How Machine Learning can help with Business Development

Accelerated business growth has always been about innovation in functionalities such as customer experience, employee management, and others. And with advanced machine learning technologies, businesses are now able to make useful changes to bring better results for their companies.

The biggest advantage of machine learning in the corporate sector is the ability to make automated decisions without taking risks in any manner. This is why the corporate sector is expecting $59.8 billion revenue with machine learning and AI by 2025. More and more companies are integrating machine learning into their businesses to innovate various aspects.

Here are the top 4 ways that businesses can leverage machine learning.

1. Bringing personalization to customer service

Businesses keep on looking for effective ways to improve the quality of customer service and reduce the investment requirements. And machine learning offers those exact solutions to obtain those goals. With ML technologies, businesses get the ability to combine their years of data related to customer services and merge it with natural language processing technology.

The NLP algorithms make customer interactions more personal by leveraging the data to provide satisfactory services. Each and every customer gets the most accurate answers to their questions, which makes them happy. Plus, the same technology reduces the need for too much investment, resulting in lower customer servicing costs.

2. Making recruitment process convenient and successful

For a long time, hiring and recruitment processes faced multiple struggles. The difficulty in shortlisting the right candidates, removing the human biases, asking the right questions and keeping it cost-effective have presented troubles for recruiters.

But now, with machine learning, it is possible to bring automation in the hiring process. Corporates are now able to shortlist candidates among thousands of applications without skipping a valuable candidate. The machine learning tools are able to analyze credentials and match them with relevant job profiles.

Also, the same technology can detect biases and remove those factors while conducting the assessment. It all makes machine learning a cost-effective and successful way of hiring people.

3. Improving finance management and handling methods

Machine learning also offers the capacity to manage financial processes of a company. In fact, the processes such as payment, invoice analysis, and others can become automatic with machine learning.

A huge number of invoices can be analyzed in no time. The companies can reduce their efforts and time on managing their finances and save a lot of cash too. Plus, the security of machine learning technologies provides protection to the processes at the same time.

4. Marketing and Management

Marketing and management can also get innovative results with machine learning. The AI tools are already being used in gathering customer data, supply chain management, and other processes.

Companies are leveraging machine learning tools to find data related from social media about products, logos, and other factors. All this data is used to create a better brand exposure and to get successful outcomes.

All in all, AI and machine learning offers innovation to almost every part of a business. So, it would be wise to integrate right tools and algorithms to improve ROI and make your business a success.

Machine Learning Algorithms

We are heralding a new dawn with 2020 – a world where neural networks, deep learning, and NLP are fast coming up as competitive differentiators. Businesses across the world are embracing Machine Learning technologies with gusto. Hence it doesn’t come as a surprise that Boston Consulting Group predicts 25% job loss due to machine learning algorithms and automation.

While data is essential for machine learning solutions, the algorithm behind it is equally essential. If you are looking for the most popular machine learning algorithms in today’s times, then check out our compilation:

1. Naïve Bayes Classifier Algorithm

This algorithm performs well with huge data spanning millions of records in the data set. There are two main forms of this algorithm – Gaussian Naïve Bayes (Apply normal distribution to continuous attribute values) and Multinomial Naïve Bayes (for data that shows multinomial distribution)

This type of algorithm shows immense value in –

1. Sentiment Analysis (used by Facebook to assess status updates)
2. Document Categorization (Google uses it for Indexing for PageRank)
3. Spam filtering (used by Google)

2. K Means clustering Algorithm

This is one of the simplest unsupervised learning algorithms that can solve the common clustering issue. It displays better clustering performance than hierarchical clustering. Its key application is by search engines like Yahoo and Google.  They use this technique to group the web pages based on the ‘relevance rate’ of the search queries.

3. Support Vector Machine Learning Algorithm

SVM algorithm continues learning from the classified data set to understand the classification pattern and apply it to new data. On training data sets, SVM is known to provide high accuracy and incredible classification performance. It uses the concept of hyperplane (a line) to classify data. The more it is away from the hyperplane, the better is its classification accuracy.

This algorithm is prominent in financial sectors where analysts try to compare stocks and its movement vis-à-vis competitive stocks or benchmark stocks.

