Category Archives: Computing and Artificial Intelligence

RPA | A step towards Digital Transformation

Digital Transformation is a long path to be covered by organizations and Robotic Process Automation, also known as RPA, is the first step towards it. RPA is a piece of highly customized software, often called a Software Robot or a BOT, that when deployed perform a variety of tasks as a part of the Digital Enterprise. Every organization, be it Banking, Finance or insurance, telecommunication operator or an energy provider hear this acronym “RPA” every time from their supervisors and in their meetings.

It can be clearly seen that both the companies and the employees can be benefited with it and this makes RPA one of the must-have in the world services. We can check the criticality of RPA with a few examples that will affect the jobs in the service sector.

  1. RPA provided the solution to the Banking sector by ensuring the quality of data, the consistency of information and the eligibility of subscribers. It also decreased the average processing time taken by an employee for data, chequebooks and documents processing performed by different applications. Employee performance was enhanced by a significant reduction in the number of clicks and app switching. Robots help the operator’s decisions while taking over the unpleasant and redundant tasks. As a result, It boosted the Employee performance by a significant reduction in the number of clicks and application switching. Robots took the unpleasant and redundant tasks and helped the operator’s decisions.
  2. With increasing population houses are also increasing and so do Electric Supply. The confusion and queries about the high electrical bill were a non-resolved issue. Client advisors are now supported by Contextor robots: these software bots automatically recover all the data giving the context of the customer who is on the telephone, launch the business data analysis engine and instantly present the response to the consultant. Presently, 90% of customer complaints are handled in minutes, from the first call itself.

One can find such in every aspect of the service sector. In large enterprises, while working on computers, employees spend a lot of time interacting with the different applications of IT. Robotic Process Automation makes accessible to every single tertiary worker a set of robots that will facilitate their remaining burden by enabling them to devote more time to assignments that leverage their knowledge and sense of human relationship.

It describes that RPA bots are not destroying jobs & opportunities, but instead, they are improving the working conditions of the employees they help, who can then concentrate more time on tasks that are interesting for them and that use their insight, intelligence and their sense of human relationships. It is clear that robots augment human capacities, without replacing them.

Data Science

How do we relate Data Science, Artificial Intelligence, Machine Learning & Deep Learning with each other?




It is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms. This means that data science helps AI to come out of the problems by linking related kind of data for future use. Basically, information pools faster and more efficiently. They play active roles in the design and implementation work of four related areas:

  • Data architecture
  • Data acquisition
  • Data analysis
  • Data archiving



In the present, AI is complex and effective but is nowhere equal to human intelligence. Humans use the data present around them and the data accumulated in the past to figure out anything and everything. However, AIs don’t have that ability just yet.

Artificial intelligence is turning up to every industry, but we must understand its limits. The principle limitation of AI is that it learns from the data. There is no other way in which knowledge can be integrated. That means any deception in the data will be reflected in the results. And any additional layers of prediction or analysis must be added separately.


Machine Learning

Machine learning is an application of artificial intelligence (AI) that provides systems ability to automatically learn and improve from experience without being exclusively programmed. It focuses on the development of computer programs that can access data and use it to learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, to look for patterns in data and make better decisions in the future based on the examples that we provide. Machine learning methods

  • Supervised machine learning algorithms
  • unsupervised machine learning algorithms
  • Semi-supervised machine learning algorithms
  • Reinforcement machine learning algorithms


Deep Learning

It is a latest technology behind driverless cars, which enables them to recognize a stop sign, or to make difference between a pedestrian from a lamppost. Models are trained by using a large set of collected data and neural network architectures


Artificial intelligence is a huge term with applications ranging from robotics to text analysis. It is still a technology under evolution and there are dispute of whether we should be aiming for high-level AI or not. Machine learning is a subset of AI that focuses on a definite range of activities.

Big Data 2018

The future of Artificial Intelligence in 6 ways

Technology moves at rapid speed, and we now have more power with us than we had in our homes in the 1990s. Artificial intelligence (AI) has been an interesting concept of science fiction for decades, but many researchers think we’re finally getting close to making AI a reality. NPR notes that in the past few years, scientists have made development in “machine learning,” using neural networks.

This is a type of “deep learning” that allows machines to process information for themselves on a very refined level, allowing them to perform difficult functions like facial recognition. Below are the 6 ways of AI which highly impacts on future

 Automated Transportation

We’re already seeing the beginnings of self-driving cars, though the vehicles are currently required to have a driver present at the wheel for safety. Inspite of these advance in developments, the technology isn’t perfect yet, and it will take a while for public acceptance to bring automated cars into widespread use. Google began testing a self-driving car in 2012, and since then, the U.S.

Cyborg Technology

One of the main circumspection of being human is simply our own bodies—and brains. AI gives the most impact on people with amputated limbs, as the brain will be able to communicate with a robotic limb to give the patient more control. This kind of cyborg technology would automatically reduce the limitations that amputees deal with on a daily basis.

