SigmaWay Blog

SigmaWay Blog tries to aggregate original and third party content for the site users. It caters to articles on Process Improvement, Lean Six Sigma, Analytics, Market Intelligence, Training ,IT Services and industries which SigmaWay caters to

The three emerging IT technologies

In this rapidly advancing digital age, information technology is evolving from being a reactive organization with limited agility to be a predictive business enabler that foresees and anticipates business needs. So, the key in developing this competence is streamlining IT processes and working smarter, not harder. With this in mind, the author, Rose de Fremery, tech writer, takes a look into three technologies that are transforming business and IT operations today. These technologies are Cloud Computing, Artificial Intelligence and Automation. Read more about these technologies at:


Rate this blog entry:
1437 Hits

AI Contributing Towards Medicine

Artificial Intelligence is spreading its wings and is coming into rescue in various fields. One such field which comes into rescue for humans is the health care sector. Combination of these two fields can bring great advancement in health care sector. Artificial Intelligence and Machine learning have already come into action in medicine. Following are the top 4 applications:

    1. Diagnosing Diseases: Not all diseases can easily be rectified. This could be time consuming and expensive. Here, various Deep Learning algorithms prove to be a solution. This focus on automatic diagnosis, making diagnosis much cheaper and accessible. 
    2. Developing Drugs Faster: Drug development is a time taking and a tedious task. It involves analytics and various rounds of testing. AI has already aced in speeding up the process.
    3. Personalizing Treatment: Same medical procedure can not be carried out on every patient. Choosing the course of treatment can be a difficult and a great responsibility. Machine Learning can automate this task. It can help in designing the right treatment plan.
    4. Improving Gene Editing: This is a technique that relies on targeting and editing specific location on the DNA. A careful selection needs to be made. Machine Learning models have successfully been able to predict target and effects successfully.

To


Rate this blog entry:
726 Hits

Working with Machine Learning

Artificial Intelligence, Machine Learning and Deep Learning are relatively newer technologies invading the fields of information technology, business etc. Though developers are walking towards this era, currently the number of experts is relatively less. The company often makes mistakes by starting up with the technologies instead of focusing on business needs. They often make mistakes by assigning out of domain work to some. For e.g. Hiring data scientists and asking them to build something interested from given database. Rather than a team must be formed of product managers, data engineers, data scientist and DevOps engineers.A team of four will be a kick start to improve our process and giving better results. Now everybody has an opportunity to improve the models, optimise the deployment and scale the business. 

Talking about ML, many projects fail due to complex structures. This could occur because of working on wrong problem, to having wrong data, failing to build a model or failing to deploy it correctly. Read more at:

Rate this blog entry:
870 Hits

Latest development of Artificial Intelligence

Researchers at MIT have integrated AI with Radio Waves for visualizing people on the other side of the wall. While it sounds like the kind of technology a SWAT team would love to have before kicking through a door, it’s already been used in a surprising way—to monitor the movements of Parkinson’s patients in their homes. The radio signals that they use are very similar to Wi-Fi but a little less powerful. The technology  depicts the people in the scene as skeleton-like stick figures, and can show them moving in real time as they do normal activities, like walk or sit down. But how does it work? Find out at:


Rate this blog entry:
439 Hits

The Change in Data Management for AI

Nowadays, Artificial Intelligence is not new to this world. AI is used in almost every field, especially in the field of business. To make business related decisions, you need data. This data is analysed and then plans and actions are decided. From identifying the problem to discover actionable business insights, right analysis of data can transform business operations and take it to higher level. 

However, with the power of AI you can automate this task of data analysing, transforming raw data into actionable business intelligence. One thing for which Artificial intelligence is hungry for is data! The more data you feed it, the better results it gives to you. But with the vast amount of data that AI requires, it also follows the concept of “Garbage in, Garbage out.” Feeding the right thing to AI should be the up-most priority to get results. For this reason, many companies are making changes in their data management space.

Read more at :

Rate this blog entry:
771 Hits

Let Machine Learn Using SVM!

Machine Learning is one of those technologies which have invaded in our lives to make it better. Without any doubt one can say that even though machine learning is in its initial phase, it has already become a part in our 24/7 running lives. Set of algorithms to use data, learn from it and then forecast future trends for that topic is expanding day by day.

Machine Learning and Data Sciences are often used together in order to predict future from varied data results available with us. One of the famous algorithm used in this field is SVM or Support Vector Machine which can be used for both regression and classification task. It uses the concept of hyperplanes and other mathematical functions in order to produce significant accuracy with less computation power. SVM has already proved itself in text categorization, image recognition, and in bioinformatics and now working in other.

To know more about how SVM works visit :


Rate this blog entry:
969 Hits

The Relationship They Share: AI, ML, DL

Artificial Intelligence, Machine Learning and Deep Learning are now the most exploring topics for any techie. In spite of enough differentiation between these terms, they are often used interchangeably. To put an end to this confusion one could say that ML and DL are nothing but cousins of AI. 

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Many applications of AI are being seen and used today. From voice-powered personal assistants like Siri and Alexa to self-driving cars and many more are applications of AI.

On the other hand, Machine learning is an artificial intelligence (AI) that is discipline geared toward the technological development of human knowledge. Machine learning allows computers to handle new situations via analysis, self-training, observation and experience.

Whereas, deep learning is a subset of machine learning which is a collection of algorithms used in ML to build and train neutral networks and act as decision making nodes.

So, though AL, ML and DL are interrelated but in this vast field of technology they all stand on their own and using them interchangeably would not be justice.

Read more at:

Rate this blog entry:
470 Hits

Learning AI - The Right Way

Nowadays, we can hear about lot of myths surrounding AI and ML. This article link has busted those myths and allowed the truth to rise to its deserved place in the informational hierarchy. The basic misconception is that most of the people link AI and ML directly to robot, which is not true. Other than this, there is a whole other discussion of AI taking over humans, to which even Stephen Hawking and Elon Musk agreed. In some economical respects, these threats seem to be real, like self-driving cars replacing human drivers. But a new trend is on rise, in which the AI is included in the business structure by shifting the human staff to a new position. AI/ML is still an infant and hence we shouldn’t fear AI takeover anytime soon. Continue at:



Rate this blog entry:
834 Hits
Sign up for our newsletter

Follow us