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Merger of Artificial Intelligence & Web Development

Merger of Artificial Intelligence & Web Development

Web development is growing at a rapid rate with main focus on enhancing user experience. Apparently Artificial Intelligence as well is spreading its wings onto the technology world by leaps and bounds. Merger of these two is evolving to be one of the most talked about subjects among the Techno Geeks. There are many significant benefits of implementing Artificial Intelligence Solutions into Web Development. Futurologists predict that properly implemented AI solutions will bring 30% revenue increase to marketplaces by 2020. Key Advantages of implementing AI Technologies into Web Development are:
  • Faster & Effective Search Experience using Voice Based Search
  • Better Interactions & Reply Predictions via Chatbots
  • Superior Customer Experience based on individual Mood & Preferences
  • Personalized Store Experience
  • Effective Marketing via Machine Learning, part of AI & Big Data Analysis
Competitive Audit: Key Essential for UX Designing

Competitive Audit: Key Essential for UX Designing

Understanding the Market Context plays a vital role in efficient and effective UX Designing. While user-centered design focuses on user needs/tasks, and information architecture focuses on content, these two aspects alone offer an incomplete picture. What is missing is the context: the environment in which the website or web application is used as well as the market in which it exists. Competitive analysis, as the name implies, is an exploration of the companies in a given industry sector or market niche that are competing with your company’s products or services for market share. Key Benefits of an In-Depth Competitive Analysis can be summarized as:
  • Better Understanding of your Competition
  • Domain Knowledge Build Up
  • Identification of the Best Practices
  • Ignition of Dialogues & Discussion among the Team
  • Exploration of various Approaches & Possibilities
GDPR - The Key Takeaways

GDPR - The Key Takeaways

The EU General Data Protection Regulation (GDPR) superseded the UK Data Protection Act 1998 on 25 May 2018. It expands the rights of individuals to control how their personal data is collected and processed, and places a range of new obligations on organizations to be more accountable for data protection. GDPR Compliance can be summarized into following Key Points:
  • Lawful, Fair & Transparent processing of Data
  • Obtain Clear & Explicit Consent of the Data Collected from the Data Subject
  • Timely Notification of Security Breach to Data Subjects & Controllers – Within 72 Hours
  • Right of the Data Subjects to Data Access
  • Right of the Data Subjects to Data Deletion or Right to be Forgotten
  • Data Portability – Right of Data Subjects to Access & Reuse their Data
  • Privacy by Design – Design your system with proper Security Protocols right from start
  • Appointment of Potential Data Protection Officer if needed
Top UI Design Trends

Top UI Design Trends

Immersive User Interface Experience in Web and Mobile Development is of vital importance for higher User Traffic and Better User Engagement. UI Designers need to start put all their efforts in making the user experience more lifelike and personalized. They need to captivate the user, whisk them away on an interactive adventure. Some of the Top UI Design Trends are
  • High Quality & Artistic Photo Content
  • Supersize Catchy Topography
  • Split Screens
  • Custom Digital Illustrations
  • Long Form Content
  • Buttonless UI
  • Vibrant and Bold Colors
  • Engaging Interface Animations
  • Full Screen Background Images
  • Hero Images for Landing Pages
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    The IoT Mania

    The IoT Mania

    The IoT buzz is growing rapidly and helping digitally transform businesses. Internet of Things is made up of devices – from "simple sensors to smartphones and wearables – connected together", to "gather information, analyse it and create an appropriate action". The quality and scope of the data across the Internet of Things generates an opportunity for much more contextualized and responsive interactions with devices to create a potential impact not only on how we live but also on how we work. We are looking at a future in which companies will indulge in digital Darwinism, using IoT, AI and Machine Data Portability. With deep learning, natural language processing, image recognition, and neural-network drive decision making, IoT technologies will help create more integrated and interactive experiences. Almost all the popular research statistics predict a steep growth in the IoT market in the coming years. Companies that develop custom IoT applications will earn huge revenues. There’s a demand for high-level programming for sensors and devices, web apps, and both B2B and B2C mobile end-user apps.
    BIG Data Analytics

    BIG Data Analytics

    Big Data Analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes. Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process. It has one or more of the following characteristics – High Volume, High Velocity, or High Variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale. Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, Businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in better and faster decisions.
    The Blockchain Technology

