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The past decade witnessed the emergence of two of the most significant technologies- virtual reality and Internet of Things.

Virtual reality refers to the use of technology to counterfeit an environment where the digital world seems real. It aims to place the user inside an experience, consequently enabling them to interact with the 3D worlds. On the other hand, Internet of Things is all about making real-world objects connect and manipulate in the digital world.

While both these technologies work to bring augmented ease to our lives, it's the convergence of the two that offer the most promising opportunities. Becoming quickly enmeshed in the prevailing times, the two disruptive technologies have largely revolutionized the industrial platform.

The meeting point of the two technologies boasts of immense potential. Let’s understand this with some examples.

1) Telepresence

The encroachment of telepresence depicts the colossal potential of the confluence of IoT and VR. If we talk about a typical video conference, the system includes a monitor screen, sound system, and codec. You can add additional speakers or a projector screen to improve the video conference experience. However, with telepresence, it is not the same.

With an aim to extend near lifelike audio and video quality, telepresence leverages compound multi-codec, multi-monitor, and multi-speakers. It has successfully transformed the way we can communicate with others over long distances. It offers the ability to look and move freely within a real-world environment, giving the illusion of actually being present there.

Telepresence has efficaciously eliminated the time and financial constraints related to travel. Offering all the benefits of a face-to-face interaction, it has made long-distance meetings exceedingly convenient.

2) Virtual Smart Cities

An increasing number of cities around the world are looking to become “smart” in order to improve comfort, reduce costs and consumption of resources and augment the quality of life of its citizens. Consequently, for the concept to materialize, it is significant that Internet of Things along with its accessibility to public grows. This will enable adequate accurate data to be amassed in cities for analysis and forecasting.

Moving ahead, these cities need to be integrated into a well-controlled virtual environment. This will allow an accurate analysis of the prevailing city conditions as well as help in making predictions of the impending future scenario. Thus, any kind of risk or disaster can be effectively monitored to simulate its effects.

3) Healthcare

The union of VR and IoT technologies has greatly assisted the healthcare field by bringing improved ease to patients as well as doctors. A competent example of this is robotic-assisted surgery, which has been in use for quite some time now. Also known as da Vinci Surgical System, it allows the surgeons to perform a least invasive surgery. A camera along with a few tools is inserted into the body through a relatively small opening that allows the surgeon to get a full view of the operating area without exposing the patient to the ordeal of a large incision.

The system includes a 3D HD vision system and small wristed devices that revolve and bend much better than the human hand, thus enabling improved vision, control, and accuracy.

But, this is just the beginning. It is anticipated that VR surgeries will soon control real da Vinci systems, permitting surgeons to operate on patients while sitting in their offices.

Final Thoughts

Considering the potential of the two technologies, more and more companies are investing into the development of new applications of both virtual reality and internet of things and because of that, in past several years so many IoT App development companies has been evolved in the market. In the following year, it is predicted to see a growing number of integration of smart objects within virtual simulations, for purposes such as leisure, training, or damage prevention.

 

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Believe it or not, but the possibilities that the Internet of Things, IoT brings to the table are countless. The internet of things, IoT continues to be the next big thing in technology, and now the new phase of the internet of things is pushing everyone hard to ask questions about the data collected by sensors and devices of IoT.  

Undoubtedly, the internet of things, IoT will generate a tsunami of data, with the swift expansion of sensors and devices connected to the IoT. The sheer volume of data being produced by the internet of things will rise exponentially in the upcoming years. This generated data can provide extremely valuable insight to figure out what’s working well and what’s not. Moreover, the internet of things, IoT, will point out the issues that often arise and provide meaningful and actionable insight into new business opportunities and potential risks as correlations and associations are made. 

Examples of IoT Data:  

  • Data that improves productivity of industries through predictive maintenance of equipment and machinery 
  • Data that assists smart cities in predicting crime rates and accidents   
  • Data that creates truly smart living homes with connected devices    
  • Data that provides doctors real-time insight into information from biochips to pacemakers 
  • Data that gives critical communication between self-driven automobiles          

That’s great news, but it’s not possible for humans to monitor, analyze and understand all of this data using traditional methods. Even if they reduce the sample size, it will simply consume too much of their time.  Undoubtedly, finding actionable insights in terabytes of machine data is not a cakewalk, just ask a data scientist. The biggest challenge is to find ways to analyze the deluge of performance data and information that the internet of things, IoT devices creates. The only possible way to keep up with the terabytes of data generated by IoT devices and sensors and gain the hidden insights that it holds is using Artificial Intelligence, commonly known as AI.  

