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Understanding Different Types of Artificial Intelligence Technology

Artificial intelligence has gained an incredible momentum in the past couple of years. The current intelligent systems have the capability of managing large amounts of data and simplifying complicated calculations very fast. But these are not the sentient machines. AI developers are trying to develop this feature in the future. In the coming years, AI systems will reach and surpass the performance of humans in solving different tasks. Different types of AI have emerged to assist other artificial intelligence systems to work smarter. In this article, we are going to have a look at different categories of artificial intelligence. Reactive Machines AI The fundamental types of artificial intelligence systems are quite reactive and they are not able to use previous experiences to advise current decisions and to configure memories. IBM’s chess-playing computer called Deep Blue defeated Garry Kasparov who is an international grandmaster in chess in the late 1990s, is one example of this type of machine. In the same way, Google’s AlphaGo defeated the top human Go experts but it can’t assess all the future moves. Its testing method is more enlightened than Deep Blue’s by using a neural network to assess the game developments. Limited Memory AI Limited memory AI is mostly used in self-driving cars. They will detect the movement of vehicles around them constantly. The static data such as lane marks, traffic lights and any curves in the road will be added to the AI machine. This helps autonomous cars to avoid getting hit by a nearby vehicle. Nearly, it will take 100 seconds for an AI system to make considered decisions in self-driving. Theory of Mind AI Theory of mind artificial intelligence is a very advanced technology. In terms of psychology, the theory of mind represents the understanding of people and things in the world that can have emotions which alter their own behavior. Still, this type of AI has not been developed completely in the society. But research shows that the way to make advancements is to begin by developing robots that are able to identify eye and face movements and act according to the looks. Self-aware AI Self-aware AI is a supplement of the theory of mind AI. This type of AI is not developed yet, but when it happens, it can configure representations about themselves. It means particular devices are tuned into cues from humans like attention spans, emotions and also able to display self-driven reactions. Artificial Narrow Intelligence (ANI) ANI is the most common technology that can be found in many aspects of our daily life. We can find this in smartphones like Cortana and Siri that help users to respond to their problems on request. This type of artificial intelligence is referred to as ‘weak AI’. Because it is not strong enough as we need it to be. Artificial General Intelligence (AGI) This type of artificial intelligence systems work like humans and is called as ‘strong AI’. Most of the robots are ANI, but few are AGI or above. Pillo robot is an example of AGI which answers to all questions with respect to the health of the family. It can distribute pills and give guidance about their health. This is a powerful technology which is necessary for living with a full-time live-in doctor. Artificial Superhuman Intelligence (ASI) This type of AI has the ability to achieve everything that a human can do and more. Alpha 2 is the first humanoid robot developed for the family. This robot is capable of managing a smart home and can operate the things in your home. It will notify you of the weather conditions and tells you interesting stories too. It is really a high-powered robot which you feel like is a member of your family. Also Read: List of Startups building websites with Artificial Intelligence Artificial Intelligence Impact on Content Marketing    
Understanding Different Types of Artificial Intelligence Technology 79 Understanding Different Types of Artificial Intelligence Technology Blog
Ruslan Bragin 18 Oct 2017
Artificial intelligence has gained an incredible momentum in the past couple of years. The current intelligent systems have the capability of managing large amounts of data and simplifying complicated...
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Facebook re-licensed React - As it happened

