How industries are reaping benefits from Virtual Reality technology

Virtual Reality technology has provided the much needed visual impetus to have a second look at products or services. It comes as no surprise at how companies these days are vying to incorporate VR tech considering the number of possibilities and the revenue markets this technology opens up.

One of the prime reasons estimated behind the immensely popular VR tech is the freedom it renders to the person using it. You no longer are bound to where the camera angle goes or what your physical environment is. VR has the power to transport you to an altogether new world that transcends the boundaries of the real world.

In this post, you will get to know how and which industries are benefiting from VR.

1. The retail sector

Retail showrooms are all about the glitz and the finesse of visual appeal. How about having something that gives the same impression but at half the cost? VR makes that possible for retail outlets everywhere.

Showrooms can now play to their strengths and display things in every shade possible and offering combination suggestions would not be that difficult a process now.

This would also give retailers an insight into what is it that pleases their customers the most and then make necessary changes.

2. Architecture

The one thing you visualize when you hear the word architecture or architects is them working on huge blue sheets with scales and high-end pens etc. With the insurgence of VR, this would be far less a hassle.

It also allows them to experiment with the various fundamentals of erecting towers by using visual layouts and lighting and discuss the various outcomes of it all. This, in turn, saves building companies a lot as they come to know of problems before the actual construction takes place.

3. Travel & Tourism

Visualising the corners of the world would no longer require going there. And that explains why this technology is a phenomenal boon to travel & tourism companies.

The tourists get to virtually visit the location even before they actually travel there, thus aiding their planning. Tourists can finalize accommodations based on the proximity of where they’d want to stay, what view they’d prefer, etc.

4. Education sector

Out of all the sectors that rely on visualization for accuracy, the education sector would be considered to be the most important. It could be any branch of education for that matter.

From engineering to medicals, from linguistics to humanities to history and even geography. Bringing in VR would aid better understanding and recollection of concepts, thus making learning a wholesome experience.

5. Healthcare

Not only would VR aid learning about the human anatomy, but it would also benefit healing it. Getting to the root cause of the matter would bring in better treatment.

Accurate VR-oriented diagnosis brings about better prognosis and this itself would improve the quality of medicine and healthcare all over the globe. This explains why the value of the virtual reality healthcare market in the U.S. has grown from $525 million (2012) to $976 million (2017).

The benefits that VR technology imparts are multifarious and more than what could be enlisted in one article. It is a wonderful era to live in and VR adds essence to existence.

Blockchain Technology in IoT

The blockchain is rapidly emerging as a hot tech trend. It has the capability to disrupt multiple industry sectors and offer endless possibilities and ventures that were once impossible but now are quite the opposite. It is also promising fruitful outcomes through mergers with other technologies

When it comes to mergers, let’s talk IoT (Internet of Things). The technology that is said to connect every device that consists of an IP address and can receive or transfer information is revolutionary in every perceivable sense. But there are challenges IoT seems to face before changing the face of technology. And if experts are to be believed in this arena, Blockchain seems to have just what it takes to address those issues and get the tech rolling.

Decentralization – a key to IOT and Blockchain merger

One of the major challenges IoT faces is its reliance on centralization. This is done via the server/client model. The problem here is that since all devices are connected through cloud servers, in order for devices to be connected, they have to travel a long, long way even if the devices are at an arm’s length.

Yes, there is no denying the fact that cloud computing has revolutionized the tech industry, but looking at how much of data would be generated in the years to come, this system looks vulnerable to an electronic cloud-burst. It is imperative to find a surefire way to overcome this as industries like Healthcare and other very important aspects of human life will soon depend on IoT for its functioning.

This is where the decentralizing approach to IoT comes in handy, as per tech experts. Decentralizing IoT networks across the billions of devices that constitute IoT overall would ensure the absence of a weak spot. In case there ever would be a single spot of issue, it would still not pose enough threat to the entire network and the network would function just like it always did, without a glitch.

How blockchain helps the cause here?

Blockchain, primarily, is a technology that allows the formation of a distributed network of transactions that would not rely on storing information in central servers but rather function on the shared nodes of a network. Blockchains use cryptography to authenticate and spot out nodes that enable them to safely add transactions.

