SigmaWay Blog

Innovations and Inventions do not stop for anyone. They are always surprising us with something new. Due to them, the world is moving towards future at a tremendous pace. Artificial Intelligence, Machine Learning and more are taking over various jobs and easing our life. 

Following are the most astonishing tech inventions of 2018:

1. Crypto Anchors: They are digital footprints that helps to check product’s authenticity. They can either be codes or small computers embedded in a product and linked with blockchain.
2. Omron Robots: Omron Robots are robots that are gradually taking place of factory workers. They not only lift heavy objects but also move at a rapid speed and can freely navigate the environment. 
3. Myo Armband: This can be used both in video games and during surgeries. They hold the sensors in place.
4. AI Microscopes: They found their application in studying underwater life. They can monitor sea creatures 24x7 providing us important data.
5. Earprint: It is a new biometric identification device what send sounds to one’s ear. This sound is echoed back and this echo is different for every person.
6. Online Supermarket: Online market is the major attraction for everyone. Sit at one place and order from anywhere makes our lives easy. Though this is not new but it has gain more attention.

Read more at https://www.techworm.net/2018/07/most-astonishing-tech-inventions-2018.html

Data is the utmost important thing for any company or individual. Data Breach is an incident that happens when sensitive, confidential or otherwise protected data is been accessed or disclosed in an unauthorized manner. Many companies intentionally or unintentionally expose and leak consumers and commercial data. According to the Breach Level Index (BLI), many organizations fails to safeguard their databases. 

For any person or company, any such incident can be confusing. In such situation, there is a need of a standard policy which must be followed. Following are few steps one should consider in order to save guard the data in case you are at risk:

1. Isolate – Isolate the machine from rest of the network if a particular hardware is on risk.
2. Document – It is important to keep detailed records of everything you do from the moment data breach is discovered.
3. Photograph – Photographs can help in solving digital data breach.
4. Interview – Any person directly or indirectly involved with the systems that were breached should be interviewed.
5. Use Your Knowledge – Learn and take steps.

To know more about it visit:  https://www.techworm.net/2018/04/first-5-steps-when-faced-with-a-data-breach.html

Improvement is a non ending necessity for any business to survive in this competitive industry. It is well known that bringing right technological change in business cuts the cost of production specially when the human labor costs too high and are replaceable with technology. In various surveys, it has been found that HR spends most of the time in transactional jobs such as data entry and minor mathematical calculations. These tasks can be automated by bots and this human resource can be provided proper certifications and they can be utilized for better tasks. There was a survey done in an organization. They implemented technology which improved the onboarding time of people from 120 minutes to 3 minutes. And the man power can be used for more important tasks such as decision making.

Read more at https://www.informationweek.com/big-data/ai-machine-learning/how-ai-and-intelligent-automation-impact-hr-practices/a/d-id/1332484?

Packing and moving your living space is quite a headache. Especially in India, most of the people try to find someone providing the cheap shifting services and hence get trapped as they do not provide safety to your good, thus resulting in an expensive net cost. Thankfully, technology is again helping humans to get a fair deal in terms of safety of good and proper quotation of charges. Some of the reallocating companies have started to use AI based softwares that can quote exact amount to be charged from customers based of the quantity and nature of goods, destination, path to destination, floor of new location, whether lift is present or not and many more relevant factors. These softwares also helps to find the price for the goods so that they can have an insurance of that amount. Customers can also use the GPS technology to track their goods while they are being shifted to a place too far.

Read more at https://www.analyticsinsight.net/relocation-industry-working-algorithms-comprehend-consumer-needs/

Python Programming language is already quite famous for its power and simplicity. It has become an all-in-one programming language because of availability of so many libraries and frameworks which makes python extensible to many areas.

The top 6 application areas of Python are as follows :-

1. Business

2. Scientific / Numeric

3. Education

4. Gaming

5. Web and internet

6. GUI applications.

Read more at https://www.technotification.com/2017/11/python-applications.html

It’s really fascinating to see how we are generating an incredible amount of data every minute. Plenty of blogs and articles have been written stating that the better utilization of this data may result in more profit generation for the organization. So here are some tips to begin with the path of better utilization.

Currently what most of the organizations see is they are Creating data, and they want to generate some additional revenue from it, but they miss how to link these two aspects. 

To begin with, they must consider to use the Big Data Engines like Hadoop, Google Bigdata, Horton Works, MongoDB or anything similar. They help to find the patterns and correlations between different types of data.

Next is Data Warehousing. Data warehousing is not just maintaining a database. It has many advantages over the traditional databases. 

And another important step is Data Visualization. From the above two steps we have the data properly managed and organized. In this step we visualize the data in a productive manner.

