Artificial Neural Networks – The basics

Artificial Neural Networks are simulations that are derived from the biological functions of ‘neurons’ which are present in the brain. Thus, Artificial Neural Networks are essentially artificial neurons configured to carry out a specific task. ANN has gained a lot of popularity as it is used to model non-linear processes.

Artificial Neural Networking allows solving problems like clustering, classification, pattern recognition, prediction, and determining outliers. This has made ANN a very useful tool.

How does ANN work?

Artificial Neural Networks acquires knowledge through learning continuously. Like in humans, the knowledge acquired is stored in the artificial neurons designed within the ANN and used to perform the required task. ANN has a wide range of syntax, semantics, and speech-tasks which help ANN solve a wide range of problems.

Some of the interesting applications of ANN are discussed below.

1. Text Classification

Applications like web searches, language identification are some of the applications that use text classification. Neural Networks are widely employed for this type of classification. Experts agree that deep learning can be applied to enhance the value delivered by text classification. Artificial Neural Networks can be applied from character-level inputs as well as abstract text content.

CovNets or Convolutional Networks can deliver good outcomes in text classification without prior knowledge of words or phrases by applying them along with deep learning and Neural Networks.

2. Semantic Parsing

Artificial Neural Networks can be actively helpful in answering questions. A Q&A system will automatically answer any question asked in natural languages like definition questions, biographical questions and so on. Using Neural Networks in these systems makes it possible to maintain a high performing question answering system.

Developers have released semantic parsing framework for answering questions using a specific knowledge base. ANN uses this framework to quickly identify the type of questions and then answers it using semantic matching. There are other frameworks available which can further improve neural networks’ performance in this field.

3. Speech Recognition

Voice technology has advanced and now it is used for automated telephone conversations, speech-only computing, and much more. Neural Networks are being used extensively in this area. neural networks can specifically be programmed to handle multiple types of queries over a wide range and with continuous learning, neural networks help you achieve a great speech recognition software.

4. Character Recognition

Character recognition has become vital in today’s world across different industry verticals. There are many practical applications in this realm. Some instances include character recognition on receipts, invoices, checks, or legal billing. The Character Recognition framework for Artificial Neural Networks has been effectively used in this field and tests have shown the accuracy to be above 85%.

5. Spell Check

Text editors help you find out misspelled words to help you rectify them. Neural networks have been incorporated in many of these text editors nowadays to provide easier spell checks. It uses the personalized spell check framework and it outperforms many other text editors that don’t use Artificial Neural Networks today.

To conclude, we can say that Artificial Neural Networks are very versatile and make a lot of jobs easier in different functions within an enterprise.

Chatbots in Recruitment

Did you know that 74% of the candidates for a job recruitment drop out after starting the job application process? The lengthy process and reams of paperwork are some factors for this stat. How good would it be if technology could present a solution that takes out the tediousness from the entire recruitment process?

Well, technology already has a solution ready that fits in perfectly in this context – chatbots

What is a chatbot?

A chatbot is an AI programme that converses with humans in a meaningful and contextual way. Their ability to be accessible to customers round the clock adds multiple business advantages. They not only elevate user experience but also reduces costs of maintaining full-time customer support personnel.

Because of the immense business value that a chatbot offers, it finds applications in multiple industries. The recruitment industry is the latest one to have enjoyed the benefits of having a chatbot. Mya and Job Pal are two examples of chatbots that are revolutionizing the recruitment industry to a great deal.

How can a great chatbot make recruitment effective

1. Save time and money

Chatbots have evolved to be smart and useful. Right from sifting resumes to answering initial queries from a job applicant, it can do it all without the need for an actual executive to sit in front of a computer to carry out these tasks. It can also determine if a particular job opening is aligning well with a particular candidate during a conversation.

2. Application process made more effective

A chatbot is a better option to engage in the initial phase of candidate application. It can save time and get the needed information by taking the course of a natural conversation. This way the high chances of midway drop-outs through the application process can be brought down significantly.

