Data Mining
James Church
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Data extraction via metadata puts images’ security at risk

20th Jul `18, 11:00 AM in Data Mining

Do you think that your images and photo albums are safe online? Are privacy settings enough to avoid…

Do you think that your images and photo albums are safe online?

Are privacy settings enough to avoid the sneak peeks of hackers on your Facebook account?

It won’t be wrong if I would say that the information is aired everywhere. The cyberspace is an expansive radius where trillions of data float publicly. Images and various photo-albums live permanently in it.

Put on your thinking cap to answer if the images capture any precious information except stills of the moment. Well, it’s true but a bit. It’s a meta-data where the value of your images gets stored. However, they capture the moments permanently. But, when it comes to deriving actual value via image data extraction, the meta-data of those snapshots proves a goldmine.

Do you know what this meta-data is? Let me present its transparent overview.

What is Meta-data?

The meta-data is a set of information. Even, a Wikipedia publishing called it the data about the data. In terms of digital marketing, it’s a description of the web pages. The keywords embedded in it are called the meta-tags. It enables the search engine bots to filter the most accurate information for catering to the researchers/users. It’s no less than an attractive advert for the online users since they decide to throng the page after going through it.

You can basically spot it in the three most famous styles as Descriptive, Structural and Administrative metadata. Each holds its unique identity and significance. In all, it’s the best tool the users ever have to extract an online source of information that meets their requirement.

What’s in the meta-data of the images?

1. Meta-data is a GPS tracker:

The metadata of your snapshot is a precious repository of information. Are you thinking, what makes it so precious?

Well, it can be an infiltration to your privacy as well. Hundreds of online tools, like Geotag Photos Panoramio and Flickr, are flocking the virtual network. They’re capable of tracing your location automatically when you clicked it. Even, your digital camera can be a spy. The Exif file format contains standard tags that trap your location data. Modern Android and iOS phones have an inbuilt GPS receiver. It may be installed in the flash connector or hot shoe of your mobile phone or digicam. If you want to conceal your whereabouts, it won’t let it happen unless you remove that data using a metadata removal tool.

A time stamp on your digital snapshot can also create the same threat. The automatically embedded GPS data can be embedded in a digital photograph. The IT experts can correlate the time printed on it with the GPS record using a map or mapping software. It makes recording & extracting the GPS data like a walkover.

Moreover, you tend to add some title, names and other information with your selfie or a groupfie. It would also prove an asset to the data extractors or data extracting companies.

2.Integrated Risk Factor:

The internet is a hub of hackers. They don’t let even a single chance to go without having a sneak peek at the photographs of the prospective victims. They employ data mining methods and tricks to catch on their prey.

A Pennsylvania citizen Luther Lewis, 20, conspired to hit a drug dealer. The GPS tracker in the latter one’s smartphone became his enemy. Although he survived the attack, yet you can’t deny that the GPS can be a conduit to pass through vital information. Even, Emma Watson is also its victim.

Likewise, there are millions of others who are victimized by the uninvited GPS data trackers.

3.Image Data extraction helps in a decision making:

Most of you are habitual to share the moment when you walk in the air. Whether it’s a special function or any ordinary day out, you love to upload your images on your Facebook or Instagram account. These platforms also save your images’ meta-data. Later on, they extract the decisions or marketing plans on the basis.

The cloud is also a pivotal data mine. It holds zillions of company employees database with their pictures. That information also gets stored in the metadata. The data extraction & data mining organizations target them to get a stronghold over the profile data. Subsequently, they streamline their production or workflows according to the analysis of that data.

Summary:

The metadata is a precious data repository wherein the data mining & research companies find the richest information for their business. The metadata of images or snapshots helps in tracing the whereabouts and the customer behavior. This vital information becomes a lifeline to many data extractors.

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Visualization
James Gorski
James Gorski
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Seven best practices of data visualization in 2018

18th Jul `18, 11:00 AM in Visualization

Data has grown increasingly complex over recent years, thanks in no small part to computers becoming fundamental to…

Data has grown increasingly complex over recent years, thanks in no small part to computers becoming fundamental to how all businesses work. This has its positives, as the more data and metrics a company can work with, the more performant and reactive the be.

The real problem is that many people are very visually-oriented, meaning a wall of text, numbers and technical terms simply aren’t all that digestible, even to people who understand what all this information means. Thankfully, people long ago mastered the creation of charts, graphs, tables and spreadsheets to intuitively represent data in easily-digested ways.

These were in fact one of the biggest driving innovations in the advancement of office software at the dawn of the computerized office, and this refinement and evolution haven’t stopped since. You’re probably all too familiar with the creation of these charts and graphs, if you’ve spent any time working in a modern office.

Sadly, you’re probably also familiar with those colleagues in your department who’re bad about providing needed visualizations, or, let’s be honest, just don’t do a great job.

This can result in one member of a team being saddled with producing all of the visualizations. Not only is this inefficient and unideal, it can also result in a lot of errors. If one person gets stuck handling all of this, they may be working with data they don’t entirely grasp, especially in situations where some specialism is present.

In other words, making sure your entire office is on the same page about data visualization is very necessary, and is only going to be more so in the future. Perhaps it’s time for your entire department to stop, take a breath, and collectively look at modern best practices for data visualization.