Facebook has now successfully re-licensed React under the MIT license. Before that Facebook was using their own ‘BSD+Patents License’. Things began to change as the Apache Foundation officially announced that none of its software projects can include Facebook’s BSD+Patents licensed code. Chris Mattmann, Apache’s legal affairs director said that frameworks, tools, and libraries maintained under Facebook's open-source-ish BSD+Patents license should not be used in any new project. Mattmann said that if anyone has developed projects with Facebook React.js license, they are allowed to exterminate it on or before August 31, 2017, and should find a license that is compatible with the foundation policies. "No new project, subproject or codebase, which has not used Facebook BSD+Patents licensed jars (or similar), are allowed to use them," Mattmann wrote. "In other words, if you haven't been using them, you aren't allowed to start. It is Cat‑X." The licenses that are not acceptable to use in any Apache project comes under Cat-X and right now it includes BSD-4-Clause, BCL, GNU LGPL, GNU GPL, Microsoft Limited Public License and more. Apache has clearly mentioned a list of licenses on their official site that are not allowed to incorporate with any Apache products. In September, Automattic declared that it would stop React inclusion with its Gutenberg editor project unless they change their license. But now there is no need to worry about this problem, as Facebook has now successfully changed the license for all its open source projects which includes React, Flow, Jest, Immutable.js and others. Explaining the conclusion, Facebook engineering director Adam Wolff wrote that “React is the foundation of a broad ecosystem of open source software for the web, and we don’t want to hold back forward progress for nontechnical reasons.” Here’s all that transpired. April 20: Question Raised to ASF Legal about RocksDB Integrations Apache Cassandra put a question to Apache Software Foundation Legal as to whether they can use RocksDB as a direct dependency to expand its storage, which was a 3 clause BSD licensed. July 15: ASF Banned BSD+Patents License After a long debate, Chris A. Mattmann, Vice President of ASF’s legal affairs stated that Facebook’s BSD+Patents license is not well-suited with Apache Software Foundation policies to use as a dependency. July 15: RocksDB Changed its License RocksDB switched to Apache 2.0/GPL2 from its BSD-plus-Patent license. This signifies that RocksDB is now consistent with all Apache Software Foundation policies. Aug 18: Facebook Stated that they are not going to Change their BSD+Patents License Facebook announced that they will continue with the same BSD+Patents license because they strongly believe that it offers some special features to their users and the change may affect them a lot. This prompted few companies such as Hacker News, freeCodeCamp and Reddit to think about React substitutes. Sep 22: Facebook Announced its Shift to MIT License Facebook declared that it is going to change the BSD+Patents license to MIT license. This is mainly because of React, which is the heart of open source software that comes under BSD license. And they don’t want to desist it for non-technical reasons. “Wordpress, a blogging platform that is maintained by the Automattic, says it is now happy and beneficial with the Facebook license change and they are ready to use Facebook open source libraries in their future projects.” Sep 25: Facebook Officially Shifted to MIT License Facebook announced officially that now it has shifted to MIT license that is agreed upon by the ASF. Sep 26: Facebook Released React 16 Facebook released the updated version of React i.e React 16 which includes license updates as well. Therefore, React now again remains as the most approved tool for web development. Facebook team has worked on it for more than a year and finally achieved its goal. Moreover, they have rewritten the code that provides special and unique features to help developers create UIs that are superior to the existing ones.      
Facebook re-licensed React - As it happened

Facebook re-licensed React - As it happened

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Facebook has now successfully re-licensed React under the MIT license. Before that Facebook was using their own ‘BSD+Patents License’. Things began to change as the Apache Foundation o...
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Microsoft Introduced Edge Browser for Android/iOS and Microsoft Launcher for Android

One week earlier, Microsoft announced its launch of Edge browser for Android and iOS platforms. Currently, the app is available as a preview version and can be downloaded from Google Play Store. Apple users should register first to avail the app and are allowed to download the app on their iPhones once the request is approved. Microsoft Edge for iOS and Android brings familiar features like your Favorites, Reading List, New Tab Page, and Reading View across your PC and phone, so, no matter the device, your browsing goes with you," VP Joe Belfiore, Microsoft Windows, and Devices Corporate said in a blog post. "But what makes Microsoft Edge really stand out is the ability to continue on your PC, which enables you to immediately open the page you're looking at right on your PC—or save it to work on later." The Edge app now operates only on Android and iOS smartphones but not on the tablets. Microsoft stated that it is planning to add the functionality for Android tablets,  iPads and some additional features like roaming passwords as well, at some future time. The app is available only in US-English and not in all languages at preview launch but Microsoft plans to bring it sooner. The company also launched Microsoft Launcher for Android, which allows users to continue their work on PC even if it is documents, photos and more. You can place your favorite people icons on the home screen as you do on Windows 10 with the help of Microsoft Launcher. Users can get access to top news, favorite activities, important events and more, just by swiping to the right on the Android screen. It also provides gesture support for Android users. Source: Microsoft Official Blog  
Microsoft Introduced Edge Browser for Android/iOS and Microsoft Launcher for Android

Microsoft Introduced Edge Browser for Android/iOS and Microsoft Launcher for Android