What’s the best part about blockchains is that it is totally tamper-proof and cannot be messed or manipulated with.

The fascinating bit about this is that the merger of IoT and Blockchains is already making all the news. Being backed by start-ups and corporate tech giants alike, this IoT-Blockchain merger is riding on a high wave.

Companies like Ethereum and Filament are putting the IoT-Blockchain merger to use and are finding some interesting implications of it.

Though there may be a lot counting on IoT to click for the good of all humanity and though Blockchains pose a genuine solution to most of them, it is far too early to say that IoT-Blockchain is the ultimate solution to it all. It isn’t yet the perfect option but it sure is promising. And for the moment, that’s enough to consider.

Blockchain Technology in Life Sciences

In the medical sector, new and innovative therapies keep improving life sciences. However, the same innovation challenges the supply chain. Life science is in a desperate need for authenticated and secure drugs that can become available whenever required.

Why is blockchain a solution?

Blockchain technology has gained an immense level of growth in terms of investment. Experts project that this growth can reach up to a $3 billion market level as we all reach 2025.

Blockchain technology is applicable in almost every step of the supply chain of life science. And at each of those steps, this technology offers a unique benefit.

1. Provenance

Verifying product’s origin point becomes immutable with this technology. Tamper-proof blocks allow the use of digital markers. This way, all the chances of counterfeit product get diminished. Plus, life science supply chain meets every regulatory requirement.

2. Record management

The industry faces a need for extensive documentation. Highly complex records are created and managed, which increases the costs and administrative activities. All these processes can become automatic with this technology. Businesses need smart contracts that include regulations and logic of processing. Hence, all business data can get verified without wasting any time or money.

3. Sensitive data security

Access control is also possible with this technology. Networks act according to incorporated rules and restrict access to critical medical information. Hence, authorities know who accesses certain information and when. This can become a great advantage for healthcare consumers as well, who want to keep their health records confidential.

4. Managing internal process

There is a huge list of internal processes that life science companies have to manage. Tracking products, transactions, and factory operations are a few major internal processes. All in all, companies have to concentrate on their products in different steps such as raw material collection, packaging as well as labeling.

Choosing this technology can integrate each and every process spread across systems. Companies can skip multiple reconciliations and track everything with the help of a single ledger. This ledger will be available to every authoritative body. So, no need to hassle.

5. Multi-party collaboration

Collaboration is the most valuable property of this technology. Hence, clinical trial officers, trial sponsors, and multiple regulators can access and share data at the same time. A secure and shareable network brings transparency to the supply chain. Hence, every considerable party receives trustworthy collaborative network to rely on.

With speed and trust in life science, this technology offers the ability to transform the industry for good. Complications can go away and sensitive data can become more secure.

Happier patients

Soon blockchain is going to enable companies to present more relevant data for patients. Accurate information, continuous product availability, and other features can improve patients’ satisfaction. However, it all comes down to the manageability of health care and drug companies.

Life science sector requires a technology boost to attain much-needed goals in terms of security, speed, and quality. Hopefully, companies will understand this requirement and move forward in this direction as soon as possible. Only time will tell!

Angular JS Technology – The tech that rules the Internet

Often described as the HTML for applications and software, AngularJS is one of the go-to tools for frontend programming nerds. An extension of Java, this frontend framework does have several features that set it apart from contemporary tools. One of the most noticeable features of AngularJS is its application on the internet. It can be argued that the use of AngularJS has made web browsers and their various extensions more interesting. Let us have a look at what makes this particular tool such a top draw.

What is AngularJS?

When talking about AngularJS there are two words that quickly follow – ‘user interface’. UI is one of the most desired aspects of a website that draws in viewers and influences their choice to make use of its various features. AngularJS is primarily used to develop applications for the internet. Since it was first launched in 2009, most applications and websites have made use of AngularJS to a great extent. AngularJS has built-in directives which are applied to incorporate new syntax into browsers.

Features 

While in its essence AngularJS may seem like any other framework with several similarities, but what truly sets it apart is its comprehensive set of features that cover pretty much any requirement you might have as a web application developer. So let us have a look at some of the most prominent features that have made AngularJS so popular.