1. Agree on a Methodology

Establish a routine for how data visualization is produced by everyone involved. Standardize your data acquisition process, visual schema and design parameters and adhere to these pretty strictly. This will ensure consistency in all visualizations, and the target audiences will become accustomed to reading them easily.

2. Understand Your Audience

Data isn’t processed the same way by everyone, and the data present may mean different things to different people. Know who your audience is, and what their occupations are. This will allow you to design your visualizations in a way that makes the most sense to them, and presents the data everyone needs.

3. Determine the Desired Outcome

Presenting visualized data is something of a call to action. Know in advance what reaction you are trying to evoke from those whom will be reading it. For example, if you want to indicate status quo, you’ll be aiming for little to no reaction. If you’re conveying positive gains, you’ll want to evoke a positive reaction.

4. Use classification

There is more than one type of visualization, and each one serves a very different purpose. For example, an operational design is intended to show the various metrics involved in regular operation of one or more business processes. Strategic visualizations are used for planning actions or reactions to situations, as well as to plan the implementation of change. Analytical visualizations provide logistics and statistics often important to those working in the company’s financial departments.

5. Use Profiling

Be sure to profile your data in logical ordering that makes common sense to everyone. This is a common pitfall in visualization, especially when it’s coming from someone to whom this has often been an afterthought. Be sure to categorize your data, putting stuff together that belongs together. Be sure to order data in a common, sequential manner of increasing or decreasing the value. Finally, be sure to be as quantitative as possible, conveying the value of the given metrics sampled in the visualization.

6. Use visual Elements as Intended

Sometimes, people like to get a little too creative, and have a tendency to be a little too artistic with how they implement visual elements in these documents. Consider the way the human brain processes a visual – which direction will the eye scan? This is known as eye tracking in UX, and it’s just as important here. Ordering elements and data in an ordinal fashion that follows this flow is imperative.

7. Fail Faster

A common term in recent years is “fail faster”. This is most commonly used in various development environments, meaning “prototype, test and try again as often as possible”. This is applicable to data visualization, though it can be tricky and even seem disparaging to cope with. Don’t spend too much time trying to get your visualization practices to a 100% success rate before trying them out on your target audience. Prototype them, get input on readability, and apply the process to real use in the meantime. This is called iterative design.

It all seems pretty straightforward when you sit back and look at it, doesn’t it? Understanding your audience, your corporate culture and human nature as a whole is the key to proper data visualization. Fortunately, once you understand these principles, it’s very easy to enforce them relatively effortlessly with the modern, more intelligent office software commonly in use.

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Hadoop
Joseph Macwan
Joseph Macwan
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Developing intricate workflows is a cakewalk with Apache Spark!

16th Jul `18, 11:00 AM in Hadoop

The most common and traditional data processing applications are becoming insufficient in handling a great volume of data…

The most common and traditional data processing applications are becoming insufficient in handling a great volume of data sets so prominent in corporate computing today. Therefore, it is a little difficult to find a platform which can help the companies stay competitive in this Big Data race.

Apache Spark is one such open source tool which is developed for Big Data processing. It is undoubtedly one of the most popular and highly used data processing software.

Advantages of using Spark

  • Even processing complex and highly difficult analytics become easier with Spark.
  • It has an amalgamated framework for handling a wide range of diverse data sets.
  • The software is known to processes Big Data a lot faster than a plenty of competitors
  • Spark consists of assistance for Structured Query Language, machine learning, streaming data as well as graph processing.
  • It also offers a very smooth application programming interfaces (APIs), which is used to process Big Data sets along with over 100 operators for modifying data.

Easy to develop intricate workflows and solve complex analytics

Sparks makes it very much possible to answer multifarious questions in a small time frame as it has the capability to store data in memory.

It contains world-class libraries, and these libraries amplify the productivity as well as efficiency of the developer. They are amalgamated flawlessly to develop complex workflows with minimal stress.

The use of Morticia

The software has empowered engineers with the capability to effortlessly develop extremely intricate and solid data flows as well as transformations. Though, logical strategies and debugging of executions rapidly become problematic to grow the current days’ prevailing mechanisms.

Debugging Spark tasks is a complex activity as it needs alignment of several sources of data. Spark’s individual execution logs, Unified Interface, along with the query should be scanned to easily develop and manage a complex workflow. However, still it is a little difficult to know how the code that an engineer writes gets hoarded into a set of junctures and RDDs.

It’s considerably tougher when SparkSQL is utilized due to the institution of a supplementary logical layer. There are other tools like the best one, Morticia, which are fit got executing analysis of highly complex workflows, visualizing, debugging etc. The tools offer a graphical representation of the spark implementation DAG at a rational level, glossed with data as it executes.

Old workflows are marked, and this allows the user to do a proper post-mortem analysis. An intuitive graph by Morticia is a nice way to visually display RDDs, spark stages, as well as connected logical operators. Every stage exhibits significant execution data like the status, number of tasks, start and end times, run-time metrics, quantity of input/output archives, input/output dimensions, as well as execution memory. The graph is formed through the integration of RDD nodes as well as the connected logical operators inside the stages.

Every node shows a plenty of valuable diagnostic data like sum of partitions, operation scope, schema, as well as the input/output records. Successfully allowing the data scientists to develop, modify and manage even the most complex of the workflows, Morticia lessens the effort of the developers and the assistance needed by core infrastructure engineers.

