional game developers, the rising adoption of AI in games will mean a change in the way games are developed. In an interview with Gamasutra, AiGameDev.com’s Alex Champandard says something interesting: “Game design that hinges on more advanced AI techniques is slowly but surely becoming more commonplace. Developers are more willing to let go and embrace more complex systems.” It’s safe to say that the notion of Game AI is changing drastically. Concepts such as smarter function-based movements, pathfinding, inclusion of genetic algorithms and rule-based AI such as fuzzy logic are being increasingly incorporated in games, although not at a very large scale. There are some implementation challenges currently as to how academic AI techniques can be brought more into games, but with time these AI algorithms and techniques are expected to embed more seamlessly with traditional game development skills. As such, in addition to knowledge of traditional game development tools and techniques, game developers will now have to also skill up on these AI techniques to make smarter, more realistic and more interactive games. Making smarter mobile games The rise of the mobile game industry today is evident from the fact that close to 50% of the game revenue in 2017 will come from mobile games - be it smartphones or tablets. The increasingly high processing power of these devices has allowed developers to create more interactive and immersive mobile games. However, it is important to note that the processing power of the mobile games is yet to catch up to their desktop counterparts, not to mention the lack of a gaming console, which is beyond comparison at this stage. To tackle this issue, mobile game developers are experimenting with different machine learning and AI algorithms to impart ‘smartness’ to mobile games, while still adhering to the processing power limits. Compare today’s mobile games to the ones 5 years back, and you’ll notice a tremendous shift in terms of the visual appearance of the games, and how interactive they have become. New machine learning and deep learning frameworks & libraries are being developed to cater specifically to the mobile platform. Google’s TensorFlow Lite and Facebook’s Caffe2 are instances of such development. Soon, these tools will come to developers’ rescue to build smarter and more interactive mobile games. In Conclusion Gone are the days when games were just about entertainment and passing time. The gaming industry is now one of the most profitable industries of today. As it continues to grow, the demands of the gaming community and the games themselves keep evolving. The need for realism in games is higher than ever, and AI has an important role to play in making games more interactive, immersive and intelligent. With the rate at which new AI techniques and algorithms are developing, it’s an exciting time for game developers to showcase their full potential. Are you ready to start building AI for your own games? Here are some books to help you get started: Practical Game AI Programming Learning game AI programming with Lua

Learning something by rote i.e., repeating it many times, perfecting a skill by practising it over and over again or building something by making minor adjustments progressively to a prototype are things that comes to us naturally as human beings. Machines can also learn this way and this is called ‘Iterative machine learning’. In most cases, iteration is an efficient learning approach that helps reach the desired end results faster and accurately without becoming a resource crunch nightmare. Now, you might wonder, isn’t iteration inherently part of any kind of machine learning? In other words, modern day machine learning techniques across the spectrum from basic regression analysis, decision trees, Bayesian networks, to advanced neural nets and deep learning algorithms have some inherent iterative component built into them. What is the need, then, for discussing iterative learning as a standalone topic? This is simply because introducing iteration externally to an algorithm can minimize the error margin and therefore help in accurate modelling.  How Iterative Learning works Let’s understand how iteration works by looking closely at what happens during a single iteration flow within a machine learning algorithm. A pre-processed training dataset is first introduced into the model. After processing and model building with the given data, the model is tested, and then the results are matched with the desired result/expected output. The feedback is then returned back to the system for the algorithm to further learn and fine tune its results. This clearly shows that two iteration processes take place here: Data Iteration - Inherent to the algorithm Model Training Iteration - Introduced externally Now, what if we did not feedback the results into the system i.e. did not allow the algorithm to learn iteratively but instead adopted a sequential approach? Would the algorithm work and would it provide the right results? Yes, the algorithm would definitely work. However, the quality of the results it produces is going to vary vastly based on a number of factors. The quality and quantity of the training dataset, the feature definition and extraction techniques employed, the robustness of the algorithm itself are among many other factors. Even if all of the above were done perfectly, there is still no guarantee that the results produced by a sequential approach will be highly accurate. In short, the results will neither be accurate nor reproducible. Iterative learning thus allows algorithms to improve model accuracy. Certain algorithms have iteration central to their design and can be scaled as per the data size. These algorithms are at the forefront of machine learning implementations because of their ability to perform faster and better. In the following sections we will discuss iteration in different sets of algorithms each from the three main machine learning approaches - supervised ML, unsupervised ML and reinforcement learning. The Boosting algorithms: Iteration in supervised ML The boosting algorithms, inherently iterative in nature, are a brilliant way to improve results by minimizing errors. They are primarily designed to reduce bias in results and transform a particular set of weak learning classifier algorithms to strong learners and to enable them to reduce errors. Some examples are: AdaBoost (Adaptive Boosting) Gradient Tree Boosting XGBoost How they work All boosting algorithms have a common classifiers which are iteratively modified to reach the desired result. Let’s take the example of finding cases of plagiarism in a certain article. The first classifier here would be to find a group of words that appear somewhere else or in another article which would result in a red flag. If we create 10 separate group of words and term them as classifiers 1 to 10, then our article will be checked on the basis of this classifier and any possible matches will be red flagged. But no red flags with these 10 classifiers would not mean a definite 100% original article. Thus, we would need to update the classifiers, create shorter groups perhaps based on the first pass and improve the accuracy with which the classifiers can find similarity with other articles. This iteration process in Boosting algorithms eventually leads us to a fairly high rate of accuracy. The reason being after each iteration, the classifiers are updated based on their performance. The ones which have close similarity with other content are updated and tweaked so that we