4. Linear regression

In simple terms, it compares the inter-relation between two variables and assesses how a change in one impacts the other and to what extent. It is used by small and medium businesses in their revenue forecasting and team growth predictions.

For instance, if the sales are following a linear trend, this algorithm can accurately forecast the possible sales in the upcoming months.

5. Artificial Neural Networks

This is a class of algorithms modeling that mimics the biological neural structure. Some of the popular algorithms in this category include Perceptron, LSTM Recurrent Neural Network, Boltzmann Machine, and Radial Basis Function Network (RBFN). They are routinely used for regression and classification problems.

These networks can combine with other networks through the input layer, output layer, and hidden layer. This, in turn, forms multiple layers which give rise to deep artificial neural networks. Their architecture expands the realm of machine learning to delve into deep learning.

Which other machine learning algorithms have you been using for meeting your specific business objectives? Do write to us and let us know.

Deep Learning in Marketing and Sales

The buzz and enthusiasm about deep learning has significantly increased over the past few years. With numerous business ventures embracing this technology for good, deep learning is earning huge popularity now. If you are aware of Machine Learning and its implications, understanding Deep Learning won’t be that tough.

Here’s a quick look at what is Deep Learning and its serious applications in the business arena.

What is Deep Learning?

DL or Deep Learning happens to be a significant part of ML. It can be referred to as a subset or subdivision of Machine Learning that maps artificial neural networks. The mapping takes place to recreate or replicate processes performed by human brain.

That’s not all. Deep Learning also plays a crucial part in the identification of speech patterns, algorithms, images, and data analytics.

In spite of this simple and easy-to-understand introduction, there’s no denying the complexities involved in Deep Learning. Implementing DL strategies and incorporating them into existing business processes isn’t an easy affair. It becomes imperative to keep numerous aspects in mind thus devising effective DL strategies.

Transforming the business world with Deep Learning

Deep Learning can be revolutionary. If implemented in the right way, this particular technology can transform business processes to a great extent. Deep Learning helps to decode complex unstructured data and derive consumer insights that are crucial for creating sales and marketing strategies.

From retail and transport to healthcare and manufacturing, DL has started making a mark in various sectors. Let’s take a look at how it’s transforming sales and marketing for businesses:

1. Automating end-to-end customer journey

As mentioned earlier, deep learning will allow marketers to access insights from unstructured data sets such as image, video analytics, speech recognition, facial recognition, text analysis tools and much more. In short, deep learning becomes a way to accurately understand the voice of a customer.

Customer feedback and expectations can be gauged on a real-time basis and business organizations can get information to upgrade their products and services. Based on these premium insights from deep leaning, Brands can articulate the right messaging to the right customer.

2. Understanding analytics from IoT products 

Home automation is creating profitable avenues for organizations across the globe. Deep learning can help businesses understand the analytics for IoT products. It helps to capture data from the machines in different scenarios and monitor them in an easy and cost-effective way.

Through these analytics, deep learning can help to understand the interactions between machines and customers better and based on the data, the performance of IoT products can be enhanced from time to time.

3. Using Chat Bots to enhance CX

The presence of chat bots has revolutionized business marketing to a great extent. Chat bots leverage data mining, artificial intelligence, and natural language processing, thus creating new ways to interact with the end user.

Through chat bots, customers have the opportunity to engage in personalized communications. Apart from ensuring unmatched consumer experience, Chat Bots help an organization to have timely conversations with users and give them product recommendations or suggestions. You can create your marketing strategies with consumer preferences in mind thus offering them targeted products.

4. On-going predictive analysis

Deep Learning plays a highly significant role in the data analysis process. Whether it’s a small or big organization, entrepreneurs will have the chance to perform successful data analytics. Predictive analysis becomes easier, and you can develop crystal clear ideas of customer preferences.

Signing off

If market reports and figures are anything to go by, Google is running 1000 deep learning projects as of now. The number of projects was only two in 2012, and this will succinctly explain the massive importance of DL in business marketing.

Artificial Intelligence and Machine Learning in the Retail Industry

While the world is busy talking about Artificial Intelligence powered technologies such as self-driven cars, machines challenging human intelligence at a game of chess, and AI technologies in recruitment, there still remains an untapped potential for AI in the retail industry.