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One week earlier, Microsoft announced its launch of Edge browser for Android and iOS platforms. Currently, the app is available as a preview version and can be downloaded from Google Play Store. Apple...
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Gartner says, by 2020, 2.3 Million jobs will be created by Artificial Intelligence

On Monday, October 2, Peter Sondergaard at Gartner Symposium said that "AI will be a net job creator starting in 2020". How robots and AI will change the future of jobs is the big question now in everyone’s mind. According to Gartner, 8 million candidates are working in the IT field but, out of them only 1,250 to 1500 candidates are familiar with artificial intelligence. Simultaneously, 10% of companies are looking to hire candidates who are expert in machine learning and AI, to yield better results. Sondergaard said that by the year 2020, AI will destroy nearly 1.8 million jobs, but at the same time, it will create 2.3 million jobs. This means that AI will bring about 5,00,000 job openings globally. He also stated that "One year later, AI augmentation of the workforce will create about $3 trillion in business value, while saving billions of hours of work … so AI will help find, develop and augment your people. AI will be a net job creator starting in 2020." Gartner analyst stated that organizations should be more innovative towards bringing the hidden skills and power of existing employees to light. “Gartner predicts one in three jobs will be converted to software, robots and smart machines by 2025,” said Gartner research director Peter Sondergaard. “New digital businesses require less labor; machines will make sense of data faster than humans can.” Source: Gartner Official Blog  
 Gartner says, by 2020, 2.3 Million jobs will be created by Artificial Intelligence

Gartner says, by 2020, 2.3 Million jobs will be created by Artificial Intelligence

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On Monday, October 2, Peter Sondergaard at Gartner Symposium said that "AI will be a net job creator starting in 2020". How robots and AI will change the future of jobs is the big question n...
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Writing your first machine learning code

The whole world is buzzing with artificial intelligence these days. Some people predict that it might change the way the world works into the future. For developers, it presents us with a fresh opportunity to be a part of a new paradigm shift.  I started learning AI about two months back and it has been a long road since. There are lots of developments happening in AI every day. From bots that develop their own language to an AI that can beat professional players in DOTA. From self-driving cars to computers that are better at diagnosing patients than experienced doctors. There is a lot of ground to cover.  Before we go further, we must understand that there are a lot of disciplines in AI. Some of them are easier to get into than others. Obviously, your first AI program cannot be to make a self-driving car. For beginners, it is best to start with a branch of machine learning  called supervised learning.   What is supervised learning? Easily put, supervised learning means you give a bunch of data to a computer program and it uses mathematical models to draw inferences on that data. This type of learning is used for very simple regression and classification problems, but are really handy in solving many real-world problems.  Let’s start Consider this very simple data set that I’ve prepared artificially just for the purpose of this example. This dataset follows three numbers X, Y, and Z. There exists a relationship between X, Y, and Z that we don’t know yet. Our goal in writing this program is to find this relationship. This data also contains some noise that is usually present in most real data sets.  82.95761557036997,15.49770283330364,53.746988734062285 41.831058370415896,74.6908398387234,76.91731716843984 0.45109458673243674,15.880369177717512,12.400022057356612 65.84526760369872,29.757778929447664,55.432668761221926 38.02804990326463,94.94571562617034,90.18671003364872 … and 95 more simple pairs like this. For us to understand much easier, let’s put the equation in mathematical terms- Z = a*X + b*Y  Our goal is to find a and b so that we can use any other pairs of X and Y to calculate Z. Such kinds of problems are solved by a mathematical technique called linear regression.  It is one of the most simple applications of Machine Learning and easiest to get into. Before we get into the more difficult aspects of machine learning, later on, it is important to understand that this exercise is very helpful to build up confidence in this field. After all, bigger battles are won with the confidence of smaller victories.  Applications of linear regression.  These values of X, Y and Z can represent anything. They can be any three variables that are related by a linear equation. For example, if you remember high school kinematics, the velocity of any given object at a given time when it starts from an initial velocity under the influence of gravity is given by the equation v(t) = u + At where A is a constant gravitational acceleration that we don’t know. If we get a lot of experimental values for u, v and t, running a simple multivariate linear regression over these values can give us the coefficient values of u and t. We will find after our analysis that the coefficient of u will come out to be 1. If our data is to be accurate, we will get the value of A as 9.8 meters per second squared.  While this is just one area I have used for illustration, it is important to understand that linear relationships occur everywhere in nature. Getting into the code We will use Javascript to write this simple regression program by using an npm module called smr.  To set up the project, you must have nodejs installed. We could have used python as well, but installing and working with Scipy is not in our scope for now. We’ll gradually go there with time. For the time being,  Start by creating an empty directory for your project  Install smr by using npm install smr.  Create a new file called index.js and put in the code.  The code is as follows:  var smr = require('smr'); var regression = new smr.Regression({ numX: 2, numY: 1 }) //read all the data by creating a read stream var lineReader = require('readline').createInterface({   input: require('fs').createReadStream('data.txt') }); //read it line by line and fit it into the regression object lineReader.on('line', function (line) {     line = line.split(',')     regression.push({x:[line[0],line[1]], y:[line[2]]})     t = regression.calculateCoefficients()     console.log(t) }); You can get the dataset from this GitHub repo that I’ve created for this code at the end of this article.  Running the code We run the code by using node index.js on the same directory as the code. Before we do that, let’s remember the data is in the form z = a*X + b*Y  The code fits the data into the regression object and prints coefficient values of a and b. When we run the code, we begin to see this output.  [ [ 0.5007233183107977 ], [ 0.7496919531492985 ] ] [ [ 0.5007720353300695 ], [ 0.749708917362593 ] ] [ [ 0.500762395739748 ], [ 0.7497046748655358 ] ] [ [ 0.5007149604193888 ], [ 0.7496660120512422 ] ] [ [ 0.5009035966316537 ], [ 0.7495249465185432 ] ] The value of a begins to converge at 0.50 and the value of y begins to converge at 0.75. This is remarkable, as when you see the repo there is a quick python script that is used to prepare the data. The value of a and b that I had chosen were indeed 0.50 and 0.75. That means our model is good and we have successfully created our first machine learning program.  Link of repo: https://github.com/archimedes14/linear_regression_simple  
Writing your first machine learning code