Notebook Workflows

Apache Spark provides unified platform that eradicates the friction amidst data assessment and production applications. Notebook Workflows offers the user the fastest, smoothest way to develop intricate workflows from their data processing code.

Notebook Workflows are basically a collection of APIs to bind together Notebooks and process them in Job Scheduler. Developers create the workflows inside notebooks, with the help of the control structures offered by the source programming language.

These Workflows are overseen by the Jobs Scheduler. And, almost each and every workflow is endowed with the production feature offered by Jobs, like timeout mechanisms and fault recovery. It also takes benefit from the version control and security attributes. This further enables the users control the evolution of intricate workflows via GitHub, and safeguarding permission to production infrastructure via only a role-based access control.

Conclusion

Apache Spark Developers offers various ways to not only create intricate workflows, but to manage them as well. Spark is not only easy to use, but is known to fasten up the process of Big Data processing as well.

Whether you select to use Spark or any of the other famous big data processing softwares, integrating any data processing software into the business operations needs preparation and expertise. It’s significant to work with a bunch of experts to diminish implementation issues.

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Machine Learning
James Warner
James Warner
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6 revolutionary things to know about Machine Learning

13th Jul `18, 11:00 AM in Machine Learning

We are stepping into an avant-garde period, powered by advances in robotics, the adoption of smart home appliances,…

We are stepping into an avant-garde period, powered by advances in robotics, the adoption of smart home appliances, intelligent retail stores, self-driving car technology etc. Machine leaning is at the forefront of all these new-age technological advancements. The development of automated machines which have the capability match up to or maybe even surpass the human intelligence in the coming time. Machine learning is undoubtedly the next ‘big’ thing. And, it is believed that most of the future technologies will be hooked on to it.

Why is machine learning important?

Machine learning is given a lot of importance because it helps in prophesying behavior and spotting patterns that humans fail to predict. Machine learning has a myriad of very useful practical applications. Through Machine Learning, it’s possible to manage formerly baffling scenarios. After understanding the Machine Learning model with efficient generalization capabilities, it can be used to take important decisions accordingly. Machine Learning enables an individual to take a decision based on a host of scenarios. One can clearly cannot write a code that has the capability to manage all the new scenarios.

Artificial Intelligence is capable of performing various activities which require learning and judgment. From self-driven cars, investment banking, many healthcare-related functions and recruitment functions, AI is already being used to accomplish various tasks in different domains.

6 revolutionary lessons about machine learning

Machine learning algorithms are capable of finding out ways to execute necessary tasks simply by generalizing from scenarios. This is more practicable and cost-effective whereas, manual programming is not that cost effective and feasible. The increased amount of ‘data available’ is sure to give rise to more number of issues related to the data captured as well. Hence, machine learning is the thing of the future as it will be used widely in computer and other fields. Though, developing effective machine learning applications need a considerable amount of “black art” that is not that easy to find in manuals.

Listed below are the 6 most valuable lessons about machine learning:

1. Generalization is the core

One of the most basic features of machine learning is that the algorithm have to generalize from the data from training to the complete domain of all unseen scenarios in the field so that it can make correct extrapolations when you use the model. This process of generalization needs that the data that we utilize to train the model has a decent and dependable sample of the interpretations in the mapping we wish the algorithm to learn. The better the quality and the higher representative, the smoother it will be for the model to understand the unidentified and fundamental “true” mapping that subsists from inputs to outputs. Generalization is the act of moving from something precise to something broad.

Machine learning algorithm is the techniques of automatically simplify from historical scenarios. They have the capability to generalize on a higher amount of data and a faster rate.

The most general mistake that all the machine learning beginners generally make is to test on training data, and till have the impression of success. If the selected classifier is then tried on new data, it is commonly no better than random guesstimating. So, if you onboard someone to develop a classifier, make sure to keep a little bit of the data with you. Also, try and test the classifier that they give you on it.

2. Learning = Representation + Evaluation + Optimization

An ML algorithm is broken into 3 parts; representation, evaluation and optimization.

Representation: The data needs to be poured into an appropriate algorithmic form. For Text classification one may extract characteristics from your full-text inputs and mold them into a bag-of-words representation. Conversely, picking a representation is synonymous with choosing the set of classifiers that it can perhaps learn. This set is named as the hypothesis space of learner.

Evaluation: It is a metric that helps us understand what we are doing at the moment. An evaluation process is required to differentiate good classifiers from not so good ones. Say, if you are trying to predict a figure across a test say for example for a set of size n, here, you can calculate Mean Absolute Error = 1n∑ni=1|observedi−predictedi| or you may even choose to use the Root Mean Squared Error = 1n∑ni=1(observedi−predictedi)2−−−−−−−−−−−−−−−−−−−−−−−−−−√

code

OR

Optimization: It refers to the process of finding out ways to select varied techniques to optimize it. For instance, we can simply try every hypothesis in our hypothesis space. Else, we may also choose to utilize a much more intelligent technique to try the most favorable hypothesis only. Also, as we are optimizing, we may utilize the evaluation function to understand if this specific hypothesis is good or not. Optimization technique allows the user to find out more about the classifier created if the evaluation function has got more than one optimum. Firstly, the beginners should start with off-the-shelf optimizers, and later on they can move to the custom-designed ones.