1) The AngularJS Community: Given its decade-long existence and the number of users it has worldwide, AngularJS has a well-developed community which serves as a repository of information which includes tutorials, tips, guides etc. The wealth of experience within the community is also something that makes this framework desirable.

2) Open Source Coding: AngularJS makes use of open-source codes which allow a great deal of flexibility for developers. The freedom to customize as the developer wishes is something that will eventually lead to better quality of a product. The level of security that this framework offers is another desirable aspect of its open source nature.

3) Documentation: AngularJS offers comprehensive documentation with all the desirable features one might possibly need to get working.

4) Two-Way Data Binding: Possibly the most desired aspect of angularJS. This type of binding facilitates the cohesion between the DOM and the model that you are using. The simplicity of this binding process is truly an appealing factor.

5) Templates: The templates that Angular JS has to offer are in traditional HTML. AngularJS acts as a buffer between the DOM and browser as it feeds the template into the browser.

6) The architecture: While AngularJS does not fully utilize the model-view-controller (MVC) architecture, it does apply the basic principles. The way in which it functions, it would be more appropriate to say that it uses the Model-View-ViewModel (MVVM). This model offers the perfect environment to begin crafting your application.

What’s new with AngularJS

Being an Open source framework, Angular users enjoy constant updates for the main framework as well as all extensions. The latest version of AngularJS, version 5.1 was released in december 2016 along with the updated version 1.6 of Angular CLI. While the framework update does not have many new features, many of the bugs in the previous versions have been fixed in this version. The regular freezes and crashes have been minimized. One of the stability fixes made is in the Service Worker package which was previously experiencing several crashes on loading. Another update added in 5.1 is the i18n update which now allows you to set local ids and customize them.

How Relevant will AngularJS be for your Future

Given its general nature and purpose, AngularJS faces severe competition in the market. There are many alternatives that are in use today for the same purpose. However, the same can be said of Java on which all these tools run.

Angular’s appeal at this point is at its highest. Developers prefer the use of Angular because of its directives and the wide range of possibilities they entail. While Python related tools do pose a threat to AngularJS, its relatively brief legacy, for now, remains unchallenged and the testaments of its potential are more than serve as a deterrent to switching completely to alternatives.

Natural Language Processing tools for Chatbots

Language understanding tools have the capacity to make chatbots smarter. These tools are designed to enhance the communication capabilities of the chatbots. The ability to understand the sentiment, create an automatic summary and find a relationship between the topics. All these abilities can make your chatbot much more effective for the users.

Here are 5 NLP tool choices that can help your chatbot deliver high impact performance in customer servicing.

1. LUIS

Microsoft offers cognitive services to provide language intelligence and other capabilities. The LUIS or Language Understanding Intelligence Service offers high-quality models that help chatbots understand various entities and intents. The availability of the models related to times, places and others enhance the performance of chatbot. The users can get much better experience, as an active understanding of the language is used.

The tool is compatible with various platforms such as KiK, Slack, Facebook Messenger, Skype, SMS, and others. You can get both free as well as paid versions of the tool.

2. RASA NLU

When you are looking for an open source method of classifying the intent, RASA NLU is your answer. The tool offers a complete set of APIs that make entity extraction highly convenient for the chatbot.The libraries come along with the tool that enhances the capacity of any bot. The corporate sectors like health, insurance, travels, telecoms and banks get the maximum advantage of this tool.

The tool is available for free and works on platforms such as Facebook Messenger, Telegram, SMS, Skype, Line, and others. This is why the tool has gained an incredible level of popularity in multiple sectors.

3. Amazon Lex

The tool can provide high-quality language capabilities to your mobile applications. The specific properties include natural language recognition and speech recognition too. Hence, the chatbot can provide both text and voice experience to the users.

This will improve the ability of the bot to assist and help the users conducting various tasks such as placing orders, opening an account, making bookings, and others. The tool offers a simple console, which makes the processes much more convenient for the chatbot.

4. API AI

Known as one of the most trustworthy language understanding tools, API AI makes brand specific interactions easier. The businesses can leverage this tool to create bots that can provide multiple services to the users.