Writing your first machine learning code

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The whole world is buzzing with artificial intelligence these days. Some people predict that it might change the way the world works into the future. For developers, it presents us with a fresh opport...
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Scala Vs Python - Choosing the best language for Apache Spark

Apache Spark is a high-speed cluster computing technology, that accelerates the Hadoop computational software process and was introduced by Apache Software Foundation. Apache Spark enhances the speed and supports multiple programming languages such as - Scala, Python, Java and R. All these 4 APIs possess their own special features and are predominant for programming in Spark. But data scientists usually prefer to learn Python and Scala for Spark, as Java does not support Read-Evaluate-Print-Loop, and R is not a general purpose language. Both Python and Scala are easy to program and help data experts get productive fast. Choosing a programming language for Apache Spark depends on the type of application to be developed. Scala vs Python for Spark Both are Object Oriented plus functional and have the same syntax and passionate support communities. Below a list of Scala Python comparison helps you choose the best programming language based on your requirements. Scala vs Python Performance Scala is a trending programming language in Big Data. It runs 10 times faster than Python, as it uses Java Virtual Machine in runtime. Python is highly productive and a very simple language to learn. Whereas, Scala, due to its high-level functional features requires more thinking and abstraction. But once you get familiar with Scala, your productivity will dramatically boost. Both are good in their specifications, but if you are working with simple intuitive logic then Python does the job greatly. And if you are developing something more complex, then go for Scala. Refactoring the Code Safely There is a need to refactor the code continuously when programming with Apache Spark. Scala is a statically typed language and Python is a dynamically typed language. Refactoring the program code of a statically typed language is much easier and hassle-free than refactoring the code of dynamic language. Many a time, developers face difficulties after modifying the program code of python. This is because it creates more bugs than fixing the older ones. So, it is better to choose Scala which is a compiled language. Scala, Python Integration The diverse and complex infrastructure of Big Data systems requests a programming language, that has the power to integrate across several services and databases. Scala, with the Play framework, has the ability to integrate easily with various concurrency primitives like Akka’s actors in the Big Data ecosystem, as it offers many reactive cores and asynchronous libraries. Scala allows developers to write maintainable, readable and efficient services. Python, using uWSGI, supports heavyweight process forking but does not support true multithreading. User-friendly Language Both Python and Scala are equally powerful languages in the context of Spark. So the desired functionality can be achieved either by using Python or Scala. But when compared to Scala, Python is very easy to understand. Python is less prolix, that helps developers to write code easily in Python for Spark. Scala vs Python for Machine Learning Python language is recommended if you are implementing Machine Learning algorithms like Graphx or GraphFrames or MLlib and data science technologies. MLlib only contains parallel Machine Learning algorithms, that are appropriate to run on a bunch of distributed data set. Developers with a good command over Python can build ML application without SPARK MLLIB. But if you are designing ML models, then Scala is the best choice because any new addition of ML algorithms will be implemented first in Scala and then Python. Scala is preferred for implementing data engineering technologies. Conclusion Python is slower but very easy to use, while Scala is fastest and moderately easy to use. Scala provides access to the latest features of the Spark, as Apache Spark is written in Scala. Language choice for programming in Apache Spark depends on the features that best fit the project needs, as each one has its own pros and cons. So, it is necessary for developers to learn both Scala and Python before choosing a programming language. Also Read: 7 Useful Machine Learning Packages in R  
Scala Vs Python - Choosing the best language for Apache Spark