Read More at https://www.informationweek.com/big-data/real-world-tools-to-help-navigate-a-data-driven-world/a/d-id/1332377?

The world is becoming more digital. Health industry has also turned mostly digital and maintains every record and every detail in digital format. This has many pros but it also bring some threats like in terms of data security. Health industry keeps a lot of information about a person like their name, date of birth, address, card numbers, contact numbers, insurance details and many more. 

There are the following 3 reasons why these data needs to be protected :- 

•  To avoid identity theft.

•  For the patient’s safety.

•  For the reputation of the hospital and healthcare providers.

To ensure data security, just installing the antivirus is not enough. There must be other features like data encryption, limited access and modification of the data and limited transmission and sharing of data. 

Read More at https://www.technotification.com/2018/07/data-protection-in-health-industry.html

For any organization, it is necessary to keep a track of the data created, data pulled, the source from where it is pulled, the format of the data they have been using till now, who can access this data and many more. There are many strategies for managing the data but there’s nothing as a perfect strategy. It varies from organization to organization and types of data. Another important factor is size of organization and its budget. The following are the three factors that can help in managing the data in a better way i.e. as per the GDPR rules.

1. Centralization of data

2. Automating data management 

3. Measuring Success.

Read More at https://www.analyticsinsight.net/how-to-overcome-the-challenges-in-managing-big-data

Tons of data is generated everyday in the industry. And making sense of this pile of data has become an important task for many businesses. To achieve this, they are turning into Big Data analytics and Data Science. Data Scientists have knowledge about various algorithms suitable for various types of data and these statistical algorithms are implemented in several programming language. Selection of the programming language depends on many factors. So here is the list of top 6 programming languages that are used by most of the data scientists and analysts.

1.  Python

2. R Programming

3. Matlab

4. Java

5. Julia

6. Scala

Read detailed review at https://www.technotification.com/2018/07/best-programming-languages-for-data-science.html

Just 5 years back, people were debating over whether to accept cloud over in-premise systems, its cost, the billing systems of cloud etc. The topic “Should we use cloud” from that time has turned into “What else should be do on cloud”. Cloud has really grown and have got its roots really deep in the industry. Most of the industry is planning to find new ways to do the business which has cloud as a major part in their systems. 

Read More at https://www.informationweek.com/cloud/software-as-a-service/the-cloud-steps-into-maturity/d/d-id/1332394

Data is like an untapped stream of gold. If used properly, it can help any business to start climbing of the stairs of success real fast. History has plenty of examples like Hershey’s, Caterpillar, Vermont Electric Power Company and many more. These companies began to store, segregate and analyse their data. The data includes their operational activities, sales, purchases, monitoring employees, data about competitors and many more. Proper usage of this data can bring a digital transformation in the organisation. Data analysis often results in cut in operational cost. But its benefits doesn’t just limit till the cost cut. Data analysis can also show a path for revenue generation.

Read more at https://www.informationweek.com/strategic-cio/digital-transformation-turning-data-into-dollars/a/d-id/1332319

Careers in machine learning, Data science, artificial intelligence, deep learning and many more are considered as one of the best choices to pursue. Now these technologies and the related jobs are considered one of the hottest and best jobs today. So, here are the list of top 5 languages prevalent in market for data science, machine learning etc.

1. Python

2. R

3. Java

4. Scala

5. C

Read More at https://www.informationweek.com/big-data/ai-machine-learning/5-top-languages-for-machine-learning-data-science/d/d-id/1332311?

Over past few decades, there has been a rapid advancement in the industry. It has become important that the strategists verify that their end users are getting the latest and quality material. Falling too behind of the advancements may result loses for the organisation. There are several symptoms that may help you to understand it’s the time to make necessary changes in your IT infrastructure to meet the requirements of the end users.

5 such symptoms are as follows :-  

1. Increasing number of IT help desk calls.

2. Hardware/software failures and outages on the rise.

3. Rise in the use of shadow IT.

4. Nobody wants to work for you.

5. New tech initiative is scrapped due to shortcomings in the infrastructure.

Read in Detail at https://www.informationweek.com/strategic-cio/it-strategy/5-signs-your-it-infrastructure-is-falling-behind/a/d-id/1332304?

Github is a very common and well known platform for learning, sharing and developing programs. It’s very helpful especially for team projects. With time Github has turned to be a platform for interactive programming learning. The most common and most chosen programming languages among the Github users are :-

• javascript

• Python

• Java

• Ruby

• PHP

• C++

• CSS

• C#

• GO

• C

Read Detailed review at https://www.technotification.com/2017/11/10-programming-languages-on-github.html

May be your organisation has just started to step into AI, but don’t worry its not too late. It takes plenty of time to embed a good helpful AI into the organisation and there’s no scope of mistake. Giants like Google, Amazon, Microsoft have implemented AI at early stages because they have plenty of data and plenty of resources. For any organisation it is important to find the best suitable aspect for AI implementation which will provide benefit at most. 