3. Data alone cannot do the job!

Generalization is the main purpose however, it has a major concern that only data is not enough, irrespective of the quantity. However, fortunately, functions we want to master are not drawn consistently from the bunch of all arithmetically possible functions! Even the most general assumptions, including, smoothness, similar examples having analogous classes, inadequate dependencies, or restricted complexity – are mostly enough to function well, and this is one of the main reasons that make machine learning so powerful. Basically all the beginners syndicate knowledge with Big data to produce programs.

4. Beware of Overfitting

We may end up just fantasizing a classifier if the data is not adequate, and is incapable of completely determining the apt classifier. This issue is termed as overfitting, and it is considered as a nuisance of ML. Noticing overfitting is beneficial, but it doesn’t resolve the issue. You have to find ways to get rid of it. Fortuitously, you have a plenty of options to try. Cross-validation helps to combat overfitting. Training with more data, regularization, removing features, early stopping, ensambling are some of the other methods to offload Overfitting.

5. Feature engineering is the key to success

Feature engineering is the technique of using core domain knowledge of the data to develop features that make ML algorithms work better. If it is done properly, it amplifies the predictive strength of the algorithms by developing features from raw data. These features simplify the complete machine learning process. Utilization of several independent features which nicely correlate with the class, then learning becomes easy.

6. Accuracy & Simplicity are different

Occam’s razor superbly states that objects should not be increased beyond the requirement. This means that of two classifiers have similar training error, the simpler of the two will probably have the nethermost test error. Every machine learning project should be initiated with a relentless aim on the business question that you wish to answer. You should start by formulating the main success principles for the analysis.

Applying Occam’s razor and selecting the model that is easiest to interpret, to elucidate, to deploy and manage are the key steps to take to build powerful machine learning programs. It is suggested to choose the simplest model that is adequately accurate, however, make sure that you know the issue deeply to know what “sufficiently accurate” implies in practice.

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Data Science
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How to export PDF data to Excel or CSV for easy analysis

12th Jul `18, 02:20 PM in Data Science

A term “data scientist” was coined only a decade ago. Since then, the job of discovering trends and…

A term “data scientist” was coined only a decade ago. Since then, the job of discovering trends and drawing conclusions from huge data sets has become one of the most sought-after professions across wide variety of industries.

Data often comes in PDFs or their worse cousin, scanned PDF, so it needs to get unlocked and usually exported to spreadsheet programs like Microsoft Excel or CSV before one can run any sort of analysis on it. This leads us to a conclusion that a reliable PDF converter that allows its users to quickly and accurately convert native and scanned PDFs to Microsoft Excel or CSV is one of the key tools in every data analyst’s toolbox.

PDF to Excel converters are widely available and come as desktop, web-based and even mobile solutions. Data analysts dealing with simple, clean and organized PDF tables can very often rely on free, usually online PDF tools such as Cometdocs for example, which is well known in the data journalism space.

However, what if you are dealing with complex, messy and, even scanned (image) PDFs which are not easy to read and parse without the advanced OCR technology? Or if your PDF table needs some adjustments like replotting or deletion of some rows and columns? Most data analysts in such cases perform simple PDF data conversion to a spreadsheet and then spend hours on cleaning it to prepare for analysis and further manipulation.

But, with the rise of data science, journalism and an ever increasing need to make sense of big data, software developers have recognized the need for more elegant and sophisticated ways to unlock data from PDF and export it in a clean and organized fashion into Excel or CSV for easy analysis. One such PDF solution is Able2Extract Professional 12 that specializes in PDF to Excel conversion. It comprises multiple features that allow users to quickly, securely, and accurately export PDF data into Excel and CSV, and even to adjust PDF tables before conversion and preview the output without leaving the software interface. Let’s take a quick look at what exactly it brings to the table when it comes to exporting PDF data into Excel (and CSV).

Export PDF to Excel or CSV Quickly and Accurately

Like some other PDF to Excel converters, Able2Extract comes with easy to use graphic interface that allows users to export PDF to Excel spreadsheets and CSV in just a few clicks. In this particular case, it is a no-brainer three step process: open, select and convert. Basically it comes down to specifying the part of the document to convert (be it a single PDF table on one page or a long multipage file with multiple tables and content) and clicking the easy recognizable Excel or CSV icon (as seen on a screen capture below). This is called the standard or automatic conversion. The data gets converted quickly and very accurately. With Able2Extract being the desktop solution, it goes without saying that your data is completely safe and secure all the time as it never leaves your computer.

image1

Custom Convert PDF to Excel

However, the real power and advantage of this software for big data users lies not in automatic conversion, but an advanced or custom conversion feature. This advanced feature allows users to set up table and row structure and preview the output before converting. So you get to control and adjust the output before conversion to Excel and thus save yourself the hassle of cleaning up spreadsheets after PDF data has been exported to Excel.

You can customize your PDF to Excel conversion using a myriad of options for setting up table structure and adjusting the look of your spreadsheet prior to conversion. These options you access by choosing the Custom PDF to Excel conversion as you can see below.

image2

From managing named table structures, rows, columns, headers, and footers, to adding, deleting, and replotting tables, you can adapt the Excel output almost beyond recognition (if that is what you would want, of course). You can preview your output after every customization to check if that is what you need.