Scala Vs Python - Choosing the best language for Apache Spark

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Apache Spark is a high-speed cluster computing technology, that accelerates the Hadoop computational software process and was introduced by Apache Software Foundation. Apache Spark enhances the speed ...
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Microsoft penetrating Cryptocurrency Market with the Blockchain technology

Microsoft corporation has chosen to enter the crypto currency market with the blockchain technology. The company is apparently collaborating with Israel's Bank Hapoalim, the first bank that uses blockchain technology for digital bank contracts. Bank contract means giving assurance from a particular bank, that borrower's indebtedness will be met even if their liability is not attained. Blending the blockchain with bank contract is the thing that happened for the first time in the history of Israel’s banking sector. Applicants do not need to visit the bank, once the application is installed and the documentation process will be highly secured and will consume less time. According to the MarketsandMarkets report, by the end of 2021, the blockchain market is expected to reach $2.31 billion from $210.2 million in 2016, at a CAGR (compound annual growth rate) of 61.5%. Since the initiation of the cryptocurrency transactions, blockchain technology has been put to practice. The technology is now more popular because of the fact that it cannot be ruptured and offers high security. Microsoft has garnered the faith of a huge number of blockchain customers, and its recent collaboration with Hapoalim bank in Israel is expected to elevate the company’s growth in the near future. Source: Microsoft Official Blog  
Microsoft penetrating Cryptocurrency Market with the Blockchain technology

Microsoft penetrating Cryptocurrency Market with the Blockchain technology

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Microsoft corporation has chosen to enter the crypto currency market with the blockchain technology. The company is apparently collaborating with Israel's Bank Hapoalim, the first bank that uses b...
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Google Safe Browsing Service now protecting more than 3 billion devices

Google has recently released a new statistics related to its Safe Browsing Service. The search giant announced that now more than 3 billion desktops or mobile devices running on Chrome, Safari, and Firefox are being protected from visiting potentially dangerous sites with this new Safe Browsing service. Additionally, many web app developers including Snapchat are using this service to protect their users. The Safe Browsing service was launched in 2007, and it was one of Google’s earliest anti-malware efforts. In the year 2016, Google released a figure of 2 million and this was the number of devices being protected by Safe Browsing service. As per a blog post by Google, “This notification is one of the visible parts of Safe Browsing, a collection of Google technologies that hunt badness—typically websites that deceive users—on the internet.” For many years, Google has been using machine learning in Safe Browsing. This has helped them in detecting much web-based malware and removing them. Google also included, “We’re continually evaluating and integrating cutting-edge new approaches to improve Safe Browsing”.  
Google Safe Browsing Service now protecting more than 3 billion devices

Google Safe Browsing Service now protecting more than 3 billion devices

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Google has recently released a new statistics related to its Safe Browsing Service. The search giant announced that now more than 3 billion desktops or mobile devices running on Chrome, Safari, and Fi...
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7 Useful Machine Learning packages in R