can get a better match. This process of improving the algorithm inherently, is termed as boosting and is currently one of the most popular methods in Supervised Machine Learning. Strengths & weaknesses The obvious advantage of this approach is that it allows minimal errors in the final model as the iteration enables the model to correct itself every time there is an error. The downside is the higher processing time and the overall memory requirement for a large number of iterations. Another important aspect is that the error fed back to train the model is done externally, which means the supervisor has control over the model and how it modifies. This in turn has a downside that the model doesn’t learn to eliminate error on its own. Hence, the model is not reusable with another set of data. In other words, the model does not learn how to become error-free by itself and hence cannot be ported to another dataset as it would need to start the learning process from scratch. Artificial Neural Networks: Iteration in unsupervised ML Neural Networks have become the poster child for unsupervised machine learning because of their accuracy in predicting data models. Some well known neural networks are: Convolutional Neural Networks   Boltzmann Machines Recurrent Neural Networks Deep Neural Networks Memory Networks How they work Artificial neural networks are highly accurate in simulating data models mainly because of their iterative process of learning. But this process is different from the one we explored earlier for Boosting algorithms. Here the process is seamless and natural and in a way it paves the way for reinforcement learning in AI systems. Neural Networks consist of electronic networks simulating the way the human brain is works. Every network has an input and output node and in-between hidden layers that consist of algorithms. The input node is given the initial data set to perform a set of actions and each iteration creates a result that is output as a string of data. This output is then matched with the actual result dataset and the error is then fed back to the input node. This error then enables the algorithms to correct themselves and reach closer and closer to the actual dataset. This process is called training the Neural Networks and each iteration improve the accuracy. The key difference between the iteration performed here as compared to how it is performed by Boosting algorithms is that here we don’t have to update the classifiers manually, the algorithms change themselves based on the error feedback. Strengths & weaknesses The main advantage of this process is obviously the level of accuracy that it can achieve on its own. The model is also reusable because it learns the means to achieve accuracy and not just gives you a direct result. The flip side of this approach is that the models can go wrong heavily and deviate completely in a different direction. This is because the induced iteration takes its own course and doesn’t need human supervision. The facebook chat-bots deviating from their original goal and communicating within themselves in a language of their own is a case in point. But as is the saying, smart things come with their own baggage. It’s a risk we would have to be ready to tackle if we want to create more accurate models and smarter systems.    Reinforcement Learning Reinforcement learning is a interesting case of machine learning where the simple neural networks are connected and together they interact with the environment to learn from their mistakes and rewards. The iteration introduced here happens in a complex way. The iteration happens in the form of reward or punishment for arriving at the correct or wrong results respectively. After each interaction of this kind, the multilayered neural networks incorporate the feedback, and then recreate the models for better accuracy. The typical type of reward and punishment method somewhat puts it in a space where it is neither supervised nor unsupervised, but exhibits traits of both and also has the added advantage of producing more accurate results. The con here is that the models are complex by design. Multilayered neural networks are difficult to handle in case of multiple iterations because each layer might respond differently to a certain reward or punishment. As such it may create inner conflict that might lead to a stalled system - one that can’t decide which direction to move next. Some Practical Implementations of Iteration Many modern day machine learning platforms and frameworks have implemented the iteration process on their own to create better data models, Apache Spark and MapR are two such examples. The way the two implement iteration is technically different and they have their merits and limitations. Let’s look at MapReduce. It reads and writes data directly onto HDFS filesystem present on the disk. Note that for every iteration to be read and written from the disk needs significant time. This in a way creates a more robust and fault tolerant system but compromises on the speed. On the other hand, Apache Spark stores the data in memory (Resilient Distributed DataSet) i.e. in the RAM. As a result, each iteration takes much less time which enables Spark to perform lightning fast data processing. But the primary problem with the Spark way of doing iteration is that dynamic memory or RAM is much less reliable than disk memory to store iteration data and perform complex operations. Hence it’s much less fault tolerant that MapR.   Bringing it together To sum up the discussion, we can look at the process of iteration and its stages in implementing machine learning models roughly as follows: Parameter Iteration: This is the first and inherent stage of iteration for any algorithm. The parameters involved in a certain algorithm are run multiple times and the best fitting parameters for the model are finalized in this process. Data Iteration: Once the model parameters are finalized, the data is put into the system and the model is simulated. Multiple sets of data are put into the system to check the parameters’ effectiveness in bringing out the desired result. Hence, if data iteration stage suggests that some of the parameters are not well suited for the model, then they are taken back to the parameter iteration stage and parameters are added or modified. Model Iteration: After the initial parameters and data sets are finalized, the model testing/ training happens. The iteration in model testing phase is all about running the same model simulation multiple times with the same parameters and data set, and then checking the amount of error, if the error varies significantly in every iteration, then there is something wrong with either the data or the parameter or both. Iterations are done to data and parameters until the model achieves accuracy.   Human Iteration: This step involves the human induced iteration where different models are put together to create a fully functional smart system. Here, multiple levels of fitting and refitting happens to achieve a coherent overall goal such as creating a driverless car system or a fully functional AI. Iteration is pivotal to creating smarter AI systems in the near future. The enormous memory requirements for performing multiple iterations on complex data sets continue to pose major challenges. But with increasingly better AI chips, storage options and data transfer techniques, these challenges are getting easier to handle. We believe iterative machine learning techniques will continue to lead the transformation of the AI landscape in the near future.  