    The Blockchain Technology

    Blockchain is poised to change IT in much the same way open-source software did a quarter of a century ago. Blockchain is a public append-only electronic ledger that can be openly shared among disparate users and that creates an unchangeable record of their transactions, each one time-stamped and linked to the previous one. Each digital record or transaction in the thread is called a block and it allows either an open or controlled set of users to participate in the electronic ledger. The ledger can be written onto with new information, but the previous information, stored in blocks, cannot be edited, adjusted or changed. This is accomplished by using cryptography to link the contents of the newly added block with each block before it, such that any change to the contents of a previous block in the chain would invalidate the data in all blocks after it. The first major application of blockchain technology was bitcoin which was released in 2009. Bitcoin is a crypto currency and the blockchain is the technology that underpins it. A crypto currency refers to a digital coin that runs on a blockchain. Blockchain will help build more efficient secure enterprise models, optimize the ecosystems and reduce the risk factor.
    Cloud Computing Trends for CIO<strong>s</strong> To Look For

    Cloud Computing Trends for CIOs To Look For

    Cloud Computing has gone from the obscure service to multi-billion dollar corporations to become an integral part of the internet ecosystem. Cloud Computing has provided Big Data with a way to store and retrieve an immense amount of information. It has evolved from personal cloud storage to entire organizations moving all of their data to the cloud. From multi-cloud strategies to cost containment and container orchestration, CIOs are getting more pragmatic and prudent when taking advantage of the cloud’s economies of scale. Top trends in Cloud Computing that Strategic Businesses and their CIOs need to look at are:
    • Growth in Cloud Services & Solutions viz SaaS, PaaS and IaaS
    • Hybrid Cloud Solutions – Cloud to Cloud & Cloud to On-Premise Connectivity
    • Cloud Storage & It’s Multi-Faceted Usage
    • Quantum Computing – The Next Rising Star
    • Edge Computing
    • Serverless Computing
    • Cloud Security Vulnerabilities
    • Disaster Recovery
    Going Kotlin for Android Development

    Going Kotlin for Android Development

    At Google I/O 2017, Kotlin was announced as the new coding alternative for Android Platform. Kotlin is an open source statically-typed programming language developed by JetBrains. Providing full compatibility with Java, it runs on the Java Virtual Machine, enables to write less code, has readable syntax and can be compiled to JavaScript source code. With Kotlin on Google Cloud Platform portal, Google focuses to make it easier for developers to find the resources related to Kotlin on Google Cloud. Key Benefits of developing Android Applications using Kotlin can be listed as below:

    Mature Language and Environment: The Kotlin release has gone through many stages before releasing the final 1.0 release. The IDE plugin works smoothly and already allows many of the features that you love and use in Java.

    Enhanced Development Speed with Less Boiler Plate Coding: Programming with Kotlin requires approximately 20% less coding compared to Java. Thus, helping Android developers to make their task simple, quick and efficient. Kotlin uses various handy tricks and methods to help you significantly reduce the extra lines of codes and create relatively smaller file sizes.

    Easier for Developers to Setup, Learn and Adapt: It is a very simple language compared to Java with lean and intuitive syntax enabling developers to learn it quickly. App Code Debugging is as well fast and easier with Kotlin. Kotlin seamlessly integrates with Android Studio, hence setting up a project in Kotlin using Android Studio is fast and simple.

    Interoperable and 100% Compatibility with Java: Kotlin is positioned as a 100% Java-interoperable programming language. It allows using the Java frameworks and libraries in your new Kotlin projects by using advanced frameworks without any need to change the whole project in Java. You can compile the project using both Java and Kotlin languages. Developers can use features of Kotlin without changing any code for the existing project or switch to a new project.

    Open Source Language and Free of Cost: As an open source language under Apache 2, you just need a Java-to-Kotlin converter tool if you want to convert your present java files. This tool is useful to change your complex codes into a simper code thus helping you to save your time and work. Kotlin supports the open developer networks and the strength of open source makes the network strong which is not restricted to any single company. This excellent programming language for Android App development is available just for free.

    Extremely Safe & Secure Language: Kotlin's code is more concise, therefore it goes without saying that a concise, compact, and clear code is implicitly a safer code with fewer crashes and reduced number of system failures. NullPointerExceptions (NPE) has been one of the most common causes of app crash. Fixing the NullPointerExceptions and protecting your code using null checks is quite a time-consuming and daunting task for developers. But with Kotlin, NPE is a thing of past as null safety comes baked into the language’s type system. Kotlin comes with a smarter and safer compiler that helps in detecting errors easily on runtime. It perform lot of checks to reduce run time errors & number of bugs in code.