Choosing a programming language for machine learning depends on the specifications of a given task and the environment where the ML activities are to be performed. Kaggler’s Favourite Tools survey says that out of 1714 Kaggler’s, 543 are preferring the open source programming language R. R is the best choice for data experts who want to understand and prospect the data, using graphs and statistical methods. It has various advanced implementations and ML packages for the top ML algorithms, that are essential to know for every data scientist to design and explore the given data. The Black Box in R is known as Package, which is a collection of pre-written codes that can be used whenever required. According to CRAN (Comprehensive R Archive Network), approximately 8,341 packages are available today. The users can install these ML packages simply by using the syntax i.e install.packages (“Name_Of_R_Package”). List of a few R packages used for Machine Learning is given below:- RODBC implements ODBC RODBC is the perfect package to choose if you want to convert the data stored in Open Database Connectivity or SQL databases into R data frame. library(RODBC) can be used to load the RODBC function and install.packages(“RODBC”) to install the RODBC package. Wordcloud Package in R Wordcloud allows you to create a graphical picture of words and you can arrange the words in a unsystematic fashion, place the most frequently used words close together in the center, identify the frequency of a specific word etc., resulting in a long lasting impression. It can be loaded with library(wordcloud) and installed using install.packages(“wordcloud”). CARET analyses the best algorithm CARET is one of the leading packages in R language. It is always a question that which is the best algorithm for a given task. This package gives the best results in finding the best algorithm and allows data scientists to run various algorithms for business problems. e1071 helps to find the conditional probability Conditional probability is essential for some forms of analyses, for example: to find the probability that a person who buys a cup also buys a saucer. This can be done with the help of e1071 package. This package of R language also provides naiveBayes( ) function based on conditional probability. Kernlab Package Machine Learning Image processing is one of the difficult tasks, which Support Vector Machine (SVM) eases to a great extent. The implementation of SVM is possible easily with Kernlab package and SVM has several Kernel functions such as laplacedot, polydot, tanhdot and much more. SVM is not at all possible and completely functional without the Kernel functions. RStudio Shiny Package The open source R package i.e Shiny allows beautiful and well-built web frameworks for developing web applications using R. Without the need of any knowledge of HTML, CSS, and JavaScript, it allows you to build interactive web applications. Lubridate Package Machine Learning The techniques used with date-times must be powerful. But in some situations, R language fails to achieve it. According to package authors, the Lubridate package has the same and catchy syntax that makes working with dates and times easy, by providing tools. Conclusion Different machine learning packages are available in the Comprehensive R Archive Network repository. All these packages are used to design efficient models, but make sure that you understand the specifications clearly before applying an algorithm. Because a small change in the parameter can change the output completely.  
7 Useful Machine Learning packages in R

7 Useful Machine Learning packages in R

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Choosing a programming language for machine learning depends on the specifications of a given task and the environment where the ML activities are to be performed. Kaggler’s Favourite Tools surv...
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7 Advantages of Developing Apps with MEAN Stack

Various technologies are developed to provide services for increasing demand of mobile and web applications across the globe. Among all, MEAN which is a free and open-source JavaScript software stack holds a great importance for developing dynamic web applications and websites. The acronym MEAN stands for the MongoDB, ExpressJS, AngularJS, and Node.js, which are open source JavaScript based technologies. A full-stack JavaScript framework which is used to develop web apps quickly and easily can be enabled by using all these powerhouse technologies together. Let us discuss some of the advantages of MEAN stack for which developers are choosing it for developing mobile apps and websites. MEAN makes the switching between client and server easier Developing apps using MEAN is simple and fast because developers are allowed to write code only in one language i.e JavaScript for both client and server side. A JavaScript specialist can manage the complete project with the help of MEAN Stack formula. With Node.js, a developer can deploy the applications on the server directly without the need of deploying it to a stand-alone server. Isomorphic Coding is possible with MEAN Transferring the code to another framework that is written in one particular framework is made easier with the help of MEAN stack. This made MEAN stack a leading edge technology and the MEAN stack development companies are considering plenty of technologies in MEAN to boost the transcendence in applications and web development projects. Highly Flexible MEAN allows you to test an application on cloud platform easily after successful completion of a development process. Applications can be easily developed, tested and introduced in the cloud. It also allows you to add extra information simply by adding the field to your form. MongoDB, specifically designed for the cloud, provides full cluster support and automatic replication. MEAN uses JSON JSON (JavaScript Object Notation) is used in both NodeJS and AngularJS. And MongoDB is a component based relational database that offers users to save documents in JSON format. But it is limited for only small to intermediate level companies. Mostly, developers prefer MEAN stack at various stages of applications and web development. Cost effective Developing apps with MEAN stack requires developers who are proficient at JavaScript, whereas LAMP stack requires developers who are expert in MySQL, JavaScript, and PHP. As less number of developers are required to develop apps using MEAN stack, the amount to be invested to hire the number of developers will also be less. So, we can say MEAN stack is highly cost effective and often, the most effective way of dealing with. High Speed and Reusability Node.js is speedy and ascendable because of its non-blocking architecture. Angular.js is an open source JavaScript framework that offers maintenance, testability, and reusability. Powerful directives of this framework progress into great testability and domain specific language. Open Source and Cloud Compatible All the MEAN stack technologies are open source and available for free. It helps the development process using libraries and public repositories and reduces the development cost. MongoDB helps to deploy cloud functionalities within the app by reducing the disk space cost. Conclusion MEAN stack is fast developing, easy to learn and easy to combine around. Any technology available in Stack can be used easily in integration with another, depending on the requirement. However, it is definitely a new cutting edge technology and innovation that will possibly rule the market shortly. Also Read:  MEAN Stack - Evolution Of Web Development
7 Advantages of Developing Apps with MEAN Stack