The conversational interface with this tool takes not much effort. Plus, the powerful features of the tool allow the bots to answer highly complex questions asked by the users. The bots get to leverage the stored knowledge along with the machine learning. Hence, the bot learns and gets better.

Some other valuable features include multilingual interactions, cross-platform support, easy integrations and others.

5. ChatScript

ChatScript allows you to create a script for the dialog conducted by the chatbot. The tool uses the scripting rules to create extensive texts. Plus, it scans documents, memorizes old interactions and manages a large volume of users.

All the mentioned tools have their own set of properties. You can add these tools and enhance the capabilities of your chatbot.

Blockchain Technology in Investment Banking

Data storage is one of the prime requirements to manage an investment bank. Banks create multiple databases to store valuable information regarding the customers, transactions, and others. These databases play a critical role while completing any task. Banks are required to offer and obtain the needed data to facilitate a smooth transaction. All the counterparties require informational pieces on a regular basis. Managing huge databases becomes a troubling and expensive task for investment banks.

It is here that Blockchain can display immense potential for investment banks.

It can become a viable answer to all the database related issues faced by the banks. Blockchain database systems provide one platform for all the counterparties to access the required data. The highly secured access system keeps the databases protected too.

According to a report, Blockchain has been able to reduce the banking costs and increase savings up to 30%. Also, it is reported that investments in Blockchain efforts in capital markets will rise from $75 million in 2015 to a staggering $400 million by 2020.

The banks can integrate all the fragments of databases together and make their office processes much more cost-effective.

1. Lower reporting costs with transparent data optimization

The blockchain provides a single source for the data. The source offers a highly optimized quality of the data for the banks, which brings an incredible level of transparency in the financial reporting.

The verification of the data becomes much easier as the authorities, clients, and customers share the data at the same time. This way, the banks get to create financial reports without investing a huge amount on gathering and verifying the data.

2. Lower processing costs with efficient operations

Blockchain centralizes the whole operation of investment banks. The operations related to finding details of customers, connecting with clients and others take much lesser effort with the continuous availability of the required data. A single source provides all information about every function.

With that, banks get to manage the digital identities efficiently. Multiple branches can use the same source to understand customers and provide quick and easy services. By doing this, the banks get to save a lot of operational costs too.

3. Lower compliance costs with transparency

With traditional database systems, banks have to spend a huge amount of money in communicating information with all the counterparties. Banks need to gather data and audit them again and again to ensure the compliance in the functions.

These costs can also be reduced with blockchain. A single database system becomes a perfect solution to bring transparency and audit transactions conveniently. The whole process becomes visible to all the authorities. As a result, the costs of compliance reduce by 30 to 50 percent.

Other benefits

There are various other functions that an investment bank needs to manage. Some of the functions include middle office work, trade support, settlement, investigations, and clearance. It is obvious that all the mentioned functions require the availability of the right data at the right time.

Banks are required to gather, analyze and confirm the data before taking any action. Hence, the availability of a blockchain database system makes these functions much more cost-effective.

Recent reports have presented positive outcomes of the blockchain technology and thus, more investment banks are exploring to implement this database technology in their work.

Technology Trends in 2018

Digital experts and tech giants have always talked about the importance of innovative computing technologies. These processes and methodologies will develop and bring down market costs to a great extent. The opportunity to innovate interactions will initiate organizational growth. Every organization would like to stay ahead of the growth curve, and that’s where they need the support of unique technologies.

What do the statistics state?

When it comes to assessing the growth and development of technologies, there’s no denying the contribution of tech giants like Apple, Amazon, Microsoft, Facebook, and Alphabet. These ventures together contributed a lot to the total market gains. In fact, federal investments in the digital market will touch the $95 billion mark by 2018. Now, we know how the technology market is going to shape up in the coming years.

Identifying the promising and prospective technologies at this juncture will be a great thing to do. We must have a look at the four exciting technologies that are expected to leave a mark in 2018!

1. IoT

Internet of Things is crucial, and it’s here to stay. According to market sources, a whopping $6 trillion will be allotted for IoT solutions within five years. If we take a look around, we will surely come across numerous smart homes.