Even if the candidate leaves midway, the chatbot can nudge him/her later on in subtle ways to try and get the entire application process carried out. From scheduling appointments for interviews to sharing information on new job openings and letting the candidate know about the application status, a chatbot can help make the job application process less cumbersome.

3. Pre-screening process made transparent

The traditional interaction between a recruiter and a candidate is filled with uneasy periods of silence post the interview. A chatbot can help fill this gap by a pre-screening process and making the entire activity an interactive and transparent one.

By instantly providing information on approval or rejection, the candidate can take the next appropriate step. This transparency of application process helps candidates get a quick update on the status of their job applications and reduces a lot of back an forth procedures for the HR management and candidate.

4. Automate routine tasks

Mechanical tasks such as sifting through resumes, scheduling interviews, and internal coordination are routine yet necessary tasks within the recruitment activity. With a chatbot, all these routine tasks can be easily automated and HR professionals can focus on more complex activities such as employee branding, improving the outreach and other management related aspects.

To wrap up, while many recruiters feel that chatbots are likely to make jobs obsolete, the fact remains that they actually make the recruiters’ job more powerful and effective rather than making them redundant.

R or Python? Which is the better for Data Science

Data Science has gained a lot of popularity in recent years and the amount of people opting for a career in data science has increased significantly.  Data science is basically an amalgam of data interpretation, algorithm design, and use of technology to solve problems.

Data scientists are provided with data which they have to mine in order to gain invaluable insights, to solve complex problems, and to provide direction to stakeholders. A data scientist should be able to dive into the given data to effectively draw helpful analysis for business decision making. This is the chief KRA of this job, thus making it slightly complex when compared to others.

If you are looking to enter the field of data science, it is important that you equip yourself with the right technical skills so that you can add value in real life corporate scenarios and carve out a lucrative career path for yourself.

Once such question that requires considerable thought is ‘which is the best language for data science, R or Python?’

In this post, we have tried our best to answer the question for you…

R or Python?

R is the best-suited choice for data scientists over the past years because the functionality of R was designed keeping data science in mind. Also, it is compatible with languages like C++ and Java. R is used by Google and hence, it is considered a reliable data science language.

When it comes to Python, it is relatively new in the field of data science and has yet to make a lasting mark. However, Python provides many benefits which have led to data scientists to choose Python as their language of choice in data science.

The financial sector switched its data science language to Python after Bank of America started using Python, mainly because it is more versatile and easier to implement. However, some people still prefer R because of its long heritage.

How to Decide

There are a lot of factors which can help you decide which programming language is best suited for you. Data science requires immense programming language proficiency and hence it is important that you choose wisely.

1. Versatility

R is considered the best by many because of all the customized packages it offers especially for data science. Python, however, is easy to learn and has a more refined syntax. In case a person is just starting out as a data scientist, it is better to opt for python.

2. Data visualization support

Data visualization is of utmost importance in data science and it is at this stage that R proves to be a better alternative than Python. Options like ggplot2 and googleVis visualization tools simply expand the utility value of visualization. Python is not as versatile as R in this matter, but it still provides visualization tools like Plot.ly or Matplotlib.

3. Deep Learning support

Data scientists are required to know about deep learning in order to mine data and Python is the more superior language here. Python provides data scientists with various packages like Theano and TensorFlow, which makes it one of the best languages for deep learning. R can also use some of these packages but Python does have an upper hand here

To wrap up, we have provided all the important factors which can help you decide which programming language will suit you better as a data scientist. Do let us know which will be your final choice. We would love to hear from you.

The importance of UI and UX for Business

“88% of online customers are less likely to return to a website after a poor experience”

“94% of a website’s first impressions are design related”

These statistics demonstrate how UX drives the ROI of any business in today’s time.

UI and UX are undoubtedly the essential components of a website and it is crucial that businesses invest in good UI and UX so that their services can be easily accessible to the end user.