7 Advantages of Developing Apps with MEAN Stack

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Various technologies are developed to provide services for increasing demand of mobile and web applications across the globe. Among all, MEAN which is a free and open-source JavaScript software stack ...
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How AI is dominating the Field of Marketing: Five Real-Time Observations

Artificial Intelligence platform of IBM, Watson is loquacious: it tells jokes, answers the questions and write songs as well. AI platform of Google can read lips better than a professional does, and it is capable enough to master video games within a short period. It can predict actions on video two seconds before it begins with MIT’s AI. Tesla has another AI platform which successfully powering the company to introduce self-driving cars. All these things are propelling us closer to the world of machines which are more intelligent than humans. Automation and artificial intelligence are profoundly transforming the trade and commerce industry. Scientists, marketers, and futurists agree to the notion that the emergence of Artificial Intelligence and Automation will have a significant impact on society. It is predicted that Artificial Intelligence will bring a complete and spontaneous change in society with more utopian outcomes. Artificial Intelligence in Marketing Field Artificial intelligence is a hot topic in marketing. It is considered as the next frontier of marketing. AI is a broad term which has covered a wide range of different technologies. The concept of AI refers to technology that is seeking to mimic human intelligence. AI includes a broad variety of capabilities such as voice, image recognition, machine learning and semantic searching. Marketers like to wax lyrical about new and exciting updated technologies. They rely on AI for image recognition and speech recognition. It also prevents data leaks in marketing and helps in targeting drones at remote communities. Marketers believe that AI will accelerate sales and marketing. In the next few years, it is expected that we are going to have autonomous, self-driving marketing automation. Machine learning will improve sales as well as marketing software and allow this software to do things without any explicit instructions. Be it predictive lead scoring, content recommendations or e-mail acquisitions, machine learning has improved all the things. AI Transforming Markets Traditional marketing or outbound marketing campaigns are far less efficient in winning and retaining a customer than once they were. AI is important to gain sustainable competitive advantage in this always connected, real world where marketers are required to deliver continuous, customized, insight driven interactions with customers on an individual basis. Brands that have understood the significance of AI and put the right system in place to scale are successful in creating a competitive advantage which is very difficult to replicate. This is because artificial intelligence is not about technology, it is about delivering the perfect combination of content with context. Big data begets big complexities and machines are better equipped to unravel the enormous complexities in case of big data. The introduction of new and more sophisticated technologies are increasing the gap between strategy and system. Marketers and leaders are seeking alternatives to close this gap. Moreover, demand for customized conversions and personalized experiences is accelerating. Which means, marketers are required to have a right system in place to deliver personalized experiences to customers. AI also has made it possible for brands to manage the execution of in-house data because it is as valuable as the brand itself. Marketers are using innovative AI based systems to execute strategies to deliver revenue growth and cost reductions. It is also mitigating the risk of damaging customer relationships. Also Read: Artificial Intelligence Impact on Content Marketing AI implication in Inbound Marketing AI applications have a significant impact in marketing. Different applications are known for playing different roles across the customer journey. Some are known for attracting customers while other are used for conversions and re-engaging past customers. You can divide these applications and their implications concerning RACE framework. Image Source: smartinsights.com How AI is dominating the Field of Marketing: Five Real Time Observations     AI, particularly machine learning is an integral part of marketing. Here is the list of five real-world examples which can help you understand how AI and machine learning is dominating the field of marketing. IBM Watson IBM Watson is a cognitive system or a strategic partnership between system and people. Having Watson means you have an AI platform for businesses which can help you uncover insights, engage with the customer in new ways and make decisions with more confidence. IBM Watson allows you to build Chatbot and virtual agents that answer customer queries and help marketers respond to their needs quickly