Artificial Intelligence (AI) and IoT    

Artificial intelligence, also known as machine intelligence (MI) is the intelligence that is exhibited by machines or software. John McCarthy, the person who coined this terminology back in 1955, describes it as "the science and engineering of making intelligent machines". In a nutshell, AI is a branch of computer science that emphasizes the creation of an intelligent machine that thinks intelligently, the way intelligent humans think and works and reacts like humans.   

In an IoT environment, Artificial Intelligence (AI) can aid business enterprises take the billions of data points they have and prune them down to what’s really helpful and actionable. The general principle is akin to that in retail applications i.e. review and analyze the data you have collected from different sources to find out similarities or patterns, so that better business decisions can be made.  

To be able to figure out the potential risks or problems, the collected data has to be analyzed in terms of what’s normal and what’s not. Abnormalities, correlations, and similarities need to be identified based on the real-time streams of data generated. The collected data combined with Artificial Intelligence makes life easier with predictive analytics, intelligent automation, and proactive intervention. 

Artificial Intelligence in IoT Applications  

  • New sensors will enable computers and smart devices to “hear,” gather sonic information about the user’s ambience   
  • Visual big data will allow computers and smart devices to gain a deeper insight of images on the screen, with the new AI app that understands the context of images

These are some of the promising applications of Artificial Intelligence in the internet of things, IoT ecosystem. The potential for highly personalized services are countless and will dramatically change the way people live. For example, Amazon.com can suggest what other books and movies you may like, helping Saavn and Gaana to determine what other songs you may love listening, and your family doctor would receive notification if you’re not feeling comfortable.  

Here Are Some Challenges Facing AI in IoT

  • Artificial Stupidity
  • Complexity
  • Safety
  • Ethical and legal Issues
  • Compatibility
  • Privacy/Security 

What’s Next? 

Gartner has predicted that by the end of next year, 6 billion connected devices will be requesting support, which means that processes, technologies, and strategies will have to be in place to respond to them. It is important to think of connected devices less as ‘things’, but more as customers or consumers of services in themselves. The need for Artificial intelligence, AI will become more prominent at the stage when the number of connected devices and sensors increase manifold.

Hope you find this post helpful. If you did, share it with your friends and colleagues. For AI and IoT Courses Online, you can do some research on Google to find the best institute that suits your needs and budget.

For any query related to this post, you can comment down below. Thanks for your time. 

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How Customer Analytics has evolved...