With most retailers now focusing towards providing an omnichannel experience for their shoppers, AI can play a crucial role in disrupting the retail industry.

Creating Smart Shops with Artificial Intelligence

While AI assistants such as Siri and Google Home help us maintain our day-to-day groceries list and change our shopping experience, an ideal situation would be to walk into a smart store without any shop attendees or long check-out queues.

Imagine a shopping experience where you can just enter a shop, pick up the stuff you want, a bunch of facial recognition algorithms process your purchase, and automatically money gets deducted from your digital wallet when you leave the store! Suddenly, shopping seems to become more of technology experience.

Amazon has been marginally successful in replicating this experience for customers with its Amazon Go grocery store. In order for shoppers to use this, they need to install the Amazon Go app. There are digital sensors on the shelves that detect when purchases are made and the money gets deducted from the customer’s account when he or she exits the shop.

Amazing isn’t it? This is what a future-ready AI store should ideally look like!

Predicting online customer behavior with Machine Learning

We live in a digital world today where every minute truckload of data about customer behavior is recorded and stored. But most companies struggle in extrapolating this data into actionable steps that can increase their ROI by 10X times.

You may have luxurious marketing and advertising budgets to target your customers, to get awesome click-through rates, but how are you making your customers tip over the fence and make a sale?

With Machine Learning tools, you can create an intelligent and automated marketing system that helps you with:

• customer segmentation
• predicting customer value and
• designing product recommendations.

But it doesn’t end with just that. Machine learning also helps us optimize our Ad budgets and invest them in the right customers.

In a world where resources and time are always limited, we are forced to make quick and smart decisions. But machine learning algorithms are programmed to analyze this huge pool of customer data within a fraction of seconds and identify customers who are 65% more likely to make a purchase. Thus, in this way, ML helps to justify and set optimum ad bids to target customers.

Product recommendation is another favorite pick for top marketers. ML helps to analyze online customer behavior in depth, such as the products they are interested in, the blogs they read, the price ranges they operate at etc. Based on all these interactions, a business can invest in curating content that makes the shopping experience as “personal” as possible.

Artificial Intelligence and Cognitive Computing in the Retail industry:

AI and cognitive computing are adding the “innovation” to the retail industry. Creating an omnichannel experience for customers has become the top most priority for retail brands.

Out of the many retail sections, in this blog post we are going to concentrate mainly on two AI integrated retail categories:

1) Product Recommendations:

IBM’s Watson, powered with its cognitive computing abilities is an excellent choice for brands who are looking to provide both in-store and online product recommendations.

• AI-powered retail store

In this example, we see how a New York based wine company called Wine4Me decided to choose the AI technology of IBM Watson to make in-store wine recommendations for its customers. The goal was to make wine shopping an easy and personalized experience.

But the first step was to provide Watson with ample data from wine tasters and also teach Watson how people ask and shop for wine. Based on the occasion, price, sweetness,  brand, and age of the wine, Watson should be able to bring up a list of wine recommendations that would suit the customer’s demand.

Now, this whole AI-powered shopping technology works as a win-win for both the retailer and the customer. Customers, on one hand, enjoy a hassle-free and engaging shopping experience while retailers can make better inventory decisions by tracking customer preferences.

• AI-powered digital store

In this example, we will talk about how the San-Francisco based premium brand The North Face integrated Watson on its digital platform to create an unparalleled shopping experience for its customers. The North Face is a premium outdoor product company specializing in outerwear, coats, backpacks etc

Though SEO filters and keyword phrases help in attracting customers to the website, most brands forget to communicate with their customers to help them find what they want. This is where AI can help you establish that user connect.

Let us assume that a user wants to buy a jacket online, then the AI platform asks the user a series of questions such as the purpose of the jacket, the time period during which he will use it, which place will be visiting, color preferences, material preferences and so on. The user is free to answer all these questions in his own style and type in any response.

With every response, the AI will trigger a series of product recommendations that closely match the user’s requirement. Now, this sounds fancy, doesn’t it?