Read More at https://www.informationweek.com/enterprises-wade-into-the-ai-pool/d/d-id/1332434?

Data is the most valuable asset for any company and any person dealing with this data needs to be cautious. Modern businesses rely on data. They store, process and access data for information gathering and use it for decision making. According to reports of 2017, a single mistake in handling this data can result into loss of nearly $3.6 million. 

However, data can be loss due to various. Few of them are:

1. Human Error

2. Hardware Failure

3. Theft

4. Online Crime

5. Natural Disaster

The best way to deal with this is to take prevention and keep an up-to-date recovery plan and a 3-2-1 backup strategy, i.e. there should be three copies of data, kept in two different mediums, and at least one of the backups should be off site.

Read about it at: https://www.bigdatanews.datasciencecentral.com/profiles/blogs/five-ways-your-business-is-at-risk-of-data-loss

The pace at which technology is moving is unmatchable. Every day some advancement in technology comes into lime light. Among the various fields in technology, one of the major and trending technology is AI. It is growing each day and proving to be a solution in many applications. Below are the 10 areas where Artificial Intelligence can be noticed:

1. Robots predicting the future
2. Robot Soldier
3. Survival Robots
4. Police using AI algorithms to predict crimes
5. AI-based medical treatment
6. Autonomous drones and weapons
7. Supercomputers with imaginations
8. AI communicating with AI
9. AI hackers
10. AI in court

Every thing in this world have a positive side and a negative side. Similarly, a few cases have been there where Artificial Intelligence have gone wrong. However, chances of improvements are always there. 

To know more about these 10 areas and other uses visit: https://www.techrepublic.com/pictures/10-terrifying-uses-of-artificial-intelligence/

Now a days, companies are investing a huge amount of money in Artificial Intelligence, Automation, Robotics etc in order to be up to date with technology. However, still there are few limitations that each of them faces. Even after adopting various technologies, the reports stated that the company still struggles in defining goals. Below are the five steps that companies must follow in order to improve and to meet their expectation for automation technology:

1. Recognize that the use of intelligent automation is transformative, and built on the use of new machines and data sources
2. Formulate a comprehensive approach to automating the service delivery model
3. Measure value vs. risk
4. Consider the “operating model” in all forms
5. Disrupt from within

Bringing innovation in always good but it is also suggested to look for other alternatives for investment as not all technologies proves to be powerful for a company.

Read more about it at https://www.techrepublic.com/article/5-steps-to-help-your-company-benefit-from-ai/

Analytics is one of the major jobs performed in companies these days. Daily operations are carried out involving data that presents us with results which helps an organization to carry out further processes and helps in decision making. Effective business intelligence is the product of data processed. This data is raw and can be either structured or unstructured. 

Firstly, one needs to manage data before processing it. Rules are to be set for the analytics process which can offer better insight and an easy processing. Below are the five rules that can help in managing your data more effectively:

1. Establish Clear Analytics Goals Before Getting Started
2. Simplify and Centralize Your Data Streams
3. Scrub Your Data Before Warehousing
4. Establish Clear Data Governance Protocols
5. Create Dynamic Data Structures

The field of data analytics is always evolving and thus it is important to create a proper structure that can help in future. By establishing them we can enhance the quality of data processing.

Read more about it at: https://www.sisense.com/blog/data-management-rules-analytics/

Artificial Intelligence is a combination of various sub topics. Whether it be Machine Learning, Deep Learning, Neural Network etc, each one of them finds shelter under artificial intelligence. Below are the basics of two important topics – Neural Network and Deep Learning.

Neural Networks: Neural network is modelled in the same way as human brain. It compromises various algorithms and aims to find relationship in data provided by us through processes that mimics like human brains. It is one of the trending technologies and is finding its applications in various fields like trading, medicine, pattern recognition etc.

Deep Learning: Deep Learning is basically a network composed of several layers. It is also known as ‘stacked neural networks’. It is a subfield of machine learning and is inspired by the structure and function of brain. It can be widely used in classification, clustering, predictions etc.

We are walking towards the era where technology will dominate and the main technology will be artificial intelligence. Hence it is important to get an insight of what we will be with in future. For a beginner, these topics might be confusing but they form the crust of artificial intelligence.

Read more about them at: https://www.technotification.com/2018/08/neural-networks-deep-learning.html