[box type="note" align="" class="" width=""]In this article by Jen Stirrup & Ruben Oliva Ramos from their book Advanced Analytics with R and Tableau, we shall look at the steps involved in prepping for any data science project taking the example of a data classification project using R and Tableau.[/box] Business Understanding When we are modeling data, it is crucial to keep the original business objectives in mind. These business objectives will direct the subsequent work in the data understanding, preparation and modeling steps, and the final evaluation and selection (after revisiting earlier steps if necessary) of a classification model or models. At later stages, this will help to streamline the project because we will be able to keep the model's performance in line with the original requirement while retaining a focus on ensuring a return on investment from the project. The main business objective is to identify individuals who are higher earners so that they can be targeted by a marketing campaign. For this purpose, we will investigate the data mining of demographic data in order to create a classification model in R. The model will be able to accurately determine whether individuals earn a salary that is above or below $50K per annum. Working with Data In this section, we will use Tableau as a visual data preparation in order to prepare the data for further analysis. Here is a summary of some of the things we will explore: Looking at columns that do not add any value to the model Columns that have so many missing categorical values that they do not predict the outcome reliably Review missing values from the columns The dataset used in this project has 49,000 records. You can see from the files that the data has been divided into a training dataset and a test set. The training dataset contains approximately 32,000 records and the test dataset around 16,000 records. It's helpful to note that there is a column that indicates the salary level or whether it is greater than or less than fifty thousand dollars per annum. This can be called a binomial label, which basically means that it can hold one or two possible values. When we import the data, we can filter for records where no income is specified. There is one record that has a NULL, and we can exclude it. Here is the filter: Let's explore the binomial label in more detail. How many records belong to each label? Let's visualize the finding. Quickly, we can see that 76 percent of the records in the dataset have a class label of <50K. Let's have a browse of the data in Tableau in order to see what the data looks like. From the grid, it's easy to see that there are 14 attributes in total. We can see the characteristics of the data: Seven polynomials: workclass, education, marital-status, occupation, relationship, race, sex, native-country One binomial: sex Six continuous attributes: age, fnlwgt, education-num, capital-gain, capital-loss, hours-per-week From the preceding chart, we can see that nearly 2 percent of the records are missing for one country, and the vast majority of individuals are from the United States. This means that we could consider the native-country feature as a candidate for removal from the model creation because the lack of variation means that it isn't going to add anything interesting to the analysis. Data Exploration We can now visualize the data in boxplots, so we can see the range of the data. In the first example, let's look at the age column, visualized as a boxplot in Tableau: We can see that the values are higher for the age characteristic, and there is a different pattern for each income level. When we look at education, we can also see a difference between the two groups: We can focus on age and education, while discarding other attributes that do not add value, such as native-country. The fnlwgt column does not add value because it is specific to the census collection process.When we visualize the race feature, it's noted that the White value appears for 85 percent of overall cases. This means that it is not likely to add much value to the predictor: Now, we can look at the number of years that people spend in education. When the education number attribute was plotted, then it can be seen that the lower values tend to predominate in the <50K class and the higher levels of time spent in education are higher in the >50K class. We can see this finding in the following figure: This finding may indicate some predictive capability in the education feature. The visualization suggests that there is a difference between both groups since the group that earns over $50K per annum does not appear much in the lower education levels. To summarize, we will focus on age and education as providing some predictive capability in determining the income level.The purpose of the model is to classify people by their earning level. Now that we have visualized the data in Tableau, we can use this information in order to model and analyze the data in R to produce the model. If you liked this article, please be sure to check out Advanced Analytics with R and Tableau which consists of this article and many useful analytics techniques with R and Tableau.