Set up and Save PDF to Excel Templates

Able2Extract also allows you to set up and save custom PDF to Excel conversion templates for later reuse. This is a real timesaver when there’s a lot of PDF files formatted in the exact same way. Here’s how to do save and load conversion template in case you want to try:

To save your custom PDF to Excel template for later reuse, go to File menu and click on the Save Custom Excel Template As, then choose the name and location for it on your drive. To load the template next you receive a file with the same layout, just go to the File menu after you’ve opened the file and selected an area for conversion, click on the Load Custom Excel Template and choose the template file you previously saved.

Convert Scanned PDFs to Excel

As already mentioned above, Able2Extract integrates OCR technology which recognizes the characters from images (when you scan the hard copy of some archived document to PDF, it is basically an image representation of those characters which can only be recognized by OCR technology). This will allow you to extract data from scanned PDF tables and files and turn it into editable Excel spreadsheets or CSV.

Quickly Convert Multiple PDFs to Excel

To wrap up the breakdown of useful Able2Extract features when it comes to handling PDF data and analyzing it in Excel or CSV, it is worth mentioning the program’s ability to convert multiple files at once using the batch feature easily accessible from the Command toolbar.

Pro Tip: Use PDF Forms to Collect Data

One more area where Able2Extract can prove handy to anyone handling data is data collection. You can use it to create and edit PDF forms which are used for collecting data.

image3

If your main concern is simply exporting PDF data to Excel and CSV for further analysis, data collection may not be of interest, but it comes with the software, so it doesn’t hurt to be aware of it. Able2Extract Professional also converts PDF to about 10 more popular file formats, lets you create and secure PDF, edit and annotate PDF, and more.

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Education
Richard D. Eddington
Richard D. Eddington
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Top 5 Artificial Intelligence tutors in education

11th Jul `18, 04:18 PM in Education

What role does AI play in learning and education? In fact, great. First of all, systems with AI…

What role does AI play in learning and education? In fact, great. First of all, systems with AI help to optimize the work from the inside. Sooner or later there will come a need to unite the work of people and smart devices. This becomes obvious, that technologies has really changed the way of learning and education.

Here are Top 5 Examples of Artificial Intelligence in Education for efficient optimization of resources.

1. iTalk2Learn

iTalk2Learn is an online learning system designed for primary school students. A joint project developed in Europe, the developers of the system require that machine learning is used to compile personal lesson plans.

The program focuses on the factions, which, according to the developers, remain an obstacle to the development of mathematics for a disproportionate number of students. Thanks to the use of seven modalities, the program declares the balance of structured activities with interactive practical work.

The program model includes functions such as speech recognition. Currently, the technology of machine learning is calibrated only for English speaking users. Developers analyze the transcripts from these records in order to “teach” the system to recognize common questions and speech images. The company claims that iTalk2Learn’s German speech recognition also works.

2. Thirdspace Learning

Thirdspace Learning is one of the largest online platform for online math in the US. Starting from 2016, the company integrates AI into its activities. The goal is to track students’ progress and optimize their services. Also, the company sets as its goal in the near future to provide its online teachers with the response on their real-time tutoring using AI.

If the student misunderstands the task or if the teacher misses something important, Thirdspace wants the artificial intelligence to identify the problems and warn the teacher before the problem begins to aggravate.

The founder of the company, Tom Hopper, stated that ThirdSpace Learning notes every training session, which is thousands of hours. According to the company’s website, more than 400 math specialists have been found and taught and up to now, more than 100,000 hours of mathematical training have been completed.

It is likely that Thirdspace seeks to increase its database to set a good foundation for its efforts to implement AI. This would be particularly suitable for finding out the styles of student learning and to institutionalize mathematical learning in a more personalized way.

3. Duolingo Chatbot

In 2016, Duolingo informed about the release of iPhone-compatible chatbots for learning to speak fluently in French, Italian, and Spanish.

In accordance with conventional bots, users proceed with the application using hints in their native language. Nevertheless, the company assures that it’s chatbot is unique because it is capable of “receiving and responding to a few thousands of unique answers” instead of a more restricted amount of hints.

Advantages include the “help me reply” function, which offers users possible answers if the person finds it difficult to answer. Unfamiliar and new words can also be translated or checked it pronunciation in real time during chats.

Nowadays, the company claims that it serves more than 150 million users. A study conducted by Ruman Veselinov, Ph.D. and John Grego, Ph.D., on the effectiveness of Duolingo for learning Spanish, reports that this platform is a really effective tool.

4. Thinkster Math

Thinkster is a mathematical training platform that combines AI and machine learning to help mathematical teachers track student performance. According to his website, Thinkster uses AI to visualize the student’s thinking process when he or she is working on a mathematical task.

The training platform records the work of the student and tracks the steps that the student has chosen to solve the mathematical problem. As a result, mathematical trainers can identify problem areas and report on strategies to increase student productivity. The goal is to personalize the learning experience of each student based on performance data.

The platform will monitor the student’s ability to solve tasks, which include distractions to achieve common mastery in this area.

5. EdTech Foundry

The Norwegian company EdTech Foundry recently announced Differ, chatbot, which can help students in education. The system is created to provide immediate answers to students’ questions, which, as a rule, are repeated every semester.

From the information provided on the company’s website it is known that the questions vary from general to more particular questions about the syllabi and the expectations of the course. Moreover, except answering questions, chatbot sometimes offers academic articles and reading to students who are relevant to their coursework. Chatbot also offers clear ways that a student can contribute to the class, for example, posting on forums.