People are already living in smart homes, waking up to smart alarms, and having smart voice assistants at home. The trend will increase over time, and the time isn’t far when every process will get automated. Internet of Things has already made an indelible impact, and it will continue to rule the tech arena in 2018 too!

2. Virtual Reality

It is highly imperative to track the growth of VR in 2018. Revenues of VR content will rise from USD 2274.24 million (2017) to USD 13964.98 million (2020). Head Mounted displays revenue too will rise from USD 3243.12 million (2017) to USD 6498.26 million (2020).

Although, this particular technology is taking giant strides towards development, there’s still room for improvement. Creation of attractive content and high-value services would be important. VR will stay, grow, and excel if experts innovate and improvise the VR-supported modules.

3. Chat-Bots

Enterprises across the globe consider customer service to be an integral aspect of their services. Consumer satisfaction is highly important, and this is where smart consumer-facing chat bots making all the difference to the CX industry.

Chat bots humanize machine interactions, thus helping you develop a personalized camaraderie with consumers on the other end. Advanced bots also leverage AI to ensure interactive UX. Looking at its ability to generate business with 47% of the consumers, the trend will surely grow and reach new heights by the end of 2018!

4. Augmented Reality

With the two most popular tech giants, Google (ARCore) and Apple (ARKit) making the first move, startups and other ventures will try their best to invest in AR. The sector is expected to grow in the next 4 years to touch $83 billion per year by 2021. The top brands are already planning to incorporate AR strategies looking at this disruptive trend.

Signing off

These trends will determine the market for innovative technologies in the coming year. If you are working in the digital sector, it’s high time to wait and watch out for them!

Why net neutrality is important now

Since its inception in the 1980’s, the Internet has offered unrestricted access to one and all. Irrespective of the content, subject matter, and information, individuals have been able to visit websites easily. It’s right here that a crucial question pops up. When it comes to using the internet and exploring the web, would you like to face restrictions?

The answer is simple, predictable, and obvious. It’s a big ‘NO.’

Content supervision and restrictions are like fatal blows on your freedom. You don’t have the liberty to browse through websites of your choice, and nothing can get worse than that. Net neutrality is important and highly crucial. While Millennials are quite vocal about the issue, traditional users are also putting up logical protests. Here’s all that you should know and understand about Net Neutrality!

Comprehending the term

The term ‘net neutrality’ has been doing the rounds for quite some time now. However, the concept isn’t quite clear to all. The concept of Net Neutrality refers to a principle that ensures complete freedom for internet users. It prohibits internet service providers from slowing down, restricting, and blocking contents. That’s not all; restrictions on certain applications can be implemented too.

If we take a look at the situation abroad, 2015 proved to be a historic year for internet users. Millions of users and numerous activists urged the ‘Federal Communications Commission’ to take a strong stand against internet restrictions. The outcome was impressive as the internet offered open and free access to all. Information sharing and access was also unrestricted and could take place without any interference.

Understanding the implications of net neutrality

Going by the concept and sensitivity of net neutrality, it’s highly imperative to understand its implications for internet users. Net neutrality is a concept that encompasses free will and openness of mass media. Quite naturally, the entire situation is critical and we should be aware of the global implications.

Here’s a quick look at what will happen without net neutrality:

• Restricted access

You won’t have control over the contents or websites. Internet service providers will gain the opportunity to decide the fate of a website. It’s them who get to decide which website wins and which doesn’t.

• Closed-down network

Internet without neutrality will resemble a closed-down network. It’s the internet service providers and telephone companies that will call the shots.

• Discrimination

Marginalized communities and those belonging to LGBT groups and indigenous races will feel neglected. The internet happens to be a weapon as well as an effective platform to voice their opinions. They won’t get the opportunity to organize, assemble, and gather targeted resources.

• Implications on the business arena

Restricted internet access will have devastating impacts on businesses too. They will suffer to a great extent, as startups won’t have the opportunity to establish their presence on the web.

The current situation

Although Net Neutrality continues to prevail, its future doesn’t seem to be secure. It’s living in a vulnerable time where equations can change at the drop of a hat. We have to wait and see what future has in store!

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!