Taking over dangerous jobs

Robots are already taking over some of the most difficult jobs available, including bomb defusing. These robots aren’t quite robots yet, according to the BBC. They are technically drones, being used as the physical counterpart for defusing bombs, but requires a human to control them, rather than using AI. Whatever their allocation, they have saved thousands of lives by taking over one of the most dangerous jobs in the world.

Solving climate change

Solving climate change might looks like a tall order from a robot, according to the development machines have more access to data than one person ever could—storing a mind-boggling number of statistics. Using big data, AI will identify the development and use that technology to solve the difficult problems.

Robot as friends

At this time, most of the robots are still emotionless and it’s hard to imagine a robot you could relate to. However, a company in Japan which has taken the advancement big steps towards a robot companion—which who can understand and feel emotions. Introduced in 2014, the Robot companion which is named as “Pepper” was sold in 2015, with all 1,000 initial units selling out within a minute. The robot was specifically programmed to read human emotions, develop its own emotions. Pepper goes on sale in the U.S. in 2016, and more sophisticated friendly robots are sure to follow.

Improved elder care

Although we don’t know the exact future, it is quite conspicuous that relating with AI will soon become an everyday activity. These interactions will apparently help our society evolve, particularly in regards to automated transportation, cyborgs, handling dangerous duties, solving climate change, and improving the care of our elders.


Big Data Market will be worth US$46.34-billion by end of 2018. This clearly indicates that big data is in a constant phase of growth and evolution. IDC estimates that the global revenue from big data will reach US$203 billion by 2020 and there will be close to 440,000 big data related job roles in the US alone with only 300,000 skilled professionals to fill them. Bidding adieu to 2017 and just in the third month of 2018, we look at the marked differences in the big data space what exciting may be on the horizon for big data in 2018. Tracking big data trends is just similar to monitor the regular shifts in the wind- the moment you sense its direction, it changes. Yet the following big data trends are likely to shape up in 2018.

The expansion of the Internet of Things (IoT) has added innumerable new sources of Big Data into the Data Management landscape and will be one of the major Big Data Trends in 2018 and beyond. Laptops, smart phones, sensors on machines, all generate huge amounts of data for the IoT.

Organizations that are flexible enough to manage and transform the data into useful Business Intelligence, this represents a significant opportunity to gain a competitive advantage (or remain competitive). As Big Data grows, businesses attempt to keep up with it, and struggle to turn the data into usable insights. Business Intelligence is key to stay competitive, and Data Analytics provides the up-to-date information needed.

In 2017, some companies expanded their services and software which translated Big Data into visualizations and graphs. This allowed researchers to gather and coordinate information about the general population more efficiently, and improve the customer experience. It also allows leaders to streamline the decision-making process.

The number of companies offering Cloud services will also continue to expand in 2018, resulting in competitive pricing, and allowing smaller businesses to access Big Data resources.


Business Intelligence in 2018

Organizational decision-making is currently undergoing a shift which will continue into 2018. In 2017, the goal of processing Big Data promoted ever-increasing efficiency and steadily decreasing costs. In turn, this has made the use of Business Intelligence, based on Big Data, more important to small and medium-sized businesses, and even start-ups. This trend will continue into 2018, and beyond, with the cost of processing Big Data continuing to drop. Expect the following

  • Use of Business Intelligence from the Cloud will increase.
  • Analytics will provide improved data visualization models and self-service software.
  • Decisions regarding expansion into new markets and geographies will be based on Big Data.

GM uses Cloud Computing, AI to Lighten up

Big Data

General Motor is the first company in North America to implement new creative, generative design software technology from Bay Area-based Software Company. It uses cloud computing and AI-based algorithms to expeditiously extract a large number of permutations of a part design, generating a wide number of high-performance, generally organic-looking geometric design based on sectors utilized by the users, such as weight, strength, material choice, fabrication method, and more. The user then regulates the best part design option.

An advanced technical field, generative design nature’s different ways to approach the design, providing limits beyond what a human alone could create. “This advanced technology provides the enormous technologies in how we can design and implement components for our vehicles to make them lighter and more efficient.

“When we combine the design technology with manufacturing advancements, such as 3D printing, our access to vehicle development is effectively transformed and is basically different to co-create with the computer.

Executives and engineers from the two companies will participate in a series of on-site engagements to explore and interchange their ideas, learning’s, and expertise. GM is on demand to access the advancement in Autodesk’s of software and technical specialists.

Eliminating the paths which are not specifically specialized to combined with parts merger yields benefits for vehicle owners including the potential for more interior space has increased the range and enhanced vehicle performance. It also searches the way for new features and advancement for customers and provides vehicle designers an outlook on which to analyze designs and shapes.

GM is the leading end-user and performs innovations in additive manufacturing. For more than three decades, GM has used 3D printing to create three-dimensional parts directly from digital data through successive addition of layers of material. GM started the first now it has the most comprehensive 3D printing capabilities in the world with more than 50 rapid prototype machines that have produced more than 250,000 prototype parts over the last decade.