To function, chatbot uses special algorithms and must accumulate and work on questions and student-teacher interaction all the time to improve its ability to give proper answers recommendations. The company reports that the results of its trial program showed five times higher the participation of students in the messages sent by the bot, compared to the teacher-human.

In the modern world, many people can already experience these opportunities on their own, and evaluate the advantages and disadvantages of AI.

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Media & Entertainment
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BDMS
 

Here is how Netflix uses data to drive success (Infographic)

10th Jul `18, 04:38 PM in Media & Entertainment

With over 100 million subscribers, there is no doubt that Netflix is the daddy of the online streaming…

With over 100 million subscribers, there is no doubt that Netflix is the daddy of the online streaming world. Netflix’s speedy rise to dominance has movie industry leaders taken aback – forcing them to ask, how on earth could one single website take on Hollywood? The answer – big data.

One of the major success stories in technological disruption, Netflix is a prime example of how big data can be leveraged to totally transform an industry. Netflix offers something wholly unique, in that it provides a service that is completely tailored to every individual user’s taste. The key to this is data. The Netflix algorithm constantly gathers data about users’ activities – what they watch, what they skip, what they search, and so on. They then use this data for a range of things, from creating personalized movie recommendations to choosing which new series to commission.

In perfecting the science of data, Netflix has managed to design a platform so tailored to each user’s needs that cinema and television simply cannot compete. To learn more, check out this very insightful infographic from the guys at Frame Your TV which shows how Netflix has taken advantage of big data to become one of today’s most successful online companies.

@SOURCE

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Artificial Intelligence
Kayla Matthews
Kayla Matthews
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This AI construction worker just figured out the fastest way to put up a building

09th Jul `18, 11:04 AM in Artificial Intelligence

Successful construction, for the most part, is about maintaining proper efficiency and productivity throughout the scope of a…

Successful construction, for the most part, is about maintaining proper efficiency and productivity throughout the scope of a project.

Whether a firm has 20 ongoing development projects or one, they must finish them all on time — sometimes even to meet strict deadlines.

Designs and architectural plans help facilitate the development of a property and make sure everything and everyone involved are right where they need to be. But then there’s the planning phase, where one must consider their supply of resources, available manpower and equipment, as well as just how long the project should take.

Generally, administrative or project managers will flesh out this plan just before the operation begins.

It’s not that the process is particularly difficult, just tedious, as it requires managers to consider nearly every aspect of a project — including alternate methods and timelines in case something goes wrong.

What if there is a better way?

California-based Alice Technologies has created an AI system called ALICE that can plan proposed projects more effectively by coming up with a detailed schedule.

The system essentially digests a 3D model of the project in question, and will then provide steps for how to build the structure, complete with the order in which to place components and how much time it should take to do a particular step.

It can do this much faster than any human ever could — within mere minutes, a user can see the results — and much more effectively, as well.

In fact, the system can identify more precise and effective ways for development, some of which even seasoned project managers might have missed. And it’s all thanks to modern technology, in this case AI, which runs on machine learning algorithms, big data systems and lots of information.

It highlights how transformative AI and similar technologies will be for the construction industry. It hearkens back to that single question: What if there is a better way?

Artificial Intelligence, the Effective and Modern Way

AI, in and of itself, is incredible. It was primarily born of the concept of automation or the idea to get things done faster, yet more effectively, with little to no input.

When a group adopts AI, they are essentially relinquishing control, in many ways, and allowing the resulting system to take the reins.

To some, this can be frightening. How in the world could anyone allow a computer or non-sentient system — driven by deep neural networks and pre-programmed algorithms — to do tasks for them, particularly things humans have done for hundreds of years?

Yet, AI is taking hold in many industries and fields today, in ways people never imagined. The health care industry is using it to solve mysterious and known ailments alike, using data analytics. Retailers use it to deliver highly targeted and personalized experiences to consumers.

It’s in our homes and mobile devices, powering virtual assistants who can react and interface with other technologies nearby. Even entertainment companies like Spotify and Netflix are using AI to build an accurate profile of our viewing habits and suggest better content.

AI is a modern, effective and reliable solution for automating and improving many tasks, processes and experiences.

Knowing this, why wouldn’t the construction industry take full advantage of its capabilities?

AI Applications in Construction and Development

One of the most powerful applications of AI in construction and development is its integration with robust business management software. Such applications are comprehensive solutions for the planning, maintenance and management of a construction crew. It can generate checklists and workflow patterns, offer collaboration and communication tools and keep everything organized.

It helps facilitate the regular operation of a crew within the confines of a budget, available manpower and resources. AI can generate detailed reports, oversee operations and help improve efficiencies by eliminating operational bottlenecks. It can even deploy in a way that keeps everyone on track — including onsite crews — without external input.

Similarly, building information modeling (BIM) applications can also integrate with AI. They essentially offer digital blueprints or structure plans, in a real-time, shared information structure. Onsite workers might have the current BIM plans pulled up on a tablet or phone.

Simultaneously, project managers may have the BIM visible in their office, complete with up-to-date information on the status of the project, revisions and potential design hiccups.

But both these applications involve planning and design. AI can also improve worksite safety, automate equipment, hardware or tedious processes, predict potential issues or events and help with site surveys and property analysis.

It already is revolutionary for the world of construction and development.