But it all boils down to data and also literally teaching the AI what would the possible use case scenarios be that users would identify themselves with. Even the most non-obvious statements and taken for granted business scenarios need to be keyed in so that the AI helps to generate the right response.

This next-generation online shopping experience is sure to make both customers and retailers conscious of their expectations. Thus, if used in the right way, AI has great potential to provide product recommendations that can increase revenue from sales by 10x.

2. In-store Sales:

AI is all set to change the traditional shopping experience when a customer walks into the store. With e-commerce booming, you find lesser customers who are willing to enter a store and interact with a store assistant.

So, how can AI help in increasing the customer foot-fall at a brick and mortar retail store? The answer is simple – by using AI-powered robots.

Pepper,  a humanoid robot from Softbank’s robotics company has been in the news for enriching the customer shopping experiences at retail stores and serving as a retail concierge. Stores across the US, who have adopted Pepper as their smart shop assistant, have recorded almost 70% increase in customer walk-ins and sales.

Now that’s huge, isn’t it?

But what does this humanoid Pepper do differently from a salesman?

Firstly, Pepper acts as a brand ambassador for the store that drives in customers. Based on sheer curiosity, customers are more likely to stop by your store to experience an interaction with a robot who could help them with their shopping decisions. It almost feels like having a friendly celebrity with whom you can take a tour of the store.

Secondly, the humanoid robot is programmed to sense movement, emotion, offer shopping advice, product information, etc. Thus, it helps the user make an informed purchase. It has a tablet positioned on its chest where the user can key in his preferences and based on that Pepper will offer suggestions.

This definitely boosts user engagement and conversations inside a store. And the best part is that Pepper is always connected to the internet and all the data thus collected is stored in the cloud. So, next time when the same customer enters the shop, it becomes easy to showcase products based on his/her interests.

Thus, Pepper helps to automate all the obvious and mundane tasks at a retail store and transform in-store shopping into an engaging and joyful experience for customers.

shopping experience while retailers can make better inventory decisions by tracking customer preferences.
add: The rise of retail AI solutions is further transforming the industry by enabling real-time analytics, personalized recommendations, and demand forecasting, leading to increased efficiency and customer satisfaction

The future of Artificial Intelligence in Retail

While we are positive and hopeful that the future of retail is likely to be dictated by AI, we also notice that there is still time until this technology gets widely accepted in stores around the world.

One of the major constraints involves high budgets, thus making it easy only for the bigger players such as Amazon, Walmart, Target etc to become early adopters of such technologies.

But having said that, it is time that retailers identify the potential that AI and Machine Learning can make to their business and take steps to make the shift soon.

If you would like to build an AI-powered solution for your retail business, then drop us a short message with your requirements

The Depth Of Deep Learning

Artificial Intelligence (AI) and Machine Learning (ML) are some of the hottest topics right now.

The term “AI” is thrown around casually every day. You hear aspiring developers saying they want to learn AI. You also hear executives saying they want to implement AI in their services. But quite often, many of these people don’t understand what AI is.

Once you’ve read this article, you will understand the basics of AI and ML. More importantly, you will understand how Deep Learning, the most popular type of ML, works.

The first step towards understanding how Deep Learning works is to grasp the differences between important terms.

Artificial Intelligence vs Machine Learning 

Artificial Intelligence is the replication of human intelligence in computers.

When AI research first started, researchers were trying to replicate human intelligence for specific tasks — like playing a game.

They introduced a vast number of rules that the computer needed to respect. The computer had a specific list of possible actions, and made decisions based on those rules.

Machine Learning refers to the ability of a machine to learn using large data sets instead of hard coded rules.

ML allows computers to learn by themselves. This type of learning takes advantage of the processing power of modern computers, which can easily process large data sets.

Supervised learning vs unsupervised learning

Supervised Learning involves using labelled data sets that have inputs and expected outputs.

When you train an AI using supervised learning, you give it an input and tell it the expected output.

If the output generated by the AI is wrong, it will readjust its calculations. This process is done iteratively over the data set, until the AI makes no more mistakes.

An example of supervised learning is a weather-predicting AI. It learns to predict weather using historical data. That training data has inputs (pressure, humidity, wind speed) and outputs (temperature).

Unsupervised Learning is the task of machine learning using data sets with no specified structure.