With an ever-increasing number of websites, the need for UI and UX is more than ever, because the competition is very high. Today, website owners prefer to hire people who have both UI and UX design experience, as knowing both provides the person better insights and facilitates more creativity.

But most often, people think UI and UX are similar. Through this post, we aim to change this notion and dive in deep into the differences.

UX and UI are Different

User experience and user interaction are two very different components of the design. The user experience of a site involves scooping out the general logical flow of a website, while UI  deals with the visual designs and graphics that go into design.

For example, when you access Gmail, it asks for your id and password, and only then it allows you to log in. This is the flow that Gmail follows or the UX of Gmail.

UI, on the other hand, is the website layout and visual designs that a user sees. This includes things such as interactions, themes, text areas, fonts, color schemes etc.

For example, in Gmail components such as the login button, your photo beside the mail id, text box, etc. are components which fall under UI design.

UX and UI roles on a website

The job of a UX designer is to basically study the market, analyze the competition and design a game plan that finally puts together a functional and cost-effective website. A user must be able to easily navigate through the website.

A UX designer concerns himself with the macro concepts of the site and makes sure that it is not cluttered or distracts the user from performing the main call-to-action such as: signing up for a service, downloading a guide, sending an inquiry etc.

UI designers, on the other hand, are more inclined towards creating an attractive and beautifully designed website for users. Also, UI design requires that you help users navigate by designing buttons in different sizes to show priority. Basically, UI designers concern themselves with the micro concepts of the website, like minute visual details to make the site look attractive.

Dropbox is an excellent example of UI/UX experience. The strong graphics and visuals on the website provoke the user to explore the website longer and browse through. The UX design of Dropbox was spot-on and it focused on encouraging the user to make just one action – to use dropbox’s cloud service for photo and document sharing.

However, in late 2017, Dropbox went through a major re-branding exercise where they moved away from the neat clean iconography and embraced a more retro-themed design which got a lot of mixed reviews. But, that is what it all boils down to – Design is not a software, it is more people-centric and hence it is important to have empathy for people when you design.

Which comes first? UX or UI?

The primary objective of any website is to showcase the products and services of any business in the most direct and innovative way. There are millions of websites that get published on the internet every hour. Hence it is necessary to create a website that is on par with the current design trends, competition and has a logical flow of the whole process.

For this purpose, UX is the first thing that designers dive into. They do a competitive analysis of the market and come up with insights which improve site functionality. UI design comes much later after all the wireframes are in place. Here is where the creative spirit of designers is put to test.

Thus, UI and UX are important components of design but play a completely different role. They are essential to drive user engagement and enable conversions.

Artificial Intelligence Trends in 2018

Artificial Intelligence (AI) has soared to unbelievable heights in recent times, and even today, with tech giants like Google and Microsoft making constant advancement in this field, AI is almost everywhere. Needless to say, there will be many things that AI will learn to do in 2018, and make the lives of people much easier than it already has. Though we don’t yet have flying cars and floating buildings, like in movies, we do have some great AI like Alexa and Google Assistant.

So, the question that arises now is – what is the future of AI in 2018? Well, some mighty intellectual people may think too much into the future regarding what things AI will be able to do, but for now, let’s you and me focus on what we can expect from AI in 2018.

1. AI will become more Human-Like

In today’s busy world, where manpower itself is not enough to handle the ever-increasing customer demands, many companies are using AI in the form of chatbots and automated answering machines to make their institutions run smoothly. Customer assistance is a major field where AI is said to make a huge progress.

However, it is likely for some people to feel weird listening to a robotic voice talking to them, which is why Artificial Intelligence companies have been working towards making their products more and more human-like, so as to encourage people to use it. AI’s like Amazon’s Alexa is a great example of a personal assistant.

2. Voice Recognition will become much Better

Almost all the smartphones today have a voice recognition technology installed in them, which enables you to talk to the AI and set appointments, call someone, set alarms, and much more. Siri and Google Assistant are some good examples.

However, it becomes irritating sometimes, when the voice recognition doesn’t hear you correctly when voice typing, and you have to edit the whole message again. This is said to change in 2018, as AI companies and their voice recognition tech is said to improve by folds.