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Marketing
Diana Bhathena
Diana-Bhathena
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How Artificial Intelligence is changing the face of marketing

06th Jul `18, 01:54 PM in Marketing

Have you, as a marketer, sat back and thought about how much energy you expend in identifying keywords,…

Have you, as a marketer, sat back and thought about how much energy you expend in identifying keywords, creating blog rollout plans, personalizing content for marketing, creating all those social posts and overall formulating content strategies?

Now, step back and imagine if a lot of these activities were performed by machines, and all you had to do was actually enhance and optimize these activities. Or simply be the thought and enabler behind the plan? Well, it’s possible today. There are a lot of tools which enable you to free up your time and give you that chance to perform better.  And this because of artificial intelligence.

Artificial intelligence is a term used to describe an array of connected technologies which are at different stages of maturity. Some of these are deep learning, voice image recognition, NLP (or Natural Language Processing) deep learning, and so on. AI is a term that in concept, actually imitates the human brain, to enable the actions it performs.

How is this done?  It’s not so hard if you break it down. Firstly, algorithms that have scoured through past data create patterns and in turn the data into predictive models. Other applications and systems then act as moderators to enable models.

AI has over the last several years seen a massive shift in accessibility. In marketing alone, AI is being used to optimize campaigns, to mass tap customers through email and ads. And to even groom customers or quantify and qualify leads. The data that this intelligence provides marketers with is immensely helpful and enables people to make scientific, data-backed decisions that can equip companies to reach better, more targeted audiences, in shorter periods of time with smarter campaigns.

Artificial Intelligence will only be able to impact marketers and their teams if the quality of the data provided is very good. The term, Garbage In – Garbage Out, is used to showcase how marketers often use data that cannot derive useful insights unless the input is of great quality.

Martech companies delivering top of the line customer experiences are all shifting to using AI in some way or another. From simplifying the process to enabling customer feedback and testimonials, to breaking down big data, many companies are using AI as the foundation for large campaigns. Be they large ad optimization campaigns or for market research! AI is helping the marketer also perform alpha and beta testing for customer experiences at a large scale – in real time! These processes would have otherwise been tedious, time-consuming and hard to process manually and in real time.

AI will over the coming years help brands and marketers work in sync on one of the most diverse spaces in the consumer market – the monetization markets. Artificial intelligence can help test various types of marketing collateral to see which lead form works or which display ads are truly liked or not, and in turn, which will convert an advertisement to a lead or a sale. Optimization of content and collateral will then, in turn, change the way products are marketed to specific audiences, basis data and backed by choice, leading to improved conversion rates.

Ask a marketer today what their biggest problem is, you are sure to hear that most will say it is maintaining the attention of the consumer through relevant content. The 3-8 second window is sometimes not enough to engage a customer, and instant gratification seems to be the need of the hour. Machine learning comes into play here. It enables enterprises to keep abreast of the rapidly changing needs of their consumers, as well as their likes, dislikes, and preferences. Models can be built and learned from which tell stories of which customer patterns liked what, while empowering the customer with the right information to make purchases or showcase interest.

One sees the future of Marketing, as the say Marketing 5.0, shift from the more traditional and digital to a more sentient and artificial. This shift will also create an array of new opportunities for marketers. Roles like Agile marketers, growth hackers, marketing technologists will soon emerge and create a diverse lot of marketers, ready to take on this next phase of the Marketing cycle, mining better consumer insights and strategically engaging with the right consumers. All while making these very consumers, feel special as they receive personalized messaging, and relevant, yet disruptive content.

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Media & Entertainment
Ranjani Ragotham
Ranjani
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AI & Big Data – TV channels create better content for their viewers

05th Jul `18, 01:43 PM in Media & Entertainment

The TV industry is a constantly expanding and evolving world. With unforgettable classics like the Star Trek series…

The TV industry is a constantly expanding and evolving world. With unforgettable classics like the Star Trek series in 1968. To the hilariously absurd mockumentary, The Office. To the dark and modern House of Cards.  Besides differences in their storytelling structures, these shows were diverse in terms of the platforms they were available on. Today, more and more people are living boring old television behind, and are turning to websites like Netflix for good shows and movies.

The growing popularity of online streaming apps and web companies like Amazon Prime and Netflix has boosted the quality of content available to viewers. Netflix’s ground-breaking predictive analysis algorithm has proven that content creation for media entertainment is just getting better and better.

However, viewers have always been eager to use a wide range of platforms for different experiences. While TV providers are fighting a tough battle against online streaming companies, they are using every means possible to stay in the race. And with the recent boom in data analytics, cable TV providers have taken full advantage of big data. Right from enhancing their customers viewing experience. To utilizing cutting-edge big data solutions to learn more about their viewers and their preferences. And subsequently, leverage these insights to adjust their channels’ content according to the demands of the audience.

But who watches TV in the age of online streaming?

Surprisingly, a large chunk of people still relies on their TV sets as a source of entertainment and news. In America alone, an approximate number of 50% of households still subscribe to cable TV providers. Such as Time Warner Cable, Charter Spectrum, and Comcast. Studies show that these households spend almost five hours a day in front of a TV.

And that’s why data is highly essential for the TV industry. Not only do providers and production companies gain profits by creating engaging content. But it also allows advertisers to create more targeted advert content. Which can be run at opportune timings for specific audiences. For example, Time Warner Cable is able to provide personalized advertisements to their viewers with the help of growing data sets available to them.