    Multi-Platform Development Support hence highly Versatile: Kotlin was created with JVM in mind so it can virtually be used on any device that can run JVM. Kotlin JS can be used to do Front End Development. With Gradle support, you can write gradle files in Kotlin. With Kotlin Native, the future is open to basically any platform, it paves way to build iOS applications as well.
    Benefits of Using Firebase for App Development

    Benefits of Using Firebase for App Development

    Firebase was founded by James Tamplin and Andrew Lee and later on acquired by Google. Firebase is a Backend-as-a-Service i.e. a Cloud Computing Service model using which web and mobile application developers can connect their applications with backend cloud storage and APIs rendered by the backend applications. It helps developers to share various features between apps built on different platforms related to Database, Config and Notifications. Firebase is a framework which helps to build portable application for your business with real-time database i.e. when one user updates a record in the database, that update would be conveyed to every single user, be those users on a website, iOS or Android device. It gives a basic and unified platform with many Google features packed-in so that developers need not configure their server while using Firebase. Benefits of using Firebase can be summarized as below:

    Real Time Database: The cloud-hosted NoSQL database is offered by Firebase real-time database that helps you store and synchronize data in JSON format among all connected clients. If you are looking to create an Android, iOS or Web App that provides real time updates to users without creating Database or API, then the best tool to opt for is Firebase. It has capabilities to manage backend components of applications.

    Hosting: Firebase provides fast, secure, static, and production-grade hosting for developers. It allows developers to efficiently deploy web apps and static content to a CDN (Content Delivery Network). The process is extremely easy in Firebase as it consists of Auto Provisioned SSL (Security Socket Layer) certificate ideas, Customized domain support, and Global CDN regardless of developers sending a single landing page or a complex web application.

    Authentication: Firebase Authentication provides instant UI libraries, backend services, and simple to utilize SDKs so that you can authenticate users over your app. It supports authentication utilizing username, email ids, or passwords. In addition, developers are allowed to let users sign in to their Firebase application either by utilizing Firebase UI as a Drop in Authentication solution or by utilizing the Firebase Authentication SDK to manually incorporate Sign-in techniques into the app.

    Content Storage: Firebase allows easy content storage. It is built for application developers who need to store and serve user-generated content, for example photos or videos. It gives secure document transfers and downloads for Firebase applications, regardless of network quality. You can utilize it to store pictures, sound, video, or other user-generated content. Firebase Storage is upheld by Google Cloud Storage, a capable, basic, and cost-effective object storage service.

    Notifications & Cloud Messaging: The Firebase Cloud Messaging offers you an opportunity to send notifications and messages to your targeted audiences for free across all devices and platforms with the help of battery-efficient connection. It gives a choice to developers and organizations looking for an adaptable notification platform which requires minimal coding effort to begin, and a graphical console for sending messages. Firebase notification allows targeted user notifications for mobile app developers.  You can send notification messages to drive user re-engagement and maintenance.

    Invites: To increase your users and compel them to download your application, word of mouth advertising stands out from the crowd. With the help of this feature, you can easily send customized messages emails and invitations to all your prospective clients.

    Remote Config: It is a cloud service that gives you a chance to change the conduct and appearance of your application without requiring users to download an application update. Your application controls when updates are applied, and it can as often as possible check for updates and apply them with a negligible effect on execution.

    Test Lab: Test Lab is utilized for testing your application on gadgets hosted in a Google data-center. It helps you to find issues that only happen on particular gadget configurations. A test result includes logs, videos, and screenshots which are available in your project in the Firebase console. Even if you haven’t composed any test code for your application.

    Crash Reporting: This feature of Firebase helps to create detailed reports of the errors which are assembled into groups of comparative stack flow triggered by the severity of effect on users. In addition to automatic reports, you can register custom events to help capture the steps which leads to a crash.

    App Indexing: This feature is used to index application in Google search results. After app indexing, If a user search related to your app, it will start the app installed in user’s device directly from the search result.

    AdMob: Admob is advertising facility of the Firebase which is used to generate profits from your app.  You can exhibit various advertisements from millions of advertisers and increase your revenue. The AdMob also provides you the option of enhancing the user experience and lets you chose the appropriate template from plethora of options.

    AdWords & Analytics: You can characterize custom audiences in the Firebase console based on device data, custom events, or user properties. Now you can easily track down your user’s journey on a number of devices. It means you would know whether he is using a smartphone, tablet or laptop. By using this you can achieve potential clients with the help of online advertisements. You can gain deep insights into promotion conversions, and run targeted advertisement campaigns using Firebase Analytics to engage your audiences & create advertising strategies. You can also export your mobile app data to BigQuery with the help of Google Analytics. It can further support in engaging more users once you match the UX based on user id.