This means that your smartphone assistant will hear your commands better, and type your voice message better.

3. Machines will be Data-Driven

Machine Learning has become necessary in today’s time. The immense growth in AI and the IoT (Internet of Things) has made companies invest capital and workforce towards advancing their AI functionalities.

This may sound crude, but AI has become essential for companies to maintain their data flow and important company data, and an AI functionality has become a vital part of every company’s functionality, and that will continue to grow in 2018. Artificial Intelligence functionality has been broadened to new horizons by all the data provided by Internet Of Things, and it will continue to grow exponentially.

In 2018 and beyond, there are a lot of advancements in AI to look forward to. While it may go mainstream in many sectors, others will have a more cautious approach before embracing the technology.

Internet of Things (IoT) in the Insurance Industry

IoT is often regarded as the next big industrial revolution after the Internet. In addition to connecting machines with machines and with people, it is also promoting an enhanced sense of awareness of our surroundings. From coffee makers to washing machines, everything can now be connected and controlled. It doesn’t come as a surprise that spending on IOT worldwide (across all markets) is expected to rise from $737 billion in 2016 to a whopping $1.29 trillion by 2020.

Internet of Things is making its presence felt across industries, and the insurance companies are no different. IoT essentially uses its massive data flow and information from various devices to calculate and come up with possible outcomes for insurance companies and policyholders, which help them to minimize their losses, and it also actively engages the policyholders to prevent mishaps. Here is how the IoT can affect the Insurance companies and insurance policyholders.

How Internet of Things benefits Insurance policyholders

Advancement in IoT is in the best interests of the policyholders. This is because IoT keeps a track of anything and everything which is on the network, which includes the policyholder, and the assets he has insured.

1. IoT and Car Insurance

Consider a case where your car is insured at an insurance company which uses IoT to assist them with insurance policies. You can ask them and get your driving habits recorded with the IoT.

If a fluctuation is seen in the insured person’s driving, it will alert the insurance company. Also, good driving habits observed by IoT can get you good discounts on insurance policies. Hence, an increase in IoT technology can prove to be very beneficial for insurance policyholders.

2. IoT and Health Insurance

A person who cares a lot about his well-being and his fitness is obviously less prone to diseases as compared to a person with a sedentary lifestyle. However, almost every fit person and a normal person pay the same premium.

IoT can help you here. With various devices available now, like Fitbit and other running apps, which track your activities, you can contact your insurer and link your apps and devices with the IoT and get discounts on your premiums.

3. IoT and Home Insurance

As a homeowner, it is natural for a person to opt for a good home insurance policy to protect his home from damages and sudden calamities.  Many a time, untoward incidents can lead to lead to losses for both policyholders as well as for insurance companies.

These things can be avoided using IoT. Devices powered by IoT can be linked with satellites and various environmental sensors which can warn insurance policyholders of a potential problem before it gets too big. It can also notify the people about a possible approaching calamity to be avoided, hence preventing too much damage.

These pointers aptly denote the immense value that IOT can have in the insurance sector. It not only segregates the right premium for the right set of policyholders but also captures essential data points that can prevent claims and payouts for the insurance companies.

How to become a Data Scientist?

Data Science is providing tremendous business value such as precision in forecasting and gaining insights on what can be beneficial for the customer. Take for instance streaming sites like YouTube etc. Data science is being used to mine data about what kind of videos attract users’ interests. This helps the site to recommend similar videos based on their specific choices and interests.

5 skills every Data Scientist should have

In today’s world, where the competition is immense, data scientists are more in demand than ever. If you are interested in entering this field and want a list of skills that you need to master then you are at the right place. We have listed 5 essential skills you need to master in order to be a good Data Scientist.

1. High-level understanding of Python, Hadoop, and SQL

As a data scientist, it is essential for you to be good at programming languages like Python, Hadoop, and SQL. The data provided to a data scientist is usually in form of large data sets and being able to understand and decode it requires good programming knowledge.