When you train an AI using unsupervised learning, you let the AI make logical classifications of the data.

An example of unsupervised learning is a behavior-predicting AI for an e-commerce website. It won’t learn by using a labelled data set of inputs and outputs.

Instead, it will create its own classification of the input data. It will tell you which kind of users are most likely to buy different products.

You’re now prepared to understand what Deep Learning is, and how it works.

Deep Learning is a machine learning method. It allows us to train an AI to predict outputs, given a set of inputs. Both supervised and unsupervised learning can be used to train the AI.

We will learn how deep learning works by building an hypothetical airplane ticket price estimation service. We will train it using a supervised learning method.

How Deep Learning can build an AI  to estimate Airplane ticket prices

We want our airplane ticket price estimator to predict the price using the following inputs (we are excluding return tickets for simplicity):

• Origin Airport
• Destination Airport
• Departure Date
• Airline

Like animals, our estimator AI’s brain has neurons. They are represented by circles. These neurons are interconnected.

The neurons are grouped into three different types of layers:

1. Input Layer
2. Hidden Layer(s)
3. Output Layer

The input layer receives input data. In our case, we have four neurons in the input layer: Origin Airport, Destination Airport, Departure Date, and Airline. The input layer passes the inputs to the first hidden layer.

The hidden layers perform mathematical computations on our inputs. One of the challenges in creating neural networks is deciding the number of hidden layers, as well as the number of neurons for each layer.

The “Deep” in Deep Learning refers to having more than one hidden layer.

The output layer returns the output data. In our case, it gives us the price prediction.

So how does it compute the price prediction?

This is where the magic of Deep Learning begins.

Each connection between neurons is associated with a weight. This weight indicates the importance of the input value. The initial weights are set randomly.

When predicting the price of an airplane ticket, the departure date is one of the heavier factors. Hence, the departure date neuron connections will have a big weight.

Each neuron has an Activation Function. These functions are hard to understand without mathematical reasoning.

Simply put, one of its purposes is to “standardize” the output from the neuron.

Once a set of input data has passed through all the layers of the neural network, it returns the output data through the output layer.

Nothing complicated, right?

Training the Neural Network

Training the AI is the hardest part of Deep Learning. Why?

1. You need a large data set.
2. You need a large amount of computational power.

For our airplane ticket price estimator, we need to find historical data of ticket prices. And due to the large amount of possible airports and departure date combinations, we need a very large list of ticket prices.

To train the AI, we need to give it the inputs from our data set, and compare its outputs with the outputs from the data set. Since the AI is still untrained, its outputs will be wrong.

Once we go through the whole data set, we can create a function that shows us how wrong the AI’s outputs were from the real outputs. This function is called the Cost Function.

Ideally, we want our cost function to be zero. That’s when our AI’s outputs are the same as the data set outputs. 

Thus at this juncture, with the help of deep learning we have almost trained the AI to project an output that is in line with the input data. This ladies and gentleman to be honest is just the basics, but deep learning along with precision is used to train the AI for supervised learning.

We hope that at this juncture you have some idea of how the different elements of Machine Learning, Deep Learning and Neuron networks all come together to create Artificial Intelligence.

Stay tuned for more interesting facts on AI and Machine Learning!

Language For The Machines

Machine learning is all about making a machine capable of learning codes and producing them automatically by analyzing the input data. This process includes the development of AI-enabled algorithms which help machine learn and produce codes. So, in a way we can say that machine learning is a part of AI (Artificial intelligence) which has been very viably used in many fields, like math, psychology, etc.

Well, this is just an intro; a more technicalities are still hidden far behind the curtains. The biggest one being learning of a machine language because only a good programmer can make use of all tools and bring out the best in the job.

So, in this article, we shall discuss about the 3 must-know languages which a programmer should know while dealing with machine learning.

1. Python

Python is the highly flexible and multi-purpose language in nature. Due to its these features, it has gained a lot of popularity among the developers, programmers and data scientists. This language has its own libraries for the purpose machine learning – Numpy and Scipy. These two libraries are enough to learn about the Linear Algebra and Kernel methods of the phenomenon of machine learning. The biggest ease with this language is that- it has easy syntax. Easy syntax, in turns, makes the whole machine learning process easy and understandable. Those who want to excel in machine learning, should start with python.