and efficiently. Watson is working with people in 45 countries and 20 industries. It is helping people to work faster and work smarter than ever before. IBM Watson is successfully transforming customer experiences with bots. Chatbots let users interact with their organizations in real time. They speak to us, answer questions and offer support within seconds. It also allows you to add a natural language interface to the app, website or device. These bots help in breaking down the barriers in fast and efficient customer communications. AI Platform of Tesla MIT ranks Tesla second in the list of World's 13 Smartest Artificial Intelligence Companies. This electric auto manufacturing company is taking advantage of machine learning technology to collect data from all of its cars on the road. The company then utilizes such data to build a better performance into its Autopilot features. Autopilot features, the results of AI technology are successful in reducing accidents by 50%.  The company makes the use of radar as a primary control sensor, camera, and additional technology as a supplement. However, AI technology is helping the car to understand the object more clearly in its way. MIT     MIT has published a paper on new artificial intelligence platform which is known as AI2. This platform makes the use of human input combined with machine learning to reduce false positives and to increase its ability to predict the cyber-attacks. Cybersecurity professionals are facing daunting tasks. They are required to protect enterprise networks from threats and limit the damages when data breaches occur. Cybersecurity is a challenge in industries where the companies are short-staffed, and they are unable to find trained staffs. In such cases, the AI2 platform can help businesses ensure cyber security. The AI2 platform is capable enough to predict cyber-attacks with 85% rate of accuracy. Uber Uber is not only a company that merely schedules rides but is also considered as a software and technology company which heavily relies on machine learning and artificial intelligence. The amount data which the company is required to handle is huge and machine learning helps executives to use that data for business value. Uber is not extremely open about the use of technology, but it helps with logistics and many other aspects of the business. Uber is continuously expanding the use of machine learning by investing in autonomous vehicles. Uber makes use of machine learning and artificial intelligence platforms to figure out ways to retain their customers and to increase customer value.   Salesforce Salesforce is the largest tech company on the list. AI has introduced Einstein platform in 2016, which is customizable for customers and make the use of machine learning, deep learning, predictive analytics, natural language processing and smart data discovery. This AI platform has introduced the larger trend among tech providers which are using AI and machine-learning technology to improve their own businesses. 451 Research’s Patience said that Salesforce is making the use of customer data including emails and collect activity data from tools such as Chatter and external sources like social media. It also makes use of signals from IoT devices and uses them to train machine learning models to drive features within the applications. Artificial intelligence is a new trend which is bringing a storm to the world of marketing. Many marketers are still in the dark. They don’t know how to leverage AI strategies in everyday marketing campaigns. AI-driven marketing solutions sound like future of marketing, but some creative minds are here to take over the most tedious and time-consuming tasks that marketers are struggling to deal with. Above mentioned real-world examples of AI suggest that it is offering marketers an unprecedented ability to improve personalization, productivity, and performance. Final Thoughts     “Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as we manage to keep the technology beneficial.” (Max Tegmark, President of the Future of Life Institute) We have only scratched the surface of Marketing to collect real-world examples of AI and machine learning technology; there is a lot more to find out! From Siri to self-driving cars, from Google’s search algorithms to IBM’s Watson to autonomous weapons, AI is a part and parcel of nearly every field. Artificial intelligence and machine learning are useful for all functions of a business. Marketing is also not an exception. AI has a significant impact on marketing, and it is going to shape further, the future of marketing and a way to forge the relationship between companies and clients.  
How AI is dominating the Field of Marketing: Five Real-Time Observations

How AI is dominating the Field of Marketing: Five Real-Time Observations

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Artificial Intelligence platform of IBM, Watson is loquacious: it tells jokes, answers the questions and write songs as well. AI platform of Google can read lips better than a professional does, and i...
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