    Accessibility to Machine Learning: Firebase provides developers the facility of Machine Learning. This benefit is available for both Android and iOS developers as well as experienced or newcomers. The ML kit has ready - to -use APIs for various mobile functionalities such as detecting the face, identifying the text, barcode scanning and labeling images etc. You have a choice between on - device and Cloud APIs that can be selected according to your needs.
    Chatbots & Natural Language Processing: A Reality Check

    Chatbots & Natural Language Processing: A Reality Check

    Chatbot is a computer program powered by a Set of Rules or by Artificial Intelligence, which conducts a conversation in natural language via auditory or textual methods, understands the intent of user, and tries to send an appropriate response.  A chatbot that relies on rules can only accept a limited number of inquiry types, and can only respond in limited ways. A chatbot that uses “Artificial Intelligence“ employs sophisticated algorithms, such as Natural Language Processing, to handle user requests. Currently, 43 percent of the chatbot market is devoted to customer service experiences. From handling customer complaints to standardized requests like adding or canceling a service, chatbots take over and add an element of guided self-service to customer interactions. Now, increasingly sophisticated chatbots are also making their way into the marketing landscape. Messaging apps are the new paradigm that consumers use to communicate. Messaging apps boast a staggering 5 billion active monthly users, and these apps are out performing social media. Connecting with consumers through their preferred lines of communication has always been a top marketing goal and chatbots are making that easier and more personalized than ever. The difficulty in building a chatbot is less a technical one and more an issue of user experience. Users quickly abandon sites that employ chatbots that provide an awkward and difficult user experience. As chatbots get more complex, and start being more lifelike, the one-size-fits-all approach starts not being viable. When choosing a chatbot vendor today or implementing your own chatbot, it is important to make sure that the chatbot you use will learn from its past experiences.  Automation is a great way to save on costs while improving efficiency and productivity, and this extends into all areas of business. Chatbots simply offer one way to help automate some of the most basic marketing activities. A major component to the success of a chatbot lies less in its ability to deliver the builders' message, and more on its ability to “listen” and deliver the message the customer needs. As on date, Chatbots still have not succeeded to execute all kinds of interactions that are currently being performed by humans related to online business. Even the most advanced chatbot abilities will be limited by the pertaining models since they are not capable of cognitive perception. We still await some revolutionary highly skilled AI to come up. Below are the reasons why Natural Language Processing is way harder than we perceive and is causing major setbacks in the Chatbot success stories:



    1. Knowledge of the world is still difficult for computers to acquire As humans, we can use our intuitions to make logical leaps, to understand what somebody is saying even if they do not explicitly tell us some of the information necessary to understand them. A computer does not have that kind of intuition, and it will never have unless it starts to experience the life outside of the texts it has been provided with.

    2. It is hard to understand whether two sentences or two concepts are equal For any given idea, we can write infinitely many sentences that roughly define the same idea. Unless what you are describing has a very specific definition, it is likely that the way you describe something is the first time somebody has defined it that exact way. This creates a problem for NLP applications, as they will never have enough data to cover all the ways in which things can be defined. No amount of data will solve this problem.

    3. Optimizing the Wrong Metrics In Machine Learning research, we try to make models that generalize to as many problems as possible. One of the key aspects of Machine Learning models is the objective (loss) function. That function defines what you are optimizing for, what the training wants to achieve. As we have mentioned before, these objective functions are generally very general. They also generally have some mathematical properties to make sure that common learning algorithms work well with them. The main problem is that you usually cannot use the exact objective function you want. There are two reasons for this: either the function you want does not have the mathematical properties necessary to work well with the existing Machine Learning ecosystem, or it is very hard to train. We therefore try to transform our problem to a more commonly used one. We might, for example, try to train a chatbot by optimizing it for guessing the next word in a sentence correctly, given the previous sentence and the current sentence. Although you can sometimes construct objective functions that aim to optimize for some metric at the expense of others, these can only be verified after the training is done.

    A chatbot that is very good at predicting the next word might struggle if the questions are given with a different phrasing than it has been trained on, producing grammatically correct but wrong answers. An even bigger problem is that we don’t exactly know what metrics we want to optimize. It is great if we can have a chatbot that gives correct answers 99% of the time. But that does not say how it gives out those answers, and whether there is a trend in the remaining 1%.

    4. The Human Bias As we are dealing with natural languages, the data you use is ultimately generated by humans. Since it is hard to gather a lot of data, we prefer to use representations of words that have been previously computed using large datasets, and use our own data to fine-tune those representations. Common choices include Wikipedia, Twitter, Common Crawl (most frequently visited websites), and Google News. The thing you have to keep in mind is that your chatbot will carry the characteristics of the underlying text data. That is generally desirable, as it makes your chatbot more human-like, but your chatbot also adopts the biases (large and small) that the people who wrote parts of your data carry.
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