However, before cable companies can do anything with big data, there remains one question to be answered. Where is the data coming from? Without the algorithms and direct connectivity which companies like Netflix have, how are cable providers getting data to come up with actionable insights?

According to an infographic shared by Telco Transformations, there are a number of ways cable companies can mine relevant data to gain insights. These sources include field operation and administrative data, geographic information systems, content and customer management systems.

Data can also be gained from customer premise equipment (CPE) which include internet modems, Wi-Fi routers, phones and other third-party equipment. Certain companies also use public data sets available, such as real estate records, and voter registration records and demographics.

Hence, by using these datasets, content suppliers, advertisers, sponsors and network providers can now create detailed customer profiles. They have a wealth of information, with which they can shape their content or channel.

By breaking down huge data sets of viewer information, they can glean useful and profitable action points.

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Analytics
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Top 6 popular Cloud Services compared [Infographic]

04th Jul `18, 02:03 PM in Analytics

Such is the range of cloud services now on offer that it can be difficult to determine which…

Such is the range of cloud services now on offer that it can be difficult to determine which one is best, especially if you’re new to the world of cloud storage. The first step is to establish what you want to get out of using the cloud – do you simply want something that’s quick to set up and easy to use, or do you require a more advanced solution with a generous storage capacity?

If this dilemma is familiar to you, then you’ll find this infographic from ERS IT Solutions  very helpful, as it compares six of the best-known cloud storage options in a digestible and practical guide. The maximum file upload size, free storage capacity, cost of use and compatible operating systems are all laid bare, while the main pros and cons of each have been extracted for readers who wish to know more about these services.

If you’re a cloud beginner, your best bet would be a user-friendly service such as Dropbox, which stores any type of file and has a very simple interface. Plus, it works on pretty much any operating system you can think of, so once you’ve downloaded the service and signed up, you can start using it straight away.

The more advanced cloud user might prefer a more comprehensive service like iCloud Drive, which allows users to upload files as large as 50GB and comes with a host of useful office productivity apps such as Keynote.  Business owners and IT professionals might also derive great benefit from Box, which allows for tasks to be set, message threads to be established and files to be edited by specified users.

Read the infographic below to see which cloud solution is best suited to your needs.

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Artificial Intelligence
Daniel Smyth
Daniel Smyth
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Useful mobile apps powered by artificial intelligence

03rd Jul `18, 01:04 PM in Artificial Intelligence

With the kind of advancements in technology today, artificial intelligence has become the talk of the town. It…

With the kind of advancements in technology today, artificial intelligence has become the talk of the town. It is demonstrated by machines where they think and work like humans. Technology today is not only confined to Apple’s SIRI or Amazon’s Alexa. There are other AI enabled apps that help your business grow and achieve tangible benefits. With the increasing need for speed, accuracy, and security, artificial intelligence has a great scope in the field of mobile application development. Below are some of the applications that prove how AI has transformed mobile-based applications.

Google assisatance

Google Assistant: One of the most widely used applications powered by AI is the Google Assistant. This is a voice-controlled app that allows you to do a variety of tasks like send messages, make phone calls, set reminders for appointments and other special occasions and many more. All you have to do is touch and hold the home button of your smartphone and say “Ok Google” to turn on the assistant. Follow the instructions as they appear.

EasilyDo

EasilyDo: It is a personalized virtual assistant that helps in identifying emails that need to be responded, reminds you about meetings and even adds the details of your travel like flights to the calendar and provides notifications. The application provides great assistance in intelligently managing your calendar, contacts, and mail.

Siftr Magic

Siftr Magic: It is another AI-powered app that helps free up space in your phone by detecting and removing the junk photos. When you have too many pictures it is a painful procedure to sit and scan them deciding what to keep and what to delete. Siftr Magic is one of the best and powerful cleaner applications available today that helps in effortlessly cleaning your phone.

Socratic

Socratic: AI-powered apps not only provide assistance in your day to day official chores but also helps solve that difficult math problem. This app takes a picture of the problem and provides step by step solution.

Recent News: This app is designed for those who love reading. Depending upon your previous reading trends the app updates latest articles about news and your other areas of interest. It has been powered by AI algorithms and also suggests relevant articles that you might want to follow.

Elsa: This is a language app that guides in improving your English accent. The app can be downloaded free of cost.

Robin: This voice-based application provides accurate navigations, weather forecasts, traffic status, etc. One can also make a call or text hands-free using the application.

Google Allo: It is an AI-powered messaging app, known to perform other tasks like adding events to the calendar and finding videos, etc.

Hound

Hound: It is another AI app that is somewhat similar to the Google voice search and helps you put up queries using your natural voice.

Microsoft Pix: This app helps in creating some amazing pictures. It not only optimises the settings automatically. But it also helps select the best shots and deletes the rest.

Cortana

Cortana: Cortana is an AI-powered app that helps in scheduling meetings, send emails or search for anything on the internet. To use the app the user needs to sign in to a Microsoft account.

Capital.com: CFD trading app is also powered by AI and provides a personalised news feed designed to make investment decisions smarter. The cfd trading app has been a popular AI enabled application that helps traders in making smart decisions. AI enabled apps have the ability to spot behavioural patterns and trends, thereby boosting you’re trading skills.

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