Only after you have understood the dataset can you mine data and identify peculiar characteristics and patterns. The work of a data scientist is more applied than theoretical.

2. Strong Decision-Making Prowess

A data scientist should have good intuition and decision making skills to identify the product and have a concrete knowledge of the complex system and data. When a data scientist has a good product intuition, he can generate general hypothesis on how he can improve the product, and get good insights.

It is also necessary for a data scientist to know how to define metrics of the product so that he knows what he can do to make it better.

3. Ability to work well in teams

Data science is not an easy job, and it is definitely not something one can do alone. A data scientist needs to have good teamwork spirit if at all he wants to do his job as per expected outcomes. Be it ETL specialists or data analysts, or even stakeholders form the business side, a data scientist will have to collaborate with many teams from time to time. Having a good team spirit makes workflow smoother.

4. Good Communication Skills

Even if a person is good in data science, and has obtained excellent insights, it is essential for him to know how to communicate his findings in a perfect manner. All the insights obtained from such deep-end and thorough research will be of no use if it is not communicated effectively. Different business units must be able to derive the much-needed data to make better business decisions.

5. Excellent Quantitative Analysis

A data scientist must mine data and identify peculiar characteristics and behavior from the given data. Hence, quantitative analysis becomes an essential skill for a data scientist as the datasets provided are very large. In order to efficiently identify products and their behavioral characteristics, a data scientist needs to have quantitative analysis as his expert skill.

Thus, if you want to become the most sought-after data scientist in the industry, then these are the five key skills you must possess to be good at your job.

Artificial Intelligence in the Banking Industry

Artificial Intelligence has taken the world by storm and has been advancing rapidly in recent times. It has shown a remarkable potential to augment human efforts and free them up from routine tasks so that they can focus on being better in strategizing and doing complex activities.

In today’s world, almost every aspect of life and business has the potential to be disrupted by AI. It is no wonder that the AI market is expected to surge past the hallowed $100 billion mark by 2025.

How will the banking sector be affected?

AI has had an effect on almost all the industrial sectors, and the banking sector is also one of them. Banks have started using AI for multiple purposes to make their institutions operate smoothly as AI bots can run all round the clock and do the jobs assigned to them flawlessly. The banking sector is using AI increasingly and so, here are 4 ways AI is impacting the banking industry.

1. Improved and Cost-Effective Customer Service

In today’s ever-growing corporate world, it is almost impossible for a human being to bear the burden of all the customers calling the bank’s support helpline. This is where AI plays an essential role.

Apart from being available to customers all round the clock, AI has drastically reduced the manpower and money required for customer service. This has majorly benefited the finance industry.

2. Better Management

Before the advent of AI, companies used to ask advice from bank experts as to how they can maximize profits and minimize taxes. However, it’s in the nature of humans to be imperfect, which is why the predictions were not correct mostly.

Now, customers who are looking for advice can directly ask the bank’s AI program about any questions related to their company. The AI-powered solution can provide a full report with all references and facts, thus helping both the bank and the company.

3. Know your future prospects and returns

With a targeted AI solution, you can keep getting continuous updates on various offers available and build on your current assets to increase your returns. Also, you don’t have to start from scratch as the AI will do all your work for you. Once you have an AI system in place, it will keep your account safe from market fluctuations.

4. Precise investment information and research

The finance sector is a volatile one, and many a time, there are crucial decisions that need to be made. In this case, it is but natural to choose the expert programming of an AI over human predictions and trust the AI’s continuous learning methods to forecast better.

If a bank has an AI systems in place, it can provide you with all the research and reference along with exact facts and figure to help you make the best possible decision. This makes AI an invaluable asset in the financial sector. Investment decisions are very crucial as customers may lose their trust over a bank in case the advisor makes a wrong decision.

Thus, AI has become a vital part of the financial sector and will continue to be, in the future. With so many benefits being derived from the industry it is natural that AI will find increased adoption in the months to come.

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.

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!