2. C Language

Developed by Denise Ritchie, C is the mother of all languages. Therefore, if you are thinking of building a predictive algorithm, this language will help you superficially. But as said, this language is the mother of all other programming language and hence, learning this would not be a cakewalk. To get started with C, you will have to have great fundamentals of basic C and its syntax. However, having mastery in this language does not mean to hold a PhD. It simply means to have strong concepts and clear fundamentals. Also, once you are nicely through the learning of C language, you can even give a try to other functional languages like Erlang, Scala, Julia, and Haskell.

3. R Language

R language is the modern version of the S language developed by the Bell Labs. This language, when combined with lexical scooping, helps in enhancing the flexibility of statistical models. In terms of machine learning, it is one of the strongest language to master. In this language, many GNU packages are available. This language can be used for creating the powerful algorithms and giving statistical visualization of those created algorithms. Currently, R language in very popularly used in educational industry, but soon it will exact famous in other fields as well.

So, as you can see the 3 most powerful languages for machine learning, it is time for you to decide which language you would start with. It would be a recommendation to start with C, which is the base of all other languages. After that, one can jump to python and R language.

Machine Learning In Marketing

AI and its associated concepts of Machine Learning and NLP are fast affecting all major functions of a business. Digital marketing too can be counted as one of the sectors that have seen the massive influence of ML seep in.

The involvement of Machine Learning into sales and marketing activities was a natural progression considering the ease with which we can store humongous amount of data and process it in much faster time with lower cost tools and resources.

Here are some reasons why machine learning is transforming digital marketing:

1 – Better campaign customization

Traditional marketers from the era of print and TV ads were stuck on broadcasting their marketing message to one and all. However, since the digital customer is different, there is a need for one-to-one engagement for better outcomes. This calls for knowing on a deeper level the presences, needs, and behaviors of the potential customers to send targeted marketing message. Machine learning can help marketers to dig deeper and sense a pattern not readily visible. This way you can customize your marketing campaign for better efficacy.

2 – Dynamic ad display

The recent case of Jivox IQ machine learning algorithm (called as Neuron) providing a much more personalized brand messaging than a brand CPG manager is a case in point where the advantages of Machine Learning can be put to practical use. This way marketers can add a touch of ‘smart’ to their digital marketing programmes and ensure better quality conversions.

3 – Better segmentation

As evident, the ‘one size fits all’ phrase has never been more wrong as with the digital marketing ecosystem. Hence marketers have employed segmentation to show the relevant ads to the right set of people at the right time.  While you may create broad segments, in order to create micro segments for better targeting you would need the incredible data processing and insight generation prowess of machine learning.

What does this evolution means for marketers?

Does this mean that marketing automation combined with machine learning will throw the careers of marketers off-balance? Of course not. It will simply mean that marketers will be spared the labor of manually processing data, going through tons of reports, sensing patterns, uncovering insights, and aiding in management decision making.

All this will now be handled by the Machine Learning algorithm in a much more effective and swifter way. They can enhance their job responsibilities to creating media strategies, identifying effective marketing channels, and trying to understand customer behavior, and create more appealing marketing campaigns.

Machine Learning is clearly revolutionizing the world of programmatic marketing. It is affecting every function of marketing – right from what flavor of marketing campaign to be directed at which customer segment, to a new way of telling brand stories.

These reasons clearly outline why marketers can no longer shy away from embracing Machine Learning to give their digital marketing campaigns the much needed competitive edge. Adopt ML into your marketing mix and see how your outcomes will be radically different from those driven by traditional marketing approaches.

Understanding Internet Of Things

“Anything that can be connected, will be connected.”

People who understand Internet Of Things and its potential will tell you that the Mantra above will be the talk of the town in the coming years. Anything you can imagine, anything at all would be connected to the internet, it is happening now and will only expand aggressively. So, wouldn’t it be imperative to know what exactly IOT is and how it works?