Customer analytics has been one of hottest buzzwords for years. Few years back it was only marketing department’s monopoly carried out with limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza.
SAS & SPSS were the leaders in providing customer analytics but it was restricted to conducting segmentation of customers who are likely to buy your products or services.
In the 90’s came web analytics, it was more popular for page hits, time on sessions, use of cookies for visitors and then using that for customer analytics.
By the late 2000s, Facebook, Twitter and all the other socialchannels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant.
With the digital age things have changed drastically. Customer issuperman now. Their mobile interactions have increased substantially and they leave digital footprint everywhere they go. They are more informed, more connected, always on and looking for exceptionally simple and easy experience.
This tsunami of data has changed the customer analytics forever.
Today customer analytics is not only restricted to marketing forchurn and retention but more focus is going on how to improve thecustomer experience and is done by every department of the organization.
A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360 degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics.
From the technology perspective, the biggest change is the introduction of big data platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation.
Then came Cloud based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure.
Predictive models of customer churn, Retention, Cross-Sell do exist today as well, but they run against more data than ever before.
Even analytics has further evolved from descriptive to predictive toprescriptive. Only showing what will happen next is not helping anymore but what actions you need to take is becoming more critical.
There are various ways customer analytics is carried out:
·       Acquiring all the customer data
·       Understanding the customer journey
·       Applying big data concepts to customer relationships
·       Finding high propensity prospects
·       Upselling by identifying related products and interests
·       Generating customer loyalty by discovering response patterns
·       Predicting customer lifetime value (CLV)
·       Identifying dissatisfied customers & churn patterns
·       Applying predictive analytics
·       Implementing continuous improvement
Hyper-personalization is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time
Now via Cognitive computing and Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learning neural network algorithms provide a game changing aspect.
Tomorrow there may not be just plain simple customer sentiment analyticsbased on feedbacks or surveys or social media, but with help of cognitive it may be what customer’s facial expressions show in real time.
There’s no doubt that customer analytics is absolutely essential for brand survival.
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In the age of Digital Transformation, Artificial Intelligence has come a long way from Siri to driverless cars.
If you have used a GPS on Google Maps to navigate in your car, purchased a book recommended to you by  Amazon or watched a movie suggested to you by Netflix, then you have interacted with artificial intelligence.
Artificial Intelligence is the capability of a machine to imitate intelligent human behavior which relies on the processing and comparison of vast amounts of data in volumes with help of big data analytics, no human being could ever absorb.
Stephen Hawking, Elon Musk, Bill Gates have recently expressed concern in the media about the risks posed by AI.
According to them, AI will soon replace all kinds of manual tasks and make humans redundant. This could be true in some sense but still this is a far cry from the current maturity levels of AI, which is still at the stage of figuring out real-world use cases.
Today machines can carry out complex actions but without a mind or thinking for themselves. Smartphones are smart because they are responding to your specific inputs.
The world’s top tech companies are in a race to build the best AI and capture that massive market, which means the technology will get better fast, and come at us at faster speed. IBM is investing billions in its Watson, Apple improving Siri, Amazon is banking on Alexa;  Google, Facebook and Microsoft are devoting their research labs to AI and robotics.
Together, they will swirl into that roaring twister, blowing down the industries and businesses in its path.
Within maybe few years, AI will be better than humans at diagnosing medical images and converting speech to emotions. But it can also be stealing millions of records from a government agency to identify targets vulnerable to extortion.
Soon you’ll be able to contact an AI doctor on your smartphone, talk to it about your symptoms, use your camera to show it anything it wants to see and get a diagnosis that tells you to either take a couple of Tylenols or see a specialist.
In all the fairy tales we have seen so far, good almost always wins over evil.
This is what we have seen in the movies like I, Robot or Avengers: Age of Ultron.  But Will Smith or team of avengers does not know that till end of the story. That’s where we are now: face to face with the demon for the first time, doing everything we can to get through the scary plot alive.
Today many companies are using AI for improving their business:
·         Geico is using Watson based cognitive computing to learn the underwriting guidelines, read the risk submissions, and effectively help underwrite
·         Google Translate applies AI in not only translating words, but in understanding the meaning of sentences to provide a true translation.
·         IBM Watson is the most prominent example of AI based question answering via petabytes of data retrieval that helps in various areas like finance, healthcare & insurance.
As Humans we are programmed from childhood either by nurture or nature to do things the way we do. All the nine emotions we have learned since then are the inseparable part of our lives.
Currently we are in the control of the planet because we are smartest species compared to all the animals.
But when, and if machines learns to love or hate, work in peace or retaliate in anger, then it’s not too far that, with the ability to consume & digest the vast amount of data, they will become more smarter & start taking control of the planet.

Only then we will be able to know that AI is helping us like Iron Man's Jarvis or planning to eradicate us like Terminator!!
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We are living in a century where technology dominates lifestyle;Digital Transformation with Big Data, IoT, Artificial Intelligence(AI) are such examples.
Over the past six months, Chatbots have dominated much of the tech conversation, the next big gold rush in the field of online marketing.
Chatbots are built to mimic human interaction, making them seem like an actual individual existing digitally. It could live in any major chat product (Facebook Messenger, Slack, Telegram, Text Messages, etc.), powered by basic rules engine or NLP and AI.
Chatbots have helped in conversation commerce in real time such as booking a cab or ordering a bouquet of flowers or pizza. Consumers will benefit from chatbots through personalization, and this is where social media plays a big part.

Here are a couple of other examples:
·       Weather bot: Get the weather whenever you ask like Poncho
·       Grocery bot:  Help me pick out and order groceries for the week like Yana, MagicX
·       News bot:  Ask it to tell you whenever something interesting happens like TechCrunch, CNN
·       Personal finance bot: It helps me manage my money better like Abe
A chatbot for an airline will function fundamentally differently from a banking bot.
People are now spending more time in messaging apps than in  social media and that is a huge turning point. Messaging apps are the platforms of the future and bots as over 90% of our time on mobile is spent on messaging platforms like Facebook messenger, Whatsapp, Wechat, Viber etc.
Typically business need to answer following questions to create a bot:
·       Do you need a constant communication back and forth with the consumer?
·       What are customers’ expectations for the interaction?
·       How will the bot act?
·       What happens when the bot fails?
Chat bots have to be great at answering questions, this is usually how they are challenged, and IBM’s Watson is probably the best question and answer system.
There are several advantage of Chatbots:
·       24×7 availability – A bot exists digitally unlike a human being, and can thus be pressed into service continuously without any interference
·       Faster response time than humans, coupled with an AI, chatbot’s machine learning and multi-tasking abilities make it a highly efficient virtual assistant
·       Bots allow for a two-way, personalized interaction between the consumer and a brand
·       Saves Resources – Employing a chatbot to handle basic customer interactions can free up valuable human resources without a decline in productivity
Tacobot Allows to order Taco Bell even more quickly.

KLM has a customer service bot that's able to check your flight status and let you know if it's been delayed.

Interacting with software at a human level is becoming more mainstream from digital assistants  like Google Home, Google Now, Apple Siri. 

Going forward people will not be able to tell the difference between human and machine.
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Today, with Digitization of everything, 80 percent the data being created is unstructured. 
Audio, Video, our social footprints, the data generated from conversations between customer service reps, tons of legal document’s texts processed in financial sectors are examples of unstructured data stored in Big Data.
Organizations are turning to natural language processing (NLP) technology to derive understanding from the myriad of these unstructured data available online and in call-logs.
Natural language processing (NLP) is the ability of computers to understand human speech as it is spoken. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Machine Learning has helped computers parse the ambiguity of human language.
Apache OpenNLP, Natural Language Toolkit(NLTK), Stanford NLP are various open source NLP libraries used in real world application below.
Here are multiple ways NLP is used today:
The most basic and well known application of NLP is Microsoft Word spell checking.
Text analysis, also known as sentiment analytics is a key use of NLP. Businesses are most concerned with comprehending how their customers feel emotionally adn use that data for betterment of their service.
Email filters are another important application of NLP. By analyzing the emails that flow through the servers, email providers can calculate the likelihood that an email is spam based its content by using Bayesian or Naive based spam filtering.
Call centers representatives engage with customers to hear list of specific complaints and problems. Mining this data for sentiment can lead to incredibly actionable intelligence that can be applied to product placement, messaging, design, or a range of other use cases.
Google and Bing and other search systems use NLP to extract terms from text to populate their indexes and to parse search queries.
Google Translate applies machine translation technologies in not only translating words, but in understanding the meaning of sentences to provide a true translation.
Many important decisions in financial markets use NLP by taking plain text announcements, and extracting the relevant info in a format that can be factored into algorithmic trading decisions. E.g. news of a merger between companies can have a big impact on trading decisions, and the speed at which the particulars of the merger, players, prices, who acquires who, can be incorporated into a trading algorithm can have profit implications in the millions of dollars.
Since the invention of the typewriter, the keyboard has been the king of human-computer interface. But today with voice recognition via virtual assistants, like Amazon’s Alexa, Google’s Now, Apple’s Siri and Microsoft’s Cortana respond to vocal prompts and do everything from finding a coffee shop to getting directions to our office and also tasks like turning on the lights in home, switching the heat on etc. depending on how digitized and wired-up our life is.
Question Answering - IBM Watson is the most prominent example of question answering via information retrieval that helps guide in various areas like healthcare, weather, insurance etc.
Therefore it is clear that Natural Language Processing takes a very important role in new machine human interfaces. It’s an essential tool for leading-edge analytics & is the near future.
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