Imagine this situation. You logout of your office swiping the access card, the sensor sends a message to your car parked below which has already started and the navigation system has already plotted the route to your home alerting you about the traffic on different possible routes. The air conditioner in your house switches on automatically before you reach the house and adjusts the temperature according to the weather outside. The coffee brewer on intimation brews some coffee for you and the refrigerator sends you a message about the missing groceries. At a single touch, before you reach your house, the coffee is brewed, the air conditioner has set the tone for your comfort and the groceries have arrived at your doorstep. This is the power of Internet Of Things.

Every single device connected to the internet and integrated to feed off data from each other to make our lives better is probably just the gist of it. And this is not just for machines, it can be even for the minutest component of your machines. From a commercial airliner to a sewing machine, from your car to a water heater, everything is soon going to be unified on a single network. The opportunities are unlimited. Most of which might be out of our analytical might. Yet harnessing even a portion of it can create something as revolutionary as the wheel or fire.

 5 Internet of Things Application

Change has always been the buzzword. It is what signifies human existence. The change was first seen in 1974 with the Automated Teller Machines(ATM), and now amazingly there are far more objects connected to the internet than people on the planet. The most accepted ones so far are vehicles with all their user enhancing systems on board. With the advent of wearable devices such as fitness trackers and smartwatches, the horizons have expanded even more. Smart Kitchens, Smart living rooms, Smart houses, imagine every single electric impulse transferred as data to the internet for other user oriented actions. And if this is just the beginning, we already have over 4.9 billion connected devices and counting.

 Now, that we have a fair idea of what really Internet of things is, let us see how it will revolutionize our lives in the coming future:

1. Smart Highways:  Roads with ‘on the go’ warnings about weather expected ahead and diversions in case of mishaps and blockages will soon be functional across the globe. Smart highways will have a unified platform that will integrate data from traffic signals, traffic sensors, vehicle movements and on board GPS. It will be similar to living organism that constantly updates the vehicles about traffic patterns, speed limits and adversities.

2. Autonomous Cars: Cars with auto park feature are already in the testing phase. Many cities across the globe have given a green signal for testing the driver-less car as well. The autonomous cars, equipped with intelligent sensors will feed off years of data from vehicle databases to analyse driving patterns and road hazards. Autonomous cars will constantly update itself to make the driving experience a risk free and a safe one.

3. Patient Surveillance: Monitoring of vital signs for patients in hospitals and elderly people in old age homes is an important application of Internet Of Things. Right from the blood pressure, heart beat and other vital stats, to minute changes like changes in the pressure and temperature patterns of the room, all the data can be monitored to ensure better patient surveillance.

4. Smart Homes: Internet Of Things can automate an entire home filled with appliances and lighting systems. A single touch of hand can automate appliances even before you enter the house. Right from your groceries to garbage disposal, every household chore can be planned and scheduled for. It will give you the comfort of controlling your appliances even when you are miles from your home.

5. Defense Systems: Defense forces across the world have already started applying this technology, some of them on their more secure systems. The defense capabilities of any nation will surge ahead and their warning systems will become more accurate.

How Internet Of Things is changing lives:

 
As discussed earlier, Home Automation with the assistance of Internet Of Things is soon going to change the way we live. Smart Homes are going to make our lives much more comfortable and hassle-free. By 2025, more than a quarter of the world’s population is expected to have a semi automated smart home.

A step towards this was taken by GoodWorkLabs, recently.They created an application for one of their clients which offers complete home automation solutions. With the Internet of Things application developed,  the user can control the functionality of  home appliances with just one touch:

1) Adjust the AC temperatures in your Bedroom before you even reach home.

2) Set an alarm so that you are never late for the important things in life.

3) Adjust the lights in your living room to create evening.

4) Enjoy movie nights by setting your favorite movie lists on the TV.

5) Never miss out on your grocery lists.

Read more: Click

These are just a few of the important applications we would see in the coming days, as mentioned earlier, the uses are limitless. We need to expand our horizons and think further ahead to employ ways that can protect human interests. Internet Of Things is the result of human evolution eventually. Everything we have ever invented, every medium we have explored and every thought we have recorded, all connected together.

Technically, it maybe devices connected to each other. In reality, it is every single person connected through their devices.

“If you think Internet has changed your life, think again. The internet of things will change it all over again.” – Brendan O’Brien, Aria Systems